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This will create a `data` folder with the documentation in it. ```python run_crawler.py``` +- For inference, run ```uvicorn documentation_query:app --host 0.0.0.0 --port 8083 &``` \ No newline at end of file diff --git a/documentation_bot/README.md b/documentation_bot/README.md new file mode 100644 index 0000000..6c546ab --- /dev/null +++ b/documentation_bot/README.md @@ -0,0 +1,8 @@ +# Documentation Bot + +- This bot reads the documentation of OpenML and trains an LLM model to answer questions about the project. + +## How to run + +- First run the crawler to get the documentation from OpenML. This will create a `data` folder with the documentation in it. ```python run_crawler.py``` +- For inference, run ```uvicorn documentation_query:app --host 0.0.0.0 --port 8083 &``` \ No newline at end of file diff --git a/documentation_bot/__init__.py b/documentation_bot/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/documentation_bot/base_urls.txt b/documentation_bot/base_urls.txt new file mode 100644 index 0000000..04c18d4 --- /dev/null +++ b/documentation_bot/base_urls.txt @@ -0,0 +1,4 @@ +https://openml.github.io/openml-python/main/ +https://docs.openml.org/ +https://openml.org/apis/ +https://github.com/openml/openml-python/tree/develop/openml \ No newline at end of file diff --git a/documentation_bot/documentation_query.py b/documentation_bot/documentation_query.py index b3ede0e..78212ff 100644 --- a/documentation_bot/documentation_query.py +++ b/documentation_bot/documentation_query.py @@ -5,16 +5,22 @@ from fastapi.responses import JSONResponse, StreamingResponse from httpx import ConnectTimeout from tenacity import retry, retry_if_exception_type, stop_after_attempt -from utils import ChromaStore, Crawler +from documentation_query_utils import ChromaStore, Crawler, stream_response +from langchain_ollama import ChatOllama -# TODO : make this into a separate thing using config recrawl_websites = False crawled_files_data_path = "../data/crawler/crawled_data.csv" chroma_path = "../data/crawler/" -model_name = "BAAI/bge-small-en" +rag_model_name = "BAAI/bge-small-en" generation_model_name = "llama3" # ollama +generation_llm = ChatOllama( + model=generation_model_name, temperature=0.0 +) +# Send test message to the generation model +generation_llm.invoke("test generation") + # Crawl the websites and save the data num_of_websites_to_crawl = None # none for all @@ -31,10 +37,10 @@ # Initialize the ChromaStore and embed the data chroma_store = ChromaStore( - model_name=model_name, + rag_model_name=rag_model_name, crawled_files_data_path=crawled_files_data_path, chroma_file_path=chroma_path, - generation_model_name=generation_model_name, + generation_llm=generation_llm, ) if recrawl_websites == True: chroma_store.read_data_and_embed() @@ -42,17 +48,6 @@ app = FastAPI() session_id = str(uuid.uuid4()) - -def stream_response(response): - for line in response: - try: - yield str(line["answer"]) - except GeneratorExit: - break - except: - yield "" - - @app.get("/documentationquery/{query}", response_class=JSONResponse) @retry(stop=stop_after_attempt(3), retry=retry_if_exception_type(ConnectTimeout)) async def get_documentation_query(query: str): diff --git a/documentation_bot/utils.py b/documentation_bot/documentation_query_utils.py similarity index 90% rename from documentation_bot/utils.py rename to documentation_bot/documentation_query_utils.py index 9931649..225bd34 100644 --- a/documentation_bot/utils.py +++ b/documentation_bot/documentation_query_utils.py @@ -17,9 +17,21 @@ from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_core.runnables.history import RunnableWithMessageHistory -from langchain_ollama import ChatOllama + from tqdm.auto import tqdm +def stream_response(response): + """ + Description: This function is used to stream the response from the model. + + """ + for line in response: + try: + yield str(line["answer"]) + except GeneratorExit: + break + except: + yield "" def find_device() -> str: """ @@ -36,6 +48,10 @@ def find_device() -> str: def get_session_history(session_id: str) -> BaseChatMessageHistory: + """ + Description: This function is used to get the chat history of a session. + + """ # print("this is the session id", session_id) if session_id not in store: store[session_id] = ChatMessageHistory() @@ -54,10 +70,8 @@ def __init__( recrawl_websites=False, num_of_websites_to_crawl=None, ): - self.base_urls = [ - "https://openml.github.io/openml-python/main/", - "https://docs.openml.org/", - ] + with open("./base_urls.txt", "r") as f: + self.base_urls = f.read().splitlines() self.crawled_files_data_path = crawled_files_data_path self.recrawl_websites = recrawl_websites self.num_of_websites_to_crawl = num_of_websites_to_crawl @@ -195,15 +209,15 @@ def do_crawl(self): class ChromaStore: def __init__( self, - model_name, + rag_model_name, crawled_files_data_path, chroma_file_path, - generation_model_name, + generation_llm, ) -> None: - self.model_name = model_name + self.rag_model_name = rag_model_name self.device = find_device() self.hf_embedding_function = HuggingFaceBgeEmbeddings( - model_name=self.model_name, + model_name=self.rag_model_name, model_kwargs={"device": self.device}, encode_kwargs={"normalize_embeddings": True}, ) @@ -225,12 +239,14 @@ def __init__( "also reformulate the question. Do NOT answer the question, " "just reformulate it if needed and otherwise return it as is." ) - self.generation_model_name = generation_model_name - self.generation_llm = ChatOllama( - model=self.generation_model_name, temperature=0.0 - ) + self.generation_llm = generation_llm + def read_data_and_embed(self): # inference + """ + Description: This function is used to read the crawled data and embed it using the Hugging Face model. + + """ if not os.path.exists(self.crawled_files_data_path): print("Crawled data does not exist. Please run the crawler first.") return @@ -238,6 +254,7 @@ def read_data_and_embed(self): # inference df = pd.read_csv(self.crawled_files_data_path) df["joined"] = df.apply(self._join_columns, axis=1) docs = DataFrameLoader(df, page_content_column="joined").load() + # Splitting the document texts into smaller chunks docs_texts = self._split_documents(docs) @@ -281,11 +298,20 @@ def _split_documents(self, docs): return splitter.split_documents(docs) def setup_inference(self, session_id: str) -> None: + """ + Description: This function is used to setup the inference for the bot. + + """ self.store = {} self.session_id = session_id def openml_page_search(self, input: str): + """ + Description: Use the Chroma vector store to search for the most relevant page to the input question , contextualize the question and answer it. + + """ + vectorstore = Chroma( persist_directory=self.chroma_file_path, embedding_function=self.hf_embedding_function, @@ -331,12 +357,7 @@ def openml_page_search(self, input: str): output_messages_key="answer", ) - # answer = conversational_rag_chain.invoke( - # {"input": f"{input}"}, - # config={ - # "configurable": {"session_id": self.session_id} - # }, # constructs a key "abc123" in `store`. - # )["answer"] + answer = conversational_rag_chain.stream( {"input": f"{input}"}, config={ diff --git a/documentation_bot/requirements.txt b/documentation_bot/requirements.txt new file mode 100644 index 0000000..9ff8533 --- /dev/null +++ b/documentation_bot/requirements.txt @@ -0,0 +1,12 @@ +beautifulsoup4==4.12.3 +fastapi==0.112.2 +httpx==0.27.0 +langchain==0.2.14 +langchain_community==0.2.12 +langchain_core==0.2.35 +langchain_ollama==0.1.1 +pandas==2.2.2 +Requests==2.32.3 +tenacity==8.3.0 +torch==2.3.0 +tqdm==4.66.4 diff --git a/documentation_bot/run_crawler.py b/documentation_bot/run_crawler.py new file mode 100644 index 0000000..e20724c --- /dev/null +++ b/documentation_bot/run_crawler.py @@ -0,0 +1,34 @@ +import os + +from documentation_query_utils import ChromaStore, Crawler + +recrawl_websites = True + +crawled_files_data_path = "../data/crawler/crawled_data.csv" +chroma_path = "../data/crawler/" +model_name = "BAAI/bge-small-en" +generation_model_name = "llama3" # ollama + +# Crawl the websites and save the data +num_of_websites_to_crawl = None # none for all + +if not os.path.exists(chroma_path): + os.makedirs(chroma_path, exist_ok=True) + +# Crawl the websites and save the data +crawler = Crawler( + crawled_files_data_path=crawled_files_data_path, + recrawl_websites=recrawl_websites, + num_of_websites_to_crawl=num_of_websites_to_crawl, +) +crawler.do_crawl() + +# Initialize the ChromaStore and embed the data +chroma_store = ChromaStore( + model_name=model_name, + crawled_files_data_path=crawled_files_data_path, + chroma_file_path=chroma_path, + generation_model_name=generation_model_name, +) +if recrawl_websites == True: + chroma_store.read_data_and_embed() diff --git a/frontend/ui.py b/frontend/ui.py index 14dec5f..1eeee7e 100644 --- a/frontend/ui.py +++ b/frontend/ui.py @@ -15,4 +15,4 @@ with st.spinner("Loading Required Data"): config_path = Path("../backend/config.json") ui_loader = UILoader(config_path) - ui_loader.generate_complete_ui() +ui_loader.generate_complete_ui() diff --git a/frontend/ui_utils.py b/frontend/ui_utils.py index fb543d7..35adcce 100644 --- a/frontend/ui_utils.py +++ b/frontend/ui_utils.py @@ -352,7 +352,7 @@ def __init__(self, config_path): st.session_state.messages = [] # container for company description and logo - def generate_logo_header( + def _generate_logo_header( self, ): @@ -367,8 +367,9 @@ def generate_logo_header( def generate_complete_ui(self): - self.generate_logo_header() + self._generate_logo_header() chat_container = st.container() + # self.disclaimer_dialog() with chat_container: with st.form(key="chat_form"): user_input = st.text_input( @@ -404,7 +405,7 @@ def create_chat_interface(self, user_input, query_type=None, ai_filter=False): with st.chat_message(name="ai"): st.write("OpenML Agent: ", "Hello! How can I help you today?") st.write( - "Note that results are powered by local LLM models and may not be accurate. Please refer to the official OpenML website for accurate information." + ":warning: Note that results are powered by local LLM models and may not be accurate. Please refer to the official OpenML website for accurate information." ) # Handle user input @@ -477,8 +478,6 @@ def display_results(self, initial_response, role): """ Description: Display the results in a DataFrame """ - # st.write("OpenML Agent: ") - try: st.dataframe(initial_response) except: diff --git a/mkdocs.yml b/mkdocs.yml index 4e64825..b518c1d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -41,7 +41,7 @@ plugins: default_handler: python handlers: python: - paths: [backend/modules, tests/, frontend, llm_service, ollama, tools, evaluation/] + paths: [backend/modules, tests/, frontend, llm_service, ollama, tools, evaluation/, documentation_bot/] load_external_modules: true show_source: true options: diff --git a/poetry.lock b/poetry.lock index b0224ec..e11031b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -12,91 +12,118 @@ files = [ ] [[package]] -name = "aiohttp" -version = "3.9.5" -description = "Async http client/server framework (asyncio)" +name = "aiohappyeyeballs" +version = 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b/start_training.sh @@ -1,3 +1,8 @@ +cd documentation_bot +python run_crawler.py +cd .. + cd backend python training.py + cd ..