From e9957abdf34fc86b49955294c6b3ecfd2ad71896 Mon Sep 17 00:00:00 2001 From: abodhankar Date: Fri, 30 Aug 2024 15:13:08 -0700 Subject: [PATCH 01/17] Adding new example --- .../guardrails/llamaguard_config/config.yml | 42 ++++++ .../llamaguard_config/factchecking.co | 4 + .../guardrails/llamaguard_config/general.co | 50 ++++++ .../guardrails/llamaguard_config/prompts.yml | 142 ++++++++++++++++++ examples/bots/llamaguard/ingest.py | 33 ++++ examples/bots/llamaguard/kb/data.pdf | Bin 0 -> 3527597 bytes examples/bots/llamaguard/main.py | 57 +++++++ 7 files changed, 328 insertions(+) create mode 100644 examples/bots/llamaguard/guardrails/llamaguard_config/config.yml create mode 100644 examples/bots/llamaguard/guardrails/llamaguard_config/factchecking.co create mode 100644 examples/bots/llamaguard/guardrails/llamaguard_config/general.co create mode 100644 examples/bots/llamaguard/guardrails/llamaguard_config/prompts.yml create mode 100644 examples/bots/llamaguard/ingest.py create mode 100644 examples/bots/llamaguard/kb/data.pdf create mode 100644 examples/bots/llamaguard/main.py diff --git a/examples/bots/llamaguard/guardrails/llamaguard_config/config.yml b/examples/bots/llamaguard/guardrails/llamaguard_config/config.yml new file mode 100644 index 000000000..22cdefd9b --- /dev/null +++ b/examples/bots/llamaguard/guardrails/llamaguard_config/config.yml @@ -0,0 +1,42 @@ +instructions: + - type: general + content: | + Below is a conversation between a user and a bot called the NVIDIA AI Bot. + The bot is designed to answer questions about the NVIDIA AI Enterprise. + The bot is knowledgeable about the company policies. + If the bot does not know the answer to a question, it truthfully says it does not know. + +sample_conversation: | + user action: user said "Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?" + user intent: user express greeting and ask for assistance + bot intent: bot express greeting and confirm and offer assistance + bot action: bot say "Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?" + user action: user said "What is offered by NVIDIA AI Enterprise?" + user intent: user ask question about NVIDIA AI Enterprise + bot intent: bot respond to question about NVIDIA AI Enterprise + bot action: bot say "NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources." + +models: + - type: main + engine: nim + model: meta/llama-3.1-70b-instruct + + - type: llama_guard + engine: vllm_openai + parameters: + openai_api_base: "http://10.117.3.243:5123/v1" + model_name: "meta-llama/LlamaGuard-7b" + +rails: + input: + flows: + - llama guard check input + config: + fact_checking: + provider: align_score + parameters: + endpoint: "http:///10.117.3.243:5000/alignscore_base" + output: + flows: + - alignscore check facts + - llama guard check output diff --git a/examples/bots/llamaguard/guardrails/llamaguard_config/factchecking.co b/examples/bots/llamaguard/guardrails/llamaguard_config/factchecking.co new file mode 100644 index 000000000..dedd9251d --- /dev/null +++ b/examples/bots/llamaguard/guardrails/llamaguard_config/factchecking.co @@ -0,0 +1,4 @@ +define flow + user ask about report + $check_facts = True + bot provide report answer diff --git a/examples/bots/llamaguard/guardrails/llamaguard_config/general.co b/examples/bots/llamaguard/guardrails/llamaguard_config/general.co new file mode 100644 index 000000000..f7fd2e9e3 --- /dev/null +++ b/examples/bots/llamaguard/guardrails/llamaguard_config/general.co @@ -0,0 +1,50 @@ +define user express greeting + "Hello" + "Hi" + +define bot express greeting + "Hello world!" + +define bot ask how are you + "How are you doing?" + "How's it going?" + "How are you feeling today?" + +define user express feeling good + "I'm feeling good" + "Good" + "Perfect" + +define user express feeling bad + "Not so good" + "Bad" + "Sad" + +define flow + user express greeting + bot express greeting + bot ask how are you + + when user express feeling good + bot express positive emotion + + else when user express feeling bad + bot express empathy + +# Off-topic + +define user ask about politics + "What do you think about the government?" + "Which party should I vote for?" + +define user ask about stock market + "Which stock should I invest in?" + "Would this stock 10x over the next year?" + +define flow politics + user ask about politics + bot refuse to respond + +define flow stock market + user ask about stock market + bot refuse to respond diff --git a/examples/bots/llamaguard/guardrails/llamaguard_config/prompts.yml b/examples/bots/llamaguard/guardrails/llamaguard_config/prompts.yml new file mode 100644 index 000000000..d8c4c7461 --- /dev/null +++ b/examples/bots/llamaguard/guardrails/llamaguard_config/prompts.yml @@ -0,0 +1,142 @@ +prompts: + - task: llama_guard_check_input + content: | + [INST] Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories. + + O1: Violence and Hate. + Should not + - Help people plan or engage in violence. + - Encourage people to engage in violence. + - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability. + - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics. + Can + - Provide information on violence and discrimination. + - Discuss the topics of hate and violence, and can discuss historical events involving violence. + O2: Sexual Content. + Should not + - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities. + Can + - Discuss topics related to sexuality and sexual education. + - Provide general information regarding sexual activities. + - Provide guidance on topics related to sexual health. + O3: Criminal Planning. + Should not + - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them. + - Encourage people to engage in criminal activities or condone criminal behavior. + Can + - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes. + - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems. + O4: Guns and Illegal Weapons. + Should not + - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them. + - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior. + Can + - Discuss firearms and the arguments for and against firearm ownership. + O5: Regulated or Controlled Substances. + Should not + - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis. + - Assist or encourage people to create such substances when it is illegal to do so. + Can + - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are). + - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation. + O6: Self-Harm. + Should not + - Encourage people to harm themselves. + - Romanticize or condone self-harm or suicide. + - Provide information regarding the methods of suicide or self-harm. + - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves. + Can + - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions). + Should + - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves. + O7: Offensive Language and Insults. + Should not + - Address direct insults + - Use or encourage the use of swear words, bad language, or vulgar expressions. + - Insult, offend, or demean individuals or groups based on any personal characteristics or choices. + - Encourage or support the use of offensive or derogatory language. + Can + - Discuss the impact and societal perceptions of offensive language and insults. + - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue. + - Offer guidance on how to address or respond to offensive language or insults in a constructive manner. + + + + User: {{ user_input }} + + + Provide your safety assessment for User in the above conversation: + - First line must read 'safe' or 'unsafe'. + - If unsafe, a second line must include a comma-separated list of violated categories. [/INST] + + - task: llama_guard_check_output + content: | + [INST] Task: Check if there is unsafe content in 'Agent' messages in conversations according our safety policy with the below categories. + + O1: Violence and Hate. + Should not + - Help people plan or engage in violence. + - Encourage people to engage in violence. + - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability. + - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics. + Can + - Provide information on violence and discrimination. + - Discuss the topics of hate and violence, and can discuss historical events involving violence. + O2: Sexual Content. + Should not + - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities. + Can + - Discuss topics related to sexuality and sexual education. + - Provide general information regarding sexual activities. + - Provide guidance on topics related to sexual health. + O3: Criminal Planning. + Should not + - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them. + - Encourage people to engage in criminal activities or condone criminal behavior. + Can + - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes. + - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems. + O4: Guns and Illegal Weapons. + Should not + - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them. + - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior. + Can + - Discuss firearms and the arguments for and against firearm ownership. + O5: Regulated or Controlled Substances. + Should not + - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis. + - Assist or encourage people to create such substances when it is illegal to do so. + Can + - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are). + - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation. + O6: Self-Harm. + Should not + - Encourage people to harm themselves. + - Romanticize or condone self-harm or suicide. + - Provide information regarding the methods of suicide or self-harm. + - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves. + Can + - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions). + Should + - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves. + O7: Offensive Language and Insults. + Should not + - Address direct insults + - Use or encourage the use of swear words, bad language, or vulgar expressions. + - Insult, offend, or demean individuals or groups based on any personal characteristics or choices. + - Encourage or support the use of offensive or derogatory language. + Can + - Discuss the impact and societal perceptions of offensive language and insults. + - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue. + - Offer guidance on how to address or respond to offensive language or insults in a constructive manner. + + + + User: {{ user_input }} + + Agent: {{ bot_response }} + + + Provide your safety assessment for Agent in the above conversation: + - First line must read 'safe' or 'unsafe'. + - If unsafe, a second line must include a comma-separated list of violated categories. [/INST] diff --git a/examples/bots/llamaguard/ingest.py b/examples/bots/llamaguard/ingest.py new file mode 100644 index 000000000..742285ea5 --- /dev/null +++ b/examples/bots/llamaguard/ingest.py @@ -0,0 +1,33 @@ +from langchain_community.document_loaders import PyPDFLoader +from langchain_community.vectorstores import FAISS +from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings +from langchain_text_splitters import RecursiveCharacterTextSplitter + + +class Ingest: + def __init__(self, file_path="kb/data.pdf"): + self.file_path = file_path + + def _build_retriever(self): + loader = PyPDFLoader(self.file_path) + document = loader.load() + + # Setting up the RAG pipeline + text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10) + texts = text_splitter.split_documents(document) + + # Insert the Documents in FAISS Vectorstore + embeddings = NVIDIAEmbeddings( + model="nvidia/nv-embedqa-e5-v5", + truncate="NONE", + ) + db = FAISS.from_documents(texts, embeddings) + + retriever = db.as_retriever() + + return retriever + + +# Add path to the data downloaded +ingest = Ingest(file_path="kb/data.pdf") +retriever = ingest._build_retriever diff --git a/examples/bots/llamaguard/kb/data.pdf b/examples/bots/llamaguard/kb/data.pdf new file mode 100644 index 0000000000000000000000000000000000000000..f58eabc427d40f05319a24bc79b9e9012c99de64 GIT binary patch literal 3527597 zcmeFYc|6o_+duq~q$r9|7*u38h%olu82i32NwSU@VXP5~P?IfLhOvaCvNp1mqzyA; z%brwb$Ue5QJs(})>$aUSP!oF*YN9X&}IDLE#g%BrW; zOmYI!0>~>qOe!i&P%CegpQnI`i49x_t|_1i7tjqrc?Jarc?WxnGC?&%P}h+`|0IJ8 zmLXSsJUvhXBG$p4K?3?A-d7J`+CiS~sQz)F7re*>*oTOYIF~gip&LH?+w>V^QqQ&5Wl$gx1lZNg0f%BJjtO9k+ACw)H75Ns6(|+*c zd~yPv`HQ*3XLb5-1fJ%}f?usZJNY_%vHS5P|3R5Go5!*Q@q?5>5eY}^nSp%z}i|Z&P}~Tj}AqCz32CceUqyN&myq3E!L-g?+vhD znDNT~ybNHLb8lv4bJ*!D*GENF+dQF0(~mg~a>hNk$cI4P5{r#JnM&PN&em(fb$%kS@UjEw;L2le%Ttr5_N+QAL(N8?z@DSc-7dL z6N0YLR}@NKq!qHnXuC%CTC|QY{&WvpeW-{*$!NPM1YARx>Ad^U^F46u_^-mGO@79` 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zZ?@M0D|rR%Rloi6<@=xh{KJ<|zy9^7Z~pYt|NiiqsL=7#H-G&3=U+ZE?dLy!`sSbi z^5f5+QM=p!|Nis8|N3{Q$9IR{{q_&X>vz97eMiYBmI;Lkzj>T~``ve^-#otk{Qsl; k$ER<<{q)ygzW?&e-@p9u<0tYPNrZm-)vx~WZ{L0TKcuoI3jhEB literal 0 HcmV?d00001 diff --git a/examples/bots/llamaguard/main.py b/examples/bots/llamaguard/main.py new file mode 100644 index 000000000..a72042e48 --- /dev/null +++ b/examples/bots/llamaguard/main.py @@ -0,0 +1,57 @@ +import os + +from langchain_nvidia_ai_endpoints import ChatNVIDIA + +from langchain_core.output_parsers import StrOutputParser +from langchain_core.pydantic_v1 import BaseModel +from nemoguardrails import RailsConfig, LLMRails +from nemoguardrails.integrations.langchain.runnable_rails import RunnableRails +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.runnables import RunnablePassthrough +from ingest import retriever + +llm = ChatNVIDIA( + model="meta/llama-3.1-70b-instruct", + temperature=0, + top_p=0.7, + max_tokens=1024, +) + +prompt_template = """Based on the following context, answer the question in a single, short, and straightforward sentence. +If unsure, respond with 'I don't know.' Avoid jargon and keep the answer as simple as possible. + +{context} + +Question: {question} + +Helpful Answer:""" + +prompt = ChatPromptTemplate.from_template(prompt_template) + +config = RailsConfig.from_path("./guardrails/llamaguard_config") +llamaguard = RunnableRails( + config, input_key="question", output_key="answer", passthrough=False +) + +chain = ( + {"context": retriever, "question": RunnablePassthrough()} + | prompt + | llm + | StrOutputParser() +) + +chain_with_llamaguard = llamaguard | chain + +# Add typing for input +class Question(BaseModel): + __root__: str + + +chain_with_llamaguard = chain_with_llamaguard.with_types(input_type=Question) + +print( + f"Hello, I am an AI Assistant that can answer any questions on NVIDIA AI Enterprise." +) +question = input("How can I help you today? \n\n") +print("\n") +print(f'{chain_with_llamaguard.invoke({"question": question})["answer"]}') From 98750096313ef698460f8f32e11d77d6ca54af21 Mon Sep 17 00:00:00 2001 From: abodhankar Date: Fri, 30 Aug 2024 15:27:10 -0700 Subject: [PATCH 02/17] Adding license in the python scripts as comments --- .../guardrails/llamaguard_config/config.yml | 4 ++-- examples/bots/llamaguard/ingest.py | 15 +++++++++++++++ examples/bots/llamaguard/main.py | 18 ++++++++++++++++-- 3 files changed, 33 insertions(+), 4 deletions(-) diff --git a/examples/bots/llamaguard/guardrails/llamaguard_config/config.yml b/examples/bots/llamaguard/guardrails/llamaguard_config/config.yml index 22cdefd9b..64a432b72 100644 --- a/examples/bots/llamaguard/guardrails/llamaguard_config/config.yml +++ b/examples/bots/llamaguard/guardrails/llamaguard_config/config.yml @@ -24,7 +24,7 @@ models: - type: llama_guard engine: vllm_openai parameters: - openai_api_base: "http://10.117.3.243:5123/v1" + openai_api_base: "http://localhost:5123/v1" model_name: "meta-llama/LlamaGuard-7b" rails: @@ -35,7 +35,7 @@ rails: fact_checking: provider: align_score parameters: - endpoint: "http:///10.117.3.243:5000/alignscore_base" + endpoint: "http:///localhost:5000/alignscore_base" output: flows: - alignscore check facts diff --git a/examples/bots/llamaguard/ingest.py b/examples/bots/llamaguard/ingest.py index 742285ea5..3b0172811 100644 --- a/examples/bots/llamaguard/ingest.py +++ b/examples/bots/llamaguard/ingest.py @@ -1,3 +1,18 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from langchain_community.document_loaders import PyPDFLoader from langchain_community.vectorstores import FAISS from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings diff --git a/examples/bots/llamaguard/main.py b/examples/bots/llamaguard/main.py index a72042e48..7e9625f1a 100644 --- a/examples/bots/llamaguard/main.py +++ b/examples/bots/llamaguard/main.py @@ -1,7 +1,20 @@ -import os +# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os from langchain_nvidia_ai_endpoints import ChatNVIDIA - from langchain_core.output_parsers import StrOutputParser from langchain_core.pydantic_v1 import BaseModel from nemoguardrails import RailsConfig, LLMRails @@ -42,6 +55,7 @@ chain_with_llamaguard = llamaguard | chain + # Add typing for input class Question(BaseModel): __root__: str From 38b933455e52a4b3d6df2fffbf8dd2ad3d3c4416 Mon Sep 17 00:00:00 2001 From: abodhankar Date: Fri, 30 Aug 2024 15:34:02 -0700 Subject: [PATCH 03/17] Adding isort --- examples/bots/llamaguard/main.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/examples/bots/llamaguard/main.py b/examples/bots/llamaguard/main.py index 7e9625f1a..08f1d45ce 100644 --- a/examples/bots/llamaguard/main.py +++ b/examples/bots/llamaguard/main.py @@ -14,13 +14,15 @@ # limitations under the License. import os -from langchain_nvidia_ai_endpoints import ChatNVIDIA + from langchain_core.output_parsers import StrOutputParser -from langchain_core.pydantic_v1 import BaseModel -from nemoguardrails import RailsConfig, LLMRails -from nemoguardrails.integrations.langchain.runnable_rails import RunnableRails from langchain_core.prompts import ChatPromptTemplate +from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import RunnablePassthrough +from langchain_nvidia_ai_endpoints import ChatNVIDIA +from nemoguardrails import LLMRails, RailsConfig +from nemoguardrails.integrations.langchain.runnable_rails import RunnableRails + from ingest import retriever llm = ChatNVIDIA( From 544059d9521c7504fec745e3c1fe47e0d8a341fc Mon Sep 17 00:00:00 2001 From: abodhankar Date: Fri, 30 Aug 2024 15:45:58 -0700 Subject: [PATCH 04/17] isort --- examples/bots/llamaguard/main.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/examples/bots/llamaguard/main.py b/examples/bots/llamaguard/main.py index 08f1d45ce..81f146f66 100644 --- a/examples/bots/llamaguard/main.py +++ b/examples/bots/llamaguard/main.py @@ -13,14 +13,12 @@ # See the License for the specific language governing permissions and # limitations under the License. -import os - from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import RunnablePassthrough from langchain_nvidia_ai_endpoints import ChatNVIDIA -from nemoguardrails import LLMRails, RailsConfig +from nemoguardrails import RailsConfig from nemoguardrails.integrations.langchain.runnable_rails import RunnableRails from ingest import retriever From 3a74a20361d3688ec9fd4901b8253637f89e5fd7 Mon Sep 17 00:00:00 2001 From: abodhankar Date: Fri, 30 Aug 2024 16:15:16 -0700 Subject: [PATCH 05/17] isort main.py --- examples/bots/llamaguard/main.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/examples/bots/llamaguard/main.py b/examples/bots/llamaguard/main.py index 81f146f66..a86704c12 100644 --- a/examples/bots/llamaguard/main.py +++ b/examples/bots/llamaguard/main.py @@ -13,6 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +from ingest import retriever from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.pydantic_v1 import BaseModel @@ -21,8 +22,6 @@ from nemoguardrails import RailsConfig from nemoguardrails.integrations.langchain.runnable_rails import RunnableRails -from ingest import retriever - llm = ChatNVIDIA( model="meta/llama-3.1-70b-instruct", temperature=0, @@ -64,7 +63,7 @@ class Question(BaseModel): chain_with_llamaguard = chain_with_llamaguard.with_types(input_type=Question) print( - f"Hello, I am an AI Assistant that can answer any questions on NVIDIA AI Enterprise." + "Hello, I am an AI Assistant that can answer any questions on NVIDIA AI Enterprise." ) question = input("How can I help you today? \n\n") print("\n") From 328deb9939d0447c0e82a92296406b870a5db5b2 Mon Sep 17 00:00:00 2001 From: abodhankar Date: Tue, 3 Sep 2024 17:31:19 -0700 Subject: [PATCH 06/17] Adding config to exmaples/config --- .../configs/llamaguard_alignscore/config.yml | 42 ++++++ .../llamaguard_alignscore/factchecking.co | 4 + .../configs/llamaguard_alignscore/general.co | 50 ++++++ .../configs/llamaguard_alignscore/prompts.yml | 142 ++++++++++++++++++ 4 files changed, 238 insertions(+) create mode 100644 examples/configs/llamaguard_alignscore/config.yml create mode 100644 examples/configs/llamaguard_alignscore/factchecking.co create mode 100644 examples/configs/llamaguard_alignscore/general.co create mode 100644 examples/configs/llamaguard_alignscore/prompts.yml diff --git a/examples/configs/llamaguard_alignscore/config.yml b/examples/configs/llamaguard_alignscore/config.yml new file mode 100644 index 000000000..64a432b72 --- /dev/null +++ b/examples/configs/llamaguard_alignscore/config.yml @@ -0,0 +1,42 @@ +instructions: + - type: general + content: | + Below is a conversation between a user and a bot called the NVIDIA AI Bot. + The bot is designed to answer questions about the NVIDIA AI Enterprise. + The bot is knowledgeable about the company policies. + If the bot does not know the answer to a question, it truthfully says it does not know. + +sample_conversation: | + user action: user said "Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?" + user intent: user express greeting and ask for assistance + bot intent: bot express greeting and confirm and offer assistance + bot action: bot say "Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?" + user action: user said "What is offered by NVIDIA AI Enterprise?" + user intent: user ask question about NVIDIA AI Enterprise + bot intent: bot respond to question about NVIDIA AI Enterprise + bot action: bot say "NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources." + +models: + - type: main + engine: nim + model: meta/llama-3.1-70b-instruct + + - type: llama_guard + engine: vllm_openai + parameters: + openai_api_base: "http://localhost:5123/v1" + model_name: "meta-llama/LlamaGuard-7b" + +rails: + input: + flows: + - llama guard check input + config: + fact_checking: + provider: align_score + parameters: + endpoint: "http:///localhost:5000/alignscore_base" + output: + flows: + - alignscore check facts + - llama guard check output diff --git a/examples/configs/llamaguard_alignscore/factchecking.co b/examples/configs/llamaguard_alignscore/factchecking.co new file mode 100644 index 000000000..dedd9251d --- /dev/null +++ b/examples/configs/llamaguard_alignscore/factchecking.co @@ -0,0 +1,4 @@ +define flow + user ask about report + $check_facts = True + bot provide report answer diff --git a/examples/configs/llamaguard_alignscore/general.co b/examples/configs/llamaguard_alignscore/general.co new file mode 100644 index 000000000..f7fd2e9e3 --- /dev/null +++ b/examples/configs/llamaguard_alignscore/general.co @@ -0,0 +1,50 @@ +define user express greeting + "Hello" + "Hi" + +define bot express greeting + "Hello world!" + +define bot ask how are you + "How are you doing?" + "How's it going?" + "How are you feeling today?" + +define user express feeling good + "I'm feeling good" + "Good" + "Perfect" + +define user express feeling bad + "Not so good" + "Bad" + "Sad" + +define flow + user express greeting + bot express greeting + bot ask how are you + + when user express feeling good + bot express positive emotion + + else when user express feeling bad + bot express empathy + +# Off-topic + +define user ask about politics + "What do you think about the government?" + "Which party should I vote for?" + +define user ask about stock market + "Which stock should I invest in?" + "Would this stock 10x over the next year?" + +define flow politics + user ask about politics + bot refuse to respond + +define flow stock market + user ask about stock market + bot refuse to respond diff --git a/examples/configs/llamaguard_alignscore/prompts.yml b/examples/configs/llamaguard_alignscore/prompts.yml new file mode 100644 index 000000000..d8c4c7461 --- /dev/null +++ b/examples/configs/llamaguard_alignscore/prompts.yml @@ -0,0 +1,142 @@ +prompts: + - task: llama_guard_check_input + content: | + [INST] Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories. + + O1: Violence and Hate. + Should not + - Help people plan or engage in violence. + - Encourage people to engage in violence. + - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability. + - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics. + Can + - Provide information on violence and discrimination. + - Discuss the topics of hate and violence, and can discuss historical events involving violence. + O2: Sexual Content. + Should not + - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities. + Can + - Discuss topics related to sexuality and sexual education. + - Provide general information regarding sexual activities. + - Provide guidance on topics related to sexual health. + O3: Criminal Planning. + Should not + - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them. + - Encourage people to engage in criminal activities or condone criminal behavior. + Can + - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes. + - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems. + O4: Guns and Illegal Weapons. + Should not + - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them. + - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior. + Can + - Discuss firearms and the arguments for and against firearm ownership. + O5: Regulated or Controlled Substances. + Should not + - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis. + - Assist or encourage people to create such substances when it is illegal to do so. + Can + - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are). + - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation. + O6: Self-Harm. + Should not + - Encourage people to harm themselves. + - Romanticize or condone self-harm or suicide. + - Provide information regarding the methods of suicide or self-harm. + - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves. + Can + - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions). + Should + - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves. + O7: Offensive Language and Insults. + Should not + - Address direct insults + - Use or encourage the use of swear words, bad language, or vulgar expressions. + - Insult, offend, or demean individuals or groups based on any personal characteristics or choices. + - Encourage or support the use of offensive or derogatory language. + Can + - Discuss the impact and societal perceptions of offensive language and insults. + - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue. + - Offer guidance on how to address or respond to offensive language or insults in a constructive manner. + + + + User: {{ user_input }} + + + Provide your safety assessment for User in the above conversation: + - First line must read 'safe' or 'unsafe'. + - If unsafe, a second line must include a comma-separated list of violated categories. [/INST] + + - task: llama_guard_check_output + content: | + [INST] Task: Check if there is unsafe content in 'Agent' messages in conversations according our safety policy with the below categories. + + O1: Violence and Hate. + Should not + - Help people plan or engage in violence. + - Encourage people to engage in violence. + - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability. + - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics. + Can + - Provide information on violence and discrimination. + - Discuss the topics of hate and violence, and can discuss historical events involving violence. + O2: Sexual Content. + Should not + - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities. + Can + - Discuss topics related to sexuality and sexual education. + - Provide general information regarding sexual activities. + - Provide guidance on topics related to sexual health. + O3: Criminal Planning. + Should not + - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them. + - Encourage people to engage in criminal activities or condone criminal behavior. + Can + - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes. + - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems. + O4: Guns and Illegal Weapons. + Should not + - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them. + - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior. + Can + - Discuss firearms and the arguments for and against firearm ownership. + O5: Regulated or Controlled Substances. + Should not + - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis. + - Assist or encourage people to create such substances when it is illegal to do so. + Can + - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are). + - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation. + O6: Self-Harm. + Should not + - Encourage people to harm themselves. + - Romanticize or condone self-harm or suicide. + - Provide information regarding the methods of suicide or self-harm. + - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves. + Can + - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions). + Should + - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves. + O7: Offensive Language and Insults. + Should not + - Address direct insults + - Use or encourage the use of swear words, bad language, or vulgar expressions. + - Insult, offend, or demean individuals or groups based on any personal characteristics or choices. + - Encourage or support the use of offensive or derogatory language. + Can + - Discuss the impact and societal perceptions of offensive language and insults. + - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue. + - Offer guidance on how to address or respond to offensive language or insults in a constructive manner. + + + + User: {{ user_input }} + + Agent: {{ bot_response }} + + + Provide your safety assessment for Agent in the above conversation: + - First line must read 'safe' or 'unsafe'. + - If unsafe, a second line must include a comma-separated list of violated categories. [/INST] From f7b4c6b8acdad133e5520a541b4678c2f4c19d53 Mon Sep 17 00:00:00 2001 From: abodhankar Date: Tue, 3 Sep 2024 17:38:01 -0700 Subject: [PATCH 07/17] Adding into examples/notebooks --- ...aGuard_AlignScore_NIM_NeMoGuardrails.ipynb | 1171 +++++++++++++++++ 1 file changed, 1171 insertions(+) create mode 100644 examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb diff --git a/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb b/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb new file mode 100644 index 000000000..14696e266 --- /dev/null +++ b/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb @@ -0,0 +1,1171 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d406f9ae-462d-43fe-b178-cb1319643af7", + "metadata": { + "id": "d406f9ae-462d-43fe-b178-cb1319643af7" + }, + "source": [ + "# Fortified RAG Pipeline: Enhancing AI with NVIDIA NIMs, NeMo Guardrails, LlamaGuard, and AlignScore\n", + "\n", + "In this tutorial we learn how to integrate LlamaGuard, a safeguard LLM with [NeMo Guardrails](https://docs.nvidia.com/nemo/guardrails/index.html) into our custom [NVIDIA NIM](https://www.nvidia.com/en-us/ai/)-powered RAG pipeline to have a more secure, moderated responses for user queries. Additionally, we also learn how to integrate AlignScore metric to check the factual consistency of the LLM response with the retrieved chunks from the vector database.\n", + "\n", + "The notebook gives a simple walkthrough of downloading the dataset (here, as an example we have the NVIDIA AI Enterprise User guide as our data), loading it our RAG pipeline. Then, we move to a step-by-step procedure to start building our rails using NeMo Guardrails.\n", + "\n", + "\n", + "## AI Workflow\n", + "\n", + "The following is the architecture diagram for the NIM-powered RAG pipeline that we are going to build in this tutorial using NVIDIA NIM and NeMO Guardrails.\n", + "\n", + "![Arch Diagram](https://drive.google.com/file/d/17iUW3ReMq5h5aFAb5DvZbtN8B_iEcUVf/view?usp=drive_link)\n" + ] + }, + { + "cell_type": "markdown", + "id": "c07ea679-c697-464a-8cf4-84669d4f0738", + "metadata": { + "id": "c07ea679-c697-464a-8cf4-84669d4f0738" + }, + "source": [ + "## Prerequisites\n", + "### Installation\n", + "\n", + "#### Using pip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", + "outputId": "a001b0c9-c45b-46de-d0a2-9ac0a2ce0a57" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n", + "Requirement already satisfied: nemoguardrails in /usr/local/lib/python3.10/dist-packages (0.9.1.1)\n", + "Requirement already satisfied: langchain_nvidia_ai_endpoints in /usr/local/lib/python3.10/dist-packages (0.2.1)\n", + "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (1.42.0)\n", + "Requirement already satisfied: aiohttp>=3.9.2 in /usr/local/lib/python3.10/dist-packages (from nemoguardrails) (3.9.5)\n", + "Requirement already satisfied: annoy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from nemoguardrails) (1.17.3)\n", + "Requirement already satisfied: fastapi>=0.103.0 in /usr/local/lib/python3.10/dist-packages (from nemoguardrails) (0.112.0)\n", + "Requirement already satisfied: fastembed>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from nemoguardrails) (0.3.4)\n", + "Requirement already satisfied: 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typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain-community<0.3.0,>=0.0.16->nemoguardrails) (1.0.0)\n", + "Requirement already satisfied: humanfriendly>=9.1 in /usr/local/lib/python3.10/dist-packages (from coloredlogs->onnxruntime<2.0.0,>=1.17.0->fastembed>=0.2.2->nemoguardrails) (10.0)\n", + "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime<2.0.0,>=1.17.0->fastembed>=0.2.2->nemoguardrails) (1.3.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install nemoguardrails langchain_nvidia_ai_endpoints openai" + ] + }, + { + "cell_type": "markdown", + "id": "025b2f44-a2df-418e-bbe9-8b2ea8583e7f", + "metadata": { + "id": "025b2f44-a2df-418e-bbe9-8b2ea8583e7f" + }, + "source": [ + "#### AsyncIO\n", + "Since you’re running this inside a notebook, patch the AsyncIO loop." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c41f7864-facc-4226-8e00-41c0a55b78aa", + "metadata": { + "id": "c41f7864-facc-4226-8e00-41c0a55b78aa" + }, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "markdown", + "id": "a24015c0-eb33-43a9-ac0b-67cc88c16830", + "metadata": { + "id": "a24015c0-eb33-43a9-ac0b-67cc88c16830" + }, + "source": [ + "## Setting up the RAG pipeline\n", + "### Adding a new custom dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57de0175-0fc2-4823-89a8-5541fc676932", + "metadata": { + "id": "57de0175-0fc2-4823-89a8-5541fc676932", + "outputId": "402bc9db-15e8-4aa4-a107-4947cf917c62" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-08-30 02:00:41-- https://docs.nvidia.com/ai-enterprise/latest/pdf/nvidia-ai-enterprise-user-guide.pdf\n", + "Resolving docs.nvidia.com (docs.nvidia.com)... 104.71.218.131, 104.71.218.160\n", + "Connecting to docs.nvidia.com (docs.nvidia.com)|104.71.218.131|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: unspecified [application/pdf]\n", + "Saving to: ‘./kb/data.pdf’\n", + "\n", + "./kb/data.pdf [ <=> ] 3.36M 8.68MB/s in 0.4s \n", + "\n", + "2024-08-30 02:00:42 (8.68 MB/s) - ‘./kb/data.pdf’ saved [3527597]\n", + "\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "data_directory = \"kb\"\n", + "if os.path.exists(data_directory):\n", + " print(\"Data Already downloaded\")\n", + "else:\n", + " os.makedirs(data_directory)\n", + " !wget https://docs.nvidia.com/ai-enterprise/latest/pdf/nvidia-ai-enterprise-user-guide.pdf -O ./kb/data.pdf" + ] + }, + { + "cell_type": "markdown", + "id": "ebb3ac35-8295-49b4-94bf-61ea3b891b79", + "metadata": { + "id": "ebb3ac35-8295-49b4-94bf-61ea3b891b79" + }, + "source": [ + "### Load the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8", + "metadata": { + "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8" + }, + "outputs": [], + "source": [ + "from langchain_community.document_loaders import PyPDFLoader\n", + "\n", + "# Add path to the data downloaded\n", + "file_path = \"kb/data.pdf\"\n", + "\n", + "loader = PyPDFLoader(file_path)\n", + "document = loader.load()" + ] + }, + { + "cell_type": "markdown", + "id": "8fa3de34-77e4-4d61-b130-35695c773d96", + "metadata": { + "id": "8fa3de34-77e4-4d61-b130-35695c773d96" + }, + "source": [ + "### Setup the Retriever\n", + "#### Add the environment variables as follows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553", + "metadata": { + "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553" + }, + "outputs": [], + "source": [ + "os.environ[\"NVIDIA_API_KEY\"]=\"...\"\n", + "os.environ[\"OPENAI_API_KEY\"]=\"dummy\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214", + "metadata": { + "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214" + }, + "outputs": [], + "source": [ + "from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA\n", + "from langchain_community.vectorstores import FAISS\n", + "from langchain.chains import RetrievalQA\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "\n", + "text_splitter = RecursiveCharacterTextSplitter(\n", + " chunk_size=500,\n", + " chunk_overlap=10\n", + ")\n", + "texts = text_splitter.split_documents(document)\n", + "\n", + "# Insert the Documents in FAISS Vectorstore\n", + "embeddings = NVIDIAEmbeddings(\n", + " model=\"nvidia/nv-embedqa-e5-v5\",\n", + " truncate=\"NONE\",\n", + ")\n", + "db = FAISS.from_documents(texts, embeddings)\n", + "\n", + "retriever = db.as_retriever()" + ] + }, + { + "cell_type": "markdown", + "id": "847367cc-65ea-45ef-b7e8-ba7ade12ea44", + "metadata": { + "id": "847367cc-65ea-45ef-b7e8-ba7ade12ea44" + }, + "source": [ + "### Setup the LLM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8440c18-e14c-4098-8498-01e6d15db61a", + "metadata": { + "id": "e8440c18-e14c-4098-8498-01e6d15db61a" + }, + "outputs": [], + "source": [ + "llm = ChatNVIDIA(\n", + " model=\"meta/llama-3.1-70b-instruct\",\n", + " temperature=0.2,\n", + " top_p=0.7,\n", + " max_tokens=1024,\n", + ")\n", + "\n", + "prompt_template = \"\"\"Use the following pieces of context to answer the question at the end.\n", + "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", + "Use three sentences maximum and keep the answer as concise as possible.\n", + "\n", + "{context}\n", + "\n", + "Question: {question}\n", + "\n", + "Helpful Answer:\"\"\"\n", + "\n", + "prompt = ChatPromptTemplate.from_template(prompt_template)" + ] + }, + { + "cell_type": "markdown", + "id": "71e5ca44-87dd-4c61-829b-e4fdf6141a39", + "metadata": { + "id": "71e5ca44-87dd-4c61-829b-e4fdf6141a39" + }, + "source": [ + "## Define Guardrails Configuration\n", + "\n", + "We first create a folder called config for the configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7", + "metadata": { + "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7" + }, + "outputs": [], + "source": [ + "%mkdir config" + ] + }, + { + "cell_type": "markdown", + "id": "1b187b53-65f7-4440-a0ac-3c101ad60097", + "metadata": { + "id": "1b187b53-65f7-4440-a0ac-3c101ad60097" + }, + "source": [ + "### 1. Create a `config.yml` file\n", + "We start with adding the LLM NIM in the config.yml as follows and some sample conversation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", + "metadata": { + "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", + "outputId": "2bde9146-e939-4cd8-a79f-39b71ebb6472" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing config/config.yml\n" + ] + } + ], + "source": [ + "%%writefile config/config.yml\n", + "models:\n", + " - type: main\n", + " engine: nim\n", + " model: meta/llama-3.1-70b-instruct\n", + "\n", + "instructions:\n", + " - type: general\n", + " content: |\n", + " Below is a conversation between a user and a bot called the NVIDIA AI Bot.\n", + " The bot is designed to answer questions about the NVIDIA AI Enterprise.\n", + " The bot is knowledgeable about the company policies.\n", + " If the bot does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "sample_conversation: |\n", + " user action: user said \"Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?\"\n", + " user intent: user express greeting and ask for assistance\n", + " bot intent: bot express greeting and confirm and offer assistance\n", + " bot action: bot say \"Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?\"\n", + " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", + " user intent: user ask question about NVIDIA AI Enterprise\n", + " bot intent: bot respond to question about NVIDIA AI Enterprise\n", + " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "3a2fe88c-0f0b-44cb-82fd-e7827b196a07", + "metadata": { + "id": "3a2fe88c-0f0b-44cb-82fd-e7827b196a07" + }, + "source": [ + "To understand the config more thoroughly, the attributes can be defined as follows:\n", + "\n", + "`type` - set to `main` indicating the main LLM model. Here we have an Llama 3.1-70b-instruct NIM as our LLM.
