langgraph/examples/rag/langgraph_self_rag_mistral_nomic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1908e8c-5970-4d9d-bc2b-d5fee7aa3baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install -U llama-cpp-python langchain-nomic langchain_community tiktoken langchainhub chromadb langchain langgraph"
   ]
  },
  {
   "attachments": {
    "e3e60bc2-6033-4d66-af6d-1dfafce5cb9f.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "848ba742-7443-4123-8115-061da9823309",
   "metadata": {},
   "source": [
    "# Self RAG\n",
    "\n",
    "Self-reflection can enhance RAG, enabling correction of poor quality retrieval or generations.\n",
    "\n",
    "Several recent papers focus on this theme, but implementing the ideas can be tricky.\n",
    "\n",
    "Here we show how to implement self-reflective RAG using `Nomic`, `Mistral`, and `LangGraph`.\n",
    "\n",
    "We'll focus on ideas from one paper, `Self RAG` [here](https://arxiv.org/abs/2310.11511).\n",
    "\n",
    "This can run fully locally (e.g., on a laptop).\n",
    "\n",
    "![Screenshot 2024-02-15 at 3.33.46 PM.png](attachment:e3e60bc2-6033-4d66-af6d-1dfafce5cb9f.png)\n",
    "\n",
    "### Embeddings\n",
    "\n",
    "We'll 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",
    "Simply: \n",
    "\n",
    "(1) Clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp):\n",
    "\n",
    "```\n",
    "git clone https://github.com/ggerganov/llama.cpp\n",
    "```\n",
    "\n",
    "(2) Download GGUF weights for Nomic's embedding model(s), allowing them to be run locally: \n",
    "\n",
    "* https://huggingface.co/nomic-ai/nomic-embed-text-v1-GGUF\n",
    "* https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF\n",
    "\n",
    "(3) Add to `llama.cpp/model` directory.\n",
    "\n",
    "(4) Build llama.cpp:\n",
    "```\n",
    "cd llama.cpp\n",
    "make\n",
    "```\n",
    "\n",
    "### 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:instruct\n",
    "```\n",
    "\n",
    "(3) Set `local_llm` to the model downloaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d35ebcdc-8510-4ce5-a0e9-8db714944c68",
   "metadata": {},
   "outputs": [],
   "source": [
    "! ollama pull mistral:instruct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "32d50725-04c1-487c-89cc-a119ec708c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ollama model name\n",
    "local_llm = \"mistral:instruct\"\n",
    "\n",
    "# Local embedding model paths (downloaded above)O\n",
    "embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.Q4_K_S.gguf\"\n",
    "# embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.f16.gguf\"\n",
    "# embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.5.f16.gguf\"\n",
    "# embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.5.Q4_K_S.gguf\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba68a46d-b617-4fdc-9113-fabdcf736feb",
   "metadata": {},
   "source": [
    "## Indexing\n",
    "\n",
    "First, let's index a popular blog post on agents. \n",
    "\n",
    "For local, we can 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",
    "We'll use the [llama.cpp](https://github.com/ggerganov/llama.cpp) integration.\n",
    "\n",
    "We'll use a local vectorstore, [Chroma](https://python.langchain.com/docs/integrations/vectorstores/chroma)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3bb9060-ad74-4470-9991-2ba167b6b8d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_community.document_loaders import WebBaseLoader\n",
    "from langchain_community.vectorstores import Chroma\n",
    "from langchain_community.embeddings import LlamaCppEmbeddings\n",
    "from langchain_nomic.embeddings import NomicEmbeddings\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",
    "embedding = LlamaCppEmbeddings(model_path=embd_model_path, n_batch=512)\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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d3339b9-5f30-4d54-bfc9-9091c6035955",
   "metadata": {},
   "source": [
    "## State\n",
    "\n",
    "Every node in our graph will modify `state`, which is dict that contains values (`question`, `documents`, etc) relevant to RAG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "90fb1dc6-c482-483a-8441-39965c401beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, TypedDict\n",
    "\n",
    "from langchain_core.messages import BaseMessage\n",
    "\n",
    "\n",
    "class GraphState(TypedDict):\n",
    "    \"\"\"\n",
    "    Represents the state of our graph.\n",
    "\n",
    "    Attributes:\n",
    "        keys: A dictionary where each key is a string.\n",
    "    \"\"\"\n",
    "\n",
    "    keys: Dict[str, any]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b89b2f21-b6b3-42db-a826-ff9d9aaec443",
   "metadata": {},
   "source": [
