From 8dfe146ecaf3540164584fdafa048b35d9addf25 Mon Sep 17 00:00:00 2001 From: Daemon <109057945+Daethyra@users.noreply.github.com> Date: Mon, 4 Dec 2023 13:36:50 -0800 Subject: [PATCH 1/3] 'docs/' subdir needs README #68 --- ...Readiness and Technical Debt Reduction.pdf | Bin ...MachineLearning-Algorithms_Cheatsheet.pdf} | Bin ...ners_guide_to_mlops-Google_whitepaper.pdf} | Bin ...e_smith-quick_reference-original-master.md | 510 ------------------ .../Prompting/Beginner-Prompt.txt | 22 - .../Prompting/ChatMarkupLanguage/chatml.md | 93 ---- .../Prompting/ChatMarkupLanguage/chatml.txt | 93 ---- ...ving with LLMs - Machine Learning - AI.pdf | Bin 766323 -> 0 bytes .../Prompting/tone_prompts.jpg | Bin 467775 -> 0 bytes docs/README.md | 19 + 10 files changed, 19 insertions(+), 718 deletions(-) rename docs/{Enterprise_Guides => Continued-Education}/A Rubric for ML Production Readiness and Technical Debt Reduction.pdf (100%) rename docs/{Enterprise_Guides/ML+Cheat+Sheet_2.pdf => Continued-Education/MachineLearning-Algorithms_Cheatsheet.pdf} (100%) rename docs/{Enterprise_Guides/practitioners_guide_to_mlops_whitepaper.pdf => Continued-Education/practitioners_guide_to_mlops-Google_whitepaper.pdf} (100%) delete mode 100644 docs/Custom-GPT-Uploadable_Knowledge_Base/langchain_serve_smith-quick_reference-original-master.md delete mode 100644 docs/Enterprise_Guides/Prompting/Beginner-Prompt.txt delete mode 100644 docs/Enterprise_Guides/Prompting/ChatMarkupLanguage/chatml.md delete mode 100644 docs/Enterprise_Guides/Prompting/ChatMarkupLanguage/chatml.txt delete mode 100644 docs/Enterprise_Guides/Prompting/Tree of Thoughts - Deliberate Problem Solving with LLMs - Machine Learning - AI.pdf delete mode 100644 docs/Enterprise_Guides/Prompting/tone_prompts.jpg create mode 100644 docs/README.md diff --git a/docs/Enterprise_Guides/A Rubric for ML Production Readiness and Technical Debt Reduction.pdf b/docs/Continued-Education/A Rubric for ML Production Readiness and Technical Debt Reduction.pdf similarity index 100% rename from docs/Enterprise_Guides/A Rubric for ML Production Readiness and Technical Debt Reduction.pdf rename to docs/Continued-Education/A Rubric for ML Production Readiness and Technical Debt Reduction.pdf diff --git a/docs/Enterprise_Guides/ML+Cheat+Sheet_2.pdf b/docs/Continued-Education/MachineLearning-Algorithms_Cheatsheet.pdf similarity index 100% rename from docs/Enterprise_Guides/ML+Cheat+Sheet_2.pdf rename to docs/Continued-Education/MachineLearning-Algorithms_Cheatsheet.pdf diff --git a/docs/Enterprise_Guides/practitioners_guide_to_mlops_whitepaper.pdf b/docs/Continued-Education/practitioners_guide_to_mlops-Google_whitepaper.pdf similarity index 100% rename from docs/Enterprise_Guides/practitioners_guide_to_mlops_whitepaper.pdf rename to docs/Continued-Education/practitioners_guide_to_mlops-Google_whitepaper.pdf diff --git a/docs/Custom-GPT-Uploadable_Knowledge_Base/langchain_serve_smith-quick_reference-original-master.md b/docs/Custom-GPT-Uploadable_Knowledge_Base/langchain_serve_smith-quick_reference-original-master.md deleted file mode 100644 index 90091c6..0000000 --- a/docs/Custom-GPT-Uploadable_Knowledge_Base/langchain_serve_smith-quick_reference-original-master.md +++ /dev/null @@ -1,510 +0,0 @@ -# LangChain/Serve/Smith Quick Reference - -## Introduction -Welcome to the comprehensive guide for LangChain, LangServe, and LangSmith. These powerful tools collectively offer a robust framework for building, deploying, and managing advanced AI and language model applications. - -- **LangChain**: A versatile toolkit for creating and managing chains of language models and AI functionalities, facilitating complex tasks and interactions. -- **LangServe**: Dedicated to server-side operations, LangServe manages the deployment and scaling of language models, ensuring efficient and reliable performance. -- **LangSmith**: Focused on tracing, debugging, and detailed analysis, LangSmith provides the necessary tools to monitor, evaluate, and improve AI applications. - -This documentation aims to provide users, developers, and AI enthusiasts with a thorough understanding of each tool's capabilities, practical applications, and best practices for integration and usage. Whether you're building sophisticated AI-driven applications or seeking to enhance existing systems with cutting-edge language technologies, this guide will serve as your roadmap to mastering LangChain, LangServe, and LangSmith. - ---- - -## Core Concepts - -### Section: Prompt + LLM -- **Objective**: To demonstrate the basic composition of a `PromptTemplate` with a `LLM` (Language Learning Model), creating a chain that takes user input, processes it, and returns the model's output. -- **Example