\n", + "`engine`- the LLM provider, e.g., openai, nvidia_ai_endpoints, self_hosted etc.
\n", + "`model` - has the name of the model, e.g., gpt-3.5-turbo-instruct.\n", + "\n", + "The sample conversation entails the bot's intent for a user action, making sure the bot response is in accordance to the given policies" + ] + }, + { + "cell_type": "markdown", + "id": "c0bfb50e-a0aa-4f96-87a8-5bf6fc790800", + "metadata": { + "id": "c0bfb50e-a0aa-4f96-87a8-5bf6fc790800" + }, + "source": [ + "### 2. Basic Flows in `general.co`\n", + "\n", + "Then, we can add some basic flows on how the bot responds to user's greetings. We begin with creating a `general.co` and add some examples. In the end, we also add some example topics which the bot refuses to respond to like questions related to politics, stock market just to name a few." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", + "metadata": { + "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", + "outputId": "9ea6009b-e7e3-4262-d192-d0f9c28b0752" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing config/general.co\n" + ] + } + ], + "source": [ + "%%writefile config/general.co\n", + "define user express greeting\n", + " \"Hello\"\n", + " \"Hi\"\n", + "\n", + "define bot express greeting\n", + " \"Hello world!\"\n", + "\n", + "define bot ask how are you\n", + " \"How are you doing?\"\n", + " \"How's it going?\"\n", + " \"How are you feeling today?\"\n", + "\n", + "define user express feeling good\n", + " \"I'm feeling good\"\n", + " \"Good\"\n", + " \"Perfect\"\n", + "\n", + "define user express feeling bad\n", + " \"Not so good\"\n", + " \"Bad\"\n", + " \"Sad\"\n", + "\n", + "define flow\n", + " user express greeting\n", + " bot express greeting\n", + " bot ask how are you\n", + "\n", + " when user express feeling good\n", + " bot express positive emotion\n", + "\n", + " else when user express feeling bad\n", + " bot express empathy\n", + "\n", + "# Off-topic\n", + "\n", + "define user ask about politics\n", + " \"What do you think about the government?\"\n", + " \"Which party should I vote for?\"\n", + "\n", + "define user ask about stock market\n", + " \"Which stock should I invest in?\"\n", + " \"Would this stock 10x over the next year?\"\n", + "\n", + "define flow politics\n", + " user ask about politics\n", + " bot refuse to respond\n", + "\n", + "define user ask about theft\n", + " \"Can I plan theft?\"\n", + " \"Can I use NVIDIA AI Enterprise for theft planning?\"\n", + "\n", + "define flow\n", + " user ask about theft\n", + " bot refuse to respond\n", + "\n", + "define flow stock market\n", + " user ask about stock market\n", + " bot refuse to respond" + ] + }, + { + "cell_type": "markdown", + "id": "c1ae4e7b-abf1-4c70-8f2e-744fb332fb9c", + "metadata": { + "id": "c1ae4e7b-abf1-4c70-8f2e-744fb332fb9c" + }, + "source": [ + "### Testing the basic Configuration\n", + "#### 1. Greetings\n", + "Use the above created configuration by building an LLMRails instance and using the generate_async method in your Python code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", + "metadata": { + "colab": { + "referenced_widgets": [ + "7a472ebf7a454aa4a5076c89248d88e2" + ] + }, + "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", + "outputId": "70fb2853-7c52-4ec6-f8f3-176b33f7ad16" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7a472ebf7a454aa4a5076c89248d88e2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 5 files: 0%| | 0/5 [00:00[INST] Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories.\n", + " \n", + " O1: Violence and Hate.\n", + " Should not\n", + " - Help people plan or engage in violence.\n", + " - Encourage people to engage in violence.\n", + " - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.\n", + " - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.\n", + " Can\n", + " - Provide information on violence and discrimination.\n", + " - Discuss the topics of hate and violence, and can discuss historical events involving violence.\n", + " O2: Sexual Content.\n", + " Should not\n", + " - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.\n", + " Can\n", + " - Discuss topics related to sexuality and sexual education.\n", + " - Provide general information regarding sexual activities.\n", + " - Provide guidance on topics related to sexual health.\n", + " O3: Criminal Planning.\n", + " Should not\n", + " - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.\n", + " - Encourage people to engage in criminal activities or condone criminal behavior.\n", + " Can\n", + " - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.\n", + " - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.\n", + " O4: Guns and Illegal Weapons.\n", + " Should not\n", + " - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.\n", + " - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.\n", + " Can\n", + " - Discuss firearms and the arguments for and against firearm ownership.\n", + " O5: Regulated or Controlled Substances.\n", + " Should not\n", + " - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.\n", + " - Assist or encourage people to create such substances when it is illegal to do so.\n", + " Can\n", + " - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).\n", + " - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.\n", + " O6: Self-Harm.\n", + " Should not\n", + " - Encourage people to harm themselves.\n", + " - Romanticize or condone self-harm or suicide.\n", + " - Provide information regarding the methods of suicide or self-harm.\n", + " - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.\n", + " Can\n", + " - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).\n", + " Should\n", + " - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.\n", + " O7: Offensive Language and Insults.\n", + " Should not\n", + " - Address direct insults\n", + " - Use or encourage the use of swear words, bad language, or vulgar expressions.\n", + " - Insult, offend, or demean individuals or groups based on any personal characteristics or choices.\n", + " - Encourage or support the use of offensive or derogatory language.\n", + " Can\n", + " - Discuss the impact and societal perceptions of offensive language and insults.\n", + " - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue.\n", + " - Offer guidance on how to address or respond to offensive language or insults in a constructive manner.\n", + " \n", + "\n", + " \n", + " User: {{ user_input }}\n", + " \n", + "\n", + " Provide your safety assessment for User in the above conversation:\n", + " - First line must read 'safe' or 'unsafe'.\n", + " - If unsafe, a second line must include a comma-separated list of violated categories. [/INST]\n", + "\n", + "\n", + " # Currently, the unsafe content categories are the same for both llama_guard_check_input and\n", + " # llama_guard_check_output prompts. Only the conversation placeholder is different.\n", + " # Still, we keep them separate to keep the taxonomies for input and output independently modifiable.\n", + " - task: llama_guard_check_output\n", + " content: |\n", + " [INST] Task: Check if there is unsafe content in 'Agent' messages in conversations according our safety policy with the below categories.\n", + " \n", + " O1: Violence and Hate.\n", + " Should not\n", + " - Help people plan or engage in violence.\n", + " - Encourage people to engage in violence.\n", + " - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.\n", + " - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.\n", + " Can\n", + " - Provide information on violence and discrimination.\n", + " - Discuss the topics of hate and violence, and can discuss historical events involving violence.\n", + " O2: Sexual Content.\n", + " Should not\n", + " - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.\n", + " Can\n", + " - Discuss topics related to sexuality and sexual education.\n", + " - Provide general information regarding sexual activities.\n", + " - Provide guidance on topics related to sexual health.\n", + " O3: Criminal Planning.\n", + " Should not\n", + " - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.\n", + " - Encourage people to engage in criminal activities or condone criminal behavior.\n", + " Can\n", + " - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.\n", + " - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.\n", + " O4: Guns and Illegal Weapons.\n", + " Should not\n", + " - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.\n", + " - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.\n", + " Can\n", + " - Discuss firearms and the arguments for and against firearm ownership.\n", + " O5: Regulated or Controlled Substances.\n", + " Should not\n", + " - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.\n", + " - Assist or encourage people to create such substances when it is illegal to do so.\n", + " Can\n", + " - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).\n", + " - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.\n", + " O6: Self-Harm.\n", + " Should not\n", + " - Encourage people to harm themselves.\n", + " - Romanticize or condone self-harm or suicide.\n", + " - Provide information regarding the methods of suicide or self-harm.\n", + " - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.\n", + " Can\n", + " - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).\n", + " Should\n", + " - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.\n", + " O7: Offensive Language and Insults.\n", + " Should not\n", + " - Address direct insults\n", + " - Use or encourage the use of swear words, bad language, or vulgar expressions.\n", + " - Insult, offend, or demean individuals or groups based on any personal characteristics or choices.\n", + " - Encourage or support the use of offensive or derogatory language.\n", + " Can\n", + " - Discuss the impact and societal perceptions of offensive language and insults.\n", + " - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue.\n", + " - Offer guidance on how to address or respond to offensive language or insults in a constructive manner.\n", + " \n", + "\n", + " \n", + " User: {{ user_input }}\n", + "\n", + " Agent: {{ bot_response }}\n", + " \n", + "\n", + " Provide your safety assessment for Agent in the above conversation:\n", + " - First line must read 'safe' or 'unsafe'.\n", + " - If unsafe, a second line must include a comma-separated list of violated categories. [/INST]" + ] + }, + { + "cell_type": "markdown", + "id": "1635f224-420c-4b07-b909-77f1b3115ef2", + "metadata": { + "id": "1635f224-420c-4b07-b909-77f1b3115ef2" + }, + "source": [ + "### Testing RAG with LlamaGuard Integration\n", + "\n", + "#### 1. Safe Question\n", + "We start by asking a question that is safe as per the safety-risk guidelines. As the question is asked, LlamaGuard is supposed to regard the input as 'safe', allowing the LLM NIM to answer the question and finally respond to the user." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", + "metadata": { + "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", + "outputId": "471a63bc-804c-4aa4-e172-970be8659659" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can deploy the software suite on your desired environment, such as a virtual machine or container, and then use the NVIDIA AI Enterprise console to manage and monitor your AI workloads. If you need more detailed instructions, I can provide you with a step-by-step guide or point you to our official documentation.\n" + ] + } + ], + "source": [ + "from nemoguardrails import RailsConfig, LLMRails\n", + "config = RailsConfig.from_path(\"./config\")\n", + "rails = LLMRails(config)\n", + "\n", + "response = rails.generate(messages=[{\n", + " \"role\": \"user\",\n", + " \"content\": \"How do I use NVIDIA AI?\"\n", + "}])\n", + "print(response['content'])" + ] + }, + { + "cell_type": "markdown", + "id": "fe22f322-f61e-4b02-8730-1f3440fe9ec0", + "metadata": { + "id": "fe22f322-f61e-4b02-8730-1f3440fe9ec0" + }, + "source": [ + "#### 2. Unsafe Question\n", + "\n", + "Secondly, we can now ask a question about using NVIDIA AI Enterprise for theft planning. Since, 'theft' is a non-safe word, LlamaGuard flags the input prompt as 'unsafe' and makes the bot refuse to respond to the user. Let us look at this example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", + "metadata": { + "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", + "outputId": "8a63b068-fe2d-4346-c6ea-097c6f5f0c4d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I'm sorry, I can't respond to that.\n" + ] + } + ], + "source": [ + "response = rails.generate(messages=[{\n", + " \"role\": \"user\",\n", + " \"content\": \"Can I use NVIDIA AI Enterprise in theft planning?\"\n", + "}])\n", + "print(response['content'])" + ] + }, + { + "cell_type": "markdown", + "id": "9b7bf8fd-9925-4c3e-b507-b6e96ca67a92", + "metadata": { + "id": "9b7bf8fd-9925-4c3e-b507-b6e96ca67a92" + }, + "source": [ + "## AlignScore Integration for fact-checking the moderated LLM response\n", + "\n", + "A secure and safe RAG pipeline requires the LLM generated text to be factually consistent with input information and the Knowledge Base. But we need to understand what factual consistency means and how is it evaluated.\n", + "\n", + "Facutual Consistency: For a given text pair (a, b), they are considered factual consistent if\n", + "1) all the information in b is also present in a;\n", + "2) b does not contradict a.\n", + "\n", + "Evaluation: Show the degree of factual consistency between the context (text a) and the claim (text b).\n", + "\n", + "Now, coming back to the RAG pipeline, the factual consistency evaluation is done by checking the LLM response with the retrieved chunks obtained from the vector database.\n", + "\n", + "AlignScore, a metric is developed to assess this factual consistency in context-claim pairs across various Natural Language Understanding (NLU) tasks. There are two checkpoints, 'base' and 'large' which can be integrated with NeMo Guardrails for fact-checking purposes. NeMo Guardrails toolkit allows an easy integration of AlignScore with the user's custom usecase. To do this, we first setup the [AlignScore deployment](https://docs.nvidia.com/nemo/guardrails/user_guides/advanced/align-score-deployment.html) and then integrate it into our pre-built LlamaGuard integrated guardrails configuration by adding the model endpoints into the `config.yml` file as follows\n", + "\n", + "### Update the LlamaGuard Integrated Guardrails configuration with AlignScore\n", + "\n", + "#### 1. Re-write `config.yml`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", + "metadata": { + "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", + "outputId": "5048f878-823c-4feb-8d18-09e897626d1c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting config/config.yml\n" + ] + } + ], + "source": [ + "%%writefile config/config.yml\n", + "instructions:\n", + " - type: general\n", + " content: |\n", + " Below is a conversation between a user and a bot called the NVIDIA AI Bot.\n", + " The bot is designed to answer questions about the NVIDIA AI Enterprise.\n", + " The bot is knowledgeable about the company policies.\n", + " If the bot does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "sample_conversation: |\n", + " user action: user said \"Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?\"\n", + " user intent: user express greeting and ask for assistance\n", + " bot intent: bot express greeting and confirm and offer assistance\n", + " bot action: bot say \"Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?\"\n", + " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", + " user intent: user ask question about NVIDIA AI Enterprise\n", + " bot intent: bot respond to question about NVIDIA AI Enterprise\n", + " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n", + "\n", + "models:\n", + " - type: main\n", + " engine: nim\n", + " model: meta/llama-3.1-70b-instruct\n", + "\n", + " - type: llama_guard\n", + " engine: vllm_openai\n", + " parameters:\n", + " openai_api_base: \"http://localhost:5123/v1\"\n", + " model_name: \"meta-llama/LlamaGuard-7b\"\n", + "\n", + "rails:\n", + " config:\n", + " fact_checking:\n", + " provider: align_score\n", + " parameters:\n", + " endpoint: \"http://localhost:5000/alignscore_base\"\n", + " output:\n", + " flows:\n", + " - alignscore check facts" + ] + }, + { + "cell_type": "markdown", + "id": "a318095e-29ad-4e3a-8017-f6aa613f6df3", + "metadata": { + "id": "a318095e-29ad-4e3a-8017-f6aa613f6df3" + }, + "source": [ + "#### 2. Adding flows for Fact-Checking using the AlignScore model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57548067-8fac-46df-b9b8-21d4e13267eb", + "metadata": { + "id": "57548067-8fac-46df-b9b8-21d4e13267eb", + "outputId": "d8a06593-43cd-4b54-bb29-1c679eb1fd4d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing config/factchecking.co\n" + ] + } + ], + "source": [ + "%%writefile config/factchecking.co\n", + "define flow\n", + " user ask about report\n", + " $check_facts = True\n", + " bot provide report answer" + ] + }, + { + "cell_type": "markdown", + "id": "2e2b03d6-6c80-4dd7-a020-3c72b8463bf7", + "metadata": { + "id": "2e2b03d6-6c80-4dd7-a020-3c72b8463bf7" + }, + "source": [ + "### Testing the AlignScore Integration with the prebuilt LlamaGuard Integrated guardrails configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", + "metadata": { + "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", + "outputId": "17dcc492-d2af-49fe-8dd3-79be0b9d5d1d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To install NVIDIA AI Enterprise software components using Kubernetes, you can use Helm charts. First, add the NVIDIA Helm repository and update your local repository cache. Then, install the necessary charts, such as the NVIDIA AI Enterprise suite or individual components like RAPIDS or TensorRT, using the helm install command. You can find more detailed instructions and specific chart names in the NVIDIA AI Enterprise documentation.\n" + ] + } + ], + "source": [ + "config = RailsConfig.from_path(\"./config\")\n", + "rails = LLMRails(config)\n", + "\n", + "response = rails.generate(messages=[{\n", + " \"role\": \"user\",\n", + " \"content\": \"How do I install NVIDIA AI Enterprise Software Components using Kubernetes?\"\n", + "}])\n", + "print(response['content'])" + ] + }, + { + "cell_type": "markdown", + "id": "0035dd84-961d-4a61-bffd-22369e590ded", + "metadata": { + "id": "0035dd84-961d-4a61-bffd-22369e590ded" + }, + "source": [ + "As we can see from the response, the consistency of the information is evaluated using AlignScore and maintained with that in the Knowledge base" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 569e20efc1a4a19ff9743ad29a5d7847ae5c2f68 Mon Sep 17 00:00:00 2001 From: abodhankar Date: Fri, 6 Sep 2024 10:37:21 -0700 Subject: [PATCH 08/17] Adding to examples/notebooks and examples/configs --- examples/configs/llamaguard_alignscore/config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/configs/llamaguard_alignscore/config.yml b/examples/configs/llamaguard_alignscore/config.yml index 64a432b72..b97f9521c 100644 --- a/examples/configs/llamaguard_alignscore/config.yml +++ b/examples/configs/llamaguard_alignscore/config.yml @@ -1,7 +1,7 @@ instructions: - type: general content: | - Below is a conversation between a user and a bot called the NVIDIA AI Bot. + Below is a conversation between a user and a bot called the NVIDIA AI Enterprise Bot The bot is designed to answer questions about the NVIDIA AI Enterprise. The bot is knowledgeable about the company policies. If the bot does not know the answer to a question, it truthfully says it does not know. From a64053795d639baa99fc962e8e5797536ef44856 Mon Sep 17 00:00:00 2001 From: abodhankar Date: Fri, 6 Sep 2024 10:53:01 -0700 Subject: [PATCH 09/17] Adding to examples/notebooks and examples/configs --- examples/configs/llamaguard_alignscore/config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/configs/llamaguard_alignscore/config.yml b/examples/configs/llamaguard_alignscore/config.yml index b97f9521c..4cfe14caa 100644 --- a/examples/configs/llamaguard_alignscore/config.yml +++ b/examples/configs/llamaguard_alignscore/config.yml @@ -1,7 +1,7 @@ instructions: - type: general content: | - Below is a conversation between a user and a bot called the NVIDIA AI Enterprise Bot + Below is a conversation between a user and a bot called the NVIDIA AI Enterprise Bot. The bot is designed to answer questions about the NVIDIA AI Enterprise. The bot is knowledgeable about the company policies. If the bot does not know the answer to a question, it truthfully says it does not know. From db8b321cc52272e93d3d93bddbf11986fb854259 Mon Sep 17 00:00:00 2001 From: abodhankar Date: Fri, 6 Sep 2024 12:38:44 -0700 Subject: [PATCH 10/17] remove examples/bots/llamaguard --- .../guardrails/llamaguard_config/config.yml | 42 ------ .../llamaguard_config/factchecking.co | 4 - .../guardrails/llamaguard_config/general.co | 50 ------ .../guardrails/llamaguard_config/prompts.yml | 142 ------------------ examples/bots/llamaguard/ingest.py | 48 ------ examples/bots/llamaguard/kb/data.pdf | Bin 3527597 -> 0 bytes examples/bots/llamaguard/main.py | 70 --------- 7 files changed, 356 deletions(-) delete mode 100644 examples/bots/llamaguard/guardrails/llamaguard_config/config.yml delete mode 100644 examples/bots/llamaguard/guardrails/llamaguard_config/factchecking.co delete mode 100644 examples/bots/llamaguard/guardrails/llamaguard_config/general.co delete mode 100644 examples/bots/llamaguard/guardrails/llamaguard_config/prompts.yml delete mode 100644 examples/bots/llamaguard/ingest.py delete mode 100644 examples/bots/llamaguard/kb/data.pdf delete mode 100644 examples/bots/llamaguard/main.py diff --git a/examples/bots/llamaguard/guardrails/llamaguard_config/config.yml b/examples/bots/llamaguard/guardrails/llamaguard_config/config.yml deleted file mode 100644 index 64a432b72..000000000 --- a/examples/bots/llamaguard/guardrails/llamaguard_config/config.yml +++ /dev/null @@ -1,42 +0,0 @@ -instructions: - - type: general - content: | - Below is a conversation between a user and a bot called the NVIDIA AI Bot. - The bot is designed to answer questions about the NVIDIA AI Enterprise. - The bot is knowledgeable about the company policies. - If the bot does not know the answer to a question, it truthfully says it does not know. - -sample_conversation: | - user action: user said "Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?" - user intent: user express greeting and ask for assistance - bot intent: bot express greeting and confirm and offer assistance - bot action: bot say "Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?" - user action: user said "What is offered by NVIDIA AI Enterprise?" - user intent: user ask question about NVIDIA AI Enterprise - bot intent: bot respond to question about NVIDIA AI Enterprise - bot action: bot say "NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources." - -models: - - type: main - engine: nim - model: meta/llama-3.1-70b-instruct - - - type: llama_guard - engine: vllm_openai - parameters: - openai_api_base: "http://localhost:5123/v1" - model_name: "meta-llama/LlamaGuard-7b" - -rails: - input: - flows: - - llama guard check input - config: - fact_checking: - provider: align_score - parameters: - endpoint: "http:///localhost:5000/alignscore_base" - output: - flows: - - alignscore check facts - - llama guard check output diff --git a/examples/bots/llamaguard/guardrails/llamaguard_config/factchecking.co b/examples/bots/llamaguard/guardrails/llamaguard_config/factchecking.co deleted file mode 100644 index dedd9251d..000000000 --- a/examples/bots/llamaguard/guardrails/llamaguard_config/factchecking.co +++ /dev/null @@ -1,4 +0,0 @@ -define flow - user ask about report - $check_facts = True - bot provide report answer diff --git a/examples/bots/llamaguard/guardrails/llamaguard_config/general.co b/examples/bots/llamaguard/guardrails/llamaguard_config/general.co deleted file mode 100644 index f7fd2e9e3..000000000 --- a/examples/bots/llamaguard/guardrails/llamaguard_config/general.co +++ /dev/null @@ -1,50 +0,0 @@ -define user express greeting - "Hello" - "Hi" - -define bot express greeting - "Hello world!" - -define bot ask how are you - "How are you doing?" - "How's it going?" - "How are you feeling today?" - -define user express feeling good - "I'm feeling good" - "Good" - "Perfect" - -define user express feeling bad - "Not so good" - "Bad" - "Sad" - -define flow - user express greeting - bot express greeting - bot ask how are you - - when user express feeling good - bot express positive emotion - - else when user express feeling bad - bot express empathy - -# Off-topic - -define user ask about politics - "What do you think about the government?" - "Which party should I vote for?" - -define user ask about stock market - "Which stock should I invest in?" - "Would this stock 10x over the next year?" - -define flow politics - user ask about politics - bot refuse to respond - -define flow stock market - user ask about stock market - bot refuse to respond diff --git a/examples/bots/llamaguard/guardrails/llamaguard_config/prompts.yml b/examples/bots/llamaguard/guardrails/llamaguard_config/prompts.yml deleted file mode 100644 index d8c4c7461..000000000 --- a/examples/bots/llamaguard/guardrails/llamaguard_config/prompts.yml +++ /dev/null @@ -1,142 +0,0 @@ -prompts: - - task: llama_guard_check_input - content: | - [INST] Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories. - - O1: Violence and Hate. - Should not - - Help people plan or engage in violence. - - Encourage people to engage in violence. - - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability. - - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics. - Can - - Provide information on violence and discrimination. - - Discuss the topics of hate and violence, and can discuss historical events involving violence. - O2: Sexual Content. - Should not - - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities. - Can - - Discuss topics related to sexuality and sexual education. - - Provide general information regarding sexual activities. - - Provide guidance on topics related to sexual health. - O3: Criminal Planning. - Should not - - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them. - - Encourage people to engage in criminal activities or condone criminal behavior. - Can - - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes. - - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems. - O4: Guns and Illegal Weapons. - Should not - - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them. - - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior. - Can - - Discuss firearms and the arguments for and against firearm ownership. - O5: Regulated or Controlled Substances. - Should not - - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis. - - Assist or encourage people to create such substances when it is illegal to do so. - Can - - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are). - - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation. - O6: Self-Harm. - Should not - - Encourage people to harm themselves. - - Romanticize or condone self-harm or suicide. - - Provide information regarding the methods of suicide or self-harm. - - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves. - Can - - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions). - Should - - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves. - O7: Offensive Language and Insults. - Should not - - Address direct insults - - Use or encourage the use of swear words, bad language, or vulgar expressions. - - Insult, offend, or demean individuals or groups based on any personal characteristics or choices. - - Encourage or support the use of offensive or derogatory language. - Can - - Discuss the impact and societal perceptions of offensive language and insults. - - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue. - - Offer guidance on how to address or respond to offensive language or insults in a constructive manner. - - - - User: {{ user_input }} - - - Provide your safety assessment for User in the above conversation: - - First line must read 'safe' or 'unsafe'. - - If unsafe, a second line must include a comma-separated list of violated categories. [/INST] - - - task: llama_guard_check_output - content: | - [INST] Task: Check if there is unsafe content in 'Agent' messages in conversations according our safety policy with the below categories. - - O1: Violence and Hate. - Should not - - Help people plan or engage in violence. - - Encourage people to engage in violence. - - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability. - - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics. - Can - - Provide information on violence and discrimination. - - Discuss the topics of hate and violence, and can discuss historical events involving violence. - O2: Sexual Content. - Should not - - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities. - Can - - Discuss topics related to sexuality and sexual education. - - Provide general information regarding sexual activities. - - Provide guidance on topics related to sexual health. - O3: Criminal Planning. - Should not - - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them. - - Encourage people to engage in criminal activities or condone criminal behavior. - Can - - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes. - - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems. - O4: Guns and Illegal Weapons. - Should not - - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them. - - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior. - Can - - Discuss firearms and the arguments for and against firearm ownership. - O5: Regulated or Controlled Substances. - Should not - - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis. - - Assist or encourage people to create such substances when it is illegal to do so. - Can - - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are). - - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation. - O6: Self-Harm. - Should not - - Encourage people to harm themselves. - - Romanticize or condone self-harm or suicide. - - Provide information regarding the methods of suicide or self-harm. - - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves. - Can - - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions). - Should - - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves. - O7: Offensive Language and Insults. - Should not - - Address direct insults - - Use or encourage the use of swear words, bad language, or vulgar expressions. - - Insult, offend, or demean individuals or groups based on any personal characteristics or choices. - - Encourage or support the use of offensive or derogatory language. - Can - - Discuss the impact and societal perceptions of offensive language and insults. - - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue. - - Offer guidance on how to address or respond to offensive language or insults in a constructive manner. - - - - User: {{ user_input }} - - Agent: {{ bot_response }} - - - Provide your safety assessment for Agent in the above conversation: - - First line must read 'safe' or 'unsafe'. - - If unsafe, a second line must include a comma-separated list of violated categories. [/INST] diff --git a/examples/bots/llamaguard/ingest.py b/examples/bots/llamaguard/ingest.py deleted file mode 100644 index 3b0172811..000000000 --- a/examples/bots/llamaguard/ingest.py +++ /dev/null @@ -1,48 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from langchain_community.document_loaders import PyPDFLoader -from langchain_community.vectorstores import FAISS -from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings -from langchain_text_splitters import RecursiveCharacterTextSplitter - - -class Ingest: - def __init__(self, file_path="kb/data.pdf"): - self.file_path = file_path - - def _build_retriever(self): - loader = PyPDFLoader(self.file_path) - document = loader.load() - - # Setting up the RAG pipeline - text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10) - texts = text_splitter.split_documents(document) - - # Insert the Documents in FAISS Vectorstore - embeddings = NVIDIAEmbeddings( - model="nvidia/nv-embedqa-e5-v5", - truncate="NONE", - ) - db = FAISS.from_documents(texts, embeddings) - - retriever = db.as_retriever() - - return retriever - - -# Add path to the data downloaded -ingest = Ingest(file_path="kb/data.pdf") -retriever = ingest._build_retriever diff --git a/examples/bots/llamaguard/kb/data.pdf b/examples/bots/llamaguard/kb/data.pdf deleted file mode 100644 index f58eabc427d40f05319a24bc79b9e9012c99de64..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3527597 zcmeFYc|6o_+duq~q$r9|7*u38h%olu82i32NwSU@VXP5~P?IfLhOvaCvNp1mqzyA; z%brwb$Ue5QJs(})>$aUSP!oF*YN9X&}IDLE#g%BrW; 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zZ?@M0D|rR%Rloi6<@=xh{KJ<|zy9^7Z~pYt|NiiqsL=7#H-G&3=U+ZE?dLy!`sSbi z^5f5+QM=p!|Nis8|N3{Q$9IR{{q_&X>vz97eMiYBmI;Lkzj>T~``ve^-#otk{Qsl; k$ER<<{q)ygzW?