langgraph/examples/rag/langgraph_crag_local.ipynb

{
 "cells": [
  {
   "attachments": {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "92ddc4f4-f7bf-4e0e-b5a5-5abd8a008b21",
   "metadata": {},
   "source": [
    "# Corrective RAG (CRAG) -- With Local LLMs\n",
    "\n",
    "Corrective-RAG (CRAG) is a strategy for RAG that incorperates self-reflection / self-grading on retrieved documents. \n",
    "\n",
    "In the paper [here](https://arxiv.org/pdf/2401.15884.pdf), a few steps are taken:\n",
    "\n",
    "* If at least one document exceeds the threshold for relevance, then it proceeds to generation\n",
    "* Before generation, it performns knowledge refinement\n",
    "* This paritions the document into \"knowledge strips\"\n",
    "* It grades each strip, and filters our irrelevant ones\n",
    "* If all documents fall below the relevance threshold or if the grader is unsure, then the framework seeks an additional datasource\n",
    "* It will use web search to supplement retrieval\n",
    " \n",
    "We will implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph):\n",
    "\n",
    "* Let's skip the knowledge refinement phase as a first pass. This can be added back as a node, if desired. \n",
    "* If *any* documents are irrelevant, let's opt to supplement retrieval with web search. \n",
    "* We'll use [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search) for web search.\n",
    "* Let's use query re-writing to optimize the query for web search.\n",
    "\n",
    "![Screenshot 2024-04-01 at 12.36.05 PM.png](attachment:d3ff129f-c0ff-4951-993c-efafc7f29dce.png)\n",
    "\n",
    "## Running\n",
    "\n",
    "This notebook can be run three ways:\n",
    "\n",
    "(1) Mistral API\n",
    "\n",
    "(2) Locally \n",
    "\n",
    "(3) CoLab: [here](https://colab.research.google.com/drive/1U5OcwWjoXZSud30q4XOk1UlIJNjaD3kX?usp=sharing) is a link to a CoLab for this notebook. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba4302f-09d9-4d2a-a18d-a6fd23704850",
   "metadata": {},
   "source": [
    "# Enviorment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a660963-bd3d-4c87-b2e4-b6e432055211",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install --quiet langchain_community tiktoken langchainhub chromadb langchain langgraph tavily-python langchain-mistralai gpt4all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "728896ab-8aca-4cc3-a152-1491a76bc620",
   "metadata": {},
   "source": [
    "### LLMs\n",
    "\n",
    "You can run this in two ways:\n",
    "\n",
    "(1) Use [Mistral API](https://auth.mistral.ai/ui/login?flow=cc5d3fa5-122b-4c87-bcd8-81e8151c6753).\n",
    "\n",
    "(2) Run locally, as shown below.\n",
    "\n",
    "#### Local Embeddings\n",
    "\n",
    "You can use `GPT4AllEmbeddings()` from Nomic, which can access use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n",
    "\n",
    "\n",
    "Follow the documentation [here](https://docs.gpt4all.io/gpt4all_python_embedding.html#supported-embedding-models).\n",
    "\n",
    "#### Local LLM\n",
    "\n",
    "(1) Download [Ollama app](https://ollama.ai/).\n",
    "\n",
    "(2) Download a `Mistral` model from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available.\n",
    "```\n",
    "ollama pull mistral\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0cd7ff0-534d-4743-ba6d-e12024a2fd84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If using Mistral API\n",
    "mistral_api_key =  <your-api-key>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f74055-d137-466c-9555-4e2da9759ddb",
   "metadata": {},
   "source": [
    "### Search\n",
    " \n",
    "We'll use [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search) for web search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28224481-4cb0-4bc6-bf88-2d2b383094df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['TAVILY_API_KEY'] = <your-api-key>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55ec4c0c-65cc-4816-86df-c40b55f9c2d5",
   "metadata": {},
   "source": [
    "### Tracing\n",
    "\n",
    "Optionally, use [LangSmith](https://docs.smith.langchain.com/) for tracing (shown at bottom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68fed362-871a-46df-8ba0-579797ff2e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n",
    "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n",
    "os.environ['LANGCHAIN_API_KEY'] = <your-api-key>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c059c3a3-7f01-4d46-8289-fde4c1b4155f",
   "metadata": {},
   "source": [
    "## Configuration\n",
    "\n",
    "Decide to run locally and select LLM to use with Ollama."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f4db331-c4d0-4c7c-a9a5-0bebc8a89c6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_local = 'Yes'\n",