Code**: -```python -from langchain.chat_models import ChatOpenAI -from langchain.prompts import ChatPromptTemplate - -# Creating a prompt template -prompt = ChatPromptTemplate.from_template("Can you tell me a joke about {topic}?") - -# Initializing the model -model = ChatOpenAI() - -# Building the chain -chain = prompt | model - -# Invoking the chain with user input -response = chain.invoke({"topic": "science"}) -print(response.content) -``` -- **Explanation**: This code block shows how to create a simple chain that asks the AI to generate a joke based on a user-provided topic. `ChatPromptTemplate` is used to format the prompt, and `ChatOpenAI` is the model that generates the response. - ---- - -### Section: Memory -- **Objective**: To illustrate how to integrate memory into a LangChain application, enabling the chain to maintain context across interactions. This is particularly useful for applications like chatbots where retaining context from previous interactions is crucial. -- **Example Code**: -```python -from langchain.chat_models import ChatOpenAI -from langchain.memory import ConversationBufferMemory -from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder - -# Initializing the chat model -model = ChatOpenAI() - -# Creating a prompt template with a placeholder for conversation history -prompt = ChatPromptTemplate.from_messages([ - ("system", "You are a helpful chatbot"), - MessagesPlaceholder(variable_name="history"), - ("human", "{input}") -]) - -# Setting up memory for the conversation -memory = ConversationBufferMemory(return_messages=True) - -# Loading initial memory variables -memory.load_memory_variables({}) - -# Building the chain with memory integration -chain = ( - {"input": "Hello, how are you today?", "history": memory.load_memory_variables()} - | prompt - | model -) - -# Invoking the chain with user input -response = chain.invoke({"input": "Tell me about LangChain"}) -print(response.content) - -# Saving the context for future interactions -memory.save_context({"input": "Tell me about LangChain"}, {"output": response.content}) -``` -- **Explanation**: This code demonstrates the use of `ConversationBufferMemory` to keep a record of the conversation. The `ChatPromptTemplate` is configured to include a history of messages, allowing the model to generate responses considering previous interactions. - ---- - -### Section: Using Tools -- **Objective**: To demonstrate how to integrate third-party tools into a LangChain application, thereby enhancing its capabilities. This example will specifically show how to use the `DuckDuckGoSearchRun` tool within a LangChain for web searches. -- **Example Code**: -```python -from langchain.chat_models import ChatOpenAI -from langchain.prompts import ChatPromptTemplate -from langchain.schema.output_parser import StrOutputParser -from langchain.tools import DuckDuckGoSearchRun - -# Installing the necessary package for DuckDuckGo search -# !pip install duckduckgo-search - -# Initializing the DuckDuckGo search tool -search = DuckDuckGoSearchRun() - -# Creating a prompt template to format user input into a search query -template = "Search for information on: {input}" -prompt = ChatPromptTemplate.from_template(template) - -# Initializing the chat model -model = ChatOpenAI() - -# Building the chain with search functionality -chain = prompt | model | StrOutputParser() | search - -# Invoking the chain with a search query -search_result = chain.invoke({"input": "the latest Python updates"}) -print(search_result) -``` -- **Explanation**: This example shows the use of `DuckDuckGoSearchRun` to perform web searches. The user's input is formatted into a search query using `ChatPromptTemplate`, passed through a chat model, and then processed by the search tool to retrieve information. - ---- - -## Advanced Features - -### Section: Embedding Router -- **Objective**: To explain and demonstrate the use of embeddings to dynamically route queries to the most relevant prompt based on semantic similarity. This advanced feature allows LangChain applications to handle a variety of inputs more intelligently. -- **Example Code**: -```python -from langchain.chat_models import ChatOpenAI -from langchain.embeddings import OpenAIEmbeddings -from langchain.prompts import PromptTemplate -from langchain.schema.output_parser import StrOutputParser -from langchain.schema.runnable import RunnableLambda, RunnablePassthrough -from langchain.utils.math import cosine_similarity - -# Creating two distinct prompt templates for different domains -physics_template = "You are a physics expert. Answer this physics question: {query}" -math_template = "You are a math expert. Answer this math question: {query}" - -# Initializing