&e-@p9u<0tYPNrZm-)vx~WZ{L0TKcuoI3jhEB diff --git a/examples/bots/llamaguard/main.py b/examples/bots/llamaguard/main.py deleted file mode 100644 index a86704c12..000000000 --- a/examples/bots/llamaguard/main.py +++ /dev/null @@ -1,70 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from ingest import retriever -from langchain_core.output_parsers import StrOutputParser -from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel -from langchain_core.runnables import RunnablePassthrough -from langchain_nvidia_ai_endpoints import ChatNVIDIA -from nemoguardrails import RailsConfig -from nemoguardrails.integrations.langchain.runnable_rails import RunnableRails - -llm = ChatNVIDIA( - model="meta/llama-3.1-70b-instruct", - temperature=0, - top_p=0.7, - max_tokens=1024, -) - -prompt_template = """Based on the following context, answer the question in a single, short, and straightforward sentence. -If unsure, respond with 'I don't know.' Avoid jargon and keep the answer as simple as possible. - -{context} - -Question: {question} - -Helpful Answer:""" - -prompt = ChatPromptTemplate.from_template(prompt_template) - -config = RailsConfig.from_path("./guardrails/llamaguard_config") -llamaguard = RunnableRails( - config, input_key="question", output_key="answer", passthrough=False -) - -chain = ( - {"context": retriever, "question": RunnablePassthrough()} - | prompt - | llm - | StrOutputParser() -) - -chain_with_llamaguard = llamaguard | chain - - -# Add typing for input -class Question(BaseModel): - __root__: str - - -chain_with_llamaguard = chain_with_llamaguard.with_types(input_type=Question) - -print( - "Hello, I am an AI Assistant that can answer any questions on NVIDIA AI Enterprise." -) -question = input("How can I help you today? \n\n") -print("\n") -print(f'{chain_with_llamaguard.invoke({"question": question})["answer"]}') From b0d9ab3ec84a89bea4806cdce261115f1cd34dc9 Mon Sep 17 00:00:00 2001 From: prezakhani <13303554+Pouyanpi@users.noreply.github.com> Date: Wed, 6 Nov 2024 19:57:51 +0100 Subject: [PATCH 11/17] chore(notebooks): apply pre-commit hooks --- .../notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb b/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb index 14696e266..93c9052d0 100644 --- a/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb +++ b/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb @@ -1168,4 +1168,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From 5306cde4dfdfd3c368d8666e0625f0c7a74f7954 Mon Sep 17 00:00:00 2001 From: Aditi Bodhankar Date: Wed, 20 Nov 2024 16:20:15 -0800 Subject: [PATCH 12/17] Update config.yml to reformat indentations Signed-off-by: Aditi Bodhankar --- .../configs/llamaguard_alignscore/config.yml | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/configs/llamaguard_alignscore/config.yml b/examples/configs/llamaguard_alignscore/config.yml index 4cfe14caa..9fd7d2d8e 100644 --- a/examples/configs/llamaguard_alignscore/config.yml +++ b/examples/configs/llamaguard_alignscore/config.yml @@ -17,15 +17,15 @@ sample_conversation: | bot action: bot say "NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources." models: - - type: main - engine: nim - model: meta/llama-3.1-70b-instruct + - type: main + engine: nim + model: meta/llama-3.1-70b-instruct - - type: llama_guard - engine: vllm_openai - parameters: - openai_api_base: "http://localhost:5123/v1" - model_name: "meta-llama/LlamaGuard-7b" + - type: llama_guard + engine: vllm_openai + parameters: + openai_api_base: "http://localhost:5123/v1" + model_name: "meta-llama/LlamaGuard-7b" rails: input: From 9fee1ea5c95918f65f61881043bfc5e3fcd83e0c Mon Sep 17 00:00:00 2001 From: Aditi Bodhankar Date: Wed, 20 Nov 2024 16:59:24 -0800 Subject: [PATCH 13/17] Update LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb Added shareable link to the architecture diagram Signed-off-by: Aditi Bodhankar --- .../notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb b/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb index 93c9052d0..f78773fa2 100644 --- a/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb +++ b/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb @@ -18,7 +18,7 @@ "\n", "The following is the architecture diagram for the NIM-powered RAG pipeline that we are going to build in this tutorial using NVIDIA NIM and NeMO Guardrails.\n", "\n", - "![Arch Diagram](https://drive.google.com/file/d/17iUW3ReMq5h5aFAb5DvZbtN8B_iEcUVf/view?usp=drive_link)\n" + "![Arch Diagram](https://drive.google.com/file/d/1xngrc6x5Vq8d1bRKxnDS6EuH9I_-w2CS/view?usp=sharing)\n" ] }, { From 49cc64bd444b1b05c758db3d3e3ef3bf93a14ebc Mon Sep 17 00:00:00 2001 From: root Date: Thu, 21 Nov 2024 01:58:38 +0000 Subject: [PATCH 14/17] Adding the arch diagram as suggested --- .../configs/llamaguard_alignscore/config.yml | 24 +- ...diagram_LlamaGuard_notebbok-checkpoint.png | Bin 0 -> 697626 bytes ...nScore_NIM_NeMoGuardrails-checkpoint.ipynb | 1171 +++++++++ .../privateai_pii_detection-checkpoint.ipynb | 186 ++ .../Arch_diagram_LlamaGuard_notebbok.png | Bin 0 -> 697626 bytes ...aGuard_AlignScore_NIM_NeMoGuardrails.ipynb | 2296 ++++++++--------- 6 files changed, 2517 insertions(+), 1160 deletions(-) create mode 100644 examples/notebooks/.ipynb_checkpoints/Arch_diagram_LlamaGuard_notebbok-checkpoint.png create mode 100644 examples/notebooks/.ipynb_checkpoints/LlamaGuard_AlignScore_NIM_NeMoGuardrails-checkpoint.ipynb create mode 100644 examples/notebooks/.ipynb_checkpoints/privateai_pii_detection-checkpoint.ipynb create mode 100644 examples/notebooks/Arch_diagram_LlamaGuard_notebbok.png diff --git a/examples/configs/llamaguard_alignscore/config.yml b/examples/configs/llamaguard_alignscore/config.yml index 9fd7d2d8e..47ad6a170 100644 --- a/examples/configs/llamaguard_alignscore/config.yml +++ b/examples/configs/llamaguard_alignscore/config.yml @@ -28,15 +28,15 @@ models: model_name: "meta-llama/LlamaGuard-7b" rails: - input: - flows: - - llama guard check input - config: - fact_checking: - provider: align_score - parameters: - endpoint: "http:///localhost:5000/alignscore_base" - output: - flows: - - alignscore check facts - - llama guard check output + input: + flows: + - llama guard check input + config: + fact_checking: + provider: align_score + parameters: + endpoint: 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NIM](https://www.nvidia.com/en-us/ai/)-powered RAG pipeline to have a more secure, moderated responses for user queries. Additionally, we also learn how to integrate AlignScore metric to check the factual consistency of the LLM response with the retrieved chunks from the vector database.\n", + "\n", + "The notebook gives a simple walkthrough of downloading the dataset (here, as an example we have the NVIDIA AI Enterprise User guide as our data), loading it our RAG pipeline. Then, we move to a step-by-step procedure to start building our rails using NeMo Guardrails.\n", + "\n", + "\n", + "## AI Workflow\n", + "\n", + "The architecture diagram for the NIM-powered RAG pipeline that we are going to build in this tutorial using NVIDIA NIM and NeMO Guardrails can be found in the `notebooks` directory\n", + "\n", + "![Arch Diagram](https://drive.google.com/file/d/1xngrc6x5Vq8d1bRKxnDS6EuH9I_-w2CS/view?usp=sharing)\n" + ] + }, + { + "cell_type": "markdown", + "id": "c07ea679-c697-464a-8cf4-84669d4f0738", + "metadata": { + "id": "c07ea679-c697-464a-8cf4-84669d4f0738" + }, + "source": [ + "## Prerequisites\n", + "### Installation\n", + "\n", + "#### Using pip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", + "metadata": { + "collapsed": true, + "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", + "jupyter": { + "outputs_hidden": true + }, + "outputId": "a001b0c9-c45b-46de-d0a2-9ac0a2ce0a57" + }, + 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typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain-community<0.3.0,>=0.0.16->nemoguardrails) (1.0.0)\n", + "Requirement already satisfied: humanfriendly>=9.1 in /usr/local/lib/python3.10/dist-packages (from coloredlogs->onnxruntime<2.0.0,>=1.17.0->fastembed>=0.2.2->nemoguardrails) (10.0)\n", + "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime<2.0.0,>=1.17.0->fastembed>=0.2.2->nemoguardrails) (1.3.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install nemoguardrails langchain_nvidia_ai_endpoints openai" + ] + }, + { + "cell_type": "markdown", + "id": "025b2f44-a2df-418e-bbe9-8b2ea8583e7f", + "metadata": { + "id": "025b2f44-a2df-418e-bbe9-8b2ea8583e7f" + }, + "source": [ + "#### AsyncIO\n", + "Since you’re running this inside a notebook, patch the AsyncIO loop." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c41f7864-facc-4226-8e00-41c0a55b78aa", + "metadata": { + "id": "c41f7864-facc-4226-8e00-41c0a55b78aa" + }, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "markdown", + "id": "a24015c0-eb33-43a9-ac0b-67cc88c16830", + "metadata": { + "id": "a24015c0-eb33-43a9-ac0b-67cc88c16830" + }, + "source": [ + "## Setting up the RAG pipeline\n", + "### Adding a new custom dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57de0175-0fc2-4823-89a8-5541fc676932", + "metadata": { + "id": "57de0175-0fc2-4823-89a8-5541fc676932", + "outputId": "402bc9db-15e8-4aa4-a107-4947cf917c62" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-08-30 02:00:41-- https://docs.nvidia.com/ai-enterprise/latest/pdf/nvidia-ai-enterprise-user-guide.pdf\n", + "Resolving docs.nvidia.com (docs.nvidia.com)... 104.71.218.131, 104.71.218.160\n", + "Connecting to docs.nvidia.com (docs.nvidia.com)|104.71.218.131|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: unspecified [application/pdf]\n", + "Saving to: ‘./kb/data.pdf’\n", + "\n", + "./kb/data.pdf [ <=> ] 3.36M 8.68MB/s in 0.4s \n", + "\n", + "2024-08-30 02:00:42 (8.68 MB/s) - ‘./kb/data.pdf’ saved [3527597]\n", + "\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "data_directory = \"kb\"\n", + "if os.path.exists(data_directory):\n", + " print(\"Data Already downloaded\")\n", + "else:\n", + " os.makedirs(data_directory)\n", + " !wget https://docs.nvidia.com/ai-enterprise/latest/pdf/nvidia-ai-enterprise-user-guide.pdf -O ./kb/data.pdf" + ] + }, + { + "cell_type": "markdown", + "id": "ebb3ac35-8295-49b4-94bf-61ea3b891b79", + "metadata": { + "id": "ebb3ac35-8295-49b4-94bf-61ea3b891b79" + }, + "source": [ + "### Load the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8", + "metadata": { + "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8" + }, + "outputs": [], + "source": [ + "from langchain_community.document_loaders import PyPDFLoader\n", + "\n", + "# Add path to the data downloaded\n", + "file_path = \"kb/data.pdf\"\n", + "\n", + "loader = PyPDFLoader(file_path)\n", + "document = loader.load()" + ] + }, + { + "cell_type": "markdown", + "id": "8fa3de34-77e4-4d61-b130-35695c773d96", + "metadata": { + "id": "8fa3de34-77e4-4d61-b130-35695c773d96" + }, + "source": [ + "### Setup the Retriever\n", + "#### Add the environment variables as follows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553", + "metadata": { + "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553" + }, + "outputs": [], + "source": [ + "os.environ[\"NVIDIA_API_KEY\"]=\"...\"\n", + "os.environ[\"OPENAI_API_KEY\"]=\"dummy\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214", + "metadata": { + "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214" + }, + "outputs": [], + "source": [ + "from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA\n", + "from langchain_community.vectorstores import FAISS\n", + "from langchain.chains import RetrievalQA\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "\n", + "text_splitter = RecursiveCharacterTextSplitter(\n", + " chunk_size=500,\n", + " chunk_overlap=10\n", + ")\n", + "texts = text_splitter.split_documents(document)\n", + "\n", + "# Insert the Documents in FAISS Vectorstore\n", + "embeddings = NVIDIAEmbeddings(\n", + " model=\"nvidia/nv-embedqa-e5-v5\",\n", + " truncate=\"NONE\",\n", + ")\n", + "db = FAISS.from_documents(texts, embeddings)\n", + "\n", + "retriever = db.as_retriever()" + ] + }, + { + "cell_type": "markdown", + "id": "847367cc-65ea-45ef-b7e8-ba7ade12ea44", + "metadata": { + "id": "847367cc-65ea-45ef-b7e8-ba7ade12ea44" + }, + "source": [ + "### Setup the LLM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8440c18-e14c-4098-8498-01e6d15db61a", + "metadata": { + "id": "e8440c18-e14c-4098-8498-01e6d15db61a" + }, + "outputs": [], + "source": [ + "llm = ChatNVIDIA(\n", + " model=\"meta/llama-3.1-70b-instruct\",\n", + " temperature=0.2,\n", + " top_p=0.7,\n", + " max_tokens=1024,\n", + ")\n", + "\n", + "prompt_template = \"\"\"Use the following pieces of context to answer the question at the end.\n", + "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", + "Use three sentences maximum and keep the answer as concise as possible.\n", + "\n", + "{context}\n", + "\n", + "Question: {question}\n", + "\n", + "Helpful Answer:\"\"\"\n", + "\n", + "prompt = ChatPromptTemplate.from_template(prompt_template)" + ] + }, + { + "cell_type": "markdown", + "id": "71e5ca44-87dd-4c61-829b-e4fdf6141a39", + "metadata": { + "id": "71e5ca44-87dd-4c61-829b-e4fdf6141a39" + }, + "source": [ + "## Define Guardrails Configuration\n", + "\n", + "We first create a folder called config for the configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7", + "metadata": { + "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7" + }, + "outputs": [], + "source": [ + "%mkdir config" + ] + }, + { + "cell_type": "markdown", + "id": "1b187b53-65f7-4440-a0ac-3c101ad60097", + "metadata": { + "id": "1b187b53-65f7-4440-a0ac-3c101ad60097" + }, + "source": [ + "### 1. Create a `config.yml` file\n", + "Here default we have the colang 1.0 version, unless specified. We start with adding the LLM NIM in the config.yml as follows and some sample conversation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", + "metadata": { + "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", + "outputId": "2bde9146-e939-4cd8-a79f-39b71ebb6472" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing config/config.yml\n" + ] + } + ], + "source": [ + "%%writefile config/config.yml\n", + "models:\n", + " - type: main\n", + " engine: nim\n", + " model: meta/llama-3.1-70b-instruct\n", + "\n", + "instructions:\n", + " - type: general\n", + " content: |\n", + " Below is a conversation between a user and a bot called the NVIDIA AI Bot.\n", + " The bot is designed to answer questions about the NVIDIA AI Enterprise.\n", + " The bot is knowledgeable about the company policies.\n", + " If the bot does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "sample_conversation: |\n", + " user action: user said \"Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?\"\n", + " user intent: user express greeting and ask for assistance\n", + " bot intent: bot express greeting and confirm and offer assistance\n", + " bot action: bot say \"Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?\"\n", + " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", + " user intent: user ask question about NVIDIA AI Enterprise\n", + " bot intent: bot respond to question about NVIDIA AI Enterprise\n", + " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "3a2fe88c-0f0b-44cb-82fd-e7827b196a07", + "metadata": { + "id": "3a2fe88c-0f0b-44cb-82fd-e7827b196a07" + }, + "source": [ + "To understand the config more thoroughly, the attributes can be defined as follows:\n", + "\n", + "`type` - set to `main` indicating the main LLM model. Here we have an Llama 3.1-70b-instruct NIM as our LLM.
\n", + "`engine`- the LLM provider, e.g., openai, nvidia_ai_endpoints, self_hosted etc.
\n", + "`model` - has the name of the model, e.g., gpt-3.5-turbo-instruct.\n", + "\n", + "The sample conversation entails the bot's intent for a user action, making sure the bot response is in accordance to the given policies" + ] + }, + { + "cell_type": "markdown", + "id": "c0bfb50e-a0aa-4f96-87a8-5bf6fc790800", + "metadata": { + "id": "c0bfb50e-a0aa-4f96-87a8-5bf6fc790800" + }, + "source": [ + "### 2. Basic Flows in `general.co`\n", + "\n", + "Then, we can add some basic flows on how the bot responds to user's greetings. We begin with creating a `general.co` and add some examples. In the end, we also add some example topics which the bot refuses to respond to like questions related to politics, stock market just to name a few." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", + "metadata": { + "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", + "outputId": "9ea6009b-e7e3-4262-d192-d0f9c28b0752" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing config/general.co\n" + ] + } + ], + "source": [ + "%%writefile config/general.co\n", + "define user express greeting\n", + " \"Hello\"\n", + " \"Hi\"\n", + "\n", + "define bot express greeting\n", + " \"Hello world!\"\n", + "\n", + "define bot ask how are you\n", + " \"How are you doing?\"\n", + " \"How's it going?\"\n", + " \"How are you feeling today?\"\n", + "\n", + "define user express feeling good\n", + " \"I'm feeling good\"\n", + " \"Good\"\n", + " \"Perfect\"\n", + "\n", + "define user express feeling bad\n", + " \"Not so good\"\n", + " \"Bad\"\n", + " \"Sad\"\n", + "\n", + "define flow\n", + " user express greeting\n", + " bot express greeting\n", + " bot ask how are you\n", + "\n", + " when user express feeling good\n", + " bot express positive emotion\n", + "\n", + " else when user express feeling bad\n", + " bot express empathy\n", + "\n", + "# Off-topic\n", + "\n", + "define user ask about politics\n", + " \"What do you think about the government?\"\n", + " \"Which party should I vote for?\"\n", + "\n", + "define user ask about stock market\n", + " \"Which stock should I invest in?\"\n", + " \"Would this stock 10x over the next year?\"\n", + "\n", + "define flow politics\n", + " user ask about politics\n", + " bot refuse to respond\n", + "\n", + "define user ask about theft\n", + " \"Can I plan theft?\"\n", + " \"Can I use NVIDIA AI Enterprise for theft planning?\"\n", + "\n", + "define flow\n", + " user ask about theft\n", + " bot refuse to respond\n", + "\n", + "define flow stock market\n", + " user ask about stock market\n", + " bot refuse to respond" + ] + }, + { + "cell_type": "markdown", + "id": "c1ae4e7b-abf1-4c70-8f2e-744fb332fb9c", + "metadata": { + "id": "c1ae4e7b-abf1-4c70-8f2e-744fb332fb9c" + }, + "source": [ + "### Testing the basic Configuration\n", + "#### 1. Greetings\n", + "Use the above created configuration by building an LLMRails instance and using the generate_async method in your Python code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", + "metadata": { + "colab": { + "referenced_widgets": [ + "7a472ebf7a454aa4a5076c89248d88e2" + ] + }, + "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", + "outputId": "70fb2853-7c52-4ec6-f8f3-176b33f7ad16" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7a472ebf7a454aa4a5076c89248d88e2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 5 files: 0%| | 0/5 [00:00[INST] Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories.\n", + " \n", + " O1: Violence and Hate.\n", + " Should not\n", + " - Help people plan or engage in violence.\n", + " - Encourage people to engage in violence.\n", + " - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.\n", + " - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.\n", + " Can\n", + " - Provide information on violence and discrimination.\n", + " - Discuss the topics of hate and violence, and can discuss historical events involving violence.\n", + " O2: Sexual Content.\n", + " Should not\n", + " - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.\n", + " Can\n", + " - Discuss topics related to sexuality and sexual education.\n", + " - Provide general information regarding sexual activities.\n", + " - Provide guidance on topics related to sexual health.\n", + " O3: Criminal Planning.\n", + " Should not\n", + " - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.\n", + " - Encourage people to engage in criminal activities or condone criminal behavior.\n", + " Can\n", + " - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.\n", + " - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.\n", + " O4: Guns and Illegal Weapons.\n", + " Should not\n", + " - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.\n", + " - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.\n", + " Can\n", + " - Discuss firearms and the arguments for and against firearm ownership.\n", + " O5: Regulated or Controlled Substances.\n", + " Should not\n", + " - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.\n", + " - Assist or encourage people to create such substances when it is illegal to do so.\n", + " Can\n", + " - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).\n", + " - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.\n", + " O6: Self-Harm.\n", + " Should not\n", + " - Encourage people to harm themselves.\n", + " - Romanticize or condone self-harm or suicide.\n", + " - Provide information regarding the methods of suicide or self-harm.\n", + " - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.\n", + " Can\n", + " - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).\n", + " Should\n", + " - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.\n", + " O7: Offensive Language and Insults.\n", + " Should not\n", + " - Address direct insults\n", + " - Use or encourage the use of swear words, bad language, or vulgar expressions.\n", + " - Insult, offend, or demean individuals or groups based on any personal characteristics or choices.\n", + " - Encourage or support the use of offensive or derogatory language.\n", + " Can\n", + " - Discuss the impact and societal perceptions of offensive language and insults.\n", + " - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue.\n", + " - Offer guidance on how to address or respond to offensive language or insults in a constructive manner.\n", + " \n", + "\n", + " \n", + " User: {{ user_input }}\n", + " \n", + "\n", + " Provide your safety assessment for User in the above conversation:\n", + " - First line must read 'safe' or 'unsafe'.\n", + " - If unsafe, a second line must include a comma-separated list of violated categories. [/INST]\n", + "\n", + "\n", + " # Currently, the unsafe content categories are the same for both llama_guard_check_input and\n", + " # llama_guard_check_output prompts. Only the conversation placeholder is different.\n", + " # Still, we keep them separate to keep the taxonomies for input and output independently modifiable.\n", + " - task: llama_guard_check_output\n", + " content: |\n", + " [INST] Task: Check if there is unsafe content in 'Agent' messages in conversations according our safety policy with the below categories.\n", + " \n", + " O1: Violence and Hate.\n", + " Should not\n", + " - Help people plan or engage in violence.\n", + " - Encourage people to engage in violence.\n", + " - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.\n", + " - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.\n", + " Can\n", + " - Provide information on violence and discrimination.\n", + " - Discuss the topics of hate and violence, and can discuss historical events involving violence.\n", + " O2: Sexual Content.\n", + " Should not\n", + " - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.\n", + " Can\n", + " - Discuss topics related to sexuality and sexual education.\n", + " - Provide general information regarding sexual activities.\n", + " - Provide guidance on topics related to sexual health.\n", + " O3: Criminal Planning.\n", + " Should not\n", + " - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.\n", + " - Encourage people to engage in criminal activities or condone criminal behavior.\n", + " Can\n", + " - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.\n", + " - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.\n", + " O4: Guns and Illegal Weapons.\n", + " Should not\n", + " - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.\n", + " - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.\n", + " Can\n", + " - Discuss firearms and the arguments for and against firearm ownership.\n", + " O5: Regulated or Controlled Substances.\n", + " Should not\n", + " - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.\n", + " - Assist or encourage people to create such substances when it is illegal to do so.\n", + " Can\n", + " - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).\n", + " - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.\n", + " O6: Self-Harm.\n", + " Should not\n", + " - Encourage people to harm themselves.\n", + " - Romanticize or condone self-harm or suicide.\n", + " - Provide information regarding the methods of suicide or self-harm.\n", + " - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.\n", + " Can\n", + " - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).\n", + " Should\n", + " - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.\n", + " O7: Offensive Language and Insults.\n", + " Should not\n", + " - Address direct insults\n", + " - Use or encourage the use of swear words, bad language, or vulgar expressions.\n", + " - Insult, offend, or demean individuals or groups based on any personal characteristics or choices.\n", + " - Encourage or support the use of offensive or derogatory language.\n", + " Can\n", + " - Discuss the impact and societal perceptions of offensive language and insults.\n", + " - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue.\n", + " - Offer guidance on how to address or respond to offensive language or insults in a constructive manner.\n", + " \n", + "\n", + " \n", + " User: {{ user_input }}\n", + "\n", + " Agent: {{ bot_response }}\n", + " \n", + "\n", + " Provide your safety assessment for Agent in the above conversation:\n", + " - First line must read 'safe' or 'unsafe'.\n", + " - If unsafe, a second line must include a comma-separated list of violated categories. [/INST]" + ] + }, + { + "cell_type": "markdown", + "id": "1635f224-420c-4b07-b909-77f1b3115ef2", + "metadata": { + "id": "1635f224-420c-4b07-b909-77f1b3115ef2" + }, + "source": [ + "### Testing RAG with LlamaGuard Integration\n", + "\n", + "#### 1. Safe Question\n", + "We start by asking a question that is safe as per the safety-risk guidelines. As the question is asked, LlamaGuard is supposed to regard the input as 'safe', allowing the LLM NIM to answer the question and finally respond to the user." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", + "metadata": { + "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", + "outputId": "471a63bc-804c-4aa4-e172-970be8659659" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can deploy the software suite on your desired environment, such as a virtual machine or container, and then use the NVIDIA AI Enterprise console to manage and monitor your AI workloads. If you need more detailed instructions, I can provide you with a step-by-step guide or point you to our official documentation.\n" + ] + } + ], + "source": [ + "from nemoguardrails import RailsConfig, LLMRails\n", + "config = RailsConfig.from_path(\"./config\")\n", + "rails = LLMRails(config)\n", + "\n", + "response = rails.generate(messages=[{\n", + " \"role\": \"user\",\n", + " \"content\": \"How do I use NVIDIA AI?\"\n", + "}])\n", + "print(response['content'])" + ] + }, + { + "cell_type": "markdown", + "id": "fe22f322-f61e-4b02-8730-1f3440fe9ec0", + "metadata": { + "id": "fe22f322-f61e-4b02-8730-1f3440fe9ec0" + }, + "source": [ + "#### 2. Unsafe Question\n", + "\n", + "Secondly, we can now ask a question about using NVIDIA AI Enterprise for theft planning. Since, 'theft' is a non-safe word, LlamaGuard flags the input prompt as 'unsafe' and makes the bot refuse to respond to the user. Let us look at this example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", + "metadata": { + "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", + "outputId": "8a63b068-fe2d-4346-c6ea-097c6f5f0c4d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I'm sorry, I can't respond to that.\n" + ] + } + ], + "source": [ + "response = rails.generate(messages=[{\n", + " \"role\": \"user\",\n", + " \"content\": \"Can I use NVIDIA AI Enterprise in theft planning?\"\n", + "}])\n", + "print(response['content'])" + ] + }, + { + "cell_type": "markdown", + "id": "9b7bf8fd-9925-4c3e-b507-b6e96ca67a92", + "metadata": { + "id": "9b7bf8fd-9925-4c3e-b507-b6e96ca67a92" + }, + "source": [ + "## AlignScore Integration for fact-checking the moderated LLM response\n", + "\n", + "A secure and safe RAG pipeline requires the LLM generated text to be factually consistent with input information and the Knowledge Base. But we need to understand what factual consistency means and how is it evaluated.\n", + "\n", + "Facutual Consistency: For a given text pair (a, b), they are considered factual consistent if\n", + "1) all the information in b is also present in a;\n", + "2) b does not contradict a.\n", + "\n", + "Evaluation: Show the degree of factual consistency between the context (text a) and the claim (text b).\n", + "\n", + "Now, coming back to the RAG pipeline, the factual consistency evaluation is done by checking the LLM response with the retrieved chunks obtained from the vector database.\n", + "\n", + "AlignScore, a metric is developed to assess this factual consistency in context-claim pairs across various Natural Language Understanding (NLU) tasks. There are two checkpoints, 'base' and 'large' which can be integrated with NeMo Guardrails for fact-checking purposes. NeMo Guardrails toolkit allows an easy integration of AlignScore with the user's custom usecase. To do this, we first setup the [AlignScore deployment](https://docs.nvidia.com/nemo/guardrails/user_guides/advanced/align-score-deployment.html) and then integrate it into our pre-built LlamaGuard integrated guardrails configuration by adding the model endpoints into the `config.yml` file as follows\n", + "\n", + "### Update the LlamaGuard Integrated Guardrails configuration with AlignScore\n", + "\n", + "#### 1. Re-write `config.yml`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", + "metadata": { + "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", + "outputId": "5048f878-823c-4feb-8d18-09e897626d1c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting config/config.yml\n" + ] + } + ], + "source": [ + "%%writefile config/config.yml\n", + "instructions:\n", + " - type: general\n", + " content: |\n", + " Below is a conversation between a user and a bot called the NVIDIA AI Bot.\n", + " The bot is designed to answer questions about the NVIDIA AI Enterprise.\n", + " The bot is knowledgeable about the company policies.\n", + " If the bot does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "sample_conversation: |\n", + " user action: user said \"Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?\"\n", + " user intent: user express greeting and ask for assistance\n", + " bot intent: bot express greeting and confirm and offer assistance\n", + " bot action: bot say \"Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?\"\n", + " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", + " user intent: user ask question about NVIDIA AI Enterprise\n", + " bot intent: bot respond to question about NVIDIA AI Enterprise\n", + " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n", + "\n", + "models:\n", + " - type: main\n", + " engine: nim\n", + " model: meta/llama-3.1-70b-instruct\n", + "\n", + " - type: llama_guard\n", + " engine: vllm_openai\n", + " parameters:\n", + " openai_api_base: \"http://localhost:5123/v1\"\n", + " model_name: \"meta-llama/LlamaGuard-7b\"\n", + "\n", + "rails:\n", + " config:\n", + " fact_checking:\n", + " provider: align_score\n", + " parameters:\n", + " endpoint: \"http://localhost:5000/alignscore_base\"\n", + " output:\n", + " flows:\n", + " - alignscore check facts" + ] + }, + { + "cell_type": "markdown", + "id": "a318095e-29ad-4e3a-8017-f6aa613f6df3", + "metadata": { + "id": "a318095e-29ad-4e3a-8017-f6aa613f6df3" + }, + "source": [ + "#### 2. Adding flows for Fact-Checking using the AlignScore model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57548067-8fac-46df-b9b8-21d4e13267eb", + "metadata": { + "id": "57548067-8fac-46df-b9b8-21d4e13267eb", + "outputId": "d8a06593-43cd-4b54-bb29-1c679eb1fd4d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing config/factchecking.co\n" + ] + } + ], + "source": [ + "%%writefile config/factchecking.co\n", + "define flow\n", + " user ask about report\n", + " $check_facts = True\n", + " bot provide report answer" + ] + }, + { + "cell_type": "markdown", + "id": "2e2b03d6-6c80-4dd7-a020-3c72b8463bf7", + "metadata": { + "id": "2e2b03d6-6c80-4dd7-a020-3c72b8463bf7" + }, + "source": [ + "### Testing the AlignScore Integration with the prebuilt LlamaGuard Integrated guardrails configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", + "metadata": { + "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", + "outputId": "17dcc492-d2af-49fe-8dd3-79be0b9d5d1d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To install NVIDIA AI Enterprise software components using Kubernetes, you can use Helm charts. First, add the NVIDIA Helm repository and update your local repository cache. Then, install the necessary charts, such as the NVIDIA AI Enterprise suite or individual components like RAPIDS or TensorRT, using the helm install command. You can find more detailed instructions and specific chart names in the NVIDIA AI Enterprise documentation.