diff --git "a/notebook_version/AB_Testing_RAG_Agent.ipynb" "b/notebook_version/AB_Testing_RAG_Agent.ipynb" new file mode 100644--- /dev/null +++ "b/notebook_version/AB_Testing_RAG_Agent.ipynb" @@ -0,0 +1,4435 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_J02wP3gdLds", + "outputId": "7e122a12-e645-4108-e710-1d3cbb904f8d" + }, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Please enter your OpenAI API key!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.document_loaders import DirectoryLoader\n", + "from langchain_community.document_loaders import PyPDFLoader\n", + "from collections import defaultdict\n", + "from langchain_core.documents import Document\n", + "\n", + "# Load all PDF documents (each page as a separate document)\n", + "path = \"data/\"\n", + "loader = DirectoryLoader(path, glob=\"*.pdf\", loader_cls=PyPDFLoader)\n", + "all_docs = loader.load()\n", + "\n", + "# Create a mapping of merged document chunks back to original pages\n", + "page_map = {}\n", + "current_index = 0\n", + "\n", + "# For source tracking, we'll store page information before merging\n", + "docs_by_source = defaultdict(list)\n", + "\n", + "# Group documents by their source file\n", + "for doc in all_docs:\n", + " source = doc.metadata.get(\"source\", \"\")\n", + " docs_by_source[source].append(doc)\n", + "\n", + "# Merge pages from the same PDF but track page ranges\n", + "merged_docs = []\n", + "for source, source_docs in docs_by_source.items():\n", + " # Sort by page number if available\n", + " source_docs.sort(key=lambda x: x.metadata.get(\"page\", 0))\n", + " \n", + " # Merge the content\n", + " merged_content = \"\"\n", + " page_ranges = []\n", + " current_pos = 0\n", + " \n", + " for doc in source_docs:\n", + " # Get the page number (1-indexed for human readability)\n", + " page_num = doc.metadata.get(\"page\", 0) + 1\n", + " \n", + " # Add a separator between pages for clarity\n", + " if merged_content:\n", + " merged_content += \"\\n\\n\"\n", + " \n", + " # Record where this page's content starts in the merged document\n", + " start_pos = len(merged_content)\n", + " merged_content += doc.page_content\n", + " end_pos = len(merged_content)\n", + " \n", + " # Store the mapping of character ranges to original page numbers\n", + " page_ranges.append({\n", + " \"start\": start_pos,\n", + " \"end\": end_pos,\n", + " \"page\": page_num,\n", + " \"source\": source\n", + " })\n", + " \n", + " # Create merged metadata that includes page mapping information\n", + " merged_metadata = {\n", + " \"source\": source,\n", + " \"title\": source.split(\"/\")[-1],\n", + " \"page_count\": len(source_docs),\n", + " \"merged\": True,\n", + " \"page_ranges\": page_ranges # Store the page ranges for later reference\n", + " }\n", + " \n", + " # Create a new document with the merged content\n", + " merged_doc = Document(page_content=merged_content, metadata=merged_metadata)\n", + " merged_docs.append(merged_doc)\n", + "\n", + "# Replace original docs with merged docs but preserve the page mapping\n", + "docs = merged_docs" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import tiktoken\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "\n", + "# tiktoken_len counts tokens (not characters) using the gpt-4o-mini tokenizer\n", + "def tiktoken_len(text):\n", + " tokens = tiktoken.encoding_for_model(\"gpt-4o-mini\").encode(\n", + " text,\n", + " )\n", + " return len(tokens)\n", + "\n", + "# Process splits to add page info based on character position\n", + "def add_page_info_to_splits(splits):\n", + " for split in splits:\n", + " # Get the start position of this chunk\n", + " start_pos = split.metadata.get(\"start_index\", 0)\n", + " end_pos = start_pos + len(split.page_content)\n", + " \n", + " # Find which page this chunk belongs to\n", + " if \"page_ranges\" in split.metadata:\n", + " for page_range in split.metadata[\"page_ranges\"]:\n", + " # If chunk significantly overlaps with this page range\n", + " if (start_pos <= page_range[\"end\"] and \n", + " end_pos >= page_range[\"start\"]):\n", + " # Use this page number\n", + " split.metadata[\"page\"] = page_range[\"page\"]\n", + " break\n", + " return splits\n", + "\n", + "# Split the text with start index tracking\n", + "text_splitter = RecursiveCharacterTextSplitter(\n", + " chunk_size = 