embeddings and chat model -embeddings = OpenAIEmbeddings() -model = ChatOpenAI() - -# Embedding the prompt templates -prompt_templates = [physics_template, math_template] -prompt_embeddings = embeddings.embed_documents(prompt_templates) - -# Defining a function to route the query to the most relevant prompt -def prompt_router(input): - query_embedding = embeddings.embed_query(input["query"]) - similarity = cosine_similarity([query_embedding], prompt_embeddings)[0] - most_similar = prompt_templates[similarity.argmax()] - return PromptTemplate.from_template(most_similar) - -# Building the chain with embedding-based routing -chain = ( - {"query": RunnablePassthrough()} - | RunnableLambda(prompt_router) - | model - | StrOutputParser() -) - -# Example query and response -response = chain.invoke({"query": "What is quantum mechanics?"}) -print(response) -``` -- **Explanation**: This code demonstrates how embeddings and cosine similarity are used to determine which prompt template is most relevant to the user's query. Based on the query's content, it chooses between a physics and a math expert prompt. The response is then generated accordingly by the chat model. - -### Section: Managing Prompt Size -- **Objective**: To illustrate strategies for managing the size of prompts within LangChain applications, ensuring they remain efficient and within the model's context window. This is crucial for maintaining performance, especially in complex chains or agents. -- **Example Code**: -```python -from langchain.agents import AgentExecutor, load_tools -from langchain.agents.format_scratchpad import format_to_openai_function_messages -from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser -from langchain.chat_models import ChatOpenAI -from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder -from langchain.tools import WikipediaQueryRun -from langchain.tools.render import format_tool_to_openai_function -from langchain.utilities import WikipediaAPIWrapper - -# Installing necessary package for Wikipedia queries -# !pip install langchain wikipedia - -# Initializing Wikipedia query tool with content character limit -wiki = WikipediaQueryRun( - api_wrapper=WikipediaAPIWrapper(top_k_results=5, doc_content_chars_max=10_000) -) -tools = [wiki] - -# Creating a prompt template with placeholders for user input and agent scratchpad -prompt = ChatPromptTemplate.from_messages([ - ("system", "You are a helpful assistant"), - ("user", "{input}"), - MessagesPlaceholder(variable_name="agent_scratchpad"), -]) -llm = ChatOpenAI(model="gpt-3.5-turbo") - -# Building an agent with a focus on managing prompt size -agent = ( - { - "input": lambda x: x["input"], - "agent_scratchpad": lambda x: format_to_openai_function_messages( - x["intermediate_steps"] - ), - } - | prompt - | llm.bind(functions=[format_tool_to_openai_function(t) for t in tools]) - | OpenAIFunctionsAgentOutputParser() -) - -# Executing the agent -agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) -response = agent_executor.invoke({ - "input": "What is the tallest mountain?" -}) -print(response) -``` -- **Explanation**: This code showcases an agent setup that includes a Wikipedia query tool and a prompt template. The agent's construction focuses on managing the prompt size by limiting the content from intermediate steps. The response to a query is generated with consideration to the prompt's overall size, ensuring efficiency. - -### Section: Agent Construction and Management -- **Objective**: To demonstrate the process of constructing and managing agents in LangChain. This includes creating agents from runnables and understanding the key components and logic involved in agent operation. -- **Example Code**: -```python -from langchain.agents import AgentExecutor, XMLAgent, tool -from langchain.chat_models import ChatAnthropic - -# Initializing the chat model with a specific model version -model = ChatAnthropic(model="claude-2") - -# Defining a custom tool for the agent -@tool -def weather_search(query: str) -> str: - """Tool to search for weather information.""" - # This is a placeholder for actual weather search logic - return "Sunny with a high of 75 degrees" - -tool_list = [weather_search] - -# Retrieving the default prompt for the XMLAgent -prompt = XMLAgent.get_default_prompt() - -# Defining logic for processing intermediate steps to a string format -def convert_intermediate_steps(intermediate_steps): - log = "" - for action, observation in intermediate_steps: - log += ( - f"{action.tool}{action.tool_input}" - f"{observation}" - ) - return log - -# Building an agent from a runnable -agent = ( - { - "question": lambda x: x["question"], - "intermediate_steps": lambda x: convert_intermediate_steps(x["intermediate_steps"]), - } - | prompt.partial(tools=lambda: "\n".join([f"{t.name}: {t.description}" for t in tool_list])) - | model.bind(stop=["", ""]) - | XMLAgent.get_default_output_parser() -) - -# Executing the agent with a specific query -agent_executor = AgentExecutor(agent=agent, tools=tool_list, verbose=True) -response = agent_executor.invoke({"question": "What's the weather in New York today?"