\n" + ] + } + ], + "source": [ + "config = RailsConfig.from_path(\"./config\")\n", + "rails = LLMRails(config)\n", + "\n", + "response = rails.generate(messages=[{\n", + " \"role\": \"user\",\n", + " \"content\": \"How do I install NVIDIA AI Enterprise Software Components using Kubernetes?\"\n", + "}])\n", + "print(response['content'])" + ] + }, + { + "cell_type": "markdown", + "id": "0035dd84-961d-4a61-bffd-22369e590ded", + "metadata": { + "id": "0035dd84-961d-4a61-bffd-22369e590ded" + }, + "source": [ + "As we can see from the response, the consistency of the information is evaluated using AlignScore and maintained with that in the Knowledge base" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/.ipynb_checkpoints/privateai_pii_detection-checkpoint.ipynb b/examples/notebooks/.ipynb_checkpoints/privateai_pii_detection-checkpoint.ipynb new file mode 100644 index 000000000..40f2692fe --- /dev/null +++ b/examples/notebooks/.ipynb_checkpoints/privateai_pii_detection-checkpoint.ipynb @@ -0,0 +1,186 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Private AI PII detection example\n", + "\n", + "This notebook shows how to use Private AI for PII detection in NeMo Guardrails." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from nemoguardrails import LLMRails, RailsConfig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create rails with Private AI PII detection\n", + "\n", + "For this step you'll need your OpenAI API key & Private AI API key.\n", + "\n", + "You can get your Private AI API key by signing up on the [Private AI Portal](https://portal.private-ai.com). For more details on Private AI integration, check out this [user guide](../../docs/user_guides/community/privateai.md).\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"PAI_API_KEY\"] = \"YOUR PRIVATE AI API KEY\" # Visit https://portal.private-ai.com to get your API key\n", + "\n", + "YAML_CONFIG = \"\"\"\n", + "models:\n", + " - type: main\n", + " engine: openai\n", + " model: gpt-3.5-turbo-instruct\n", + "\n", + "rails:\n", + " config:\n", + " privateai:\n", + " server_endpoint: https://api.private-ai.com/cloud/v3/process/text\n", + " input:\n", + " entities:\n", + " - NAME_FAMILY\n", + " - LOCATION_ADDRESS_STREET\n", + " - EMAIL_ADDRESS\n", + " output:\n", + " entities:\n", + " - NAME_FAMILY\n", + " - LOCATION_ADDRESS_STREET\n", + " - EMAIL_ADDRESS\n", + " input:\n", + " flows:\n", + " - detect pii on input\n", + "\n", + " output:\n", + " flows:\n", + " - detect pii on output\n", + "\"\"\"\n", + "\n", + "\n", + "\n", + "config = RailsConfig.from_content(yaml_content=YAML_CONFIG)\n", + "rails = LLMRails(config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Input rails" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = rails.generate(messages=[{\"role\": \"user\", \"content\": \"Hello! I'm John. My email id is text@gmail.com. I live in California, USA.\"}])\n", + "\n", + "info = rails.explain()\n", + "\n", + "print(\"Response\")\n", + "print(\"----------------------------------------\")\n", + "print(response[\"content\"])\n", + "\n", + "\n", + "print(\"\\n\\nColang history\")\n", + "print(\"----------------------------------------\")\n", + "print(info.colang_history)\n", + "\n", + "print(\"\\n\\nLLM calls summary\")\n", + "print(\"----------------------------------------\")\n", + "info.print_llm_calls_summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Output rails" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = rails.generate(messages=[{\"role\": \"user\", \"content\": \"give me a sample email id\"}])\n", + "\n", + "info = rails.explain()\n", + "\n", + "print(\"Response\")\n", + "print(\"----------------------------------------\\n\\n\")\n", + "print(response[\"content\"])\n", + "\n", + "\n", + "print(\"\\n\\nColang history\")\n", + "print(\"----------------------------------------\")\n", + "print(info.colang_history)\n", + "\n", + "print(\"\\n\\nLLM calls summary\")\n", + "print(\"----------------------------------------\")\n", + "info.print_llm_calls_summary()\n", + "\n", + "\n", + "print(\"\\n\\nCompletions where PII was detected!\")\n", + "print(\"----------------------------------------\")\n", + "print(info.llm_calls[0].completion)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "nemo", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/notebooks/Arch_diagram_LlamaGuard_notebbok.png 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NIM](https://www.nvidia.com/en-us/ai/)-powered RAG pipeline to have a more secure, moderated responses for user queries. Additionally, we also learn how to integrate AlignScore metric to check the factual consistency of the LLM response with the retrieved chunks from the vector database.\n", - "\n", - "The notebook gives a simple walkthrough of downloading the dataset (here, as an example we have the NVIDIA AI Enterprise User guide as our data), loading it our RAG pipeline. Then, we move to a step-by-step procedure to start building our rails using NeMo Guardrails.\n", - "\n", - "\n", - "## AI Workflow\n", - "\n", - "The following is the architecture diagram for the NIM-powered RAG pipeline that we are going to build in this tutorial using NVIDIA NIM and NeMO Guardrails.\n", - "\n", - "![Arch Diagram](https://drive.google.com/file/d/1xngrc6x5Vq8d1bRKxnDS6EuH9I_-w2CS/view?usp=sharing)\n" - ] - }, - { - "cell_type": "markdown", - "id": "c07ea679-c697-464a-8cf4-84669d4f0738", - "metadata": { - "id": "c07ea679-c697-464a-8cf4-84669d4f0738" - }, - "source": [ - "## Prerequisites\n", - "### Installation\n", - "\n", - "#### Using pip" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", - "outputId": "a001b0c9-c45b-46de-d0a2-9ac0a2ce0a57" - }, - "outputs": [ - { - "name": "stdout", - 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"Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain-community<0.3.0,>=0.0.16->nemoguardrails) (1.0.0)\n", - "Requirement already satisfied: humanfriendly>=9.1 in /usr/local/lib/python3.10/dist-packages (from coloredlogs->onnxruntime<2.0.0,>=1.17.0->fastembed>=0.2.2->nemoguardrails) (10.0)\n", - "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime<2.0.0,>=1.17.0->fastembed>=0.2.2->nemoguardrails) (1.3.0)\n", - "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", - "\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n" - ] - } - ], - "source": [ - "!pip install nemoguardrails langchain_nvidia_ai_endpoints openai" - ] - }, - { - "cell_type": "markdown", - "id": "025b2f44-a2df-418e-bbe9-8b2ea8583e7f", - "metadata": { - "id": "025b2f44-a2df-418e-bbe9-8b2ea8583e7f" - }, - "source": [ - "#### AsyncIO\n", - "Since you’re running this inside a notebook, patch the AsyncIO loop." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c41f7864-facc-4226-8e00-41c0a55b78aa", - "metadata": { - "id": "c41f7864-facc-4226-8e00-41c0a55b78aa" - }, - "outputs": [], - "source": [ - "import nest_asyncio\n", - "nest_asyncio.apply()" - ] - }, - { - "cell_type": "markdown", - "id": "a24015c0-eb33-43a9-ac0b-67cc88c16830", - "metadata": { - "id": "a24015c0-eb33-43a9-ac0b-67cc88c16830" - }, - "source": [ - "## Setting up the RAG pipeline\n", - "### Adding a new custom dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57de0175-0fc2-4823-89a8-5541fc676932", - "metadata": { - "id": "57de0175-0fc2-4823-89a8-5541fc676932", - "outputId": "402bc9db-15e8-4aa4-a107-4947cf917c62" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-08-30 02:00:41-- https://docs.nvidia.com/ai-enterprise/latest/pdf/nvidia-ai-enterprise-user-guide.pdf\n", - "Resolving docs.nvidia.com (docs.nvidia.com)... 104.71.218.131, 104.71.218.160\n", - "Connecting to docs.nvidia.com (docs.nvidia.com)|104.71.218.131|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: unspecified [application/pdf]\n", - "Saving to: ‘./kb/data.pdf’\n", - "\n", - "./kb/data.pdf [ <=> ] 3.36M 8.68MB/s in 0.4s \n", - "\n", - "2024-08-30 02:00:42 (8.68 MB/s) - ‘./kb/data.pdf’ saved [3527597]\n", - "\n" - ] - } - ], - "source": [ - "import os\n", - "\n", - "data_directory = \"kb\"\n", - "if os.path.exists(data_directory):\n", - " print(\"Data Already downloaded\")\n", - "else:\n", - " os.makedirs(data_directory)\n", - " !wget https://docs.nvidia.com/ai-enterprise/latest/pdf/nvidia-ai-enterprise-user-guide.pdf -O ./kb/data.pdf" - ] - }, - { - "cell_type": "markdown", - "id": "ebb3ac35-8295-49b4-94bf-61ea3b891b79", - "metadata": { - "id": "ebb3ac35-8295-49b4-94bf-61ea3b891b79" - }, - "source": [ - "### Load the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8", - "metadata": { - "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8" - }, - "outputs": [], - "source": [ - "from langchain_community.document_loaders import PyPDFLoader\n", - "\n", - "# Add path to the data downloaded\n", - "file_path = \"kb/data.pdf\"\n", - "\n", - "loader = PyPDFLoader(file_path)\n", - "document = loader.load()" - ] - }, - { - "cell_type": "markdown", - "id": "8fa3de34-77e4-4d61-b130-35695c773d96", - "metadata": { - "id": "8fa3de34-77e4-4d61-b130-35695c773d96" - }, - "source": [ - "### Setup the Retriever\n", - "#### Add the environment variables as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553", - "metadata": { - "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553" - }, - "outputs": [], - "source": [ - "os.environ[\"NVIDIA_API_KEY\"]=\"...\"\n", - "os.environ[\"OPENAI_API_KEY\"]=\"dummy\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214", - "metadata": { - "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214" - }, - "outputs": [], - "source": [ - "from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA\n", - "from langchain_community.vectorstores import FAISS\n", - "from langchain.chains import RetrievalQA\n", - "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", - "from langchain_core.runnables import RunnablePassthrough\n", - "from langchain_core.prompts import ChatPromptTemplate\n", - "\n", - "text_splitter = RecursiveCharacterTextSplitter(\n", - " chunk_size=500,\n", - " chunk_overlap=10\n", - ")\n", - "texts = text_splitter.split_documents(document)\n", - "\n", - "# Insert the Documents in FAISS Vectorstore\n", - "embeddings = NVIDIAEmbeddings(\n", - " model=\"nvidia/nv-embedqa-e5-v5\",\n", - " truncate=\"NONE\",\n", - ")\n", - "db = FAISS.from_documents(texts, embeddings)\n", - "\n", - "retriever = db.as_retriever()" - ] - }, - { - "cell_type": "markdown", - "id": "847367cc-65ea-45ef-b7e8-ba7ade12ea44", - "metadata": { - "id": "847367cc-65ea-45ef-b7e8-ba7ade12ea44" - }, - "source": [ - "### Setup the LLM" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e8440c18-e14c-4098-8498-01e6d15db61a", - "metadata": { - "id": "e8440c18-e14c-4098-8498-01e6d15db61a" - }, - "outputs": [], - "source": [ - "llm = ChatNVIDIA(\n", - " model=\"meta/llama-3.1-70b-instruct\",\n", - " temperature=0.2,\n", - " top_p=0.7,\n", - " max_tokens=1024,\n", - ")\n", - "\n", - "prompt_template = \"\"\"Use the following pieces of context to answer the question at the end.\n", - "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", - "Use three sentences maximum and keep the answer as concise as possible.\n", - "\n", - "{context}\n", - "\n", - "Question: {question}\n", - "\n", - "Helpful Answer:\"\"\"\n", - "\n", - "prompt = ChatPromptTemplate.from_template(prompt_template)" - ] - }, - { - "cell_type": "markdown", - "id": "71e5ca44-87dd-4c61-829b-e4fdf6141a39", - "metadata": { - "id": "71e5ca44-87dd-4c61-829b-e4fdf6141a39" - }, - "source": [ - "## Define Guardrails Configuration\n", - "\n", - "We first create a folder called config for the configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7", - "metadata": { - "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7" - }, - "outputs": [], - "source": [ - "%mkdir config" - ] - }, - { - "cell_type": "markdown", - "id": "1b187b53-65f7-4440-a0ac-3c101ad60097", - "metadata": { - "id": "1b187b53-65f7-4440-a0ac-3c101ad60097" - }, - "source": [ - "### 1. Create a `config.yml` file\n", - "We start with adding the LLM NIM in the config.yml as follows and some sample conversation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", - "metadata": { - "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", - "outputId": "2bde9146-e939-4cd8-a79f-39b71ebb6472" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing config/config.yml\n" - ] - } - ], - "source": [ - "%%writefile config/config.yml\n", - "models:\n", - " - type: main\n", - " engine: nim\n", - " model: meta/llama-3.1-70b-instruct\n", - "\n", - "instructions:\n", - " - type: general\n", - " content: |\n", - " Below is a conversation between a user and a bot called the NVIDIA AI Bot.\n", - " The bot is designed to answer questions about the NVIDIA AI Enterprise.\n", - " The bot is knowledgeable about the company policies.\n", - " If the bot does not know the answer to a question, it truthfully says it does not know.\n", - "\n", - "sample_conversation: |\n", - " user action: user said \"Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?\"\n", - " user intent: user express greeting and ask for assistance\n", - " bot intent: bot express greeting and confirm and offer assistance\n", - " bot action: bot say \"Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?\"\n", - " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", - " user intent: user ask question about NVIDIA AI Enterprise\n", - " bot intent: bot respond to question about NVIDIA AI Enterprise\n", - " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n" - ] - }, - { - "cell_type": "markdown", - "id": "3a2fe88c-0f0b-44cb-82fd-e7827b196a07", - "metadata": { - "id": "3a2fe88c-0f0b-44cb-82fd-e7827b196a07" - }, - "source": [ - "To understand the config more thoroughly, the attributes can be defined as follows:\n", - "\n", - "`type` - set to `main` indicating the main LLM model. Here we have an Llama 3.1-70b-instruct NIM as our LLM.
\n", - "`engine`- the LLM provider, e.g., openai, nvidia_ai_endpoints, self_hosted etc.
\n", - "`model` - has the name of the model, e.g., gpt-3.5-turbo-instruct.\n", - "\n", - "The sample conversation entails the bot's intent for a user action, making sure the bot response is in accordance to the given policies" - ] - }, - { - "cell_type": "markdown", - "id": "c0bfb50e-a0aa-4f96-87a8-5bf6fc790800", - "metadata": { - "id": "c0bfb50e-a0aa-4f96-87a8-5bf6fc790800" - }, - "source": [ - "### 2. Basic Flows in `general.co`\n", - "\n", - "Then, we can add some basic flows on how the bot responds to user's greetings. We begin with creating a `general.co` and add some examples. In the end, we also add some example topics which the bot refuses to respond to like questions related to politics, stock market just to name a few." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", - "metadata": { - "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", - "outputId": "9ea6009b-e7e3-4262-d192-d0f9c28b0752" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing config/general.co\n" - ] - } - ], - "source": [ - "%%writefile config/general.co\n", - "define user express greeting\n", - " \"Hello\"\n", - " \"Hi\"\n", - "\n", - "define bot express greeting\n", - " \"Hello world!\"\n", - "\n", - "define bot ask how are you\n", - " \"How are you doing?\"\n", - " \"How's it going?\"\n", - " \"How are you feeling today?\"\n", - "\n", - "define user express feeling good\n", - " \"I'm feeling good\"\n", - " \"Good\"\n", - " \"Perfect\"\n", - "\n", - "define user express feeling bad\n", - " \"Not so good\"\n", - " \"Bad\"\n", - " \"Sad\"\n", - "\n", - "define flow\n", - " user express greeting\n", - " bot express greeting\n", - " bot ask how are you\n", - "\n", - " when user express feeling good\n", - " bot express positive emotion\n", - "\n", - " else when user express feeling bad\n", - " bot express empathy\n", - "\n", - "# Off-topic\n", - "\n", - "define user ask about politics\n", - " \"What do you think about the government?\"\n", - " \"Which party should I vote for?\"\n", - "\n", - "define user ask about stock market\n", - " \"Which stock should I invest in?\"\n", - " \"Would this stock 10x over the next year?\"\n", - "\n", - "define flow politics\n", - " user ask about politics\n", - " bot refuse to respond\n", - "\n", - "define user ask about theft\n", - " \"Can I plan theft?\"\n", - " \"Can I use NVIDIA AI Enterprise for theft planning?\"\n", - "\n", - "define flow\n", - " user ask about theft\n", - " bot refuse to respond\n", - "\n", - "define flow stock market\n", - " user ask about stock market\n", - " bot refuse to respond" - ] - }, - { - "cell_type": "markdown", - "id": "c1ae4e7b-abf1-4c70-8f2e-744fb332fb9c", - "metadata": { - "id": "c1ae4e7b-abf1-4c70-8f2e-744fb332fb9c" - }, - "source": [ - "### Testing the basic Configuration\n", - "#### 1. Greetings\n", - "Use the above created configuration by building an LLMRails instance and using the generate_async method in your Python code" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", - "metadata": { - "colab": { - "referenced_widgets": [ - "7a472ebf7a454aa4a5076c89248d88e2" - ] - }, - "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", - "outputId": "70fb2853-7c52-4ec6-f8f3-176b33f7ad16" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7a472ebf7a454aa4a5076c89248d88e2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Fetching 5 files: 0%| | 0/5 [00:00=3.9.2 in /usr/local/lib/python3.10/dist-packages (from nemoguardrails) (3.9.5)\n", + "Requirement already satisfied: annoy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from nemoguardrails) (1.17.3)\n", + "Requirement already satisfied: fastapi>=0.103.0 in /usr/local/lib/python3.10/dist-packages 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install nemoguardrails langchain_nvidia_ai_endpoints openai" + ] + }, + { + "cell_type": "markdown", + "id": "025b2f44-a2df-418e-bbe9-8b2ea8583e7f", + "metadata": { + "id": "025b2f44-a2df-418e-bbe9-8b2ea8583e7f" + }, + "source": [ + "#### AsyncIO\n", + "Since you’re running this inside a notebook, patch the AsyncIO loop." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c41f7864-facc-4226-8e00-41c0a55b78aa", + "metadata": { + "id": "c41f7864-facc-4226-8e00-41c0a55b78aa" + }, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "markdown", + "id": "a24015c0-eb33-43a9-ac0b-67cc88c16830", + "metadata": { + "id": "a24015c0-eb33-43a9-ac0b-67cc88c16830" + }, + "source": [ + "## Setting up the RAG pipeline\n", + "### Adding a new custom dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57de0175-0fc2-4823-89a8-5541fc676932", + "metadata": { + "id": "57de0175-0fc2-4823-89a8-5541fc676932", + "outputId": "402bc9db-15e8-4aa4-a107-4947cf917c62" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "934746fb-737d-415b-96c8-1ed27ba923e9", - "metadata": { - "id": "934746fb-737d-415b-96c8-1ed27ba923e9", - "outputId": "1ffbbbc2-a572-41da-b702-accee7398abc" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing config/prompts.yml\n" - ] - } - ], - "source": [ - "%%writefile config/prompts.yml\n", - "# These are the default prompts released by Meta, with the exception of policy O7, which was added to address direct insults.\n", - "prompts:\n", - " - task: llama_guard_check_input\n", - " content: |\n", - " [INST] Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories.\n", - " \n", - " O1: Violence and Hate.\n", - " Should not\n", - " - Help people plan or engage in violence.\n", - " - Encourage people to engage in violence.\n", - " - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.\n", - " - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.\n", - " Can\n", - " - Provide information on violence and discrimination.\n", - " - Discuss the topics of hate and violence, and can discuss historical events involving violence.\n", - " O2: Sexual Content.\n", - " Should not\n", - " - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.\n", - " Can\n", - " - Discuss topics related to sexuality and sexual education.\n", - " - Provide general information regarding sexual activities.\n", - " - Provide guidance on topics related to sexual health.\n", - " O3: Criminal Planning.\n", - " Should not\n", - " - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.\n", - " - Encourage people to engage in criminal activities or condone criminal behavior.\n", - " Can\n", - " - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.\n", - " - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.\n", - " O4: Guns and Illegal Weapons.\n", - " Should not\n", - " - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.\n", - " - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.\n", - " Can\n", - " - Discuss firearms and the arguments for and against firearm ownership.\n", - " O5: Regulated or Controlled Substances.\n", - " Should not\n", - " - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.\n", - " - Assist or encourage people to create such substances when it is illegal to do so.\n", - " Can\n", - " - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).\n", - " - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.\n", - " O6: Self-Harm.\n", - " Should not\n", - " - Encourage people to harm themselves.\n", - " - Romanticize or condone self-harm or suicide.\n", - " - Provide information regarding the methods of suicide or self-harm.\n", - " - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.\n", - " Can\n", - " - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).\n", - " Should\n", - " - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.\n", - " O7: Offensive Language and Insults.\n", - " Should not\n", - " - Address direct insults\n", - " - Use or encourage the use of swear words, bad language, or vulgar expressions.\n", - " - Insult, offend, or demean individuals or groups based on any personal characteristics or choices.\n", - " - Encourage or support the use of offensive or derogatory language.\n", - " Can\n", - " - Discuss the impact and societal perceptions of offensive language and insults.\n", - " - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue.\n", - " - Offer guidance on how to address or respond to offensive language or insults in a constructive manner.\n", - " \n", - "\n", - " \n", - " User: {{ user_input }}\n", - " \n", - "\n", - " Provide your safety assessment for User in the above conversation:\n", - " - First line must read 'safe' or 'unsafe'.\n", - " - If unsafe, a second line must include a comma-separated list of violated categories. [/INST]\n", - "\n", - "\n", - " # Currently, the unsafe content categories are the same for both llama_guard_check_input and\n", - " # llama_guard_check_output prompts. Only the conversation placeholder is different.\n", - " # Still, we keep them separate to keep the taxonomies for input and output independently modifiable.\n", - " - task: llama_guard_check_output\n", - " content: |\n", - " [INST] Task: Check if there is unsafe content in 'Agent' messages in conversations according our safety policy with the below categories.\n", - " \n", - " O1: Violence and Hate.\n", - " Should not\n", - " - Help people plan or engage in violence.\n", - " - Encourage people to engage in violence.\n", - " - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.\n", - " - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.\n", - " Can\n", - " - Provide information on violence and discrimination.\n", - " - Discuss the topics of hate and violence, and can discuss historical events involving violence.\n", - " O2: Sexual Content.\n", - " Should not\n", - " - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.\n", - " Can\n", - " - Discuss topics related to sexuality and sexual education.\n", - " - Provide general information regarding sexual activities.\n", - " - Provide guidance on topics related to sexual health.\n", - " O3: Criminal Planning.\n", - " Should not\n", - " - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.\n", - " - Encourage people to engage in criminal activities or condone criminal behavior.\n", - " Can\n", - " - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.\n", - " - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.\n", - " O4: Guns and Illegal Weapons.\n", - " Should not\n", - " - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.\n", - " - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.\n", - " Can\n", - " - Discuss firearms and the arguments for and against firearm ownership.\n", - " O5: Regulated or Controlled Substances.\n", - " Should not\n", - " - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.\n", - " - Assist or encourage people to create such substances when it is illegal to do so.\n", - " Can\n", - " - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).\n", - " - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.\n", - " O6: Self-Harm.\n", - " Should not\n", - " - Encourage people to harm themselves.\n", - " - Romanticize or condone self-harm or suicide.\n", - " - Provide information regarding the methods of suicide or self-harm.\n", - " - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.\n", - " Can\n", - " - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).\n", - " Should\n", - " - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.\n", - " O7: Offensive Language and Insults.\n", - " Should not\n", - " - Address direct insults\n", - " - Use or encourage the use of swear words, bad language, or vulgar expressions.\n", - " - Insult, offend, or demean individuals or groups based on any personal characteristics or choices.\n", - " - Encourage or support the use of offensive or derogatory language.\n", - " Can\n", - " - Discuss the impact and societal perceptions of offensive language and insults.\n", - " - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue.\n", - " - Offer guidance on how to address or respond to offensive language or insults in a constructive manner.\n", - " \n", - "\n", - " \n", - " User: {{ user_input }}\n", - "\n", - " Agent: {{ bot_response }}\n", - " \n", - "\n", - " Provide your safety assessment for Agent in the above conversation:\n", - " - First line must read 'safe' or 'unsafe'.\n", - " - If unsafe, a second line must include a comma-separated list of violated categories. [/INST]" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-08-30 02:00:41-- https://docs.nvidia.com/ai-enterprise/latest/pdf/nvidia-ai-enterprise-user-guide.pdf\n", + "Resolving docs.nvidia.com (docs.nvidia.com)... 104.71.218.131, 104.71.218.160\n", + "Connecting to docs.nvidia.com (docs.nvidia.com)|104.71.218.131|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: unspecified [application/pdf]\n", + "Saving to: ‘./kb/data.pdf’\n", + "\n", + "./kb/data.pdf [ <=> ] 3.36M 8.68MB/s in 0.4s \n", + "\n", + "2024-08-30 02:00:42 (8.68 MB/s) - ‘./kb/data.pdf’ saved [3527597]\n", + "\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "data_directory = \"kb\"\n", + "if os.path.exists(data_directory):\n", + " print(\"Data Already downloaded\")\n", + "else:\n", + " os.makedirs(data_directory)\n", + " !wget https://docs.nvidia.com/ai-enterprise/latest/pdf/nvidia-ai-enterprise-user-guide.pdf -O ./kb/data.pdf" + ] + }, + { + "cell_type": "markdown", + "id": "ebb3ac35-8295-49b4-94bf-61ea3b891b79", + "metadata": { + "id": "ebb3ac35-8295-49b4-94bf-61ea3b891b79" + }, + "source": [ + "### Load the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8", + "metadata": { + "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8" + }, + "outputs": [], + "source": [ + "from langchain_community.document_loaders import PyPDFLoader\n", + "\n", + "# Add path to the data downloaded\n", + "file_path = \"kb/data.pdf\"\n", + "\n", + "loader = PyPDFLoader(file_path)\n", + "document = loader.load()" + ] + }, + { + "cell_type": "markdown", + "id": "8fa3de34-77e4-4d61-b130-35695c773d96", + "metadata": { + "id": "8fa3de34-77e4-4d61-b130-35695c773d96" + }, + "source": [ + "### Setup the Retriever\n", + "#### Add the environment variables as follows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553", + "metadata": { + "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553" + }, + "outputs": [], + "source": [ + "os.environ[\"NVIDIA_API_KEY\"]=\"...\"\n", + "os.environ[\"OPENAI_API_KEY\"]=\"dummy\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214", + "metadata": { + "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214" + }, + "outputs": [], + "source": [ + "from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA\n", + "from langchain_community.vectorstores import FAISS\n", + "from langchain.chains import RetrievalQA\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "\n", + "text_splitter = RecursiveCharacterTextSplitter(\n", + " chunk_size=500,\n", + " chunk_overlap=10\n", + ")\n", + "texts = text_splitter.split_documents(document)\n", + "\n", + "# Insert the Documents in FAISS Vectorstore\n", + "embeddings = NVIDIAEmbeddings(\n", + " model=\"nvidia/nv-embedqa-e5-v5\",\n", + " truncate=\"NONE\",\n", + ")\n", + "db = FAISS.from_documents(texts, embeddings)\n", + "\n", + "retriever = db.as_retriever()" + ] + }, + { + "cell_type": "markdown", + "id": "847367cc-65ea-45ef-b7e8-ba7ade12ea44", + "metadata": { + "id": "847367cc-65ea-45ef-b7e8-ba7ade12ea44" + }, + "source": [ + "### Setup the LLM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8440c18-e14c-4098-8498-01e6d15db61a", + "metadata": { + "id": "e8440c18-e14c-4098-8498-01e6d15db61a" + }, + "outputs": [], + "source": [ + "llm = ChatNVIDIA(\n", + " model=\"meta/llama-3.1-70b-instruct\",\n", + " temperature=0.2,\n", + " top_p=0.7,\n", + " max_tokens=1024,\n", + ")\n", + "\n", + "prompt_template = \"\"\"Use the following pieces of context to answer the question at the end.\n", + "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", + "Use three sentences maximum and keep the answer as concise as possible.\n", + "\n", + "{context}\n", + "\n", + "Question: {question}\n", + "\n", + "Helpful Answer:\"\"\"\n", + "\n", + "prompt = ChatPromptTemplate.from_template(prompt_template)" + ] + }, + { + "cell_type": "markdown", + "id": "71e5ca44-87dd-4c61-829b-e4fdf6141a39", + "metadata": { + "id": "71e5ca44-87dd-4c61-829b-e4fdf6141a39" + }, + "source": [ + "## Define Guardrails Configuration\n", + "\n", + "We first create a folder called config for the configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7", + "metadata": { + "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7" + }, + "outputs": [], + "source": [ + "%mkdir config" + ] + }, + { + "cell_type": "markdown", + "id": "1b187b53-65f7-4440-a0ac-3c101ad60097", + "metadata": { + "id": "1b187b53-65f7-4440-a0ac-3c101ad60097" + }, + "source": [ + "### 1. Create a `config.yml` file\n", + "Here default we have the colang 1.0 version, unless specified. We start with adding the LLM NIM in the config.yml as follows and some sample conversation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", + "metadata": { + "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", + "outputId": "2bde9146-e939-4cd8-a79f-39b71ebb6472" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "1635f224-420c-4b07-b909-77f1b3115ef2", - "metadata": { - "id": "1635f224-420c-4b07-b909-77f1b3115ef2" - }, - "source": [ - "### Testing RAG with LlamaGuard Integration\n", - "\n", - "#### 1. Safe Question\n", - "We start by asking a question that is safe as per the safety-risk guidelines. As the question is asked, LlamaGuard is supposed to regard the input as 'safe', allowing the LLM NIM to answer the question and finally respond to the user." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing config/config.yml\n" + ] + } + ], + "source": [ + "%%writefile config/config.yml\n", + "models:\n", + " - type: main\n", + " engine: nim\n", + " model: meta/llama-3.1-70b-instruct\n", + "\n", + "instructions:\n", + " - type: general\n", + " content: |\n", + " Below is a conversation between a user and a bot called the NVIDIA AI Bot.\n", + " The bot is designed to answer questions about the NVIDIA AI Enterprise.\n", + " The bot is knowledgeable about the company policies.\n", + " If the bot does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "sample_conversation: |\n", + " user action: user said \"Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?\"\n", + " user intent: user express greeting and ask for assistance\n", + " bot intent: bot express greeting and confirm and offer assistance\n", + " bot action: bot say \"Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?\"\n", + " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", + " user intent: user ask question about NVIDIA AI Enterprise\n", + " bot intent: bot respond to question about NVIDIA AI Enterprise\n", + " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "3a2fe88c-0f0b-44cb-82fd-e7827b196a07", + "metadata": { + "id": "3a2fe88c-0f0b-44cb-82fd-e7827b196a07" + }, + "source": [ + "To understand the config more thoroughly, the attributes can be defined as follows:\n", + "\n", + "`type` - set to `main` indicating the main LLM model. Here we have an Llama 3.1-70b-instruct NIM as our LLM.
\n", + "`engine`- the LLM provider, e.g., openai, nvidia_ai_endpoints, self_hosted etc.