    "local_llm = \"mistral:latest\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e2b6eed-3b3f-44b5-a34a-4ade1e94caf0",
   "metadata": {},
   "source": [
    "## Index\n",
    "\n",
    "Let's index 3 blog posts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb8b789b-475b-4e1b-9c66-03504c837830",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.document_loaders import WebBaseLoader\n",
    "from langchain_community.vectorstores import Chroma\n",
    "from langchain_community.embeddings import GPT4AllEmbeddings\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_mistralai import MistralAIEmbeddings\n",
    "\n",
    "# Load\n",
    "url = \"https://lilianweng.github.io/posts/2023-06-23-agent/\"\n",
    "loader = WebBaseLoader(url)\n",
    "docs = loader.load()\n",
    "\n",
    "# Split\n",
    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
    "    chunk_size=500, chunk_overlap=100\n",
    ")\n",
    "all_splits = text_splitter.split_documents(docs)\n",
    "\n",
    "# Embed and index\n",
    "if run_local == \"Yes\":\n",
    "    embedding = GPT4AllEmbeddings()\n",
    "else:\n",
    "    embedding = MistralAIEmbeddings(mistral_api_key=mistral_api_key)\n",
    "\n",
    "# Index\n",
    "vectorstore = Chroma.from_documents(\n",
    "    documents=all_splits,\n",
    "    collection_name=\"rag-chroma\",\n",
    "    embedding=embedding,\n",
    ")\n",
    "retriever = vectorstore.as_retriever()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fe7fd10a-f64a-48de-a116-6d5890def1af",
   "metadata": {},
   "source": [
    "## LLMs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e75c029-6c10-47c7-871c-1f4932b25309",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'score': 'yes'}\n"
     ]
    }
   ],
   "source": [
    "### Retrieval Grader \n",
    "\n",
    "from langchain.prompts import PromptTemplate\n",
    "from langchain_community.chat_models import ChatOllama\n",
    "from langchain_mistralai.chat_models import ChatMistralAI\n",
    "from langchain_core.output_parsers import JsonOutputParser\n",
    "\n",
    "# LLM\n",
    "if run_local == \"Yes\":\n",
    "    llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
    "else:\n",
    "    llm = ChatMistralAI(\n",
    "        model=\"mistral-medium\", temperature=0, mistral_api_key=mistral_api_key\n",
    "    )\n",
    "\n",
    "prompt = PromptTemplate(\n",
    "    template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
    "    Here is the retrieved document: \\n\\n {document} \\n\\n\n",
    "    Here is the user question: {question} \\n\n",
    "    If the document contains keywords related to the user question, grade it as relevant. \\n\n",
    "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
    "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \\n\n",
    "    Provide the binary score as a JSON with a single key 'score' and no premable or explaination.\"\"\",\n",
    "    input_variables=[\"question\", \"document\"],\n",
    ")\n",
    "\n",
    "retrieval_grader = prompt | llm | JsonOutputParser()\n",
    "question = \"agent memory\"\n",
    "docs = retriever.get_relevant_documents(question)\n",
    "doc_txt = docs[1].page_content\n",
    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dad03302-bd93-43fc-949e-af51a3298cfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The given text discusses the concept of building autonomous agents using a large language model (LLM) as its core controller. The text highlights several key components of an LLM-powered agent system, including observation, retrieval, reflection, planning & reacting, and relationships between agents. It also mentions some challenges such as finite context length, long-term planning and task decomposition, and reliability of natural language interface. The text provides examples of proof-of-concept demos like AutoGPT and discusses their limitations. The architecture of the generative agent is also described, which results in emergent social behavior.\n"
     ]
    }
   ],
   "source": [
    "### Generate\n",
    "\n",
    "from langchain import hub\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "\n",
    "# Prompt\n",
    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
    "\n",
    "# LLM\n",
    "if run_local == \"Yes\":\n",
    "    llm = ChatOllama(model=local_llm, temperature=0)\n",
    "else:\n",
    "    llm = ChatMistralAI(\n",
    "        model=\"mistral-medium\", temperature=0, mistral_api_key=mistral_api_key\n",
    "    )\n",
    "\n",
    "# Post-processing\n",
    "def format_docs(docs):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
    "\n",
    "# Chain\n",