    "### Nodes and Edges\n",
    "\n",
    "Every node in the graph we laid out above is a function.\n",
    "\n",
    "Each node will modify the state in some way.\n",
    "\n",
    "Each edge will choose which node to call next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5324ea49-5745-47b5-a0a5-bf58c8babe46",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import operator\n",
    "from typing import Annotated, Sequence, TypedDict\n",
    "\n",
    "from langchain import hub\n",
    "from langchain.prompts import PromptTemplate\n",
    "from langchain_community.vectorstores import Chroma\n",
    "from langchain_core.messages import BaseMessage, FunctionMessage\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain_community.chat_models import ChatOllama\n",
    "from langchain_core.output_parsers import JsonOutputParser\n",
    "\n",
    "### Nodes ###\n",
    "\n",
    "\n",
    "def retrieve(state):\n",
    "    \"\"\"\n",
    "    Retrieve documents\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
    "    \"\"\"\n",
    "    print(\"---RETRIEVE---\")\n",
    "    state_dict = state[\"keys\"]\n",
    "    question = state_dict[\"question\"]\n",
    "    documents = retriever.get_relevant_documents(question)\n",
    "    return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
    "\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",
    "    state_dict = state[\"keys\"]\n",
    "    question = state_dict[\"question\"]\n",
    "    documents = state_dict[\"documents\"]\n",
    "\n",
    "    # Prompt\n",
    "    prompt = hub.pull(\"rlm/rag-prompt\")\n",
    "\n",
    "    # LLM\n",
    "    llm = ChatOllama(model=local_llm, temperature=0)\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\": documents, \"question\": question})\n",
    "    return {\n",
    "        \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
    "    }\n",
    "\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 relevant documents\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---CHECK RELEVANCE---\")\n",
    "    state_dict = state[\"keys\"]\n",
    "    question = state_dict[\"question\"]\n",
    "    documents = state_dict[\"documents\"]\n",
    "\n",
    "    # LLM\n",
    "    llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
    "\n",
    "    # Prompt\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 {context} \\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\",\"context\"],\n",
    "    )\n",
    "\n",
    "    # Chain\n",
    "    chain = prompt | llm | JsonOutputParser()\n",
    "\n",
    "    # Score\n",
    "    filtered_docs = []\n",
    "    for d in documents:\n",
    "        score = chain.invoke(\n",
    "            {\n",
    "                \"question\": question,\n",
    "                \"context\": d.page_content,\n",
    "            }\n",
    "        )\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",
    "            continue\n",
    "\n",
    "    return {\"keys\": {\"documents\": filtered_docs, \"question\": question}}\n",
    "\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",
    "    state_dict = state[\"keys\"]\n",
    "    question = state_dict[\"question\"]\n",
    "    documents = state_dict[\"documents\"]\n",
    "\n",
    "    # LLM\n",
    "    llm = ChatOllama(model=local_llm, temperature=0)\n",
    "    \n",
    "    # Create a prompt template with format instructions and the query\n",
    "    prompt = PromptTemplate(\n",
    "        template=\"\"\"You are generating questions that is well optimized for retrieval. \\n \n",
    "        Look at the input and try to reason about the underlying sematic intent / meaning. \\n \n",
    "        Here is the initial question:\n",
    "        \\n ------- \\n\n",
    "        {question} \n",
    "        \\n ------- \\n\n",
    "        Formulate an improved question:\"\"\",\n",
    "        input_variables=[\"question\"],\n",
    "    )\n",
    "\n",
    "    # Chain\n",
    "    chain = prompt | llm | StrOutputParser()\n",
    "    better_question = chain.invoke({\"question\": question})\n",
    "\n",
    "    return {\"keys\": {\"documents\": documents, \"question\": better_question}}\n",
    "\n",
    "\n",
    "def prepare_for_final_grade(state):\n",
    "    \"\"\"\n",
    "    Passthrough state for final grade.\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): The current graph state\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---FINAL GRADE---\")\n",
    "    state_dict = state[\"keys\"]\n",
    "    question = state_dict[\"question\"]\n",
    "    documents = state_dict[\"documents\"]\n",
    "    generation = state_dict[\"generation\"]\n",
    "\n",
    "    return {\n",
    "        \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
    "    }\n",
    "\n",