}) -print(response) -``` -- **Explanation**: This code block illustrates how to build an agent using LangChain's `XMLAgent`. The agent includes a custom tool for weather information and logic to process and format intermediate steps. The agent is executed with a specific query, demonstrating its ability to manage and utilize its components effectively. - ---- - -### Section: Code Writing with LangChain -- **Objective**: To showcase how LangChain can be utilized for writing and executing Python code. This feature enhances the AI's ability to assist in programming tasks, making it a valuable tool for developers. -- **Example Code**: -```python -from langchain.chat_models import ChatOpenAI -from langchain.prompts import ChatPromptTemplate -from langchain.schema.output_parser import StrOutputParser -from langchain_experimental.utilities import PythonREPL - -# Creating a prompt template to instruct the model to write Python code -template = "Write Python code to solve the following problem: {problem}" -prompt = ChatPromptTemplate.from_messages([("system", template), ("human", "{problem}")]) - -# Initializing the chat model -model = ChatOpenAI() - -# Function to sanitize and extract Python code from the model's output -def sanitize_output(text): - _, after = text.split("```python") - return after.split("```")[0] - -# Building the chain for code writing -chain = prompt | model | StrOutputParser() | sanitize_output | PythonREPL().run - -# Invoking the chain with a programming problem -problem = "calculate the factorial of a number" -code_result = chain.invoke({"problem": problem}) -print(code_result) -``` -- **Explanation**: This code block demonstrates how LangChain can be used to automatically generate Python code in response to a given problem statement. The `ChatPromptTemplate` guides the AI to focus on code generation, and the output is sanitized and executed using `PythonREPL`. This illustrates LangChain's capability in automating and assisting with coding tasks. - ---- - -### Section: LangServe - -#### Basic Deployment and Querying with GPT-3.5-Turbo -- **Example**: Deploying and querying the GPT-3.5-Turbo model using LangServe. -- **Objective**: To illustrate the use of LangServe within the LangChain ecosystem. LangServe is designed to facilitate server-side functionalities for managing and deploying language models, making it an essential tool for scalable and efficient AI applications. -```python -from langserve import LangServeClient - -# Initialize the LangServe client -langserve_client = LangServeClient(api_url="https://api.langserve.com") - -# Deploying the GPT-3.5-Turbo model -model_config = { - "model_name": "gpt-3.5-turbo", - "description": "GPT-3.5 Turbo model for general-purpose use" -} -deployment_response = langserve_client.deploy_model(model_config) -print("Deployment Status:", deployment_response.status) - -# Sending a query to the deployed model -query = "Explain the concept of machine learning in simple terms." -response = langserve_client.query_model(model_name="gpt-3.5-turbo", query=query) -print("Model Response:", response.content) -``` - -#### Advanced Deployment and Custom Configuration -- **Example**: Utilizing LangServe for deploying custom-configured models for specialized tasks. -```python -# Custom deployment with specific parameters -advanced_model_config = { - "model_name": "custom-gpt-model", - "description": "A custom-configured GPT model for specialized tasks", - "parameters": { - "temperature": 0.7, - "max_tokens": 150 - } -} -langserve_client.deploy_model(advanced_model_config) - -# Querying the custom model -custom_query = "Generate a technical summary of quantum computing." -custom_response = langserve_client.query_model(model_name="custom-gpt-model", query=custom_query) -print("Custom Model Response:", custom_response.content) -``` - -#### Model Management and Analytics -- **Example**: Managing deployed models and accessing detailed analytics. -```python -# Fetching model analytics -model_analytics = langserve_client.get_model_analytics(model_name="gpt-3.5-turbo") -print("Model Usage Analytics:", model_analytics) - -# Updating a deployed model's configuration -update_config = { - "temperature": 0.5, - "max_tokens": 200 -} -langserve_client.update_model_config(model_name="gpt-3.5-turbo", new_config=update_config) - -# Retrieving