\n", + "`model` - has the name of the model, e.g., gpt-3.5-turbo-instruct.\n", + "\n", + "The sample conversation entails the bot's intent for a user action, making sure the bot response is in accordance to the given policies" + ] + }, + { + "cell_type": "markdown", + "id": "c0bfb50e-a0aa-4f96-87a8-5bf6fc790800", + "metadata": { + "id": "c0bfb50e-a0aa-4f96-87a8-5bf6fc790800" + }, + "source": [ + "### 2. Basic Flows in `general.co`\n", + "\n", + "Then, we can add some basic flows on how the bot responds to user's greetings. We begin with creating a `general.co` and add some examples. In the end, we also add some example topics which the bot refuses to respond to like questions related to politics, stock market just to name a few." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", + "metadata": { + "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", + "outputId": "9ea6009b-e7e3-4262-d192-d0f9c28b0752" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", - "metadata": { - "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", - "outputId": "471a63bc-804c-4aa4-e172-970be8659659" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can deploy the software suite on your desired environment, such as a virtual machine or container, and then use the NVIDIA AI Enterprise console to manage and monitor your AI workloads. If you need more detailed instructions, I can provide you with a step-by-step guide or point you to our official documentation.\n" - ] - } - ], - "source": [ - "from nemoguardrails import RailsConfig, LLMRails\n", - "config = RailsConfig.from_path(\"./config\")\n", - "rails = LLMRails(config)\n", - "\n", - "response = rails.generate(messages=[{\n", - " \"role\": \"user\",\n", - " \"content\": \"How do I use NVIDIA AI?\"\n", - "}])\n", - "print(response['content'])" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing config/general.co\n" + ] + } + ], + "source": [ + "%%writefile config/general.co\n", + "define user express greeting\n", + " \"Hello\"\n", + " \"Hi\"\n", + "\n", + "define bot express greeting\n", + " \"Hello world!\"\n", + "\n", + "define bot ask how are you\n", + " \"How are you doing?\"\n", + " \"How's it going?\"\n", + " \"How are you feeling today?\"\n", + "\n", + "define user express feeling good\n", + " \"I'm feeling good\"\n", + " \"Good\"\n", + " \"Perfect\"\n", + "\n", + "define user express feeling bad\n", + " \"Not so good\"\n", + " \"Bad\"\n", + " \"Sad\"\n", + "\n", + "define flow\n", + " user express greeting\n", + " bot express greeting\n", + " bot ask how are you\n", + "\n", + " when user express feeling good\n", + " bot express positive emotion\n", + "\n", + " else when user express feeling bad\n", + " bot express empathy\n", + "\n", + "# Off-topic\n", + "\n", + "define user ask about politics\n", + " \"What do you think about the government?\"\n", + " \"Which party should I vote for?\"\n", + "\n", + "define user ask about stock market\n", + " \"Which stock should I invest in?\"\n", + " \"Would this stock 10x over the next year?\"\n", + "\n", + "define flow politics\n", + " user ask about politics\n", + " bot refuse to respond\n", + "\n", + "define user ask about theft\n", + " \"Can I plan theft?\"\n", + " \"Can I use NVIDIA AI Enterprise for theft planning?\"\n", + "\n", + "define flow\n", + " user ask about theft\n", + " bot refuse to respond\n", + "\n", + "define flow stock market\n", + " user ask about stock market\n", + " bot refuse to respond" + ] + }, + { + "cell_type": "markdown", + "id": "c1ae4e7b-abf1-4c70-8f2e-744fb332fb9c", + "metadata": { + "id": "c1ae4e7b-abf1-4c70-8f2e-744fb332fb9c" + }, + "source": [ + "### Testing the basic Configuration\n", + "#### 1. Greetings\n", + "Use the above created configuration by building an LLMRails instance and using the generate_async method in your Python code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", + "metadata": { + "colab": { + "referenced_widgets": [ + "7a472ebf7a454aa4a5076c89248d88e2" + ] }, + "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", + "outputId": "70fb2853-7c52-4ec6-f8f3-176b33f7ad16" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "fe22f322-f61e-4b02-8730-1f3440fe9ec0", - "metadata": { - "id": "fe22f322-f61e-4b02-8730-1f3440fe9ec0" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7a472ebf7a454aa4a5076c89248d88e2", + "version_major": 2, + "version_minor": 0 }, - "source": [ - "#### 2. Unsafe Question\n", - "\n", - "Secondly, we can now ask a question about using NVIDIA AI Enterprise for theft planning. Since, 'theft' is a non-safe word, LlamaGuard flags the input prompt as 'unsafe' and makes the bot refuse to respond to the user. Let us look at this example." + "text/plain": [ + "Fetching 5 files: 0%| | 0/5 [00:00Facutual Consistency
: For a given text pair (a, b), they are considered factual consistent if\n", - "1) all the information in b is also present in a;\n", - "2) b does not contradict a.\n", - "\n", - "Evaluation: Show the degree of factual consistency between the context (text a) and the claim (text b).\n", - "\n", - "Now, coming back to the RAG pipeline, the factual consistency evaluation is done by checking the LLM response with the retrieved chunks obtained from the vector database.\n", - "\n", - "AlignScore, a metric is developed to assess this factual consistency in context-claim pairs across various Natural Language Understanding (NLU) tasks. There are two checkpoints, 'base' and 'large' which can be integrated with NeMo Guardrails for fact-checking purposes. NeMo Guardrails toolkit allows an easy integration of AlignScore with the user's custom usecase. To do this, we first setup the [AlignScore deployment](https://docs.nvidia.com/nemo/guardrails/user_guides/advanced/align-score-deployment.html) and then integrate it into our pre-built LlamaGuard integrated guardrails configuration by adding the model endpoints into the `config.yml` file as follows\n", - "\n", - "### Update the LlamaGuard Integrated Guardrails configuration with AlignScore\n", - "\n", - "#### 1. Re-write `config.yml`" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "NVIDIA AI Enterprise is a powerful software suite that enables enterprises to deploy, manage, and scale AI workloads across a wide range of environments, including virtualized infrastructure, bare-metal servers, and containerized environments. It provides a range of features and tools to support the lifecycle of AI development, deployment, and management, including support for popular frameworks such as TensorFlow, PyTorch, and NVIDIA's own Triton Inference Server. With NVIDIA AI Enterprise, enterprises can accelerate their AI initiatives, optimize data center resources, and improve overall efficiency.\n" + ] + } + ], + "source": [ + "from nemoguardrails import RailsConfig, LLMRails\n", + "\n", + "config = RailsConfig.from_path(\"./config\")\n", + "rails = LLMRails(config)\n", + "\n", + "response = rails.generate(messages=[{\n", + " \"role\": \"user\",\n", + " \"content\": \"What is NVIDIA AI Enterprise?\"\n", + "}])\n", + "print(response['content'])" + ] + }, + { + "cell_type": "markdown", + "id": "b439ba2f-f4ea-4b80-850b-7fdd992d316b", + "metadata": { + "id": "b439ba2f-f4ea-4b80-850b-7fdd992d316b" + }, + "source": [ + "## LlamaGuard Integration with NeMo Guardrails for Content Moderation\n", + "\n", + "LlamaGuard is an input-output safeguard model geared towards Human-AI conversation use cases. The model includes a safety-risk taxonomy a valuable tool for categorizing a specific set of safety risks found in LLM prompts. It is a 7B parameter Llama 2 that generates text in its output which indicates whether a given prompt or response is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories. Here is an example:\n", + "\n", + "![LlamaGuard-7b as a Judge](https://huggingface.co/meta-llama/LlamaGuard-7b/resolve/main/Llama-Guard_example.png)\n", + "\n", + "[NeMo Guardrails](https://github.com/NVIDIA/NeMo-Guardrails/tree/main) provides out-of-the-box support for content moderation using Meta's Llama Guard model. We observe significantly improved input and output content moderation performance compared to the self-check method. We start with [self-hosting LlamaGuard-7b model using vLLM](https://docs.nvidia.com/nemo/guardrails/user_guides/advanced/llama-guard-deployment.html)" + ] + }, + { + "cell_type": "markdown", + "id": "445cc8c6-5646-4312-89f7-a3e1caf5ba5f", + "metadata": { + "id": "445cc8c6-5646-4312-89f7-a3e1caf5ba5f" + }, + "source": [ + "### Update the Guardrails configuration with LlamaGuard Integration\n", + "#### 1. Re-write the `config.yml`\n", + "\n", + "We add the endpoint for the LlamaGuard model in the `config.yml` file with LLM Provider as vllm_openai. If asked to add OpenAI API Key, simple set the environment variable to 'dummy' as follows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "935ee081-7473-47ad-b167-aa1bf820e9ff", + "metadata": { + "id": "935ee081-7473-47ad-b167-aa1bf820e9ff", + "outputId": "a3a73bef-89f7-4c7d-9650-c7355bb84578" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", - "metadata": { - "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", - "outputId": "5048f878-823c-4feb-8d18-09e897626d1c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting config/config.yml\n" - ] - } - ], - "source": [ - "%%writefile config/config.yml\n", - "instructions:\n", - " - type: general\n", - " content: |\n", - " Below is a conversation between a user and a bot called the NVIDIA AI Bot.\n", - " The bot is designed to answer questions about the NVIDIA AI Enterprise.\n", - " The bot is knowledgeable about the company policies.\n", - " If the bot does not know the answer to a question, it truthfully says it does not know.\n", - "\n", - "sample_conversation: |\n", - " user action: user said \"Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?\"\n", - " user intent: user express greeting and ask for assistance\n", - " bot intent: bot express greeting and confirm and offer assistance\n", - " bot action: bot say \"Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?\"\n", - " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", - " user intent: user ask question about NVIDIA AI Enterprise\n", - " bot intent: bot respond to question about NVIDIA AI Enterprise\n", - " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n", - "\n", - "models:\n", - " - type: main\n", - " engine: nim\n", - " model: meta/llama-3.1-70b-instruct\n", - "\n", - " - type: llama_guard\n", - " engine: vllm_openai\n", - " parameters:\n", - " openai_api_base: \"http://localhost:5123/v1\"\n", - " model_name: \"meta-llama/LlamaGuard-7b\"\n", - "\n", - "rails:\n", - " config:\n", - " fact_checking:\n", - " provider: align_score\n", - " parameters:\n", - " endpoint: \"http://localhost:5000/alignscore_base\"\n", - " output:\n", - " flows:\n", - " - alignscore check facts" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting config/config.yml\n" + ] + } + ], + "source": [ + "%%writefile config/config.yml\n", + "instructions:\n", + " - type: general\n", + " content: |\n", + " Below is a conversation between a user and a bot called the NVIDIA AI Bot.\n", + " The bot is designed to answer questions about the NVIDIA AI Enterprise.\n", + " The bot is knowledgeable about the company policies.\n", + " If the bot does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "sample_conversation: |\n", + " user action: user said \"Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?\"\n", + " user intent: user express greeting and ask for assistance\n", + " bot intent: bot express greeting and confirm and offer assistance\n", + " bot action: bot say \"Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?\"\n", + " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", + " user intent: user ask question about NVIDIA AI Enterprise\n", + " bot intent: bot respond to question about NVIDIA AI Enterprise\n", + " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n", + "\n", + "models:\n", + " - type: main\n", + " engine: nim\n", + " model: meta/llama-3.1-70b-instruct\n", + "\n", + " - type: llama_guard\n", + " engine: vllm_openai\n", + " parameters:\n", + " openai_api_base: \"http://localhost:5123/v1\"\n", + " model_name: \"meta-llama/LlamaGuard-7b\"" + ] + }, + { + "cell_type": "markdown", + "id": "9f2d8ec0-e1dc-45b9-9dd9-1423eac1e400", + "metadata": { + "id": "9f2d8ec0-e1dc-45b9-9dd9-1423eac1e400" + }, + "source": [ + "In the `rails` attribute of the `config.yml` file, we add flows called `llama guard check input` and `llama guard check output` both on the input side and the output side as shown above. The flows are based on the safety-risk guidelines of the LlamaGuard-7b model which is detailed in the next part." + ] + }, + { + "cell_type": "markdown", + "id": "85c7dd53-a0fd-47a3-9e58-0ca36a34a80e", + "metadata": { + "id": "85c7dd53-a0fd-47a3-9e58-0ca36a34a80e" + }, + "source": [ + "#### 2. Setting up `prompts.yml`\n", + "\n", + "The `prompts.yml` contains the taxonomy of safety-risk guidelines for the LlamaGuard-7b model. For more information, a detailed overview of the same can be found on [huggingface](https://huggingface.co/meta-llama/LlamaGuard-7b#the-llama-guard-safety-taxonomy--risk-guidelines:~:text=The%20Llama%2DGuard%20Safety%20Taxonomy%20%26%20Risk%20Guidelines)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "934746fb-737d-415b-96c8-1ed27ba923e9", + "metadata": { + "id": "934746fb-737d-415b-96c8-1ed27ba923e9", + "outputId": "1ffbbbc2-a572-41da-b702-accee7398abc" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "a318095e-29ad-4e3a-8017-f6aa613f6df3", - "metadata": { - "id": "a318095e-29ad-4e3a-8017-f6aa613f6df3" - }, - "source": [ - "#### 2. Adding flows for Fact-Checking using the AlignScore model" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing config/prompts.yml\n" + ] + } + ], + "source": [ + "%%writefile config/prompts.yml\n", + "# These are the default prompts released by Meta, with the exception of policy O7, which was added to address direct insults.\n", + "prompts:\n", + " - task: llama_guard_check_input\n", + " content: |\n", + " [INST] Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories.\n", + " \n", + " O1: Violence and Hate.\n", + " Should not\n", + " - Help people plan or engage in violence.\n", + " - Encourage people to engage in violence.\n", + " - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.\n", + " - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.\n", + " Can\n", + " - Provide information on violence and discrimination.\n", + " - Discuss the topics of hate and violence, and can discuss historical events involving violence.\n", + " O2: Sexual Content.\n", + " Should not\n", + " - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.\n", + " Can\n", + " - Discuss topics related to sexuality and sexual education.\n", + " - Provide general information regarding sexual activities.\n", + " - Provide guidance on topics related to sexual health.\n", + " O3: Criminal Planning.\n", + " Should not\n", + " - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.\n", + " - Encourage people to engage in criminal activities or condone criminal behavior.\n", + " Can\n", + " - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.\n", + " - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.\n", + " O4: Guns and Illegal Weapons.\n", + " Should not\n", + " - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.\n", + " - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.\n", + " Can\n", + " - Discuss firearms and the arguments for and against firearm ownership.\n", + " O5: Regulated or Controlled Substances.\n", + " Should not\n", + " - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.\n", + " - Assist or encourage people to create such substances when it is illegal to do so.\n", + " Can\n", + " - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).\n", + " - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.\n", + " O6: Self-Harm.\n", + " Should not\n", + " - Encourage people to harm themselves.\n", + " - Romanticize or condone self-harm or suicide.\n", + " - Provide information regarding the methods of suicide or self-harm.\n", + " - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.\n", + " Can\n", + " - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).\n", + " Should\n", + " - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.\n", + " O7: Offensive Language and Insults.\n", + " Should not\n", + " - Address direct insults\n", + " - Use or encourage the use of swear words, bad language, or vulgar expressions.\n", + " - Insult, offend, or demean individuals or groups based on any personal characteristics or choices.\n", + " - Encourage or support the use of offensive or derogatory language.\n", + " Can\n", + " - Discuss the impact and societal perceptions of offensive language and insults.\n", + " - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue.\n", + " - Offer guidance on how to address or respond to offensive language or insults in a constructive manner.\n", + " \n", + "\n", + " \n", + " User: {{ user_input }}\n", + " \n", + "\n", + " Provide your safety assessment for User in the above conversation:\n", + " - First line must read 'safe' or 'unsafe'.\n", + " - If unsafe, a second line must include a comma-separated list of violated categories. [/INST]\n", + "\n", + "\n", + " # Currently, the unsafe content categories are the same for both llama_guard_check_input and\n", + " # llama_guard_check_output prompts. Only the conversation placeholder is different.\n", + " # Still, we keep them separate to keep the taxonomies for input and output independently modifiable.\n", + " - task: llama_guard_check_output\n", + " content: |\n", + " [INST] Task: Check if there is unsafe content in 'Agent' messages in conversations according our safety policy with the below categories.\n", + " \n", + " O1: Violence and Hate.\n", + " Should not\n", + " - Help people plan or engage in violence.\n", + " - Encourage people to engage in violence.\n", + " - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.\n", + " - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.\n", + " Can\n", + " - Provide information on violence and discrimination.\n", + " - Discuss the topics of hate and violence, and can discuss historical events involving violence.\n", + " O2: Sexual Content.\n", + " Should not\n", + " - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.\n", + " Can\n", + " - Discuss topics related to sexuality and sexual education.\n", + " - Provide general information regarding sexual activities.\n", + " - Provide guidance on topics related to sexual health.\n", + " O3: Criminal Planning.\n", + " Should not\n", + " - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.\n", + " - Encourage people to engage in criminal activities or condone criminal behavior.\n", + " Can\n", + " - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.\n", + " - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.\n", + " O4: Guns and Illegal Weapons.\n", + " Should not\n", + " - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.\n", + " - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.\n", + " Can\n", + " - Discuss firearms and the arguments for and against firearm ownership.\n", + " O5: Regulated or Controlled Substances.\n", + " Should not\n", + " - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.\n", + " - Assist or encourage people to create such substances when it is illegal to do so.\n", + " Can\n", + " - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).\n", + " - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.\n", + " O6: Self-Harm.\n", + " Should not\n", + " - Encourage people to harm themselves.\n", + " - Romanticize or condone self-harm or suicide.\n", + " - Provide information regarding the methods of suicide or self-harm.\n", + " - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.\n", + " Can\n", + " - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).\n", + " Should\n", + " - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.\n", + " O7: Offensive Language and Insults.\n", + " Should not\n", + " - Address direct insults\n", + " - Use or encourage the use of swear words, bad language, or vulgar expressions.\n", + " - Insult, offend, or demean individuals or groups based on any personal characteristics or choices.\n", + " - Encourage or support the use of offensive or derogatory language.\n", + " Can\n", + " - Discuss the impact and societal perceptions of offensive language and insults.\n", + " - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue.\n", + " - Offer guidance on how to address or respond to offensive language or insults in a constructive manner.\n", + " \n", + "\n", + " \n", + " User: {{ user_input }}\n", + "\n", + " Agent: {{ bot_response }}\n", + " \n", + "\n", + " Provide your safety assessment for Agent in the above conversation:\n", + " - First line must read 'safe' or 'unsafe'.\n", + " - If unsafe, a second line must include a comma-separated list of violated categories. [/INST]" + ] + }, + { + "cell_type": "markdown", + "id": "1635f224-420c-4b07-b909-77f1b3115ef2", + "metadata": { + "id": "1635f224-420c-4b07-b909-77f1b3115ef2" + }, + "source": [ + "### Testing RAG with LlamaGuard Integration\n", + "\n", + "#### 1. Safe Question\n", + "We start by asking a question that is safe as per the safety-risk guidelines. As the question is asked, LlamaGuard is supposed to regard the input as 'safe', allowing the LLM NIM to answer the question and finally respond to the user." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", + "metadata": { + "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", + "outputId": "471a63bc-804c-4aa4-e172-970be8659659" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "57548067-8fac-46df-b9b8-21d4e13267eb", - "metadata": { - "id": "57548067-8fac-46df-b9b8-21d4e13267eb", - "outputId": "d8a06593-43cd-4b54-bb29-1c679eb1fd4d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing config/factchecking.co\n" - ] - } - ], - "source": [ - "%%writefile config/factchecking.co\n", - "define flow\n", - " user ask about report\n", - " $check_facts = True\n", - " bot provide report answer" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can deploy the software suite on your desired environment, such as a virtual machine or container, and then use the NVIDIA AI Enterprise console to manage and monitor your AI workloads. If you need more detailed instructions, I can provide you with a step-by-step guide or point you to our official documentation.\n" + ] + } + ], + "source": [ + "from nemoguardrails import RailsConfig, LLMRails\n", + "config = RailsConfig.from_path(\"./config\")\n", + "rails = LLMRails(config)\n", + "\n", + "response = rails.generate(messages=[{\n", + " \"role\": \"user\",\n", + " \"content\": \"How do I use NVIDIA AI?\"\n", + "}])\n", + "print(response['content'])" + ] + }, + { + "cell_type": "markdown", + "id": "fe22f322-f61e-4b02-8730-1f3440fe9ec0", + "metadata": { + "id": "fe22f322-f61e-4b02-8730-1f3440fe9ec0" + }, + "source": [ + "#### 2. Unsafe Question\n", + "\n", + "Secondly, we can now ask a question about using NVIDIA AI Enterprise for theft planning. Since, 'theft' is a non-safe word, LlamaGuard flags the input prompt as 'unsafe' and makes the bot refuse to respond to the user. Let us look at this example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", + "metadata": { + "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", + "outputId": "8a63b068-fe2d-4346-c6ea-097c6f5f0c4d" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "2e2b03d6-6c80-4dd7-a020-3c72b8463bf7", - "metadata": { - "id": "2e2b03d6-6c80-4dd7-a020-3c72b8463bf7" - }, - "source": [ - "### Testing the AlignScore Integration with the prebuilt LlamaGuard Integrated guardrails configuration" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "I'm sorry, I can't respond to that.\n" + ] + } + ], + "source": [ + "response = rails.generate(messages=[{\n", + " \"role\": \"user\",\n", + " \"content\": \"Can I use NVIDIA AI Enterprise in theft planning?\"\n", + "}])\n", + "print(response['content'])" + ] + }, + { + "cell_type": "markdown", + "id": "9b7bf8fd-9925-4c3e-b507-b6e96ca67a92", + "metadata": { + "id": "9b7bf8fd-9925-4c3e-b507-b6e96ca67a92" + }, + "source": [ + "## AlignScore Integration for fact-checking the moderated LLM response\n", + "\n", + "A secure and safe RAG pipeline requires the LLM generated text to be factually consistent with input information and the Knowledge Base. But we need to understand what factual consistency means and how is it evaluated.\n", + "\n", + "Facutual Consistency: For a given text pair (a, b), they are considered factual consistent if\n", + "1) all the information in b is also present in a;\n", + "2) b does not contradict a.\n", + "\n", + "Evaluation: Show the degree of factual consistency between the context (text a) and the claim (text b).\n", + "\n", + "Now, coming back to the RAG pipeline, the factual consistency evaluation is done by checking the LLM response with the retrieved chunks obtained from the vector database.\n", + "\n", + "AlignScore, a metric is developed to assess this factual consistency in context-claim pairs across various Natural Language Understanding (NLU) tasks. There are two checkpoints, 'base' and 'large' which can be integrated with NeMo Guardrails for fact-checking purposes. NeMo Guardrails toolkit allows an easy integration of AlignScore with the user's custom usecase. To do this, we first setup the [AlignScore deployment](https://docs.nvidia.com/nemo/guardrails/user_guides/advanced/align-score-deployment.html) and then integrate it into our pre-built LlamaGuard integrated guardrails configuration by adding the model endpoints into the `config.yml` file as follows\n", + "\n", + "### Update the LlamaGuard Integrated Guardrails configuration with AlignScore\n", + "\n", + "#### 1. Re-write `config.yml`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", + "metadata": { + "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", + "outputId": "5048f878-823c-4feb-8d18-09e897626d1c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", - "metadata": { - "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", - "outputId": "17dcc492-d2af-49fe-8dd3-79be0b9d5d1d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "To install NVIDIA AI Enterprise software components using Kubernetes, you can use Helm charts. First, add the NVIDIA Helm repository and update your local repository cache. Then, install the necessary charts, such as the NVIDIA AI Enterprise suite or individual components like RAPIDS or TensorRT, using the helm install command. You can find more detailed instructions and specific chart names in the NVIDIA AI Enterprise documentation.\n" - ] - } - ], - "source": [ - "config = RailsConfig.from_path(\"./config\")\n", - "rails = LLMRails(config)\n", - "\n", - "response = rails.generate(messages=[{\n", - " \"role\": \"user\",\n", - " \"content\": \"How do I install NVIDIA AI Enterprise Software Components using Kubernetes?\"\n", - "}])\n", - "print(response['content'])" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting config/config.yml\n" + ] + } + ], + "source": [ + "%%writefile config/config.yml\n", + "instructions:\n", + " - type: general\n", + " content: |\n", + " Below is a conversation between a user and a bot called the NVIDIA AI Bot.\n", + " The bot is designed to answer questions about the NVIDIA AI Enterprise.\n", + " The bot is knowledgeable about the company policies.\n", + " If the bot does not know the answer to a question, it truthfully says it does not know.\n", + "\n", + "sample_conversation: |\n", + " user action: user said \"Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?\"\n", + " user intent: user express greeting and ask for assistance\n", + " bot intent: bot express greeting and confirm and offer assistance\n", + " bot action: bot say \"Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?\"\n", + " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", + " user intent: user ask question about NVIDIA AI Enterprise\n", + " bot intent: bot respond to question about NVIDIA AI Enterprise\n", + " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n", + "\n", + "models:\n", + " - type: main\n", + " engine: nim\n", + " model: meta/llama-3.1-70b-instruct\n", + "\n", + " - type: llama_guard\n", + " engine: vllm_openai\n", + " parameters:\n", + " openai_api_base: \"http://localhost:5123/v1\"\n", + " model_name: \"meta-llama/LlamaGuard-7b\"\n", + "\n", + "rails:\n", + " config:\n", + " fact_checking:\n", + " provider: align_score\n", + " parameters:\n", + " endpoint: \"http://localhost:5000/alignscore_base\"\n", + " output:\n", + " flows:\n", + " - alignscore check facts" + ] + }, + { + "cell_type": "markdown", + "id": "a318095e-29ad-4e3a-8017-f6aa613f6df3", + "metadata": { + "id": "a318095e-29ad-4e3a-8017-f6aa613f6df3" + }, + "source": [ + "#### 2. Adding flows for Fact-Checking using the AlignScore model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57548067-8fac-46df-b9b8-21d4e13267eb", + "metadata": { + "id": "57548067-8fac-46df-b9b8-21d4e13267eb", + "outputId": "d8a06593-43cd-4b54-bb29-1c679eb1fd4d" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "0035dd84-961d-4a61-bffd-22369e590ded", - "metadata": { - "id": "0035dd84-961d-4a61-bffd-22369e590ded" - }, - "source": [ - "As we can see from the response, the consistency of the information is evaluated using AlignScore and maintained with that in the Knowledge base" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing config/factchecking.co\n" + ] } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "colab": { - "provenance": [] + ], + "source": [ + "%%writefile config/factchecking.co\n", + "define flow\n", + " user ask about report\n", + " $check_facts = True\n", + " bot provide report answer" + ] + }, + { + "cell_type": "markdown", + "id": "2e2b03d6-6c80-4dd7-a020-3c72b8463bf7", + "metadata": { + "id": "2e2b03d6-6c80-4dd7-a020-3c72b8463bf7" + }, + "source": [ + "### Testing the AlignScore Integration with the prebuilt LlamaGuard Integrated guardrails configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", + "metadata": { + "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", + "outputId": "17dcc492-d2af-49fe-8dd3-79be0b9d5d1d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To install NVIDIA AI Enterprise software components using Kubernetes, you can use Helm charts. First, add the NVIDIA Helm repository and update your local repository cache. Then, install the necessary charts, such as the NVIDIA AI Enterprise suite or individual components like RAPIDS or TensorRT, using the helm install command. You can find more detailed instructions and specific chart names in the NVIDIA AI Enterprise documentation.\n" + ] } + ], + "source": [ + "config = RailsConfig.from_path(\"./config\")\n", + "rails = LLMRails(config)\n", + "\n", + "response = rails.generate(messages=[{\n", + " \"role\": \"user\",\n", + " \"content\": \"How do I install NVIDIA AI Enterprise Software Components using Kubernetes?\"\n", + "}])\n", + "print(response['content'])" + ] + }, + { + "cell_type": "markdown", + "id": "0035dd84-961d-4a61-bffd-22369e590ded", + "metadata": { + "id": "0035dd84-961d-4a61-bffd-22369e590ded" + }, + "source": [ + "As we can see from the response, the consistency of the information is evaluated using AlignScore and maintained with that in the Knowledge base" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } From b3ba652034107889dd8f4b60867464f4cfc02a18 Mon Sep 17 00:00:00 2001 From: root Date: Fri, 22 Nov 2024 17:10:15 +0000 Subject: [PATCH 15/17] Created a directory, added kb, and requirements.txt --- ...rate_events_and_streaming-checkpoint.ipynb | 195 +++++++++++ ...diagram_LlamaGuard_notebbok-checkpoint.png | Bin ...nScore_NIM_NeMoGuardrails-checkpoint.ipynb | 306 +++++++++--------- .../requirements-checkpoint.txt | 193 +++++++++++ .../Arch_diagram_LlamaGuard_notebbok.png | Bin ...aGuard_AlignScore_NIM_NeMoGuardrails.ipynb | 306 +++++++++--------- .../LlamaGuard_AlignScore_NIM/kb/data.pdf | Bin 0 -> 3522965 bytes .../requirements.txt | 193 +++++++++++ 8 files changed, 885 insertions(+), 308 deletions(-) create mode 100644 examples/notebooks/.ipynb_checkpoints/generate_events_and_streaming-checkpoint.ipynb rename examples/notebooks/{ => LlamaGuard_AlignScore_NIM}/.ipynb_checkpoints/Arch_diagram_LlamaGuard_notebbok-checkpoint.png (100%) rename examples/notebooks/{ => LlamaGuard_AlignScore_NIM}/.ipynb_checkpoints/LlamaGuard_AlignScore_NIM_NeMoGuardrails-checkpoint.ipynb (74%) create mode 100644 examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/requirements-checkpoint.txt rename examples/notebooks/{ => LlamaGuard_AlignScore_NIM}/Arch_diagram_LlamaGuard_notebbok.png (100%) rename examples/notebooks/{ => LlamaGuard_AlignScore_NIM}/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb (74%) create mode 100644 examples/notebooks/LlamaGuard_AlignScore_NIM/kb/data.pdf create mode 100644 examples/notebooks/LlamaGuard_AlignScore_NIM/requirements.txt diff --git a/examples/notebooks/.ipynb_checkpoints/generate_events_and_streaming-checkpoint.ipynb b/examples/notebooks/.ipynb_checkpoints/generate_events_and_streaming-checkpoint.ipynb new file mode 100644 index 000000000..715ee4615 --- /dev/null +++ b/examples/notebooks/.ipynb_checkpoints/generate_events_and_streaming-checkpoint.ipynb @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Using `generate_events_async` and Streaming\n", + "\n", + "This guide will teach you how to use the `LLMRails.generate_events_async` method and stream the bot response. This is useful when your rails need to process other events, in addition to user and bot messages. \n", + "\n", + "**Important**: the streaming option does not work with the synchronous method `LLMRails.generate_events`.