    "\n",
    "### Edges ###\n",
    "\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 state of the agent, including all keys.\n",
    "\n",
    "    Returns:\n",
    "        str: Next node to call\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---DECIDE TO GENERATE---\")\n",
    "    state_dict = state[\"keys\"]\n",
    "    question = state_dict[\"question\"]\n",
    "    filtered_documents = state_dict[\"documents\"]\n",
    "\n",
    "    if not filtered_documents:\n",
    "        # All documents have been filtered check_relevance\n",
    "        # We will re-generate a new query\n",
    "        print(\"---DECISION: TRANSFORM QUERY---\")\n",
    "        return \"transform_query\"\n",
    "    else:\n",
    "        # We have relevant documents, so generate answer\n",
    "        print(\"---DECISION: GENERATE---\")\n",
    "        return \"generate\"\n",
    "\n",
    "\n",
    "def grade_generation_v_documents(state):\n",
    "    \"\"\"\n",
    "    Determines whether the generation is grounded in the document.\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current state of the agent, including all keys.\n",
    "\n",
    "    Returns:\n",
    "        str: Binary decision\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---GRADE GENERATION vs DOCUMENTS---\")\n",
    "    state_dict = state[\"keys\"]\n",
    "    question = state_dict[\"question\"]\n",
    "    documents = state_dict[\"documents\"]\n",
    "    generation = state_dict[\"generation\"]\n",
    "\n",
    "    # LLM\n",
    "    llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
    "\n",
    "    # Prompt\n",
    "    prompt = PromptTemplate(\n",
    "        template=\"\"\"You are a grader assessing whether an answer is grounded in / supported by a set of facts. \\n \n",
    "        Here are the facts:\n",
    "        \\n ------- \\n\n",
    "        {documents} \n",
    "        \\n ------- \\n\n",
    "        Here is the answer: {generation}\n",
    "        Give a binary score 'yes' or 'no' score to indicate whether the answer is grounded in / supported by a set of facts. \\n\n",
    "        Provide the binary score as a JSON with a single key 'score' and no premable or explaination.\"\"\",\n",
    "        input_variables=[\"generation\", \"documents\"],\n",
    "    )\n",
    "\n",
    "    # Chain\n",
    "    chain = prompt | llm | JsonOutputParser()\n",
    "    score = chain.invoke({\"generation\": generation, \"documents\": documents})\n",
    "    grade = score[\"score\"]\n",
    "\n",
    "    if grade == \"yes\":\n",
    "        print(\"---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\")\n",
    "        return \"supported\"\n",
    "    else:\n",
    "        print(\"---DECISION: NOT SUPPORTED, GENERATE AGAIN---\")\n",
    "        return \"not supported\"\n",
    "\n",
    "\n",
    "def grade_generation_v_question(state):\n",
    "    \"\"\"\n",
    "    Determines whether the generation addresses the question.\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current state of the agent, including all keys.\n",
    "\n",
    "    Returns:\n",
    "        str: Binary decision\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---GRADE GENERATION vs QUESTION---\")\n",
    "    state_dict = state[\"keys\"]\n",
    "    question = state_dict[\"question\"]\n",
    "    documents = state_dict[\"documents\"]\n",
    "    generation = state_dict[\"generation\"]\n",
    "\n",
    "    llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
    "\n",
    "    # Prompt\n",
    "    prompt = PromptTemplate(\n",
    "        template=\"\"\"You are a grader assessing whether an answer is useful to resolve a question. \\n \n",
    "        Here is the answer:\n",
    "        \\n ------- \\n\n",
    "        {generation} \n",
    "        \\n ------- \\n\n",
    "        Here is the question: {question}\n",
    "        Give a binary score 'yes' or 'no' to indicate whether the answer is useful to resolve a question. \\n\n",
    "        Provide the binary score as a JSON with a single key 'score' and no premable or explaination.\"\"\",\n",
    "        input_variables=[\"generation\", \"question\"],\n",
    "    )\n",
    "\n",
    "    # Prompt\n",
    "    chain = prompt | llm | JsonOutputParser()\n",
    "    score = chain.invoke({\"generation\": generation, \"question\": question})\n",
    "    grade = score[\"score\"]\n",
    "\n",
    "    if grade == \"yes\":\n",
    "        print(\"---DECISION: USEFUL---\")\n",
    "        return \"useful\"\n",
    "    else:\n",
    "        print(\"---DECISION: NOT USEFUL---\")\n",
    "        return \"not useful\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf3826c-b668-4f0b-bf83-81c40baaaf02",
   "metadata": {},
   "source": [
    "## Build Graph\n",