updated model details -updated_model_details = langserve_client.get_model_details(model_name="gpt-3.5-turbo") -print("Updated Model Details:", updated_model_details) -``` - -#### Integration with LangChain Applications -- **Example**: Demonstrating seamless integration of LangServe with LangChain. -```python -from langchain.chains import SimpleChain - -# Building a SimpleChain with a LangServe deployed model -chain = SimpleChain(model_name="gpt-3.5-turbo", langserve_client=langserve_client) - -# Executing the chain with a user query -chain_response = chain.execute("What are the latest trends in AI?") -print("Chain Response using LangServe Model:", chain_response) -``` - -#### LangSmith Tracing for Enhanced Monitoring -- **Objective**: Showcasing the use of LangSmith tracing within LangServe for detailed monitoring and analysis. -- **Example Code**: -```python -from langserve import LangServeClient -from langsmith import Tracing - -# Initialize LangServe client and enable LangSmith tracing -langserve_client = LangServeClient(api_url="https://api.langserve.com") -Tracing.enable() - -# Deploying a model with tracing enabled -model_config = { - "model_name": "gpt-3.5-turbo", - "description": "GPT-3.5 Turbo model with LangSmith tracing" -} -langserve_client.deploy_model(model_config) - -# Query with tracing for detailed interaction logs -query = "Explain the impact of AI on environmental sustainability." -response = langserve_client.query_model(model_name="gpt-3.5-turbo", query=query) -print("Traced Model Response:", response.content) - -# Retrieve and analyze trace logs -trace_logs = Tracing.get_logs() -print("Trace Logs:", trace_logs) -``` -- **Explanation**: This section highlights the integration of LangSmith tracing in LangServe, enhancing the capability to monitor and analyze model interactions. It is particularly valuable for understanding model behavior, performance optimization, and debugging complex scenarios. - -### LangSmith Enhanced Capabilities: Integrating Lilac, Prompt Versioning, and More - -#### Introduction -LangSmith, complemented by tools like Lilac, offers advanced capabilities for data analysis and prompt management. This section explores how to leverage these tools for enhanced functionality in LangSmith, incorporating prompt versioning, retrieval QA chains, and editable prompt templates. - -#### Integrating Lilac for Enhanced Data Analysis -- **Functionality**: Utilize Lilac to import, enrich, and analyze datasets from LangSmith. -- **Workflow**: - 1. Query datasets from LangSmith. - 2. Import and enrich datasets using Lilac's advanced analysis tools. - 3. Export the processed data for further application within LangSmith. - -#### Advanced Prompt Management with Versioning -- **Functionality**: Manage different versions of prompts in LangSmith to ensure consistency and accuracy. -- **Application**: - 1. Track and manage versions of prompts. - 2. Apply specific prompt versions in complex deployments like retrieval QA chains. - -#### Retrieval QA Chains -- **Functionality**: Configure retrieval QA chains in LangSmith, leveraging the specific versions of prompts for precise information retrieval. -- **Implementation**: - 1. Define the prompt and its version for the QA chain. - 2. Execute queries using the retrieval QA chain to obtain accurate results. - -#### Editable Prompt Templates -- **Functionality**: Use editable prompt templates to customize and experiment with different prompt structures in LangSmith. -- **Usage**: - 1. Create and edit prompt templates dynamically. - 2. Apply edited templates in LangSmith workflows for varied applications. - -#### Comprehensive Code Example -```python -# Import necessary libraries -# Import necessary libraries -import langchain -from langchain.prompt_templates import EditablePromptTemplate -# Assuming LangSmith and Lilac libraries are imported correctly - -# LangSmith setup (assuming required configurations and authentications are done) -langsmith.initialize(api_key="YOUR_LANGSMITH_API_KEY", endpoint="https://api.langsmith.com") - -# Query and fetch datasets from LangSmith using the list_runs method -project_runs = langsmith.client.list_runs(project_name="your_project_name") - -# Import dataset into Lilac and enrich it -lilac_dataset = lilac.import_dataset(project_runs) -lilac_dataset.compute_signal(lilac.PIISignal(), 'question') # Example signal -lilac_dataset.compute_signal(lilac.NearDuplicateSignal(), 'output') # Another example signal - -# Export the enriched dataset for integration with LangSmith -exported_dataset = lilac.export_dataset(lilac_dataset) - -# Implementing Prompt Versioning (assuming the existence of such functionality in LangSmith) -prompt_version = 'specific_version_hash' -prompt_name = 'your_prompt_name' -prompt = langsmith.load_prompt(prompt_name, version=prompt_version) - -# Configuring a Retrieval QA Chain with the versioned prompt -qa_chain = langchain.RetrievalQAChain(prompt=prompt) - -# Execute a query using the QA Chain -query_result = qa_chain.query("What is LangSmith's functionality?") -print(f"QA Chain Query Result: {query_result}") - -# Editable Prompt Templates for dynamic prompt editing -editable_prompt = EditablePromptTemplate(prompt_name) -editable_prompt.edit(new_template="New template content for LangSmith") -edited_prompt = editable_prompt.apply() - -# Example usage of the edited prompt in a LangSmith application -edited_prompt_result = langsmith.run_prompt(edited_prompt, input_data="Sample input for edited prompt") -print(f"Edited Prompt Result: {edited_prompt_result}") - -# Final step: Integrate the exported dataset back into LangSmith for further use -integration_status = langsmith.integrate_dataset(exported_dataset) -if integration_status.success: - print("Dataset successfully integrated back into LangSmith.") -else: - print(f"Integration failed with error: {integration_status.error}") -``` - -#### Conclusion -By integrating these diverse functionalities, LangSmith users can significantly enhance their language model applications. This synergy between LangSmith and tools like Lilac, along with advanced prompt management techniques, paves the way for more sophisticated and effective AI solutions. - ---- - -## Conclusion - -In this guide, we have explored the intricate functionalities and applications of LangChain, LangServe, and LangSmith. From building complex AI models with LangChain to deploying and managing them efficiently with LangServe, and ensuring their optimum performance through LangSmith's tracing and debugging, these tools form a comprehensive ecosystem for advanced AI development. - -As the field of AI continues to evolve, so will the capabilities and applications of these tools. Please continually explore new features, updates, and best practices to stay ahead in the rapidly advancing world of AI and language models. No document is truly timeless in its teachings, for subsequent wisdom is built upon such. - - -For further learning and support, explore the following resources: - -- [LangChain Interface](https://python.langchain.com/docs/expression_language/interface) -- [LangChain Cookbook - Prompt + LLM](https://python.langchain.com/docs/expression_language/cookbook/prompt_llm_parser) -- [LangChain Cookbook - Embedding Router](https://python.langchain.com/docs/expression_language/cookbook/embedding_router) -- [LangChain Cookbook - Agent](https://python.langchain.com/docs/expression_language/cookbook/agent) -- [LangChain Cookbook - Code Writing](https://python.langchain.com/docs/expression_language/cookbook/code_writing) -- [LangChain Cookbook - Memory](https://python.langchain.com/docs/expression_language/cookbook/memory) -- [LangChain Cookbook - Managing Prompt Size](https://python.langchain.com/docs/expression_language/cookbook/prompt_size) -- [LangChain Cookbook - Tools](https://python.langchain.com/docs/expression_language/cookbook/tools) - -Thank you for engaging with this documentation. May it be a valuable resource in your journey to mastering LangChain, LangServe, and LangSmith. - ---- diff --git a/docs/Enterprise_Guides/Prompting/Beginner-Prompt.txt b/docs/Enterprise_Guides/Prompting/Beginner-Prompt.txt deleted file mode 100644 index ee8dfac..0000000 --- a/docs/Enterprise_Guides/Prompting/Beginner-Prompt.txt +++ /dev/null @@ -1,22 +0,0 @@ -Let's work this out in a step by step way to ensure we have the right answer. - -=== -``` -import openai -import pinecone # type: ignore -import configparser - -system_prompt = [{ - "role": "system", "content": " \ - - You are an AI programming assistant with a very blunt, straightforward, direct demeanor. \ - - Your assignment is to assist the user in developing software. \ - - Be meticulous. \ - - Workflow=((step-by-step) + \"We'll have to ensure we have the right answer before committing to a decision\") \ - " -}] - -# set prompt -user_prompt = [{ - -}] -``` \ No newline at end of file diff --git a/docs/Enterprise_Guides/Prompting/ChatMarkupLanguage/chatml.md b/docs/Enterprise_Guides/Prompting/ChatMarkupLanguage/chatml.md deleted file mode 100644 index 783e91d..0000000 --- a/docs/Enterprise_Guides/Prompting/ChatMarkupLanguage/chatml.md +++ /dev/null @@ -1,93 +0,0 @@ -(This document is a preview of the underlying format consumed by -ChatGPT models. As a developer, you can use our [higher-level -API](https://platform.openai.com/docs/guides/chat) and won't need to -interact directly with this