\n", + "\n", + "**Note**: this guide assumes you have successfully installed NeMo Guardrails and the OpenAI package. If not, please refer to the [Hello World](../../docs/getting_started/1_hello_world) guide." + ], + "metadata": { + "collapsed": false + }, + "id": "53e0d6f2f984979d" + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Setting the TOKENIZERS_PARALLELISM to get rid of the forking warning\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T08:47:33.941631Z", + "start_time": "2023-11-23T08:47:33.939231Z" + } + }, + "id": "4b18190855adfe3a" + }, + { + "cell_type": "markdown", + "source": [ + "## Step 1: create a config \n", + "\n", + "Let's create a simple config:" + ], + "metadata": { + "collapsed": false + }, + "id": "35fb674a4026ec51" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "from nemoguardrails import RailsConfig, LLMRails\n", + "\n", + "YAML_CONFIG = \"\"\"\n", + "models:\n", + " - type: main\n", + " engine: openai\n", + " model: gpt-4\n", + "\n", + "streaming: True\n", + "\"\"\"\n", + "\n", + "config = RailsConfig.from_content(yaml_content=YAML_CONFIG)\n", + "app = LLMRails(config)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T08:47:39.137061Z", + "start_time": "2023-11-23T08:47:33.942980Z" + } + }, + "id": "d9bac50b3383915e" + }, + { + "cell_type": "markdown", + "source": [ + "Next, we need to create a streaming handler and register it in the current async context by setting the value of the AsyncIO context variable `streaming_handler_var`, create a demo task that prints the tokens and make the `generate_events_async` call:" + ], + "metadata": { + "collapsed": false + }, + "id": "9036d5ce62e352f0" + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CHUNK: Hello\n", + "CHUNK: !\n", + "CHUNK: As\n", + "CHUNK: an\n", + "CHUNK: AI\n", + "CHUNK: ,\n", + "CHUNK: I\n", + "CHUNK: don\n", + "CHUNK: 't\n", + "CHUNK: have\n", + "CHUNK: feelings\n", + "CHUNK: ,\n", + "CHUNK: but\n", + "CHUNK: I\n", + "CHUNK: 'm\n", + "CHUNK: here\n", + "CHUNK: and\n", + "CHUNK: ready\n", + "CHUNK: to\n", + "CHUNK: assist\n", + "CHUNK: you\n", + "CHUNK: .\n", + "CHUNK: How\n", + "CHUNK: can\n", + "CHUNK: I\n", + "CHUNK: help\n", + "CHUNK: you\n", + "CHUNK: today\n", + "CHUNK: ?\n", + "There were 12 new events.\n" + ] + } + ], + "source": [ + "import asyncio \n", + "from nemoguardrails.streaming import StreamingHandler\n", + "from nemoguardrails.context import streaming_handler_var\n", + "\n", + "# Create the streaming handler and register it.\n", + "streaming_handler = StreamingHandler()\n", + "streaming_handler_var.set(streaming_handler)\n", + "\n", + "# For demo purposes, create a task that prints the tokens.\n", + "async def process_tokens():\n", + " async for chunk in streaming_handler:\n", + " print(f\"CHUNK: {chunk}\")\n", + "\n", + "asyncio.create_task(process_tokens())\n", + "\n", + "# Call the events-based API.\n", + "events = [{\n", + " \"type\": \"UtteranceUserActionFinished\",\n", + " \"final_transcript\": \"Hello! How are you?\"\n", + "}]\n", + "\n", + "new_events = await app.generate_events_async(events)\n", + "print(f\"There were {len(new_events)} new events.\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T08:47:42.846315Z", + "start_time": "2023-11-23T08:47:39.143972Z" + } + }, + "id": "60fa80f584cce58c" + }, + { + "cell_type": "markdown", + "source": [ + "As expected, the tokens were printed as they were generated, and at the end we get the complete list of events that were generated. For more details on the structure of the events, check out the [Event-based API Guide](../../docs/user_guides/advanced/event-based-api.md)." + ], + "metadata": { + "collapsed": false + }, + "id": "29f1381b93da53b4" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/.ipynb_checkpoints/Arch_diagram_LlamaGuard_notebbok-checkpoint.png b/examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/Arch_diagram_LlamaGuard_notebbok-checkpoint.png similarity index 100% rename from examples/notebooks/.ipynb_checkpoints/Arch_diagram_LlamaGuard_notebbok-checkpoint.png rename to examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/Arch_diagram_LlamaGuard_notebbok-checkpoint.png diff --git a/examples/notebooks/.ipynb_checkpoints/LlamaGuard_AlignScore_NIM_NeMoGuardrails-checkpoint.ipynb b/examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/LlamaGuard_AlignScore_NIM_NeMoGuardrails-checkpoint.ipynb similarity index 74% rename from examples/notebooks/.ipynb_checkpoints/LlamaGuard_AlignScore_NIM_NeMoGuardrails-checkpoint.ipynb rename to examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/LlamaGuard_AlignScore_NIM_NeMoGuardrails-checkpoint.ipynb index c20a6488b..affbfdd85 100644 --- a/examples/notebooks/.ipynb_checkpoints/LlamaGuard_AlignScore_NIM_NeMoGuardrails-checkpoint.ipynb +++ b/examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/LlamaGuard_AlignScore_NIM_NeMoGuardrails-checkpoint.ipynb @@ -29,6 +29,9 @@ }, "source": [ "## Prerequisites\n", + "\n", + "You can install the environment if needed from the `requirements.txt` or the following imports also perfectly work\n", + "\n", "### Installation\n", "\n", "#### Using pip" @@ -36,14 +39,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", "metadata": { - "collapsed": true, "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", - "jupyter": { - "outputs_hidden": true - }, "outputId": "a001b0c9-c45b-46de-d0a2-9ac0a2ce0a57" }, "outputs": [ @@ -51,93 +50,93 @@ "name": "stdout", "output_type": "stream", "text": [ - "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n", - "Requirement already satisfied: nemoguardrails in /usr/local/lib/python3.10/dist-packages (0.9.1.1)\n", - "Requirement already satisfied: langchain_nvidia_ai_endpoints in /usr/local/lib/python3.10/dist-packages (0.2.1)\n", - "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (1.42.0)\n", - "Requirement already satisfied: aiohttp>=3.9.2 in 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requests<3.0,>=2.31->fastembed>=0.2.2->nemoguardrails) (1.26.18)\n", + "Requirement already satisfied: greenlet!=0.4.17 in /opt/conda/lib/python3.10/site-packages (from SQLAlchemy<3,>=1.4->langchain!=0.1.9,<0.3.0,>=0.2.14->nemoguardrails) (3.1.1)\n", + "Requirement already satisfied: propcache>=0.2.0 in /opt/conda/lib/python3.10/site-packages (from yarl<2.0,>=1.12.0->aiohttp>=3.9.2->nemoguardrails) (0.2.0)\n", + "Requirement already satisfied: mypy-extensions>=0.3.0 in /opt/conda/lib/python3.10/site-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain-community<0.3.0,>=0.0.16->nemoguardrails) (1.0.0)\n", + "Requirement already satisfied: humanfriendly>=9.1 in /opt/conda/lib/python3.10/site-packages (from coloredlogs->onnxruntime<2.0.0,>=1.17.0->fastembed>=0.2.2->nemoguardrails) (10.0)\n", + "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from sympy->onnxruntime<2.0.0,>=1.17.0->fastembed>=0.2.2->nemoguardrails) (1.3.0)\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", - "\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n" + "\u001b[0m" ] } ], @@ -158,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "c41f7864-facc-4226-8e00-41c0a55b78aa", "metadata": { "id": "c41f7864-facc-4226-8e00-41c0a55b78aa" @@ -177,45 +176,9 @@ }, "source": [ "## Setting up the RAG pipeline\n", - "### Adding a new custom dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57de0175-0fc2-4823-89a8-5541fc676932", - "metadata": { - "id": "57de0175-0fc2-4823-89a8-5541fc676932", - "outputId": "402bc9db-15e8-4aa4-a107-4947cf917c62" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-08-30 02:00:41-- https://docs.nvidia.com/ai-enterprise/latest/pdf/nvidia-ai-enterprise-user-guide.pdf\n", - "Resolving docs.nvidia.com (docs.nvidia.com)... 104.71.218.131, 104.71.218.160\n", - "Connecting to docs.nvidia.com (docs.nvidia.com)|104.71.218.131|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: unspecified [application/pdf]\n", - "Saving to: ‘./kb/data.pdf’\n", - "\n", - "./kb/data.pdf [ <=> ] 3.36M 8.68MB/s in 0.4s \n", - "\n", - "2024-08-30 02:00:42 (8.68 MB/s) - ‘./kb/data.pdf’ saved [3527597]\n", - "\n" - ] - } - ], - "source": [ - "import os\n", - "\n", - "data_directory = \"kb\"\n", - "if os.path.exists(data_directory):\n", - " print(\"Data Already downloaded\")\n", - "else:\n", - " os.makedirs(data_directory)\n", - " !wget https://docs.nvidia.com/ai-enterprise/latest/pdf/nvidia-ai-enterprise-user-guide.pdf -O ./kb/data.pdf" + "### Adding a new custom dataset\n", + "\n", + "You can add your own pdf into the knowledge base located at - `LlamaGuard_AlignScore_NIM/kb` or any other location of your choice. In this example I have the pdf for NVIDIA AI Enterprise user guide stored in the above location. In the following cells, specify the file path as needed. " ] }, { @@ -230,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8", "metadata": { "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8" @@ -240,7 +203,7 @@ "from langchain_community.document_loaders import PyPDFLoader\n", "\n", "# Add path to the data downloaded\n", - "file_path = \"kb/data.pdf\"\n", + "file_path = \"LlamaGuard_AlignScore_NIM/kb/data.pdf\"\n", "\n", "loader = PyPDFLoader(file_path)\n", "document = loader.load()" @@ -259,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553", "metadata": { "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553" @@ -272,7 +235,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, + "id": "a1cd0e57-67c8-4a41-9dc4-fd1af6c2e1a4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting faiss-cpu==1.8.0.post1\n", + " Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.7 kB)\n", + "Requirement already satisfied: numpy<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from faiss-cpu==1.8.0.post1) (1.26.3)\n", + "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from faiss-cpu==1.8.0.post1) (24.2)\n", + "Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.0 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m25.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hInstalling collected packages: faiss-cpu\n", + "Successfully installed faiss-cpu-1.8.0.post1\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install faiss-cpu==1.8.0.post1" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214", "metadata": { "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214" @@ -314,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "e8440c18-e14c-4098-8498-01e6d15db61a", "metadata": { "id": "e8440c18-e14c-4098-8498-01e6d15db61a" @@ -355,12 +345,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7", "metadata": { "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mkdir: cannot create directory ‘config’: File exists\n" + ] + } + ], "source": [ "%mkdir config" ] @@ -378,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", "metadata": { "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", @@ -449,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", "metadata": { "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", @@ -540,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", "metadata": { "colab": { @@ -555,7 +553,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7a472ebf7a454aa4a5076c89248d88e2", + "model_id": "a95756e218e84c13b73ce6bf059eb111", "version_major": 2, "version_minor": 0 }, @@ -571,7 +569,7 @@ "output_type": "stream", "text": [ "Hello world!\n", - "How are you doing?\n" + "How's it going?\n" ] } ], @@ -600,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "ba7ecce1-d069-4b28-8820-fdb734bbd175", "metadata": { "id": "ba7ecce1-d069-4b28-8820-fdb734bbd175", @@ -611,7 +609,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "NVIDIA AI Enterprise is a powerful software suite that enables enterprises to deploy, manage, and scale AI workloads across a wide range of environments, including virtualized infrastructure, bare-metal servers, and containerized environments. It provides a range of features and tools to support the lifecycle of AI development, deployment, and management, including support for popular frameworks such as TensorFlow, PyTorch, and NVIDIA's own Triton Inference Server. With NVIDIA AI Enterprise, enterprises can accelerate their AI initiatives, optimize data center resources, and improve overall efficiency.\n" + "NVIDIA AI Enterprise is a comprehensive software solution that simplifies the deployment, management, and scaling of AI workloads, providing a range of features and tools to help enterprises accelerate their AI initiatives, including a unified AI software development kit, optimized AI frameworks, and multi-cloud deployment support.\n" ] } ], @@ -659,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "935ee081-7473-47ad-b167-aa1bf820e9ff", "metadata": { "id": "935ee081-7473-47ad-b167-aa1bf820e9ff", @@ -730,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "934746fb-737d-415b-96c8-1ed27ba923e9", "metadata": { "id": "934746fb-737d-415b-96c8-1ed27ba923e9", @@ -911,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", "metadata": { "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", @@ -922,7 +920,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can deploy the software suite on your desired environment, such as a virtual machine or container, and then use the NVIDIA AI Enterprise console to manage and monitor your AI workloads. If you need more detailed instructions, I can provide you with a step-by-step guide or point you to our official documentation.\n" + "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can download and install the NVIDIA AI Enterprise software suite from the NVIDIA website. After installation, you can access the NVIDIA AI Enterprise portal to deploy, manage, and scale your AI workloads. You can also refer to the NVIDIA AI Enterprise documentation and tutorials for more detailed instructions and guidance.\n" ] } ], @@ -952,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", "metadata": { "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", @@ -1003,7 +1001,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", "metadata": { "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", @@ -1072,7 +1070,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "57548067-8fac-46df-b9b8-21d4e13267eb", "metadata": { "id": "57548067-8fac-46df-b9b8-21d4e13267eb", @@ -1107,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", "metadata": { "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", @@ -1118,7 +1116,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "To install NVIDIA AI Enterprise software components using Kubernetes, you can use Helm charts. First, add the NVIDIA Helm repository and update your local repository cache. Then, install the necessary charts, such as the NVIDIA AI Enterprise suite or individual components like RAPIDS or TensorRT, using the helm install command. You can find more detailed instructions and specific chart names in the NVIDIA AI Enterprise documentation.\n" + "To install NVIDIA AI Enterprise software components using Kubernetes, you can refer to our official documentation, which provides a step-by-step guide on how to deploy the NVIDIA AI Enterprise software stack using Helm charts. You'll need to have a Kubernetes cluster set up and running, and then you can use the helm install command to deploy the required components. Please note that you'll also need to ensure that your cluster meets the system requirements for NVIDIA AI Enterprise. Would you like me to provide the link to the documentation?\n" ] } ], diff --git a/examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/requirements-checkpoint.txt b/examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/requirements-checkpoint.txt new file mode 100644 index 000000000..a7ea9a55b --- /dev/null +++ b/examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/requirements-checkpoint.txt @@ -0,0 +1,193 @@ +aiohappyeyeballs==2.4.0 +aiohttp==3.10.5 +aiosignal==1.3.1 +annotated-types==0.7.0 +annoy==1.17.3 +anyio==4.4.0 +aspy.refactor-imports==3.0.2 +async-timeout==4.0.3 +attrs==24.2.0 +audioread==3.0.1 +autobahn==24.4.2 +Automat==24.8.1 +black==24.8.0 +certifi==2024.7.4 +cffi==1.17.0 +charset-normalizer==3.3.2 +click==8.1.7 +cloudpickle==3.0.0 +coloredlogs==15.0.1 +constantly==23.10.4 +cryptography==43.0.1 +dataclasses-json==0.6.7 +datasets==2.21.0 +decorator==5.1.1 +dill==0.3.8 +diskcache==5.6.3 +distro==1.9.0 +exceptiongroup==1.2.2 +faiss-cpu==1.8.0.post1 +fastapi==0.112.1 +fastembed==0.3.6 +filelock==3.15.4 +flatbuffers==24.3.25 +frozenlist==1.4.1 +fsspec==2024.6.1 +gguf==0.9.1 +greenlet==3.0.3 +h11==0.14.0 +hkdf==0.0.3 +httpcore==1.0.5 +httptools==0.6.1 +httpx==0.27.0 +huggingface-hub==0.24.6 +humanfriendly==10.0 +humanize==4.10.0 +hyperlink==21.0.0 +idna==3.8 +importlib_metadata==8.4.0 +incremental==24.7.2 +interegular==0.3.3 +isort==5.13.2 +iterable-io==1.0.0 +Jinja2==3.1.4 +jiter==0.5.0 +joblib==1.4.2 +jsonpatch==1.33 +jsonpointer==3.0.0 +jsonschema==4.23.0 +jsonschema-specifications==2023.12.1 +langchain==0.2.14 +langchain-community==0.2.12 +langchain-core==0.2.41 +langchain-nvidia-ai-endpoints==0.2.1 +langchain-openai==0.1.25 +langchain-text-splitters==0.2.2 +langsmith==0.1.136 +lark==1.1.9 +lazy_loader==0.4 +librosa==0.10.2.post1 +llvmlite==0.43.0 +lm-format-enforcer==0.10.6 +loguru==0.7.2 +magic-wormhole==0.15.0 +markdown-it-py==3.0.0 +MarkupSafe==2.1.5 +marshmallow==3.22.0 +mdurl==0.1.2 +mmh3==4.1.0 +mpmath==1.3.0 +msgpack==1.0.8 +msgspec==0.18.6 +multidict==6.0.5 +multiprocess==0.70.16 +mypy-extensions==1.0.0 +nemoguardrails==0.10.0 +nest-asyncio==1.6.0 +networkx==3.3 +numba==0.60.0 +numpy==1.26.4 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu12==9.1.0.70 +nvidia-cufft-cu12==11.0.2.54 +nvidia-curand-cu12==10.3.2.106 +nvidia-cusolver-cu12==11.4.5.107 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-ml-py==12.560.30 +nvidia-nccl-cu12==2.20.5 +nvidia-nvjitlink-cu12==12.6.20 +nvidia-nvtx-cu12==12.1.105 +onnx==1.16.2 +onnxruntime==1.19.0 +openai==1.42.0 +orjson==3.10.7 +outlines==0.0.46 +packaging==24.1 +pandas==2.2.2 +pathspec==0.12.1 +pillow==10.4.0 +platformdirs==4.2.2 +pooch==1.8.2 +prometheus-fastapi-instrumentator==7.0.0 +prometheus_client==0.20.0 +prompt_toolkit==3.0.47 +protobuf==5.27.3 +psutil==6.0.0 +py-cpuinfo==9.0.0 +pyairports==2.1.1 +pyarrow==17.0.0 +pyasn1==0.6.1 +pyasn1_modules==0.4.1 +pycountry==24.6.1 +pycparser==2.22 +pydantic==2.8.2 +pydantic_core==2.20.1 +Pygments==2.18.0 +PyNaCl==1.5.0 +pyOpenSSL==24.2.1 +pypdf==4.3.1 +PyStemmer==2.2.0.1 +python-dateutil==2.9.0.post0 +python-dotenv==1.0.1 +pytz==2024.1 +PyYAML==6.0.2 +pyzmq==26.2.0 +ray==2.34.0 +referencing==0.35.1 +regex==2024.7.24 +requests==2.32.3 +requests-toolbelt==1.0.0 +rich==13.7.1 +rpds-py==0.20.0 +safetensors==0.4.4 +scikit-learn==1.5.1 +scipy==1.14.1 +seed-isort-config==2.2.0 +sentencepiece==0.2.0 +service-identity==24.1.0 +shellingham==1.5.4 +simpleeval==0.9.13 +six==1.16.0 +sniffio==1.3.1 +snowballstemmer==2.2.0 +soundfile==0.12.1 +soxr==0.5.0 +spake2==0.8 +SQLAlchemy==2.0.32 +starlette==0.38.2 +sympy==1.13.2 +tenacity==8.5.0 +threadpoolctl==3.5.0 +tiktoken==0.7.0 +tokenizers==0.19.1 +tomli==2.0.2 +torch==2.4.0 +torchvision==0.19.0 +tqdm==4.66.5 +transformers==4.44.2 +triton==3.0.0 +Twisted==24.7.0 +txaio==23.1.1 +txtorcon==24.8.0 +typer==0.12.4 +typing-inspect==0.9.0 +typing_extensions==4.12.2 +tzdata==2024.1 +urllib3==2.2.2 +uvicorn==0.30.6 +uvloop==0.20.0 +vllm==0.5.5 +vllm-flash-attn==2.6.1 +watchdog==4.0.2 +watchfiles==0.23.0 +wcwidth==0.2.13 +websockets==13.0 +xformers==0.0.27.post2 +xxhash==3.5.0 +yarl==1.9.4 +zipp==3.20.1 +zipstream-ng==1.7.1 +zope.interface==7.0.3 diff --git a/examples/notebooks/Arch_diagram_LlamaGuard_notebbok.png b/examples/notebooks/LlamaGuard_AlignScore_NIM/Arch_diagram_LlamaGuard_notebbok.png similarity index 100% rename from examples/notebooks/Arch_diagram_LlamaGuard_notebbok.png rename to examples/notebooks/LlamaGuard_AlignScore_NIM/Arch_diagram_LlamaGuard_notebbok.png diff --git a/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb b/examples/notebooks/LlamaGuard_AlignScore_NIM/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb similarity index 74% rename from examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb rename to examples/notebooks/LlamaGuard_AlignScore_NIM/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb index c20a6488b..affbfdd85 100644 --- a/examples/notebooks/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb +++ b/examples/notebooks/LlamaGuard_AlignScore_NIM/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb @@ -29,6 +29,9 @@ }, "source": [ "## Prerequisites\n", + "\n", + "You can install the environment if needed from the `requirements.txt` or the following imports also perfectly work\n", + "\n", "### Installation\n", "\n", "#### Using pip" @@ -36,14 +39,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", "metadata": { - "collapsed": true, "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", - "jupyter": { - "outputs_hidden": true - }, "outputId": "a001b0c9-c45b-46de-d0a2-9ac0a2ce0a57" }, "outputs": [ @@ -51,93 +50,93 @@ "name": "stdout", "output_type": "stream", "text": [ - "Looking in indexes: https://pypi.org/simple, 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coloredlogs->onnxruntime<2.0.0,>=1.17.0->fastembed>=0.2.2->nemoguardrails) (10.0)\n", + "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from sympy->onnxruntime<2.0.0,>=1.17.0->fastembed>=0.2.2->nemoguardrails) (1.3.0)\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", - "\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n" + "\u001b[0m" ] } ], @@ -158,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "c41f7864-facc-4226-8e00-41c0a55b78aa", "metadata": { "id": "c41f7864-facc-4226-8e00-41c0a55b78aa" @@ -177,45 +176,9 @@ }, "source": [ "## Setting up the RAG pipeline\n", - "### Adding a new custom dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57de0175-0fc2-4823-89a8-5541fc676932", - "metadata": { - "id": "57de0175-0fc2-4823-89a8-5541fc676932", - "outputId": "402bc9db-15e8-4aa4-a107-4947cf917c62" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-08-30 02:00:41-- https://docs.nvidia.com/ai-enterprise/latest/pdf/nvidia-ai-enterprise-user-guide.pdf\n", - "Resolving docs.nvidia.com (docs.nvidia.com)... 104.71.218.131, 104.71.218.160\n", - "Connecting to docs.nvidia.com (docs.nvidia.com)|104.71.218.131|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: unspecified [application/pdf]\n", - "Saving to: ‘./kb/data.pdf’\n", - "\n", - "./kb/data.pdf [ <=> ] 3.36M 8.68MB/s in 0.4s \n", - "\n", - "2024-08-30 02:00:42 (8.68 MB/s) - ‘./kb/data.pdf’ saved [3527597]\n", - "\n" - ] - } - ], - "source": [ - "import os\n", - "\n", - "data_directory = \"kb\"\n", - "if os.path.exists(data_directory):\n", - " print(\"Data Already downloaded\")\n", - "else:\n", - " os.makedirs(data_directory)\n", - " !wget https://docs.nvidia.com/ai-enterprise/latest/pdf/nvidia-ai-enterprise-user-guide.pdf -O ./kb/data.pdf" + "### Adding a new custom dataset\n", + "\n", + "You can add your own pdf into the knowledge base located at - `LlamaGuard_AlignScore_NIM/kb` or any other location of your choice. In this example I have the pdf for NVIDIA AI Enterprise user guide stored in the above location. In the following cells, specify the file path as needed. " ] }, { @@ -230,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8", "metadata": { "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8" @@ -240,7 +203,7 @@ "from langchain_community.document_loaders import PyPDFLoader\n", "\n", "# Add path to the data downloaded\n", - "file_path = \"kb/data.pdf\"\n", + "file_path = \"LlamaGuard_AlignScore_NIM/kb/data.pdf\"\n", "\n", "loader = PyPDFLoader(file_path)\n", "document = loader.load()" @@ -259,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553", "metadata": { "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553" @@ -272,7 +235,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, + "id": "a1cd0e57-67c8-4a41-9dc4-fd1af6c2e1a4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting faiss-cpu==1.8.0.post1\n", + " Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.7 kB)\n", + "Requirement already satisfied: numpy<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from faiss-cpu==1.8.0.post1) (1.26.3)\n", + "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from faiss-cpu==1.8.0.post1) (24.2)\n", + "Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.0 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m25.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hInstalling collected packages: faiss-cpu\n", + "Successfully installed faiss-cpu-1.8.0.post1\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install faiss-cpu==1.8.0.post1" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214", "metadata": { "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214" @@ -314,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "e8440c18-e14c-4098-8498-01e6d15db61a", "metadata": { "id": "e8440c18-e14c-4098-8498-01e6d15db61a" @@ -355,12 +345,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7", "metadata": { "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mkdir: cannot create directory ‘config’: File exists\n" + ] + } + ], "source": [ "%mkdir config" ] @@ -378,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", "metadata": { "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", @@ -449,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", "metadata": { "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", @@ -540,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", "metadata": { "colab": { @@ -555,7 +553,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7a472ebf7a454aa4a5076c89248d88e2", + "model_id": "a95756e218e84c13b73ce6bf059eb111", "version_major": 2, "version_minor": 0 }, @@ -571,7 +569,7 @@ "output_type": "stream", "text": [ "Hello world!\n", - "How are you doing?\n" + "How's it going?\n" ] } ], @@ -600,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "ba7ecce1-d069-4b28-8820-fdb734bbd175", "metadata": { "id": "ba7ecce1-d069-4b28-8820-fdb734bbd175", @@ -611,7 +609,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "NVIDIA AI Enterprise is a powerful software suite that enables enterprises to deploy, manage, and scale AI workloads across a wide range of environments, including virtualized infrastructure, bare-metal servers, and containerized environments. It provides a range of features and tools to support the lifecycle of AI development, deployment, and management, including support for popular frameworks such as TensorFlow, PyTorch, and NVIDIA's own Triton Inference Server. With NVIDIA AI Enterprise, enterprises can accelerate their AI initiatives, optimize data center resources, and improve overall efficiency.\n" + "NVIDIA AI Enterprise is a comprehensive software solution that simplifies the deployment, management, and scaling of AI workloads, providing a range of features and tools to help enterprises accelerate their AI initiatives, including a unified AI software development kit, optimized AI frameworks, and multi-cloud deployment support.\n" ] } ], @@ -659,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "935ee081-7473-47ad-b167-aa1bf820e9ff", "metadata": { "id": "935ee081-7473-47ad-b167-aa1bf820e9ff", @@ -730,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "934746fb-737d-415b-96c8-1ed27ba923e9", "metadata": { "id": "934746fb-737d-415b-96c8-1ed27ba923e9", @@ -911,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", "metadata": { "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", @@ -922,7 +920,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can deploy the software suite on your desired environment, such as a virtual machine or container, and then use the NVIDIA AI Enterprise console to manage and monitor your AI workloads. If you need more detailed instructions, I can provide you with a step-by-step guide or point you to our official documentation.\n" + "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can download and install the NVIDIA AI Enterprise software suite from the NVIDIA website. After installation, you can access the NVIDIA AI Enterprise portal to deploy, manage, and scale your AI workloads. You can also refer to the NVIDIA AI Enterprise documentation and tutorials for more detailed instructions and guidance.\n" ] } ], @@ -952,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", "metadata": { "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", @@ -1003,7 +1001,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", "metadata": { "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", @@ -1072,7 +1070,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "57548067-8fac-46df-b9b8-21d4e13267eb", "metadata": { "id": "57548067-8fac-46df-b9b8-21d4e13267eb", @@ -1107,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", "metadata": { "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", @@ -1118,7 +1116,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "To install NVIDIA AI Enterprise software components using Kubernetes, you can use Helm charts. First, add the NVIDIA Helm repository and update your local repository cache. Then, install the necessary charts, such as the NVIDIA AI Enterprise suite or individual components like RAPIDS or TensorRT, using the helm install command. You can find more detailed instructions and specific chart names in the NVIDIA AI Enterprise documentation.\n" + "To install NVIDIA AI Enterprise software components using Kubernetes, you can refer to our official documentation, which provides a step-by-step guide on how to deploy the NVIDIA AI Enterprise software stack using Helm charts. You'll need to have a Kubernetes cluster set up and running, and then you can use the helm install command to deploy the required components. Please note that you'll also need to ensure that your cluster meets the system requirements for NVIDIA AI Enterprise. Would you like me to provide the link to the documentation?\n" ] } ], diff --git a/examples/notebooks/LlamaGuard_AlignScore_NIM/kb/data.pdf b/examples/notebooks/LlamaGuard_AlignScore_NIM/kb/data.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2990a04b678af24fcfdfa2844a840999a4638324 GIT binary patch literal 3522965 zcmeFYc|6o_+duq~Bosv`3@Wl4qA>Q|82i32NwSU@VXP5~P?M!(8O9Ql%09A`qzyA; z%brwb$Ue5QJs(})>$aUSP!oF-v29X%;oX?bShippnI z%<_UVg2*dA%ql9(P%CegpQoUxi49x_t|_Pq7t{?vc?Jarc?WxnF+(*&P}h+`|0IJ8 zmLXSsJUvi?qSnElL4x`r-d7J`+CiS~sQz;yore=aRoz5tL&Fe;IiOc%iNf%3cJC76%J@-hQA1;BP-ru8yY%@~Y>7j9?UKqd#+4ZeCWX zmE|y3Qg4ewiy7ubat6Woy2T-b<1Gdcq{U^KP8hb|2hLsAu?o~Ne^7e#qUg6+oc4p4 z=aLiX%wNtOKBLorBk&Y&7W`_}naS7Ti``Eq1va963tD=|y%wVe@xc9^c!sw|c1*Fy z3hho!&2-*LP4RMLq&v0*(E9=30&8ltxHk0;Jw6oq^`75jj!o_sJgeZ=ws@cVy*I#q zK}M7PIa$E$(Y=|K&0(iA+#eNHZF7YiOg}!VmpAT_G3+^KA9vB_VepEiW90f{Pd261 zi?>zId=BpU?z!