    "rag_chain = prompt | llm | StrOutputParser()\n",
    "\n",
    "# Run\n",
    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
    "print(generation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4b61211-70b5-4471-a714-feb9cc91e860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' What is agent memory and how can it be effectively utilized in vector database retrieval?'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Question Re-writer\n",
    "\n",
    "# LLM\n",
    "if run_local == \"Yes\":\n",
    "    llm = ChatOllama(model=local_llm, temperature=0)\n",
    "else:\n",
    "    llm = ChatMistralAI(\n",
    "        model=\"mistral-medium\", temperature=0, mistral_api_key=mistral_api_key\n",
    "    )\n",
    "\n",
    "# Prompt \n",
    "re_write_prompt = PromptTemplate(\n",
    "    template=\"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
    "     for vectorstore retrieval. Look at the initial and formulate an improved question. \\n\n",
    "     Here is the initial question: \\n\\n {question}. Improved question with no preamble: \\n \"\"\",\n",
    "    input_variables=[\"generation\", \"question\"],\n",
    ")\n",
    "\n",
    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
    "question_rewriter.invoke({\"question\": question})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d7fde29-e62e-4445-80f9-122eee0a3922",
   "metadata": {},
   "source": [
    "## Web Search Tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b36a2f36-bc5f-408d-a5e8-3fa203c233f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Search\n",
    "\n",
    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
    "web_search_tool = TavilySearchResults(k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3421cf0-9067-43fe-8681-0d3189d15dd3",
   "metadata": {},
   "source": [
    "# Graph \n",
    "\n",
    "Capture the flow in as a graph.\n",
    "\n",
    "## Graph state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "10028794-2fbc-43f9-aa4c-7fe3abd69c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing_extensions import TypedDict\n",
    "from typing import List\n",
    "\n",
    "class GraphState(TypedDict):\n",
    "    \"\"\"\n",
    "    Represents the state of our graph.\n",
    "\n",
    "    Attributes:\n",
    "        question: question\n",
    "        generation: LLM generation\n",
    "        web_search: whether to add search\n",
    "        documents: list of documents \n",
    "    \"\"\"\n",
    "    question : str\n",
    "    generation : str\n",
    "    web_search : str\n",
    "    documents : List[str]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "447d1333-082d-479a-a6fa-0ac0df78bb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema import Document\n",
    "\n",
    "def retrieve(state):\n",
    "    \"\"\"\n",
    "    Retrieve documents\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:ß\n",
    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
    "    \"\"\"\n",
    "    print(\"---RETRIEVE---\")\n",
    "    question = state[\"question\"]\n",
    "\n",
    "    # Retrieval\n",
    "    documents = retriever.get_relevant_documents(question)\n",
    "    return {\"documents\": documents, \"question\": question}\n",
    "\n",
    "def generate(state):\n",
    "    \"\"\"\n",
    "    Generate answer\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): New key added to state, generation, that contains LLM generation\n",
    "    \"\"\"\n",
    "    print(\"---GENERATE---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state[\"documents\"]\n",
    "    \n",
    "    # RAG generation\n",
    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
    "\n",
    "def grade_documents(state):\n",
    "    \"\"\"\n",
    "    Determines whether the retrieved documents are relevant to the question.\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): Updates documents key with only filtered relevant documents\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state[\"documents\"]\n",
    "    \n",
    "    # Score each doc\n",
    "    filtered_docs = []\n",
    "    web_search = \"No\"\n",
    "    for d in documents:\n",
    "        score = retrieval_grader.invoke({\"question\": question, \"document\": d.page_content})\n",
    "        grade = score['score']\n",
    "        if grade == \"yes\":\n",
    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
    "            filtered_docs.append(d)\n",
    "        else:\n",
    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
    "            web_search = \"Yes\"\n",
    "            continue\n",
    "    return {\"documents\": filtered_docs, \"question\": question, \"web_search\": web_search}\n",
    "\n",