format today β€”Β but expect to have the -option in the future!) - -Traditionally, GPT models consumed unstructured text. ChatGPT models -instead expect a structured format, called Chat Markup Language -(ChatML for short). -ChatML documents consist of a sequence of messages. Each message -contains a header (which today consists of who said it, but in the -future will contain other metadata) and contents (which today is a -text payload, but in the future will contain other datatypes). -We are still evolving ChatML, but the current version (ChatML v0) can -be represented with our upcoming "list of dicts" JSON format as -follows: -``` -[ - {"token": "<|im_start|>"}, - "system\nYou are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.\nKnowledge cutoff: 2021-09-01\nCurrent date: 2023-03-01", - {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"}, - "user\nHow are you", - {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"}, - "assistant\nI am doing well!", - {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"}, - "user\nHow are you now?", - {"token": "<|im_end|>"}, "\n" -] -``` -You could also represent it in the classic "unsafe raw string" -format. However, this format inherently allows injections from user -input containing special-token syntax, similar to SQL injections: -``` -<|im_start|>system -You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible. -Knowledge cutoff: 2021-09-01 -Current date: 2023-03-01<|im_end|> -<|im_start|>user -How are you<|im_end|> -<|im_start|>assistant -I am doing well!<|im_end|> -<|im_start|>user -How are you now?<|im_end|> -``` -## Non-chat use-cases -ChatML can be applied to classic GPT use-cases that are not -traditionally thought of as chat. For example, instruction following -(where a user requests for the AI to complete an instruction) can be -implemented as a ChatML query like the following: -``` -[ - {"token": "<|im_start|>"}, - "user\nList off some good ideas:", - {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"}, - "assistant" -] -``` -We do not currently allow autocompleting of partial messages, -``` -[ - {"token": "<|im_start|>"}, - "system\nPlease autocomplete the user's message.", - {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"}, - "user\nThis morning I decided to eat a giant" -] -``` -Note that ChatML makes explicit to the model the source of each piece -of text, and particularly shows the boundary between human and AI -text. This gives an opportunity to mitigate and eventually solve -injections, as the model can tell which instructions come from the -developer, the user, or its own input. -## Few-shot prompting -In general, we recommend adding few-shot examples using separate -`system` messages with a `name` field of `example_user` or -`example_assistant`. For example, here is a 1-shot prompt: -``` -<|im_start|>system -Translate from English to French -<|im_end|> -<|im_start|>system name=example_user -How are you? -<|im_end|> -<|im_start|>system name=example_assistant -Comment allez-vous? -<|im_end|> -<|im_start|>user -{{user input here}}<|im_end|> -``` -If adding instructions in the `system` message doesn't work, you can -also try putting them into a `user` message. (In the near future, we -will train our models to be much more steerable via the system -message. But to date, we have trained only on a few system messages, -so the models pay much more attention to user examples.) diff --git a/docs/Enterprise_Guides/Prompting/ChatMarkupLanguage/chatml.txt b/docs/Enterprise_Guides/Prompting/ChatMarkupLanguage/chatml.txt deleted file mode 100644 index 783e91d..0000000 --- a/docs/Enterprise_Guides/Prompting/ChatMarkupLanguage/chatml.txt +++ /dev/null @@ -1,93 +0,0 @@ -(This document is a preview of the underlying format consumed by -ChatGPT models. As a developer, you can use our [higher-level -API](https://platform.openai.com/docs/guides/chat) and won't need to -interact directly with this format today β€”Β but expect to have the -option in the future!) - -Traditionally, GPT models consumed unstructured text. ChatGPT models -instead expect a structured format, called Chat Markup Language -(ChatML for short). -ChatML documents consist of a sequence of messages. Each message -contains a header (which today consists of who said it, but in the -future will contain other metadata) and contents (which today is a -text payload, but in the future will contain other datatypes). -We are still evolving ChatML, but the current version (ChatML v0) can -be represented with our upcoming "list of dicts" JSON format as -follows: -``` -[ - {"token": "<|im_start|>"}, - "system\nYou are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.