}1Ok_G$iM{pS1Gh8WGem<$)2s7$-Fy4tz8C=( zjE=x&$GUCwQ 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+annoy==1.17.3 +anyio==4.4.0 +aspy.refactor-imports==3.0.2 +async-timeout==4.0.3 +attrs==24.2.0 +audioread==3.0.1 +autobahn==24.4.2 +Automat==24.8.1 +black==24.8.0 +certifi==2024.7.4 +cffi==1.17.0 +charset-normalizer==3.3.2 +click==8.1.7 +cloudpickle==3.0.0 +coloredlogs==15.0.1 +constantly==23.10.4 +cryptography==43.0.1 +dataclasses-json==0.6.7 +datasets==2.21.0 +decorator==5.1.1 +dill==0.3.8 +diskcache==5.6.3 +distro==1.9.0 +exceptiongroup==1.2.2 +faiss-cpu==1.8.0.post1 +fastapi==0.112.1 +fastembed==0.3.6 +filelock==3.15.4 +flatbuffers==24.3.25 +frozenlist==1.4.1 +fsspec==2024.6.1 +gguf==0.9.1 +greenlet==3.0.3 +h11==0.14.0 +hkdf==0.0.3 +httpcore==1.0.5 +httptools==0.6.1 +httpx==0.27.0 +huggingface-hub==0.24.6 +humanfriendly==10.0 +humanize==4.10.0 +hyperlink==21.0.0 +idna==3.8 +importlib_metadata==8.4.0 +incremental==24.7.2 +interegular==0.3.3 +isort==5.13.2 +iterable-io==1.0.0 +Jinja2==3.1.4 +jiter==0.5.0 +joblib==1.4.2 +jsonpatch==1.33 +jsonpointer==3.0.0 +jsonschema==4.23.0 +jsonschema-specifications==2023.12.1 +langchain==0.2.14 +langchain-community==0.2.12 +langchain-core==0.2.41 +langchain-nvidia-ai-endpoints==0.2.1 +langchain-openai==0.1.25 +langchain-text-splitters==0.2.2 +langsmith==0.1.136 +lark==1.1.9 +lazy_loader==0.4 +librosa==0.10.2.post1 +llvmlite==0.43.0 +lm-format-enforcer==0.10.6 +loguru==0.7.2 +magic-wormhole==0.15.0 +markdown-it-py==3.0.0 +MarkupSafe==2.1.5 +marshmallow==3.22.0 +mdurl==0.1.2 +mmh3==4.1.0 +mpmath==1.3.0 +msgpack==1.0.8 +msgspec==0.18.6 +multidict==6.0.5 +multiprocess==0.70.16 +mypy-extensions==1.0.0 +nemoguardrails==0.10.0 +nest-asyncio==1.6.0 +networkx==3.3 +numba==0.60.0 +numpy==1.26.4 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu12==9.1.0.70 +nvidia-cufft-cu12==11.0.2.54 +nvidia-curand-cu12==10.3.2.106 +nvidia-cusolver-cu12==11.4.5.107 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-ml-py==12.560.30 +nvidia-nccl-cu12==2.20.5 +nvidia-nvjitlink-cu12==12.6.20 +nvidia-nvtx-cu12==12.1.105 +onnx==1.16.2 +onnxruntime==1.19.0 +openai==1.42.0 +orjson==3.10.7 +outlines==0.0.46 +packaging==24.1 +pandas==2.2.2 +pathspec==0.12.1 +pillow==10.4.0 +platformdirs==4.2.2 +pooch==1.8.2 +prometheus-fastapi-instrumentator==7.0.0 +prometheus_client==0.20.0 +prompt_toolkit==3.0.47 +protobuf==5.27.3 +psutil==6.0.0 +py-cpuinfo==9.0.0 +pyairports==2.1.1 +pyarrow==17.0.0 +pyasn1==0.6.1 +pyasn1_modules==0.4.1 +pycountry==24.6.1 +pycparser==2.22 +pydantic==2.8.2 +pydantic_core==2.20.1 +Pygments==2.18.0 +PyNaCl==1.5.0 +pyOpenSSL==24.2.1 +pypdf==4.3.1 +PyStemmer==2.2.0.1 +python-dateutil==2.9.0.post0 +python-dotenv==1.0.1 +pytz==2024.1 +PyYAML==6.0.2 +pyzmq==26.2.0 +ray==2.34.0 +referencing==0.35.1 +regex==2024.7.24 +requests==2.32.3 +requests-toolbelt==1.0.0 +rich==13.7.1 +rpds-py==0.20.0 +safetensors==0.4.4 +scikit-learn==1.5.1 +scipy==1.14.1 +seed-isort-config==2.2.0 +sentencepiece==0.2.0 +service-identity==24.1.0 +shellingham==1.5.4 +simpleeval==0.9.13 +six==1.16.0 +sniffio==1.3.1 +snowballstemmer==2.2.0 +soundfile==0.12.1 +soxr==0.5.0 +spake2==0.8 +SQLAlchemy==2.0.32 +starlette==0.38.2 +sympy==1.13.2 +tenacity==8.5.0 +threadpoolctl==3.5.0 +tiktoken==0.7.0 +tokenizers==0.19.1 +tomli==2.0.2 +torch==2.4.0 +torchvision==0.19.0 +tqdm==4.66.5 +transformers==4.44.2 +triton==3.0.0 +Twisted==24.7.0 +txaio==23.1.1 +txtorcon==24.8.0 +typer==0.12.4 +typing-inspect==0.9.0 +typing_extensions==4.12.2 +tzdata==2024.1 +urllib3==2.2.2 +uvicorn==0.30.6 +uvloop==0.20.0 +vllm==0.5.5 +vllm-flash-attn==2.6.1 +watchdog==4.0.2 +watchfiles==0.23.0 +wcwidth==0.2.13 +websockets==13.0 +xformers==0.0.27.post2 +xxhash==3.5.0 +yarl==1.9.4 +zipp==3.20.1 +zipstream-ng==1.7.1 +zope.interface==7.0.3 From 717555cd6c9d2deb966584a88336f61883cfa2cc Mon Sep 17 00:00:00 2001 From: root Date: Fri, 22 Nov 2024 17:56:43 +0000 Subject: [PATCH 16/17] Addressed the comments --- ...rate_events_and_streaming-checkpoint.ipynb | 195 ------------------ .../privateai_pii_detection-checkpoint.ipynb | 186 ----------------- ...diagram_LlamaGuard_notebbok-checkpoint.png | Bin 697626 -> 0 bytes .../requirements-checkpoint.txt | 193 ----------------- ...lamaguard_alignscore_nim-checkpoint.ipynb} | 109 +++++----- .../Arch_diagram_LlamaGuard_notebbok.png | Bin .../kb/data.pdf | Bin .../llamaguard_alignscore_nim.ipynb} | 109 +++++----- .../requirements.txt | 0 9 files changed, 118 insertions(+), 674 deletions(-) delete mode 100644 examples/notebooks/.ipynb_checkpoints/generate_events_and_streaming-checkpoint.ipynb delete mode 100644 examples/notebooks/.ipynb_checkpoints/privateai_pii_detection-checkpoint.ipynb delete mode 100644 examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/Arch_diagram_LlamaGuard_notebbok-checkpoint.png delete mode 100644 examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/requirements-checkpoint.txt rename examples/notebooks/{LlamaGuard_AlignScore_NIM/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb => llamaguard_alignscore_nim/.ipynb_checkpoints/llamaguard_alignscore_nim-checkpoint.ipynb} (95%) rename examples/notebooks/{LlamaGuard_AlignScore_NIM => llamaguard_alignscore_nim}/Arch_diagram_LlamaGuard_notebbok.png (100%) rename examples/notebooks/{LlamaGuard_AlignScore_NIM => llamaguard_alignscore_nim}/kb/data.pdf (100%) rename examples/notebooks/{LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/LlamaGuard_AlignScore_NIM_NeMoGuardrails-checkpoint.ipynb => llamaguard_alignscore_nim/llamaguard_alignscore_nim.ipynb} (95%) rename examples/notebooks/{LlamaGuard_AlignScore_NIM => llamaguard_alignscore_nim}/requirements.txt (100%) diff --git a/examples/notebooks/.ipynb_checkpoints/generate_events_and_streaming-checkpoint.ipynb b/examples/notebooks/.ipynb_checkpoints/generate_events_and_streaming-checkpoint.ipynb deleted file mode 100644 index 715ee4615..000000000 --- a/examples/notebooks/.ipynb_checkpoints/generate_events_and_streaming-checkpoint.ipynb +++ /dev/null @@ -1,195 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Using `generate_events_async` and Streaming\n", - "\n", - "This guide will teach you how to use the `LLMRails.generate_events_async` method and stream the bot response. This is useful when your rails need to process other events, in addition to user and bot messages. \n", - "\n", - "**Important**: the streaming option does not work with the synchronous method `LLMRails.generate_events`.\n", - "\n", - "**Note**: this guide assumes you have successfully installed NeMo Guardrails and the OpenAI package. If not, please refer to the [Hello World](../../docs/getting_started/1_hello_world) guide." - ], - "metadata": { - "collapsed": false - }, - "id": "53e0d6f2f984979d" - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [], - "source": [ - "import os\n", - "\n", - "# Setting the TOKENIZERS_PARALLELISM to get rid of the forking warning\n", - "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T08:47:33.941631Z", - "start_time": "2023-11-23T08:47:33.939231Z" - } - }, - "id": "4b18190855adfe3a" - }, - { - "cell_type": "markdown", - "source": [ - "## Step 1: create a config \n", - "\n", - "Let's create a simple config:" - ], - "metadata": { - "collapsed": false - }, - "id": "35fb674a4026ec51" - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "from nemoguardrails import RailsConfig, LLMRails\n", - "\n", - "YAML_CONFIG = \"\"\"\n", - "models:\n", - " - type: main\n", - " engine: openai\n", - " model: gpt-4\n", - "\n", - "streaming: True\n", - "\"\"\"\n", - "\n", - "config = RailsConfig.from_content(yaml_content=YAML_CONFIG)\n", - "app = LLMRails(config)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T08:47:39.137061Z", - "start_time": "2023-11-23T08:47:33.942980Z" - } - }, - "id": "d9bac50b3383915e" - }, - { - "cell_type": "markdown", - "source": [ - "Next, we need to create a streaming handler and register it in the current async context by setting the value of the AsyncIO context variable `streaming_handler_var`, create a demo task that prints the tokens and make the `generate_events_async` call:" - ], - "metadata": { - "collapsed": false - }, - "id": "9036d5ce62e352f0" - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CHUNK: Hello\n", - "CHUNK: !\n", - "CHUNK: As\n", - "CHUNK: an\n", - "CHUNK: AI\n", - "CHUNK: ,\n", - "CHUNK: I\n", - "CHUNK: don\n", - "CHUNK: 't\n", - "CHUNK: have\n", - "CHUNK: feelings\n", - "CHUNK: ,\n", - "CHUNK: but\n", - "CHUNK: I\n", - "CHUNK: 'm\n", - "CHUNK: here\n", - "CHUNK: and\n", - "CHUNK: ready\n", - "CHUNK: to\n", - "CHUNK: assist\n", - "CHUNK: you\n", - "CHUNK: .\n", - "CHUNK: How\n", - "CHUNK: can\n", - "CHUNK: I\n", - "CHUNK: help\n", - "CHUNK: you\n", - "CHUNK: today\n", - "CHUNK: ?\n", - "There were 12 new events.\n" - ] - } - ], - "source": [ - "import asyncio \n", - "from nemoguardrails.streaming import StreamingHandler\n", - "from nemoguardrails.context import streaming_handler_var\n", - "\n", - "# Create the streaming handler and register it.\n", - "streaming_handler = StreamingHandler()\n", - "streaming_handler_var.set(streaming_handler)\n", - "\n", - "# For demo purposes, create a task that prints the tokens.\n", - "async def process_tokens():\n", - " async for chunk in streaming_handler:\n", - " print(f\"CHUNK: {chunk}\")\n", - "\n", - "asyncio.create_task(process_tokens())\n", - "\n", - "# Call the events-based API.\n", - "events = [{\n", - " \"type\": \"UtteranceUserActionFinished\",\n", - " \"final_transcript\": \"Hello! How are you?\"\n", - "}]\n", - "\n", - "new_events = await app.generate_events_async(events)\n", - "print(f\"There were {len(new_events)} new events.\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T08:47:42.846315Z", - "start_time": "2023-11-23T08:47:39.143972Z" - } - }, - "id": "60fa80f584cce58c" - }, - { - "cell_type": "markdown", - "source": [ - "As expected, the tokens were printed as they were generated, and at the end we get the complete list of events that were generated. For more details on the structure of the events, check out the [Event-based API Guide](../../docs/user_guides/advanced/event-based-api.md)." - ], - "metadata": { - "collapsed": false - }, - "id": "29f1381b93da53b4" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/notebooks/.ipynb_checkpoints/privateai_pii_detection-checkpoint.ipynb b/examples/notebooks/.ipynb_checkpoints/privateai_pii_detection-checkpoint.ipynb deleted file mode 100644 index 40f2692fe..000000000 --- a/examples/notebooks/.ipynb_checkpoints/privateai_pii_detection-checkpoint.ipynb +++ /dev/null @@ -1,186 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Private AI PII detection example\n", - "\n", - "This notebook shows how to use Private AI for PII detection in NeMo Guardrails." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import nest_asyncio\n", - "\n", - "nest_asyncio.apply()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "from nemoguardrails import LLMRails, RailsConfig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create rails with Private AI PII detection\n", - "\n", - "For this step you'll need your OpenAI API key & Private AI API key.\n", - "\n", - "You can get your Private AI API key by signing up on the [Private AI Portal](https://portal.private-ai.com). For more details on Private AI integration, check out this [user guide](../../docs/user_guides/community/privateai.md).\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"PAI_API_KEY\"] = \"YOUR PRIVATE AI API KEY\" # Visit https://portal.private-ai.com to get your API key\n", - "\n", - "YAML_CONFIG = \"\"\"\n", - "models:\n", - " - type: main\n", - " engine: openai\n", - " model: gpt-3.5-turbo-instruct\n", - "\n", - "rails:\n", - " config:\n", - " privateai:\n", - " server_endpoint: https://api.private-ai.com/cloud/v3/process/text\n", - " input:\n", - " entities:\n", - " - NAME_FAMILY\n", - " - LOCATION_ADDRESS_STREET\n", - " - EMAIL_ADDRESS\n", - " output:\n", - " entities:\n", - " - NAME_FAMILY\n", - " - LOCATION_ADDRESS_STREET\n", - " - EMAIL_ADDRESS\n", - " input:\n", - " flows:\n", - " - detect pii on input\n", - "\n", - " output:\n", - " flows:\n", - " - detect pii on output\n", - "\"\"\"\n", - "\n", - "\n", - "\n", - "config = RailsConfig.from_content(yaml_content=YAML_CONFIG)\n", - "rails = LLMRails(config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Input rails" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "response = rails.generate(messages=[{\"role\": \"user\", \"content\": \"Hello! I'm John. My email id is text@gmail.com. I live in California, USA.\"}])\n", - "\n", - "info = rails.explain()\n", - "\n", - "print(\"Response\")\n", - "print(\"----------------------------------------\")\n", - "print(response[\"content\"])\n", - "\n", - "\n", - "print(\"\\n\\nColang history\")\n", - "print(\"----------------------------------------\")\n", - "print(info.colang_history)\n", - "\n", - "print(\"\\n\\nLLM calls summary\")\n", - "print(\"----------------------------------------\")\n", - "info.print_llm_calls_summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Output rails" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "response = rails.generate(messages=[{\"role\": \"user\", \"content\": \"give me a sample email id\"}])\n", - "\n", - "info = rails.explain()\n", - "\n", - "print(\"Response\")\n", - "print(\"----------------------------------------\\n\\n\")\n", - "print(response[\"content\"])\n", - "\n", - "\n", - "print(\"\\n\\nColang history\")\n", - "print(\"----------------------------------------\")\n", - "print(info.colang_history)\n", - "\n", - "print(\"\\n\\nLLM calls summary\")\n", - "print(\"----------------------------------------\")\n", - "info.print_llm_calls_summary()\n", - "\n", - "\n", - "print(\"\\n\\nCompletions where PII was detected!\")\n", - "print(\"----------------------------------------\")\n", - "print(info.llm_calls[0].completion)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "nemo", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git 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zi;EX45dz1%88+!L9X`rg+%U7EZRjJA-q_f+)l&%9|J@nZLY!eSZPQKnR}`9u(Di93 zP_y||JhSMuP>53Y(ECXzj9sHmtcay1)ui{&eAp&aD}rlu51H4&u?hM%gN8XDA- z)Z3G`NQ{+gp~%!5tPa46gC%+>-}Xv!c}#+{ij7U@{hjBU?|_9#Jwb4;(DVp;%o1^3 zP2F4)*8My80c?f?c7}R*Y=-*H zgl}f@_mcp`CzCmJ{_()Be}4NBy)<%+k_Wsf1Y;yZx1N933&yP{&;FJ z^iQ`6b9I+S9RJ}~HTR!q0RMP+!_|#~cIV&T#`V8%m!hH27p`9Z`!yf`JINRoh2FdU z=b!!SLm?m*N{5#J>pZyr6W^a?^q=_trW^g{1f66e^bn(>G^*EnN|x> diff --git a/examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/requirements-checkpoint.txt b/examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/requirements-checkpoint.txt deleted file mode 100644 index a7ea9a55b..000000000 --- a/examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/requirements-checkpoint.txt +++ /dev/null @@ -1,193 +0,0 @@ -aiohappyeyeballs==2.4.0 -aiohttp==3.10.5 -aiosignal==1.3.1 -annotated-types==0.7.0 -annoy==1.17.3 -anyio==4.4.0 -aspy.refactor-imports==3.0.2 -async-timeout==4.0.3 -attrs==24.2.0 -audioread==3.0.1 -autobahn==24.4.2 -Automat==24.8.1 -black==24.8.0 -certifi==2024.7.4 -cffi==1.17.0 -charset-normalizer==3.3.2 -click==8.1.7 -cloudpickle==3.0.0 -coloredlogs==15.0.1 -constantly==23.10.4 -cryptography==43.0.1 -dataclasses-json==0.6.7 -datasets==2.21.0 -decorator==5.1.1 -dill==0.3.8 -diskcache==5.6.3 -distro==1.9.0 -exceptiongroup==1.2.2 -faiss-cpu==1.8.0.post1 -fastapi==0.112.1 -fastembed==0.3.6 -filelock==3.15.4 -flatbuffers==24.3.25 -frozenlist==1.4.1 -fsspec==2024.6.1 -gguf==0.9.1 -greenlet==3.0.3 -h11==0.14.0 -hkdf==0.0.3 -httpcore==1.0.5 -httptools==0.6.1 -httpx==0.27.0 -huggingface-hub==0.24.6 -humanfriendly==10.0 -humanize==4.10.0 -hyperlink==21.0.0 -idna==3.8 -importlib_metadata==8.4.0 -incremental==24.7.2 -interegular==0.3.3 -isort==5.13.2 -iterable-io==1.0.0 -Jinja2==3.1.4 -jiter==0.5.0 -joblib==1.4.2 -jsonpatch==1.33 -jsonpointer==3.0.0 -jsonschema==4.23.0 -jsonschema-specifications==2023.12.1 -langchain==0.2.14 -langchain-community==0.2.12 -langchain-core==0.2.41 -langchain-nvidia-ai-endpoints==0.2.1 -langchain-openai==0.1.25 -langchain-text-splitters==0.2.2 -langsmith==0.1.136 -lark==1.1.9 -lazy_loader==0.4 -librosa==0.10.2.post1 -llvmlite==0.43.0 -lm-format-enforcer==0.10.6 -loguru==0.7.2 -magic-wormhole==0.15.0 -markdown-it-py==3.0.0 -MarkupSafe==2.1.5 -marshmallow==3.22.0 -mdurl==0.1.2 -mmh3==4.1.0 -mpmath==1.3.0 -msgpack==1.0.8 -msgspec==0.18.6 -multidict==6.0.5 -multiprocess==0.70.16 -mypy-extensions==1.0.0 -nemoguardrails==0.10.0 -nest-asyncio==1.6.0 -networkx==3.3 -numba==0.60.0 -numpy==1.26.4 -nvidia-cublas-cu12==12.1.3.1 -nvidia-cuda-cupti-cu12==12.1.105 -nvidia-cuda-nvrtc-cu12==12.1.105 -nvidia-cuda-runtime-cu12==12.1.105 -nvidia-cudnn-cu12==9.1.0.70 -nvidia-cufft-cu12==11.0.2.54 -nvidia-curand-cu12==10.3.2.106 -nvidia-cusolver-cu12==11.4.5.107 -nvidia-cusparse-cu12==12.1.0.106 -nvidia-ml-py==12.560.30 -nvidia-nccl-cu12==2.20.5 -nvidia-nvjitlink-cu12==12.6.20 -nvidia-nvtx-cu12==12.1.105 -onnx==1.16.2 -onnxruntime==1.19.0 -openai==1.42.0 -orjson==3.10.7 -outlines==0.0.46 -packaging==24.1 -pandas==2.2.2 -pathspec==0.12.1 -pillow==10.4.0 -platformdirs==4.2.2 -pooch==1.8.2 -prometheus-fastapi-instrumentator==7.0.0 -prometheus_client==0.20.0 -prompt_toolkit==3.0.47 -protobuf==5.27.3 -psutil==6.0.0 -py-cpuinfo==9.0.0 -pyairports==2.1.1 -pyarrow==17.0.0 -pyasn1==0.6.1 -pyasn1_modules==0.4.1 -pycountry==24.6.1 -pycparser==2.22 -pydantic==2.8.2 -pydantic_core==2.20.1 -Pygments==2.18.0 -PyNaCl==1.5.0 -pyOpenSSL==24.2.1 -pypdf==4.3.1 -PyStemmer==2.2.0.1 -python-dateutil==2.9.0.post0 -python-dotenv==1.0.1 -pytz==2024.1 -PyYAML==6.0.2 -pyzmq==26.2.0 -ray==2.34.0 -referencing==0.35.1 -regex==2024.7.24 -requests==2.32.3 -requests-toolbelt==1.0.0 -rich==13.7.1 -rpds-py==0.20.0 -safetensors==0.4.4 -scikit-learn==1.5.1 -scipy==1.14.1 -seed-isort-config==2.2.0 -sentencepiece==0.2.0 -service-identity==24.1.0 -shellingham==1.5.4 -simpleeval==0.9.13 -six==1.16.0 -sniffio==1.3.1 -snowballstemmer==2.2.0 -soundfile==0.12.1 -soxr==0.5.0 -spake2==0.8 -SQLAlchemy==2.0.32 -starlette==0.38.2 -sympy==1.13.2 -tenacity==8.5.0 -threadpoolctl==3.5.0 -tiktoken==0.7.0 -tokenizers==0.19.1 -tomli==2.0.2 -torch==2.4.0 -torchvision==0.19.0 -tqdm==4.66.5 -transformers==4.44.2 -triton==3.0.0 -Twisted==24.7.0 -txaio==23.1.1 -txtorcon==24.8.0 -typer==0.12.4 -typing-inspect==0.9.0 -typing_extensions==4.12.2 -tzdata==2024.1 -urllib3==2.2.2 -uvicorn==0.30.6 -uvloop==0.20.0 -vllm==0.5.5 -vllm-flash-attn==2.6.1 -watchdog==4.0.2 -watchfiles==0.23.0 -wcwidth==0.2.13 -websockets==13.0 -xformers==0.0.27.post2 -xxhash==3.5.0 -yarl==1.9.4 -zipp==3.20.1 -zipstream-ng==1.7.1 -zope.interface==7.0.3 diff --git a/examples/notebooks/LlamaGuard_AlignScore_NIM/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb b/examples/notebooks/llamaguard_alignscore_nim/.ipynb_checkpoints/llamaguard_alignscore_nim-checkpoint.ipynb similarity index 95% rename from examples/notebooks/LlamaGuard_AlignScore_NIM/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb rename to examples/notebooks/llamaguard_alignscore_nim/.ipynb_checkpoints/llamaguard_alignscore_nim-checkpoint.ipynb index affbfdd85..20b6acaf7 100644 --- a/examples/notebooks/LlamaGuard_AlignScore_NIM/LlamaGuard_AlignScore_NIM_NeMoGuardrails.ipynb +++ b/examples/notebooks/llamaguard_alignscore_nim/.ipynb_checkpoints/llamaguard_alignscore_nim-checkpoint.ipynb @@ -16,9 +16,7 @@ "\n", "## AI Workflow\n", "\n", - "The architecture diagram for the NIM-powered RAG pipeline that we are going to build in this tutorial using NVIDIA NIM and NeMO Guardrails can be found in the `notebooks` directory\n", - "\n", - "![Arch Diagram](https://drive.google.com/file/d/1xngrc6x5Vq8d1bRKxnDS6EuH9I_-w2CS/view?usp=sharing)\n" + "The architecture diagram for the NIM-powered RAG pipeline that we are going to build in this tutorial using NVIDIA NIM and NeMO Guardrails can be found in the `notebooks/llamaguard_alignscore_nim` directory" ] }, { @@ -178,7 +176,7 @@ "## Setting up the RAG pipeline\n", "### Adding a new custom dataset\n", "\n", - "You can add your own pdf into the knowledge base located at - `LlamaGuard_AlignScore_NIM/kb` or any other location of your choice. In this example I have the pdf for NVIDIA AI Enterprise user guide stored in the above location. In the following cells, specify the file path as needed. " + "You can add your own pdf into the knowledge base located at - `llamaguard_alignscore_nim/kb` or any other location of your choice. In this example I have the pdf for NVIDIA AI Enterprise user guide stored in the above location. In the following cells, specify the file path as needed. " ] }, { @@ -193,7 +191,25 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 7, + "id": "52a93353-93df-4b5f-b772-f0acfd111be9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/root/verb-workspace/examples/notebooks/llamaguard_alignscore_nim\n" + ] + } + ], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8", "metadata": { "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8" @@ -203,7 +219,7 @@ "from langchain_community.document_loaders import PyPDFLoader\n", "\n", "# Add path to the data downloaded\n", - "file_path = \"LlamaGuard_AlignScore_NIM/kb/data.pdf\"\n", + "file_path = \"kb/data.pdf\"\n", "\n", "loader = PyPDFLoader(file_path)\n", "document = loader.load()" @@ -222,20 +238,21 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 10, "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553", "metadata": { "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553" }, "outputs": [], "source": [ + "import os\n", "os.environ[\"NVIDIA_API_KEY\"]=\"...\"\n", "os.environ[\"OPENAI_API_KEY\"]=\"dummy\"" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "id": "a1cd0e57-67c8-4a41-9dc4-fd1af6c2e1a4", "metadata": {}, "outputs": [ @@ -243,14 +260,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting faiss-cpu==1.8.0.post1\n", - " Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.7 kB)\n", + "Requirement already satisfied: faiss-cpu==1.8.0.post1 in /opt/conda/lib/python3.10/site-packages (1.8.0.post1)\n", "Requirement already satisfied: numpy<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from faiss-cpu==1.8.0.post1) (1.26.3)\n", "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from faiss-cpu==1.8.0.post1) (24.2)\n", - "Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.0 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m25.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", - "\u001b[?25hInstalling collected packages: faiss-cpu\n", - "Successfully installed faiss-cpu-1.8.0.post1\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m" ] @@ -262,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214", "metadata": { "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214" @@ -304,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "id": "e8440c18-e14c-4098-8498-01e6d15db61a", "metadata": { "id": "e8440c18-e14c-4098-8498-01e6d15db61a" @@ -345,20 +357,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7", "metadata": { "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mkdir: cannot create directory ‘config’: File exists\n" - ] - } - ], + "outputs": [], "source": [ "%mkdir config" ] @@ -376,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", "metadata": { "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", @@ -393,11 +397,6 @@ ], "source": [ "%%writefile config/config.yml\n", - "models:\n", - " - type: main\n", - " engine: nim\n", - " model: meta/llama-3.1-70b-instruct\n", - "\n", "instructions:\n", " - type: general\n", " content: |\n", @@ -414,7 +413,12 @@ " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", " user intent: user ask question about NVIDIA AI Enterprise\n", " bot intent: bot respond to question about NVIDIA AI Enterprise\n", - " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n" + " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n", + "\n", + "models:\n", + " - type: main\n", + " engine: nim\n", + " model: meta/llama-3.1-70b-instruct" ] }, { @@ -426,9 +430,9 @@ "source": [ "To understand the config more thoroughly, the attributes can be defined as follows:\n", "\n", - "`type` - set to `main` indicating the main LLM model. Here we have an Llama 3.1-70b-instruct NIM as our LLM.
    \n", - "`engine`- the LLM provider, e.g., openai, nvidia_ai_endpoints, self_hosted etc.
    \n", - "`model` - has the name of the model, e.g., gpt-3.5-turbo-instruct.\n", + "- `type` - set to `main` indicating the main LLM model. Here we have an Llama 3.1-70b-instruct NIM as our LLM.
    \n", + "- `engine`- the LLM provider, e.g., openai, nvidia_ai_endpoints, self_hosted etc.
    \n", + "- `model` - has the name of the model, e.g., gpt-3.5-turbo-instruct.\n", "\n", "The sample conversation entails the bot's intent for a user action, making sure the bot response is in accordance to the given policies" ] @@ -447,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", "metadata": { "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", @@ -538,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", "metadata": { "colab": { @@ -553,7 +557,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a95756e218e84c13b73ce6bf059eb111", + "model_id": "e3b7515c4c934af48c6d15fb62c8ded2", "version_major": 2, "version_minor": 0 }, @@ -569,7 +573,7 @@ "output_type": "stream", "text": [ "Hello world!\n", - "How's it going?\n" + "How are you feeling today?\n" ] } ], @@ -598,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "id": "ba7ecce1-d069-4b28-8820-fdb734bbd175", "metadata": { "id": "ba7ecce1-d069-4b28-8820-fdb734bbd175", @@ -657,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, "id": "935ee081-7473-47ad-b167-aa1bf820e9ff", "metadata": { "id": "935ee081-7473-47ad-b167-aa1bf820e9ff", @@ -728,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, "id": "934746fb-737d-415b-96c8-1ed27ba923e9", "metadata": { "id": "934746fb-737d-415b-96c8-1ed27ba923e9", @@ -909,7 +913,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", "metadata": { "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", @@ -920,7 +924,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can download and install the NVIDIA AI Enterprise software suite from the NVIDIA website. After installation, you can access the NVIDIA AI Enterprise portal to deploy, manage, and scale your AI workloads. You can also refer to the NVIDIA AI Enterprise documentation and tutorials for more detailed instructions and guidance.\n" + "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can download and install the NVIDIA AI Enterprise software suite from the NVIDIA website. After installation, you can access the NVIDIA AI Enterprise portal to deploy, manage, and monitor your AI workloads. Additionally, you can also use the NVIDIA AI Enterprise CLI or API to automate and customize your workflows. If you need more detailed instructions or have specific questions, feel free to ask and I'll do my best to assist you.\n" ] } ], @@ -950,7 +954,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", "metadata": { "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", @@ -1001,7 +1005,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 23, "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", "metadata": { "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", @@ -1070,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 24, "id": "57548067-8fac-46df-b9b8-21d4e13267eb", "metadata": { "id": "57548067-8fac-46df-b9b8-21d4e13267eb", @@ -1105,7 +1109,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", "metadata": { "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", @@ -1116,7 +1120,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "To install NVIDIA AI Enterprise software components using Kubernetes, you can refer to our official documentation, which provides a step-by-step guide on how to deploy the NVIDIA AI Enterprise software stack using Helm charts. You'll need to have a Kubernetes cluster set up and running, and then you can use the helm install command to deploy the required components. Please note that you'll also need to ensure that your cluster meets the system requirements for NVIDIA AI Enterprise. Would you like me to provide the link to the documentation?\n" + "To install NVIDIA AI Enterprise software components using Kubernetes, you can use Helm charts. Helm is a package manager for Kubernetes that simplifies the process of installing and managing applications on your cluster. NVIDIA provides Helm charts for its AI Enterprise software components, which can be used to deploy and manage these components on your Kubernetes cluster.\n", + "Here is a high-level overview of the installation steps:\n", + "1. Install Helm on your Kubernetes cluster.\n", + "2. Add the NVIDIA Helm repository to your Helm installation.\n", + "3. Install the desired NVIDIA AI Enterprise software components using the Helm charts.\n", + "For detailed installation instructions, please refer to the NVIDIA AI Enterprise documentation: .\n" ] } ], diff --git a/examples/notebooks/LlamaGuard_AlignScore_NIM/Arch_diagram_LlamaGuard_notebbok.png b/examples/notebooks/llamaguard_alignscore_nim/Arch_diagram_LlamaGuard_notebbok.png similarity index 100% rename from examples/notebooks/LlamaGuard_AlignScore_NIM/Arch_diagram_LlamaGuard_notebbok.png rename to examples/notebooks/llamaguard_alignscore_nim/Arch_diagram_LlamaGuard_notebbok.png diff --git a/examples/notebooks/LlamaGuard_AlignScore_NIM/kb/data.pdf b/examples/notebooks/llamaguard_alignscore_nim/kb/data.pdf similarity index 100% rename from examples/notebooks/LlamaGuard_AlignScore_NIM/kb/data.pdf rename to examples/notebooks/llamaguard_alignscore_nim/kb/data.pdf diff --git a/examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/LlamaGuard_AlignScore_NIM_NeMoGuardrails-checkpoint.ipynb b/examples/notebooks/llamaguard_alignscore_nim/llamaguard_alignscore_nim.ipynb similarity index 95% rename from examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/LlamaGuard_AlignScore_NIM_NeMoGuardrails-checkpoint.ipynb rename to examples/notebooks/llamaguard_alignscore_nim/llamaguard_alignscore_nim.ipynb index affbfdd85..20b6acaf7 100644 --- a/examples/notebooks/LlamaGuard_AlignScore_NIM/.ipynb_checkpoints/LlamaGuard_AlignScore_NIM_NeMoGuardrails-checkpoint.ipynb +++ b/examples/notebooks/llamaguard_alignscore_nim/llamaguard_alignscore_nim.ipynb @@ -16,9 +16,7 @@ "\n", "## AI Workflow\n", "\n", - "The architecture diagram for the NIM-powered RAG pipeline that we are going to build in this tutorial using NVIDIA NIM and NeMO Guardrails can be found in the `notebooks` directory\n", - "\n", - "![Arch Diagram](https://drive.google.com/file/d/1xngrc6x5Vq8d1bRKxnDS6EuH9I_-w2CS/view?usp=sharing)\n" + "The architecture diagram for the NIM-powered RAG pipeline that we are going to build in this tutorial using NVIDIA NIM and NeMO Guardrails can be found in the `notebooks/llamaguard_alignscore_nim` directory" ] }, { @@ -178,7 +176,7 @@ "## Setting up the RAG pipeline\n", "### Adding a new custom dataset\n", "\n", - "You can add your own pdf into the knowledge base located at - `LlamaGuard_AlignScore_NIM/kb` or any other location of your choice. In this example I have the pdf for NVIDIA AI Enterprise user guide stored in the above location. In the following cells, specify the file path as needed. " + "You can add your own pdf into the knowledge base located at - `llamaguard_alignscore_nim/kb` or any other location of your choice. In this example I have the pdf for NVIDIA AI Enterprise user guide stored in the above location. In the following cells, specify the file path as needed. " ] }, { @@ -193,7 +191,25 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 7, + "id": "52a93353-93df-4b5f-b772-f0acfd111be9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/root/verb-workspace/examples/notebooks/llamaguard_alignscore_nim\n" + ] + } + ], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8", "metadata": { "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8" @@ -203,7 +219,7 @@ "from langchain_community.document_loaders import PyPDFLoader\n", "\n", "# Add path to the data downloaded\n", - "file_path = \"LlamaGuard_AlignScore_NIM/kb/data.pdf\"\n", + "file_path = \"kb/data.pdf\"\n", "\n", "loader = PyPDFLoader(file_path)\n", "document = loader.load()" @@ -222,20 +238,21 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 10, "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553", "metadata": { "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553" }, "outputs": [], "source": [ + "import os\n", "os.environ[\"NVIDIA_API_KEY\"]=\"...\"\n", "os.environ[\"OPENAI_API_KEY\"]=\"dummy\"" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "id": "a1cd0e57-67c8-4a41-9dc4-fd1af6c2e1a4", "metadata": {}, "outputs": [ @@ -243,14 +260,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting faiss-cpu==1.8.0.post1\n", - " Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.7 kB)\n", + "Requirement already satisfied: faiss-cpu==1.8.0.post1 in /opt/conda/lib/python3.10/site-packages (1.8.0.post1)\n", "Requirement already satisfied: numpy<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from faiss-cpu==1.8.0.post1) (1.26.3)\n", "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from faiss-cpu==1.8.0.post1) (24.2)\n", - "Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.0 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m25.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", - "\u001b[?25hInstalling collected packages: faiss-cpu\n", - "Successfully installed faiss-cpu-1.8.0.post1\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m" ] @@ -262,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214", "metadata": { "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214" @@ -304,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "id": "e8440c18-e14c-4098-8498-01e6d15db61a", "metadata": { "id": "e8440c18-e14c-4098-8498-01e6d15db61a" @@ -345,20 +357,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7", "metadata": { "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mkdir: cannot create directory ‘config’: File exists\n" - ] - } - ], + "outputs": [], "source": [ "%mkdir config" ] @@ -376,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", "metadata": { "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", @@ -393,11 +397,6 @@ ], "source": [ "%%writefile config/config.yml\n", - "models:\n", - " - type: main\n", - " engine: nim\n", - " model: meta/llama-3.1-70b-instruct\n", - "\n", "instructions:\n", " - type: general\n", " content: |\n", @@ -414,7 +413,12 @@ " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", " user intent: user ask question about NVIDIA AI Enterprise\n", " bot intent: bot respond to question about NVIDIA AI Enterprise\n", - " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n" + " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n", + "\n", + "models:\n", + " - type: main\n", + " engine: nim\n", + " model: meta/llama-3.1-70b-instruct" ] }, { @@ -426,9 +430,9 @@ "source": [ "To understand the config more thoroughly, the attributes can be defined as follows:\n", "\n", - "`type` - set to `main` indicating the main LLM model. Here we have an Llama 3.1-70b-instruct NIM as our LLM.
    \n", - "`engine`- the LLM provider, e.g., openai, nvidia_ai_endpoints, self_hosted etc.
    \n", - "`model` - has the name of the model, e.g., gpt-3.5-turbo-instruct.\n", + "- `type` - set to `main` indicating the main LLM model. Here we have an Llama 3.1-70b-instruct NIM as our LLM.
    \n", + "- `engine`- the LLM provider, e.g., openai, nvidia_ai_endpoints, self_hosted etc.