300,\n", + " chunk_overlap = 50,\n", + " length_function = tiktoken_len,\n", + " add_start_index = True # Add this parameter\n", + ")\n", + "\n", + "# Split and then process to add page information\n", + "raw_splits = text_splitter.split_documents(docs)\n", + "split_chunks = add_page_info_to_splits(raw_splits)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai.embeddings import OpenAIEmbeddings\n", + "\n", + "embedding_model = OpenAIEmbeddings(model=\"text-embedding-3-small\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.vectorstores import Qdrant\n", + "\n", + "\"\"\" \n", + "The line about memory means that the database will be stored in the RAM instead \n", + "of on disk. This is faster, but means that the database will be lost when the \n", + "program ends.\n", + "\n", + "collection_name creates a named collection within Qdrant (similar to a \n", + "table in a SQLdatabase).\n", + "\"\"\"\n", + "qdrant_vectorstore = Qdrant.from_documents(\n", + " split_chunks,\n", + " embedding_model,\n", + " location=\":memory:\",\n", + " collection_name=\"kohavi_ab_testing_pdf_collection\",\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "qdrant_retriever = qdrant_vectorstore.as_retriever(search_kwargs={\"k\": 5})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.prompts import ChatPromptTemplate\n", + "\n", + "RAG_PROMPT = \"\"\"\n", + "CONTEXT:\n", + "{context}\n", + "\n", + "QUERY:\n", + "{question}\n", + "\n", + "You are a helpful assistant. Use the available context to answer the question. Do not use your own knowledge! If you cannot answer the question based on the context, you must say \"I don't know\".\n", + "\"\"\"\n", + "\n", + "rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "REPHRASE_QUERY_PROMPT = \"\"\"\n", + "\n", + "QUERY:\n", + "{question}\n", + "\n", + "You are a helpful assistant. Rephrase the provided query to be more specific and to the point in order to improve retrieval in our RAG pipeline about AB Testing.\n", + "\"\"\"\n", + "\n", + "rephrase_query_prompt = ChatPromptTemplate.from_template(REPHRASE_QUERY_PROMPT)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import ChatOpenAI\n", + "\n", + "openai_chat_model = ChatOpenAI(model=\"gpt-4.1-mini\", temperature=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from operator import itemgetter\n", + "from langchain.schema.output_parser import StrOutputParser\n", + "\n", + "rag_chain = (\n", + " {\"context\": itemgetter(\"question\") | qdrant_retriever, \"question\": itemgetter(\"question\")}\n", + " | rag_prompt | openai_chat_model | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "rephrased_question = (\n", + " {\"question\": itemgetter(\"question\")}\n", + " | rephrase_query_prompt | openai_chat_model | StrOutputParser()\n", + ")\n", + "\n", + "\n", + "rephrased_rag_chain = (\n", + " {\"context\": rephrased_question | qdrant_retriever, \"question\": rephrased_question}\n", + " | rag_prompt | openai_chat_model | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Any, Callable, List, Optional, TypedDict, Union\n", + "\n", + "from langchain.agents import AgentExecutor, create_openai_functions_agent\n", + "from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser\n", + "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", + "from langchain_core.messages import AIMessage, BaseMessage, HumanMessage\n", + "from langchain_core.runnables import Runnable\n", + "from langchain_core.tools import BaseTool\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "from langgraph.graph import END, StateGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Annotated, List, TypedDict, Dict, Any\n", + "\n", + "import functools\n", + "import operator\n", + "\n", + "from langchain_core.messages import AIMessage, BaseMessage, HumanMessage\n", + "from langchain_openai.chat_models import ChatOpenAI\n", + "import functools\n", + "\n", + "class GraphState(TypedDict):\n", + " messages: Annotated[List[BaseMessage], operator.add]\n", + " sources: Annotated[List[Dict[str, Any]], operator.add] # Track all sources\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def rag_chain_node(state: GraphState) -> GraphState:\n", + " query = state[\"messages\"][-1].content\n", + " \n", + " # 1. Retrieve documents once\n", + " docs = qdrant_retriever.get_relevant_documents(query)\n", + " \n", + " # 2. Extract sources from the documents\n", + " sources = []\n", + " for doc in docs:\n", + " source_path = doc.metadata.get(\"source\", \"\")\n", + " filename = source_path.split(\"/\")[-1] if \"/\" in source_path else source_path\n", + " \n", + " sources.append({\n", + " \"title\": filename,\n", + " \"page\": doc.metadata.get(\"page\", \"unknown\"),\n", + " })\n", + " \n", + " # 3. Use a simplified RAG chain without retrieval\n", + " # Create context from documents\n", + " context = \"\\n\\n\".join([doc.page_content for doc in docs])\n", + " \n", + " # Format the prompt with context and query\n", + " formatted_prompt = rag_prompt.format(context=context, question=query)\n", + " \n", + " # Send to the model and parse the output\n", + " response = openai_chat_model.invoke(formatted_prompt)\n", + " response_text = StrOutputParser().invoke(response)\n", + " \n", + " return {\n", + " \"messages\": [AIMessage(content=response_text)],\n", + " \"sources\": sources\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.tools.arxiv.tool import ArxivQueryRun\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Annotated, List, Tuple, Union\n", + "from langchain_core.tools import tool\n", + "\n", + "@tool\n", + "def retrieve_information(\n", + " query: Annotated[str, \"query to ask the retrieve information tool\"]\n", + " ):\n", + " \"\"\"Use Retrieval Augmented Generation to retrieve information about AB Testing.\"\"\"\n", + " # 1. Retrieve documents\n", + " docs = qdrant_retriever.get_relevant_documents(query)\n", + " \n", + " # 2. Extract and store sources\n", + " sources = []\n", + " for doc in docs:\n", + " source_path = doc.metadata.get(\"source\", \"\")\n", + " filename = source_path.split(\"/\")[-1] if \"/\" in source_path else source_path\n", + " \n", + " sources.append({\n", + " \"title\": filename,\n", + " \"page\": doc.metadata.get(\"page\", \"unknown\"),\n", + " })\n", + " \n", + " # Store sources for later access\n", + " retrieve_information.last_sources = sources\n", + " \n", + " # 3. Return just the formatted document contents\n", + " formatted_content = \"\\n\\n\".join([f\"Retrieved Information: {i+1}\\n{doc.page_content}\" \n", + " for i, doc in enumerate(docs)])\n", + " return formatted_content" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "@tool\n", + "def retrieve_information_with_rephrased_query(\n", + " query: Annotated[str, \"query to be rephrased before asking the retrieve information tool\"]\n", + " ):\n", + " \"\"\"This tool will intelligently rephrase your AB testing query and then will use Retrieval Augmented Generation to retrieve information about the rephrased query.\"\"\"\n", + " \n", + " # 1. Rephrase the query first\n", + " rephrased_query = rephrase_query_prompt.format(question=query)\n", + " rephrased_query = openai_chat_model.invoke(rephrased_query)\n", + " rephrased_query = StrOutputParser().invoke(rephrased_query)\n", + " \n", + " # 2. Retrieve documents using the rephrased query\n", + " docs = qdrant_retriever.get_relevant_documents(rephrased_query)\n", + " \n", + " # 3. Extract and store sources\n", + " sources = []\n", + " for doc in docs:\n", + " source_path = doc.metadata.get(\"source\", \"\")\n", + " filename = source_path.split(\"/\")[-1] if \"/\" in source_path else source_path\n", + " \n", + " sources.append({\n", + " \"title\": filename,\n", + " \"page\": doc.metadata.get(\"page\", \"unknown\"),\n", + " })\n", + " \n", + " # Store sources for later access\n", + " retrieve_information_with_rephrased_query.last_sources = sources\n", + " \n", + " # 4. Return formatted content with rephrased query\n", + " formatted_content = f\"Rephrased query: {rephrased_query}\\n\\n\" + \"\\n\\n\".join(\n", + " [f\"Retrieved Information: {i+1}\\n{doc.page_content}\" for i, doc in enumerate(docs)]\n", + " )\n", + " return formatted_content" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "tool_belt = [\n", + " retrieve_information,\n", + " retrieve_information_with_rephrased_query,\n", + " ArxivQueryRun(),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.prebuilt import ToolNode\n", + "\n", + "tool_node = ToolNode(tool_belt)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "model = ChatOpenAI(model=\"gpt-4.1\", temperature=0)\n", + "model = model.bind_tools(tool_belt)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "def call_model(state):\n", + " messages = state[\"messages\"]\n", + " \n", + " # Get the model to generate a response based on messages\n", + " response = model.invoke(messages)\n", + " \n", + " # Extract sources if available by examining the last message\n", + " sources = []\n", + " if len(messages) > 0:\n", + " last_message = messages[-1]\n", + " if hasattr(last_message, 'content'):\n", + " content = last_message.content\n", + " \n", + " # Check for specific patterns in the content\n", + " if isinstance(content, str):\n", + " if \"Rephrased query:\" in content and hasattr(retrieve_information_with_rephrased_query, \"last_sources\"):\n", + " sources = retrieve_information_with_rephrased_query.last_sources\n", + " elif \"Retrieved Information:\" in content and hasattr(retrieve_information, \"last_sources\"):\n", + " sources = retrieve_information.last_sources\n", + " elif \"Title:\" in content and \"Authors:\" in content: # ArxivQueryRun pattern\n", + " # Extract paper titles from ArXiv results\n", + " titles = re.findall(r\"Title: (.*?)$\", content, re.MULTILINE)\n", + " sources = [{\"title\": title, \"type\": \"arxiv_paper\"} for title in titles]\n", + " \n", + " # Return both the response and sources\n", + " return {\n", + " \"messages\": [response],\n", + " \"sources\": sources\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def should_continue(state):\n", + " last_message = state[\"messages\"][-1]\n", + "\n", + " if last_message.tool_calls:\n", + " return \"action\"\n", + "\n", + " return \"end\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.prompts import PromptTemplate\n", + "\n", + "def NonAB_Testing_or_helpful_RAG_or_continue(state):\n", + " initial_query = state[\"messages\"][0]\n", + " final_response = state[\"messages\"][-1]\n", + "\n", + "\n", + " prompt_template = \"\"\"\\\n", + " \n", + " Given an initial query, determine if the initial query is related to AB Testing (even vaguely e.g. statistics, A/B testing, etc.) or not. If not related to AB Testing, return 'Y'. If related to AB Testing, then given the initial query and a final response, determine if the final response is extremely helpful or not. If extremely helpful, return 'Y'. If not extremely helpful, return 'N'.\n", + "\n", + " Initial Query:\n", + " {initial_query}\n", + "\n", + " Final Response:\n", + " {final_response}\"\"\"\n", + "\n", + " prompt_template = PromptTemplate.from_template(prompt_template)\n", + "\n", + " helpfulness_check_model = ChatOpenAI(model=\"gpt-4.1\", temperature=0)\n", + "\n", + " helpfulness_chain = prompt_template | helpfulness_check_model | StrOutputParser()\n", + "\n", + " helpfulness_response = helpfulness_chain.invoke({\"initial_query\" : initial_query.content, \"final_response\" : final_response.content})\n", + "\n", + " if \"Y\" in helpfulness_response:\n", + " return \"end\"\n", + " else:\n", + " return \"agent\"" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.graph import StateGraph, END\n", + "\n", + "uncompiled_graph = StateGraph(GraphState)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uncompiled_graph.add_node(\"Initial_RAG\", rag_chain_node)\n", + "uncompiled_graph.add_node(\"agent\", call_model)\n", + "uncompiled_graph.add_node(\"action\", tool_node)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uncompiled_graph.set_entry_point(\"Initial_RAG\")\n", + "\n", + "uncompiled_graph.add_conditional_edges(\n", + " \"Initial_RAG\",\n", + " NonAB_Testing_or_helpful_RAG_or_continue,\n", + " {\n", + " \"agent\": \"agent\",\n", + " \"end\": END\n", + " }\n", + ")\n", + "\n", + "uncompiled_graph.add_conditional_edges(\n", + " \"agent\",\n", + " should_continue,\n", + " {\n", + " \"action\": \"action\",\n", + " \"end\": END\n", + " }\n", + ")\n", + "\n", + "uncompiled_graph.add_edge(\"action\", \"agent\") \n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "compiled_graph = uncompiled_graph.compile()" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": ".venv", + "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": "3.13.1" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0107beec40af42ffa66b216070f97d5c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": 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