\nKnowledge cutoff: 2021-09-01\nCurrent date: 2023-03-01", - {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"}, - "user\nHow are you", - {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"}, - "assistant\nI am doing well!", - {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"}, - "user\nHow are you now?", - {"token": "<|im_end|>"}, "\n" -] -``` -You could also represent it in the classic "unsafe raw string" -format. However, this format inherently allows injections from user -input containing special-token syntax, similar to SQL injections: -``` -<|im_start|>system -You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible. -Knowledge cutoff: 2021-09-01 -Current date: 2023-03-01<|im_end|> -<|im_start|>user -How are you<|im_end|> -<|im_start|>assistant -I am doing well!<|im_end|> -<|im_start|>user -How are you now?<|im_end|> -``` -## Non-chat use-cases -ChatML can be applied to classic GPT use-cases that are not -traditionally thought of as chat. For example, instruction following -(where a user requests for the AI to complete an instruction) can be -implemented as a ChatML query like the following: -``` -[ - {"token": "<|im_start|>"}, - "user\nList off some good ideas:", - {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"}, - "assistant" -] -``` -We do not currently allow autocompleting of partial messages, -``` -[ - {"token": "<|im_start|>"}, - "system\nPlease autocomplete the user's message.", - {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"}, - "user\nThis morning I decided to eat a giant" -] -``` -Note that ChatML makes explicit to the model the source of each piece -of text, and particularly shows the boundary between human and AI -text. This gives an opportunity to mitigate and eventually solve -injections, as the model can tell which instructions come from the -developer, the user, or its own input. -## Few-shot prompting -In general, we recommend adding few-shot examples using separate -`system` messages with a `name` field of `example_user` or -`example_assistant`. For example, here is a 1-shot prompt: -``` -<|im_start|>system -Translate from English to French -<|im_end|> -<|im_start|>system name=example_user -How are you? -<|im_end|> -<|im_start|>system name=example_assistant -Comment allez-vous? -<|im_end|> -<|im_start|>user -{{user input here}}<|im_end|> -``` -If adding instructions in the `system` message doesn't work, you can -also try putting them into a `user` message. (In the near future, we -will train our models to be much more steerable via the system -message. 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diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..f33c314 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,19 @@ +## Document assortment + +### Continued Education + +Reading meant to give context of the AI's "thought" process' limitations. + +Highlights: +1. Lost In the Middle: How Language Models Use Long Contexts + - Abstract: While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze the performance of language models on two tasks that require identifying relevant information in their input contexts: multi-document question answering and key-value retrieval. +2. "Needle In A Haystack" Analysis: Pressure Testing GPT-4 & Claude 2.1's Long Context Retrieval Accuracy + - An exported email from Greg Kramradt's mailing list. He personally pressure tested these models with the intention of helping others make smarter decisions when it comes to retrieval. + +### Custom-GPT - Uploadable Knowledge Base + +Files currently in use for my CustomGPTs. + +### Jupyter Notebooks + +A collection of notebooks that teach learners how to build retrieval augmented generation (RAG) AI. 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Language Models." authors = [ {name = "Daethyra", email = "109057945+Daethyra@users.noreply.github.com"}, ] dependencies = [ - "chardet>=5.2.0", - "numpy==1.25.1", - "aiohttp>=3.9.0", "openai>=0.28.1", "pinecone-client>=2.2.4", "tokenizers>=0.14.1", "safetensors>=0.4.0", "transformers>=4.34.1", "langchain>=0.0.27", - "pandas>=2.0.3", "langserve[all]>=0.0.32", "torch>=2.1.1", - "accelerate>=0.25.0", - #"pyaudio>=0.2.14", - "PyPDF2>=3.0.1", - "Pillow>=10.1.0", ] requires-python = ">=3.9, <=3.10.12" readme = "README.md" From f74a65b379cc34fac3d50df57027021262273ffe Mon Sep 17 00:00:00 2001 From: Daemon <109057945+Daethyra@users.noreply.github.com> Date: Mon, 4 Dec 2023 13:42:12 -0800 Subject: [PATCH 3/3] Deleted .github/.archive --- .github/.archive/2023-10-18_Mindmap.jpg | Bin 78597 -> 0 bytes .github/.archive/Basic-GPT-GUI/.env.template | 4 - .github/.archive/Basic-GPT-GUI/.gitignore | 160 ----------- .github/.archive/Basic-GPT-GUI/main.py | 20 -- 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