    \n", + "- `model` - has the name of the model, e.g., gpt-3.5-turbo-instruct.\n", "\n", "The sample conversation entails the bot's intent for a user action, making sure the bot response is in accordance to the given policies" ] @@ -447,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", "metadata": { "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", @@ -538,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", "metadata": { "colab": { @@ -553,7 +557,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a95756e218e84c13b73ce6bf059eb111", + "model_id": "e3b7515c4c934af48c6d15fb62c8ded2", "version_major": 2, "version_minor": 0 }, @@ -569,7 +573,7 @@ "output_type": "stream", "text": [ "Hello world!\n", - "How's it going?\n" + "How are you feeling today?\n" ] } ], @@ -598,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "id": "ba7ecce1-d069-4b28-8820-fdb734bbd175", "metadata": { "id": "ba7ecce1-d069-4b28-8820-fdb734bbd175", @@ -657,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, "id": "935ee081-7473-47ad-b167-aa1bf820e9ff", "metadata": { "id": "935ee081-7473-47ad-b167-aa1bf820e9ff", @@ -728,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, "id": "934746fb-737d-415b-96c8-1ed27ba923e9", "metadata": { "id": "934746fb-737d-415b-96c8-1ed27ba923e9", @@ -909,7 +913,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", "metadata": { "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", @@ -920,7 +924,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can download and install the NVIDIA AI Enterprise software suite from the NVIDIA website. After installation, you can access the NVIDIA AI Enterprise portal to deploy, manage, and scale your AI workloads. You can also refer to the NVIDIA AI Enterprise documentation and tutorials for more detailed instructions and guidance.\n" + "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can download and install the NVIDIA AI Enterprise software suite from the NVIDIA website. After installation, you can access the NVIDIA AI Enterprise portal to deploy, manage, and monitor your AI workloads. Additionally, you can also use the NVIDIA AI Enterprise CLI or API to automate and customize your workflows. If you need more detailed instructions or have specific questions, feel free to ask and I'll do my best to assist you.\n" ] } ], @@ -950,7 +954,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", "metadata": { "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", @@ -1001,7 +1005,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 23, "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", "metadata": { "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", @@ -1070,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 24, "id": "57548067-8fac-46df-b9b8-21d4e13267eb", "metadata": { "id": "57548067-8fac-46df-b9b8-21d4e13267eb", @@ -1105,7 +1109,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", "metadata": { "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", @@ -1116,7 +1120,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "To install NVIDIA AI Enterprise software components using Kubernetes, you can refer to our official documentation, which provides a step-by-step guide on how to deploy the NVIDIA AI Enterprise software stack using Helm charts. You'll need to have a Kubernetes cluster set up and running, and then you can use the helm install command to deploy the required components. Please note that you'll also need to ensure that your cluster meets the system requirements for NVIDIA AI Enterprise. Would you like me to provide the link to the documentation?\n" + "To install NVIDIA AI Enterprise software components using Kubernetes, you can use Helm charts. Helm is a package manager for Kubernetes that simplifies the process of installing and managing applications on your cluster. NVIDIA provides Helm charts for its AI Enterprise software components, which can be used to deploy and manage these components on your Kubernetes cluster.\n", + "Here is a high-level overview of the installation steps:\n", + "1. Install Helm on your Kubernetes cluster.\n", + "2. Add the NVIDIA Helm repository to your Helm installation.\n", + "3. Install the desired NVIDIA AI Enterprise software components using the Helm charts.\n", + "For detailed installation instructions, please refer to the NVIDIA AI Enterprise documentation: .\n" ] } ], diff --git a/examples/notebooks/LlamaGuard_AlignScore_NIM/requirements.txt b/examples/notebooks/llamaguard_alignscore_nim/requirements.txt similarity index 100% rename from examples/notebooks/LlamaGuard_AlignScore_NIM/requirements.txt rename to examples/notebooks/llamaguard_alignscore_nim/requirements.txt From f97438cd59d6fa2b7c9321a04c63b1870230d23d Mon Sep 17 00:00:00 2001 From: root Date: Fri, 22 Nov 2024 18:47:46 +0000 Subject: [PATCH 17/17] Add pip install -q -r req.txt --- ...llamaguard_alignscore_nim-checkpoint.ipynb | 1178 ----------------- .../llamaguard_alignscore_nim.ipynb | 156 +-- 2 files changed, 17 insertions(+), 1317 deletions(-) delete mode 100644 examples/notebooks/llamaguard_alignscore_nim/.ipynb_checkpoints/llamaguard_alignscore_nim-checkpoint.ipynb diff --git a/examples/notebooks/llamaguard_alignscore_nim/.ipynb_checkpoints/llamaguard_alignscore_nim-checkpoint.ipynb b/examples/notebooks/llamaguard_alignscore_nim/.ipynb_checkpoints/llamaguard_alignscore_nim-checkpoint.ipynb deleted file mode 100644 index 20b6acaf7..000000000 --- a/examples/notebooks/llamaguard_alignscore_nim/.ipynb_checkpoints/llamaguard_alignscore_nim-checkpoint.ipynb +++ /dev/null @@ -1,1178 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d406f9ae-462d-43fe-b178-cb1319643af7", - "metadata": { - "id": "d406f9ae-462d-43fe-b178-cb1319643af7" - }, - "source": [ - "# Fortified RAG Pipeline: Enhancing AI with NVIDIA NIMs, NeMo Guardrails, LlamaGuard, and AlignScore\n", - "\n", - "In this tutorial we learn how to integrate LlamaGuard, a safeguard LLM with [NeMo Guardrails](https://docs.nvidia.com/nemo/guardrails/index.html) into our custom [NVIDIA NIM](https://www.nvidia.com/en-us/ai/)-powered RAG pipeline to have a more secure, moderated responses for user queries. Additionally, we also learn how to integrate AlignScore metric to check the factual consistency of the LLM response with the retrieved chunks from the vector database.\n", - "\n", - "The notebook gives a simple walkthrough of downloading the dataset (here, as an example we have the NVIDIA AI Enterprise User guide as our data), loading it our RAG pipeline. Then, we move to a step-by-step procedure to start building our rails using NeMo Guardrails.\n", - "\n", - "\n", - "## AI Workflow\n", - "\n", - "The architecture diagram for the NIM-powered RAG pipeline that we are going to build in this tutorial using NVIDIA NIM and NeMO Guardrails can be found in the `notebooks/llamaguard_alignscore_nim` directory" - ] - }, - { - "cell_type": "markdown", - "id": "c07ea679-c697-464a-8cf4-84669d4f0738", - "metadata": { - "id": "c07ea679-c697-464a-8cf4-84669d4f0738" - }, - "source": [ - "## Prerequisites\n", - "\n", - "You can install the environment if needed from the `requirements.txt` or the following imports also perfectly work\n", - "\n", - "### Installation\n", - "\n", - "#### Using pip" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", - "metadata": { - "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", - "outputId": "a001b0c9-c45b-46de-d0a2-9ac0a2ce0a57" - }, - "outputs": [ - { - "name": "stdout", - 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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", - "\u001b[0m" - ] - } - ], - "source": [ - "!pip install nemoguardrails langchain_nvidia_ai_endpoints openai" - ] - }, - { - "cell_type": "markdown", - "id": "025b2f44-a2df-418e-bbe9-8b2ea8583e7f", - "metadata": { - "id": "025b2f44-a2df-418e-bbe9-8b2ea8583e7f" - }, - "source": [ - "#### AsyncIO\n", - "Since you’re running this inside a notebook, patch the AsyncIO loop." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c41f7864-facc-4226-8e00-41c0a55b78aa", - "metadata": { - "id": "c41f7864-facc-4226-8e00-41c0a55b78aa" - }, - "outputs": [], - "source": [ - "import nest_asyncio\n", - "nest_asyncio.apply()" - ] - }, - { - "cell_type": "markdown", - "id": "a24015c0-eb33-43a9-ac0b-67cc88c16830", - "metadata": { - "id": "a24015c0-eb33-43a9-ac0b-67cc88c16830" - }, - "source": [ - "## Setting up the RAG pipeline\n", - "### Adding a new custom dataset\n", - "\n", - "You can add your own pdf into the knowledge base located at - `llamaguard_alignscore_nim/kb` or any other location of your choice. In this example I have the pdf for NVIDIA AI Enterprise user guide stored in the above location. In the following cells, specify the file path as needed. " - ] - }, - { - "cell_type": "markdown", - "id": "ebb3ac35-8295-49b4-94bf-61ea3b891b79", - "metadata": { - "id": "ebb3ac35-8295-49b4-94bf-61ea3b891b79" - }, - "source": [ - "### Load the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "52a93353-93df-4b5f-b772-f0acfd111be9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/root/verb-workspace/examples/notebooks/llamaguard_alignscore_nim\n" - ] - } - ], - "source": [ - "!pwd" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8", - "metadata": { - "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8" - }, - "outputs": [], - "source": [ - "from langchain_community.document_loaders import PyPDFLoader\n", - "\n", - "# Add path to the data downloaded\n", - "file_path = \"kb/data.pdf\"\n", - "\n", - "loader = PyPDFLoader(file_path)\n", - "document = loader.load()" - ] - }, - { - "cell_type": "markdown", - "id": "8fa3de34-77e4-4d61-b130-35695c773d96", - "metadata": { - "id": "8fa3de34-77e4-4d61-b130-35695c773d96" - }, - "source": [ - "### Setup the Retriever\n", - "#### Add the environment variables as follows" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553", - "metadata": { - "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553" - }, - "outputs": [], - "source": [ - "import os\n", - "os.environ[\"NVIDIA_API_KEY\"]=\"...\"\n", - "os.environ[\"OPENAI_API_KEY\"]=\"dummy\"" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a1cd0e57-67c8-4a41-9dc4-fd1af6c2e1a4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: faiss-cpu==1.8.0.post1 in /opt/conda/lib/python3.10/site-packages (1.8.0.post1)\n", - "Requirement already satisfied: numpy<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from faiss-cpu==1.8.0.post1) (1.26.3)\n", - "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from faiss-cpu==1.8.0.post1) (24.2)\n", - "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", - "\u001b[0m" - ] - } - ], - "source": [ - "!pip install faiss-cpu==1.8.0.post1" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214", - "metadata": { - "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214" - }, - "outputs": [], - "source": [ - "from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA\n", - "from langchain_community.vectorstores import FAISS\n", - "from langchain.chains import RetrievalQA\n", - "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", - "from langchain_core.runnables import RunnablePassthrough\n", - "from langchain_core.prompts import ChatPromptTemplate\n", - "\n", - "text_splitter = RecursiveCharacterTextSplitter(\n", - " chunk_size=500,\n", - " chunk_overlap=10\n", - ")\n", - "texts = text_splitter.split_documents(document)\n", - "\n", - "# Insert the Documents in FAISS Vectorstore\n", - "embeddings = NVIDIAEmbeddings(\n", - " model=\"nvidia/nv-embedqa-e5-v5\",\n", - " truncate=\"NONE\",\n", - ")\n", - "db = FAISS.from_documents(texts, embeddings)\n", - "\n", - "retriever = db.as_retriever()" - ] - }, - { - "cell_type": "markdown", - "id": "847367cc-65ea-45ef-b7e8-ba7ade12ea44", - "metadata": { - "id": "847367cc-65ea-45ef-b7e8-ba7ade12ea44" - }, - "source": [ - "### Setup the LLM" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e8440c18-e14c-4098-8498-01e6d15db61a", - "metadata": { - "id": "e8440c18-e14c-4098-8498-01e6d15db61a" - }, - "outputs": [], - "source": [ - "llm = ChatNVIDIA(\n", - " model=\"meta/llama-3.1-70b-instruct\",\n", - " temperature=0.2,\n", - " top_p=0.7,\n", - " max_tokens=1024,\n", - ")\n", - "\n", - "prompt_template = \"\"\"Use the following pieces of context to answer the question at the end.\n", - "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", - "Use three sentences maximum and keep the answer as concise as possible.\n", - "\n", - "{context}\n", - "\n", - "Question: {question}\n", - "\n", - "Helpful Answer:\"\"\"\n", - "\n", - "prompt = ChatPromptTemplate.from_template(prompt_template)" - ] - }, - { - "cell_type": "markdown", - "id": "71e5ca44-87dd-4c61-829b-e4fdf6141a39", - "metadata": { - "id": "71e5ca44-87dd-4c61-829b-e4fdf6141a39" - }, - "source": [ - "## Define Guardrails Configuration\n", - "\n", - "We first create a folder called config for the configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7", - "metadata": { - "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7" - }, - "outputs": [], - "source": [ - "%mkdir config" - ] - }, - { - "cell_type": "markdown", - "id": "1b187b53-65f7-4440-a0ac-3c101ad60097", - "metadata": { - "id": "1b187b53-65f7-4440-a0ac-3c101ad60097" - }, - "source": [ - "### 1. Create a `config.yml` file\n", - "Here default we have the colang 1.0 version, unless specified. We start with adding the LLM NIM in the config.yml as follows and some sample conversation" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", - "metadata": { - "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", - "outputId": "2bde9146-e939-4cd8-a79f-39b71ebb6472" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing config/config.yml\n" - ] - } - ], - "source": [ - "%%writefile config/config.yml\n", - "instructions:\n", - " - type: general\n", - " content: |\n", - " Below is a conversation between a user and a bot called the NVIDIA AI Bot.\n", - " The bot is designed to answer questions about the NVIDIA AI Enterprise.\n", - " The bot is knowledgeable about the company policies.\n", - " If the bot does not know the answer to a question, it truthfully says it does not know.\n", - "\n", - "sample_conversation: |\n", - " user action: user said \"Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?\"\n", - " user intent: user express greeting and ask for assistance\n", - " bot intent: bot express greeting and confirm and offer assistance\n", - " bot action: bot say \"Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?\"\n", - " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", - " user intent: user ask question about NVIDIA AI Enterprise\n", - " bot intent: bot respond to question about NVIDIA AI Enterprise\n", - " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n", - "\n", - "models:\n", - " - type: main\n", - " engine: nim\n", - " model: meta/llama-3.1-70b-instruct" - ] - }, - { - "cell_type": "markdown", - "id": "3a2fe88c-0f0b-44cb-82fd-e7827b196a07", - "metadata": { - "id": "3a2fe88c-0f0b-44cb-82fd-e7827b196a07" - }, - "source": [ - "To understand the config more thoroughly, the attributes can be defined as follows:\n", - "\n", - "- `type` - set to `main` indicating the main LLM model. Here we have an Llama 3.1-70b-instruct NIM as our LLM.
    \n", - "- `engine`- the LLM provider, e.g., openai, nvidia_ai_endpoints, self_hosted etc.
    \n", - "- `model` - has the name of the model, e.g., gpt-3.5-turbo-instruct.\n", - "\n", - "The sample conversation entails the bot's intent for a user action, making sure the bot response is in accordance to the given policies" - ] - }, - { - "cell_type": "markdown", - "id": "c0bfb50e-a0aa-4f96-87a8-5bf6fc790800", - "metadata": { - "id": "c0bfb50e-a0aa-4f96-87a8-5bf6fc790800" - }, - "source": [ - "### 2. Basic Flows in `general.co`\n", - "\n", - "Then, we can add some basic flows on how the bot responds to user's greetings. We begin with creating a `general.co` and add some examples. In the end, we also add some example topics which the bot refuses to respond to like questions related to politics, stock market just to name a few." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", - "metadata": { - "id": "57fc1112-0709-443b-b77c-fc925cd9f3f2", - "outputId": "9ea6009b-e7e3-4262-d192-d0f9c28b0752" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing config/general.co\n" - ] - } - ], - "source": [ - "%%writefile config/general.co\n", - "define user express greeting\n", - " \"Hello\"\n", - " \"Hi\"\n", - "\n", - "define bot express greeting\n", - " \"Hello world!\"\n", - "\n", - "define bot ask how are you\n", - " \"How are you doing?\"\n", - " \"How's it going?\"\n", - " \"How are you feeling today?\"\n", - "\n", - "define user express feeling good\n", - " \"I'm feeling good\"\n", - " \"Good\"\n", - " \"Perfect\"\n", - "\n", - "define user express feeling bad\n", - " \"Not so good\"\n", - " \"Bad\"\n", - " \"Sad\"\n", - "\n", - "define flow\n", - " user express greeting\n", - " bot express greeting\n", - " bot ask how are you\n", - "\n", - " when user express feeling good\n", - " bot express positive emotion\n", - "\n", - " else when user express feeling bad\n", - " bot express empathy\n", - "\n", - "# Off-topic\n", - "\n", - "define user ask about politics\n", - " \"What do you think about the government?\"\n", - " \"Which party should I vote for?\"\n", - "\n", - "define user ask about stock market\n", - " \"Which stock should I invest in?\"\n", - " \"Would this stock 10x over the next year?\"\n", - "\n", - "define flow politics\n", - " user ask about politics\n", - " bot refuse to respond\n", - "\n", - "define user ask about theft\n", - " \"Can I plan theft?\"\n", - " \"Can I use NVIDIA AI Enterprise for theft planning?\"\n", - "\n", - "define flow\n", - " user ask about theft\n", - " bot refuse to respond\n", - "\n", - "define flow stock market\n", - " user ask about stock market\n", - " bot refuse to respond" - ] - }, - { - "cell_type": "markdown", - "id": "c1ae4e7b-abf1-4c70-8f2e-744fb332fb9c", - "metadata": { - "id": "c1ae4e7b-abf1-4c70-8f2e-744fb332fb9c" - }, - "source": [ - "### Testing the basic Configuration\n", - "#### 1. Greetings\n", - "Use the above created configuration by building an LLMRails instance and using the generate_async method in your Python code" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", - "metadata": { - "colab": { - "referenced_widgets": [ - "7a472ebf7a454aa4a5076c89248d88e2" - ] - }, - "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", - "outputId": "70fb2853-7c52-4ec6-f8f3-176b33f7ad16" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e3b7515c4c934af48c6d15fb62c8ded2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Fetching 5 files: 0%| | 0/5 [00:00[INST] Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories.\n", - " \n", - " O1: Violence and Hate.\n", - " Should not\n", - " - Help people plan or engage in violence.\n", - " - Encourage people to engage in violence.\n", - " - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.\n", - " - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.\n", - " Can\n", - " - Provide information on violence and discrimination.\n", - " - Discuss the topics of hate and violence, and can discuss historical events involving violence.\n", - " O2: Sexual Content.\n", - " Should not\n", - " - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.\n", - " Can\n", - " - Discuss topics related to sexuality and sexual education.\n", - " - Provide general information regarding sexual activities.\n", - " - Provide guidance on topics related to sexual health.\n", - " O3: Criminal Planning.\n", - " Should not\n", - " - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.\n", - " - Encourage people to engage in criminal activities or condone criminal behavior.\n", - " Can\n", - " - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.\n", - " - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.\n", - " O4: Guns and Illegal Weapons.\n", - " Should not\n", - " - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.\n", - " - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.\n", - " Can\n", - " - Discuss firearms and the arguments for and against firearm ownership.\n", - " O5: Regulated or Controlled Substances.\n", - " Should not\n", - " - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.\n", - " - Assist or encourage people to create such substances when it is illegal to do so.\n", - " Can\n", - " - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).\n", - " - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.\n", - " O6: Self-Harm.\n", - " Should not\n", - " - Encourage people to harm themselves.\n", - " - Romanticize or condone self-harm or suicide.\n", - " - Provide information regarding the methods of suicide or self-harm.\n", - " - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.\n", - " Can\n", - " - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).\n", - " Should\n", - " - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.\n", - " O7: Offensive Language and Insults.\n", - " Should not\n", - " - Address direct insults\n", - " - Use or encourage the use of swear words, bad language, or vulgar expressions.\n", - " - Insult, offend, or demean individuals or groups based on any personal characteristics or choices.\n", - " - Encourage or support the use of offensive or derogatory language.\n", - " Can\n", - " - Discuss the impact and societal perceptions of offensive language and insults.\n", - " - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue.\n", - " - Offer guidance on how to address or respond to offensive language or insults in a constructive manner.\n", - " \n", - "\n", - " \n", - " User: {{ user_input }}\n", - " \n", - "\n", - " Provide your safety assessment for User in the above conversation:\n", - " - First line must read 'safe' or 'unsafe'.\n", - " - If unsafe, a second line must include a comma-separated list of violated categories. [/INST]\n", - "\n", - "\n", - " # Currently, the unsafe content categories are the same for both llama_guard_check_input and\n", - " # llama_guard_check_output prompts. Only the conversation placeholder is different.\n", - " # Still, we keep them separate to keep the taxonomies for input and output independently modifiable.\n", - " - task: llama_guard_check_output\n", - " content: |\n", - " [INST] Task: Check if there is unsafe content in 'Agent' messages in conversations according our safety policy with the below categories.\n", - " \n", - " O1: Violence and Hate.\n", - " Should not\n", - " - Help people plan or engage in violence.\n", - " - Encourage people to engage in violence.\n", - " - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.\n", - " - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.\n", - " Can\n", - " - Provide information on violence and discrimination.\n", - " - Discuss the topics of hate and violence, and can discuss historical events involving violence.\n", - " O2: Sexual Content.\n", - " Should not\n", - " - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.\n", - " Can\n", - " - Discuss topics related to sexuality and sexual education.\n", - " - Provide general information regarding sexual activities.\n", - " - Provide guidance on topics related to sexual health.\n", - " O3: Criminal Planning.\n", - " Should not\n", - " - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.\n", - " - Encourage people to engage in criminal activities or condone criminal behavior.\n", - " Can\n", - " - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.\n", - " - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.\n", - " O4: Guns and Illegal Weapons.\n", - " Should not\n", - " - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.\n", - " - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.\n", - " Can\n", - " - Discuss firearms and the arguments for and against firearm ownership.\n", - " O5: Regulated or Controlled Substances.\n", - " Should not\n", - " - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.\n", - " - Assist or encourage people to create such substances when it is illegal to do so.\n", - " Can\n", - " - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).\n", - " - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.\n", - " O6: Self-Harm.\n", - " Should not\n", - " - Encourage people to harm themselves.\n", - " - Romanticize or condone self-harm or suicide.\n", - " - Provide information regarding the methods of suicide or self-harm.\n", - " - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.\n", - " Can\n", - " - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).\n", - " Should\n", - " - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.\n", - " O7: Offensive Language and Insults.\n", - " Should not\n", - " - Address direct insults\n", - " - Use or encourage the use of swear words, bad language, or vulgar expressions.\n", - " - Insult, offend, or demean individuals or groups based on any personal characteristics or choices.\n", - " - Encourage or support the use of offensive or derogatory language.\n", - " Can\n", - " - Discuss the impact and societal perceptions of offensive language and insults.\n", - " - Provide information on communication strategies that avoid the use of offensive language and promote respectful dialogue.\n", - " - Offer guidance on how to address or respond to offensive language or insults in a constructive manner.\n", - " \n", - "\n", - " \n", - " User: {{ user_input }}\n", - "\n", - " Agent: {{ bot_response }}\n", - " \n", - "\n", - " Provide your safety assessment for Agent in the above conversation:\n", - " - First line must read 'safe' or 'unsafe'.\n", - " - If unsafe, a second line must include a comma-separated list of violated categories. [/INST]" - ] - }, - { - "cell_type": "markdown", - "id": "1635f224-420c-4b07-b909-77f1b3115ef2", - "metadata": { - "id": "1635f224-420c-4b07-b909-77f1b3115ef2" - }, - "source": [ - "### Testing RAG with LlamaGuard Integration\n", - "\n", - "#### 1. Safe Question\n", - "We start by asking a question that is safe as per the safety-risk guidelines. As the question is asked, LlamaGuard is supposed to regard the input as 'safe', allowing the LLM NIM to answer the question and finally respond to the user." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", - "metadata": { - "id": "5b6b807c-9912-44b7-aad3-6e07b70607ad", - "outputId": "471a63bc-804c-4aa4-e172-970be8659659" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You can use NVIDIA AI Enterprise by first ensuring your system meets the necessary hardware and software requirements. Once you've met the requirements, you can download and install the NVIDIA AI Enterprise software suite from the NVIDIA website. After installation, you can access the NVIDIA AI Enterprise portal to deploy, manage, and monitor your AI workloads. Additionally, you can also use the NVIDIA AI Enterprise CLI or API to automate and customize your workflows. If you need more detailed instructions or have specific questions, feel free to ask and I'll do my best to assist you.\n" - ] - } - ], - "source": [ - "from nemoguardrails import RailsConfig, LLMRails\n", - "config = RailsConfig.from_path(\"./config\")\n", - "rails = LLMRails(config)\n", - "\n", - "response = rails.generate(messages=[{\n", - " \"role\": \"user\",\n", - " \"content\": \"How do I use NVIDIA AI?\"\n", - "}])\n", - "print(response['content'])" - ] - }, - { - "cell_type": "markdown", - "id": "fe22f322-f61e-4b02-8730-1f3440fe9ec0", - "metadata": { - "id": "fe22f322-f61e-4b02-8730-1f3440fe9ec0" - }, - "source": [ - "#### 2. Unsafe Question\n", - "\n", - "Secondly, we can now ask a question about using NVIDIA AI Enterprise for theft planning. Since, 'theft' is a non-safe word, LlamaGuard flags the input prompt as 'unsafe' and makes the bot refuse to respond to the user. Let us look at this example." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", - "metadata": { - "id": "b41eac75-0156-4c57-a3ea-ad896fe6b622", - "outputId": "8a63b068-fe2d-4346-c6ea-097c6f5f0c4d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I'm sorry, I can't respond to that.\n" - ] - } - ], - "source": [ - "response = rails.generate(messages=[{\n", - " \"role\": \"user\",\n", - " \"content\": \"Can I use NVIDIA AI Enterprise in theft planning?\"\n", - "}])\n", - "print(response['content'])" - ] - }, - { - "cell_type": "markdown", - "id": "9b7bf8fd-9925-4c3e-b507-b6e96ca67a92", - "metadata": { - "id": "9b7bf8fd-9925-4c3e-b507-b6e96ca67a92" - }, - "source": [ - "## AlignScore Integration for fact-checking the moderated LLM response\n", - "\n", - "A secure and safe RAG pipeline requires the LLM generated text to be factually consistent with input information and the Knowledge Base. But we need to understand what factual consistency means and how is it evaluated.\n", - "\n", - "Facutual Consistency: For a given text pair (a, b), they are considered factual consistent if\n", - "1) all the information in b is also present in a;\n", - "2) b does not contradict a.\n", - "\n", - "Evaluation: Show the degree of factual consistency between the context (text a) and the claim (text b).\n", - "\n", - "Now, coming back to the RAG pipeline, the factual consistency evaluation is done by checking the LLM response with the retrieved chunks obtained from the vector database.\n", - "\n", - "AlignScore, a metric is developed to assess this factual consistency in context-claim pairs across various Natural Language Understanding (NLU) tasks. There are two checkpoints, 'base' and 'large' which can be integrated with NeMo Guardrails for fact-checking purposes. NeMo Guardrails toolkit allows an easy integration of AlignScore with the user's custom usecase. To do this, we first setup the [AlignScore deployment](https://docs.nvidia.com/nemo/guardrails/user_guides/advanced/align-score-deployment.html) and then integrate it into our pre-built LlamaGuard integrated guardrails configuration by adding the model endpoints into the `config.yml` file as follows\n", - "\n", - "### Update the LlamaGuard Integrated Guardrails configuration with AlignScore\n", - "\n", - "#### 1. Re-write `config.yml`" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", - "metadata": { - "id": "a9943587-8909-4605-9668-ff1bf4ba5a62", - "outputId": "5048f878-823c-4feb-8d18-09e897626d1c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting config/config.yml\n" - ] - } - ], - "source": [ - "%%writefile config/config.yml\n", - "instructions:\n", - " - type: general\n", - " content: |\n", - " Below is a conversation between a user and a bot called the NVIDIA AI Bot.\n", - " The bot is designed to answer questions about the NVIDIA AI Enterprise.\n", - " The bot is knowledgeable about the company policies.\n", - " If the bot does not know the answer to a question, it truthfully says it does not know.\n", - "\n", - "sample_conversation: |\n", - " user action: user said \"Hi there. Can you help me with some questions I have about NVIDIA AI Enterprise?\"\n", - " user intent: user express greeting and ask for assistance\n", - " bot intent: bot express greeting and confirm and offer assistance\n", - " bot action: bot say \"Hi there! I'm here to help answer any questions you may have about NVIDIA AI Enterprise. What would you like to know?\"\n", - " user action: user said \"What is offered by NVIDIA AI Enterprise?\"\n", - " user intent: user ask question about NVIDIA AI Enterprise\n", - " bot intent: bot respond to question about NVIDIA AI Enterprise\n", - " bot action: bot say \"NVIDIA AI Enterprise is a software suite that enables enterprises to easily deploy, manage, and scale AI workloads across bare-metal servers, virtual machines, and containerized environments, allowing you to accelerate your AI initiatives and optimize data center resources.\"\n", - "\n", - "models:\n", - " - type: main\n", - " engine: nim\n", - " model: meta/llama-3.1-70b-instruct\n", - "\n", - " - type: llama_guard\n", - " engine: vllm_openai\n", - " parameters:\n", - " openai_api_base: \"http://localhost:5123/v1\"\n", - " model_name: \"meta-llama/LlamaGuard-7b\"\n", - "\n", - "rails:\n", - " config:\n", - " fact_checking:\n", - " provider: align_score\n", - " parameters:\n", - " endpoint: \"http://localhost:5000/alignscore_base\"\n", - " output:\n", - " flows:\n", - " - alignscore check facts" - ] - }, - { - "cell_type": "markdown", - "id": "a318095e-29ad-4e3a-8017-f6aa613f6df3", - "metadata": { - "id": "a318095e-29ad-4e3a-8017-f6aa613f6df3" - }, - "source": [ - "#### 2. Adding flows for Fact-Checking using the AlignScore model" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "57548067-8fac-46df-b9b8-21d4e13267eb", - "metadata": { - "id": "57548067-8fac-46df-b9b8-21d4e13267eb", - "outputId": "d8a06593-43cd-4b54-bb29-1c679eb1fd4d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing config/factchecking.co\n" - ] - } - ], - "source": [ - "%%writefile config/factchecking.co\n", - "define flow\n", - " user ask about report\n", - " $check_facts = True\n", - " bot provide report answer" - ] - }, - { - "cell_type": "markdown", - "id": "2e2b03d6-6c80-4dd7-a020-3c72b8463bf7", - "metadata": { - "id": "2e2b03d6-6c80-4dd7-a020-3c72b8463bf7" - }, - "source": [ - "### Testing the AlignScore Integration with the prebuilt LlamaGuard Integrated guardrails configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", - "metadata": { - "id": "f097460f-09e8-42e3-86dd-f52e0e0b2993", - "outputId": "17dcc492-d2af-49fe-8dd3-79be0b9d5d1d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "To install NVIDIA AI Enterprise software components using Kubernetes, you can use Helm charts. Helm is a package manager for Kubernetes that simplifies the process of installing and managing applications on your cluster. NVIDIA provides Helm charts for its AI Enterprise software components, which can be used to deploy and manage these components on your Kubernetes cluster.\n", - "Here is a high-level overview of the installation steps:\n", - "1. Install Helm on your Kubernetes cluster.\n", - "2. Add the NVIDIA Helm repository to your Helm installation.\n", - "3. Install the desired NVIDIA AI Enterprise software components using the Helm charts.\n", - "For detailed installation instructions, please refer to the NVIDIA AI Enterprise documentation: .\n" - ] - } - ], - "source": [ - "config = RailsConfig.from_path(\"./config\")\n", - "rails = LLMRails(config)\n", - "\n", - "response = rails.generate(messages=[{\n", - " \"role\": \"user\",\n", - " \"content\": \"How do I install NVIDIA AI Enterprise Software Components using Kubernetes?\"\n", - "}])\n", - "print(response['content'])" - ] - }, - { - "cell_type": "markdown", - "id": "0035dd84-961d-4a61-bffd-22369e590ded", - "metadata": { - "id": "0035dd84-961d-4a61-bffd-22369e590ded" - }, - "source": [ - "As we can see from the response, the consistency of the information is evaluated using AlignScore and maintained with that in the Knowledge base" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/notebooks/llamaguard_alignscore_nim/llamaguard_alignscore_nim.ipynb b/examples/notebooks/llamaguard_alignscore_nim/llamaguard_alignscore_nim.ipynb index 20b6acaf7..989f4fb60 100644 --- a/examples/notebooks/llamaguard_alignscore_nim/llamaguard_alignscore_nim.ipynb +++ b/examples/notebooks/llamaguard_alignscore_nim/llamaguard_alignscore_nim.ipynb @@ -28,7 +28,7 @@ "source": [ "## Prerequisites\n", "\n", - "You can install the environment if needed from the `requirements.txt` or the following imports also perfectly work\n", + "You can install the environment if needed from the `requirements.txt`\n", "\n", "### Installation\n", "\n", @@ -37,109 +37,24 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", + "execution_count": 36, + "id": "a7419777-667f-4bb1-99bd-aefa66c4a0b3", "metadata": { - "id": "db756a94-1ae7-4467-a771-05c3a68bc74a", - "outputId": "a001b0c9-c45b-46de-d0a2-9ac0a2ce0a57" + "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: nemoguardrails in /opt/conda/lib/python3.10/site-packages (0.10.1)\n", - "Requirement already satisfied: langchain_nvidia_ai_endpoints in /opt/conda/lib/python3.10/site-packages (0.2.2)\n", - "Requirement already satisfied: openai in /opt/conda/lib/python3.10/site-packages (1.54.3)\n", - "Requirement already satisfied: aiohttp>=3.9.2 in /opt/conda/lib/python3.10/site-packages (from nemoguardrails) (3.10.10)\n", - "Requirement already satisfied: annoy>=1.17.3 in /opt/conda/lib/python3.10/site-packages (from nemoguardrails) (1.17.3)\n", - "Requirement already satisfied: fastapi>=0.103.0 in /opt/conda/lib/python3.10/site-packages (from nemoguardrails) (0.115.4)\n", - 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"Requirement already satisfied: humanfriendly>=9.1 in /opt/conda/lib/python3.10/site-packages (from coloredlogs->onnxruntime<2.0.0,>=1.17.0->fastembed>=0.2.2->nemoguardrails) (10.0)\n", - "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from sympy->onnxruntime<2.0.0,>=1.17.0->fastembed>=0.2.2->nemoguardrails) (1.3.0)\n", - "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "^C\n", + "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n", "\u001b[0m" ] } ], "source": [ - "!pip install nemoguardrails langchain_nvidia_ai_endpoints openai" + "!pip install -q -r requirements.txt" ] }, { @@ -155,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 27, "id": "c41f7864-facc-4226-8e00-41c0a55b78aa", "metadata": { "id": "c41f7864-facc-4226-8e00-41c0a55b78aa" @@ -191,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 28, "id": "52a93353-93df-4b5f-b772-f0acfd111be9", "metadata": {}, "outputs": [ @@ -209,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 29, "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8", "metadata": { "id": "7e34d126-72e9-42b3-bda9-d70c352ddfe8" @@ -238,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 30, "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553", "metadata": { "id": "adcaf83f-4da4-481e-a7b5-97962e4e2553" @@ -252,29 +167,7 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "a1cd0e57-67c8-4a41-9dc4-fd1af6c2e1a4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: faiss-cpu==1.8.0.post1 in /opt/conda/lib/python3.10/site-packages (1.8.0.post1)\n", - "Requirement already satisfied: numpy<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from faiss-cpu==1.8.0.post1) (1.26.3)\n", - "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from faiss-cpu==1.8.0.post1) (24.2)\n", - "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", - "\u001b[0m" - ] - } - ], - "source": [ - "!pip install faiss-cpu==1.8.0.post1" - ] - }, - { - "cell_type": "code", - "execution_count": 12, + "execution_count": 31, "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214", "metadata": { "id": "ce8f0a63-c366-4adb-b88f-ef23e181d214" @@ -316,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 32, "id": "e8440c18-e14c-4098-8498-01e6d15db61a", "metadata": { "id": "e8440c18-e14c-4098-8498-01e6d15db61a" @@ -357,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7", "metadata": { "id": "b20aae58-41d9-43ad-9161-7aec0bac49a7" @@ -380,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 34, "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", "metadata": { "id": "2ee29193-1af0-47cc-9850-b572fe59dd56", @@ -542,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 35, "id": "d8488768-3833-45b6-80d0-85b6bc3a7172", "metadata": { "colab": { @@ -554,26 +447,11 @@ "outputId": "70fb2853-7c52-4ec6-f8f3-176b33f7ad16" }, "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e3b7515c4c934af48c6d15fb62c8ded2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Fetching 5 files: 0%| | 0/5 [00:00

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