    "def transform_query(state):\n",
    "    \"\"\"\n",
    "    Transform the query to produce a better question.\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): Updates question key with a re-phrased question\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---TRANSFORM QUERY---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state[\"documents\"]\n",
    "\n",
    "    # Re-write question\n",
    "    better_question = question_rewriter.invoke({\"question\": question})\n",
    "    return {\"documents\": documents, \"question\": better_question}\n",
    "    \n",
    "def web_search(state):\n",
    "    \"\"\"\n",
    "    Web search based on the re-phrased question.\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): Updates documents key with appended web results\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---WEB SEARCH---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state[\"documents\"]\n",
    "\n",
    "    # Web search\n",
    "    docs = web_search_tool.invoke({\"query\": question})\n",
    "    web_results = \"\\n\".join([d[\"content\"] for d in docs])\n",
    "    web_results = Document(page_content=web_results)\n",
    "    documents.append(web_results)\n",
    "\n",
    "    return {\"documents\": documents, \"question\": question}\n",
    "\n",
    "### Edges\n",
    "\n",
    "def decide_to_generate(state):\n",
    "    \"\"\"\n",
    "    Determines whether to generate an answer, or re-generate a question.\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        str: Binary decision for next node to call\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
    "    question = state[\"question\"]\n",
    "    web_search = state[\"web_search\"]\n",
    "    filtered_documents = state[\"documents\"]\n",
    "\n",
    "    if web_search == \"Yes\":\n",
    "        # All documents have been filtered check_relevance\n",
    "        # We will re-generate a new query\n",
    "        print(\"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\")\n",
    "        return \"transform_query\"\n",
    "    else:\n",
    "        # We have relevant documents, so generate answer\n",
    "        print(\"---DECISION: GENERATE---\")\n",
    "        return \"generate\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6096626d-dfa5-48e0-8a24-3747b298bc67",
   "metadata": {},
   "source": [
    "## Build Graph\n",
    "\n",
    "This just follows the flow we outlined in the figure above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0a63776c-f9cd-46ce-b8cf-95c066dc5b06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langgraph.graph import END, StateGraph\n",
    "\n",
    "workflow = StateGraph(GraphState)\n",
    "\n",
    "# Define the nodes\n",
    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
    "workflow.add_node(\"generate\", generate)  # generatae\n",
    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
    "workflow.add_node(\"web_search_node\", web_search)  # web search\n",
    "\n",
    "# Build graph\n",
    "workflow.set_entry_point(\"retrieve\")\n",
    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
    "workflow.add_conditional_edges(\n",
    "    \"grade_documents\",\n",
    "    decide_to_generate,\n",
    "    {\n",
    "        \"transform_query\": \"transform_query\",\n",
    "        \"generate\": \"generate\",\n",
    "    },\n",
    ")\n",
    "workflow.add_edge(\"transform_query\", \"web_search_node\")\n",
    "workflow.add_edge(\"web_search_node\", \"generate\")\n",
    "workflow.add_edge(\"generate\", END)\n",
    "\n",
    "# Compile\n",
    "app = workflow.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ab1d8df-a74e-4b48-a30b-e39bbfd5925a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---RETRIEVE---\n",
      "\"Node 'retrieve':\"\n",
      "'\\n---\\n'\n",
      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "\"Node 'grade_documents':\"\n",
      "'\\n---\\n'\n",
      "---ASSESS GRADED DOCUMENTS---\n",
      "---DECISION: GENERATE---\n",
      "---GENERATE---\n",
      "\"Node 'generate':\"\n",
      "'\\n---\\n'\n",
      "\"Node '__end__':\"\n",
      "'\\n---\\n'\n",
      "(' The given text discusses the concept of building autonomous agents using '\n",
      " 'large language models (LLMs) as their core controllers. LLMs have the '\n",
      " 'potential to be powerful general problem solvers, extending beyond '\n",
      " 'generating well-written copies, stories, essays, and programs. In an '\n",
      " \"LLM-powered agent system, the model functions as the agent's brain, \"\n",
      " 'complemented by several key components: planning, memory, and tool use.\\n'\n",
      " '\\n'\n",
      " '1. Planning: The agent breaks down large tasks into smaller subgoals for '\n",
      " 'efficient handling of complex tasks and can do self-criticism and '\n",
      " 'self-reflection to improve results.\\n'\n",
      " '2. Memory: Short-term memory is utilized for in-context learning, while '\n",
      " 'long-term memory provides the capability to retain and recall information '\n",
      " 'over extended periods by leveraging an external vector store and fast '\n",
      " 'retrieval.\\n'\n",
      " '3. Tool use: The agent learns to call external APIs for missing information, '\n",
      " 'including current information, code execution capability, access to '\n",
      " 'proprietary information sources, and more.\\n'\n",
      " '\\n'\n",
      " 'The text also discusses the types of memory in human brains, including '\n",
      " 'sensory memory, short-term memory (STM), and long-term memory (LTM). Sensory '\n",
      " 'memory provides the ability to retain impressions of sensory information for '\n",
      " 'a few seconds, while STM stores information needed for complex cognitive '\n",
      " 'tasks and lasts for 20-30 seconds. LTM can store information for remarkably '\n",
      " 'long periods with an essentially unlimited storage capacity and has two '\n",
      " 'subtypes: explicit/declarative memory (memory of facts and events) and '\n",
      " 'implicit/procedural memory (skills and routines).\\n'\n",
      " '\\n'\n",
      " 'The text also includes a figure comparing different methods, including AD, '\n",
      " 'ED, source policies, and RL^2, on environments that require memory and '\n",
      " 'exploration.')\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "# Run\n",
    "inputs = {\"question\": \"What are the types of agent memory?\"}\n",
    "for output in app.stream(inputs):\n",
    "    for key, value in output.items():\n",
    "        # Node\n",
    "        pprint(f\"Node '{key}':\")\n",
    "        # Optional: print full state at each node\n",
    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
    "    pprint(\"\\n---\\n\")\n",
    "\n",
    "# Final generation\n",
    "pprint(value[\"generation\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ee2be9-2368-46ea-9edd-dc064a7c7c96",
   "metadata": {},
   "source": [
    "Trace: \n",
    "\n",
    "https://smith.langchain.com/public/731df833-57de-4612-8fe8-07cb424bc9a6/r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "deb28175-27a1-4afc-9747-2983e87fc881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---RETRIEVE---\n",
      "\"Node 'retrieve':\"\n",
      "'\\n---\\n'\n",
      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
      "---GRADE: DOCUMENT NOT RELEVANT---\n",
      "---GRADE: DOCUMENT NOT RELEVANT---\n",
      "---GRADE: DOCUMENT NOT RELEVANT---\n",
      "---GRADE: DOCUMENT NOT RELEVANT---\n",
      "\"Node 'grade_documents':\"\n",
      "'\\n---\\n'\n",
      "---ASSESS GRADED DOCUMENTS---\n",
      "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\n",
      "---TRANSFORM QUERY---\n",
      "\"Node 'transform_query':\"\n",
      "'\\n---\\n'\n",
      "---WEB SEARCH---\n",
      "\"Node 'web_search_node':\"\n",
      "'\\n---\\n'\n",
      "---GENERATE---\n",
      "\"Node 'generate':\"\n",
      "'\\n---\\n'\n",
      "\"Node '__end__':\"\n",
      "'\\n---\\n'\n",
      "(' AlphaCodium is a new approach to code generation by LLMs, proposed in a '\n",
      " 'paper titled \"Code Generation with AlphaCodium: From Prompt Engineering to '\n",
      " 'Flow Engineering.\" It\\'s described as a test-based, multi-stage flow that '\n",
      " 'improves the performance of LLMs on code problems without requiring '\n",
      " 'fine-tuning. The iterative process involves repeatedly running and fixing '\n",
      " 'generated code against input-output tests, with two key elements being '\n",
      " 'generating additional data for the process and enrichment.')\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "# Run\n",
    "inputs = {\"question\": \"How does the AlphaCodium paper work?\"}\n",
    "for output in app.stream(inputs):\n",
    "    for key, value in output.items():\n",
    "        # Node\n",
    "        pprint(f\"Node '{key}':\")\n",
    "        # Optional: print full state at each node\n",
    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
    "    pprint(\"\\n---\\n\")\n",
    "\n",
    "# Final generation\n",
    "pprint(value[\"generation\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "598fe73e-f4e6-479f-8d9d-fc81680fff21",
   "metadata": {},
   "source": [
    "Trace: \n",
    "\n",
    "https://smith.langchain.com/public/c8b75f1b-38b7-48f2-a399-7ebb969d34f6/r"
   ]
  }
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