    "\n",
    "This just follows the flow we outlined in the figure above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5605dee4-b2df-46ae-a640-cc2ed90c21a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pprint\n",
    "\n",
    "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(\"prepare_for_final_grade\", prepare_for_final_grade)  # passthrough\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\", \"retrieve\")\n",
    "workflow.add_conditional_edges(\n",
    "    \"generate\",\n",
    "    grade_generation_v_documents,\n",
    "    {\n",
    "        \"supported\": \"prepare_for_final_grade\",\n",
    "        \"not supported\": \"generate\",\n",
    "    },\n",
    ")\n",
    "workflow.add_conditional_edges(\n",
    "    \"prepare_for_final_grade\",\n",
    "    grade_generation_v_question,\n",
    "    {\n",
    "        \"useful\": END,\n",
    "        \"not useful\": \"transform_query\",\n",
    "    },\n",
    ")\n",
    "\n",
    "# Compile\n",
    "app = workflow.compile()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105ae1b5-6963-4186-bb83-6d6cb96d095f",
   "metadata": {},
   "source": [
    "## Run\n",
    "\n",
    "Trace for below run: https://smith.langchain.com/public/928651fd-85b3-49ff-b481-bd28417645e5/r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "26a64f7d-0c14-4e31-a67f-63021dee626e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "llama_print_timings:        load time =     149.49 ms\n",
      "llama_print_timings:      sample time =       0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)\n",
      "llama_print_timings: prompt eval time =      17.39 ms /    12 tokens (    1.45 ms per token,   690.01 tokens per second)\n",
      "llama_print_timings:        eval time =       0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)\n",
      "llama_print_timings:       total time =      17.39 ms /    13 tokens\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---RETRIEVE---\n",
      "\"Node 'retrieve':\"\n",
      "'\\n---\\n'\n",
      "---CHECK RELEVANCE---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "\"Node 'grade_documents':\"\n",
      "'\\n---\\n'\n",
      "---DECIDE TO GENERATE---\n",
      "---DECISION: GENERATE---\n",
      "---GENERATE---\n",
      "\"Node 'generate':\"\n",
      "'\\n---\\n'\n",
      "---GRADE GENERATION vs DOCUMENTS---\n",
      "---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\n",
      "---FINAL GRADE---\n",
      "\"Node 'prepare_for_final_grade':\"\n",
      "'\\n---\\n'\n",
      "---GRADE GENERATION vs QUESTION---\n",
      "---DECISION: USEFUL---\n",
      "\"Node '__end__':\"\n",
      "'\\n---\\n'\n",
      "(' In a LLM (large language model)-powered autonomous agent system, LLM '\n",
      " 'functions as the agent’s brain, complemented by several key components. One '\n",
      " 'of these components is memory. Memory can be defined as the processes used '\n",
      " 'to acquire, store, retain, and later retrieve information. There are several '\n",
      " 'types of memory in human brains:\\n'\n",
      " '\\n'\n",
      " '1. Sensory Memory: This is the earliest stage of memory, providing the '\n",
      " 'ability to retain impressions of sensory information (visual, auditory, etc) '\n",
      " 'after the original stimuli have ended. Sensory memory typically only lasts '\n",
      " 'for up to a few seconds. Subcategories include iconic memory (visual), '\n",
      " 'echoic memory (auditory), and haptic memory (touch).\\n'\n",
      " '2. Short-Term Memory (STM) or Working Memory: It stores information that we '\n",
      " 'are currently aware of and needed to carry out complex cognitive tasks such '\n",
      " 'as learning and reasoning. Short-term memory is believed to have the '\n",
      " 'capacity of about 7 items (Miller 1956) and lasts for 20-30 seconds.\\n'\n",
      " '3. Long-Term Memory (LTM): Long-term memory can store information for a '\n",
      " 'remarkably long time, ranging from a few days to decades, with an '\n",
      " 'essentially unlimited storage capacity. There are two subtypes of LTM:\\n'\n",
      " '   * Explicit / declarative memory: This is memory of facts and events, and '\n",
      " 'refers to those memories that can be consciously recalled, including '\n",
      " 'episodic memory (events and experiences) and semantic memory (facts and '\n",
      " 'concepts).\\n'\n",
      " '   * Implicit / procedural memory: This type of memory is unconscious and '\n",
      " 'involves skills and routines that are performed automatically, like riding a '\n",
      " 'bike or typing on a keyboard.\\n'\n",
      " '\\n'\n",
      " 'We can roughly consider the following mappings in an LLM-powered agent '\n",
      " 'system:\\n'\n",
      " '\\n'\n",
      " '* Sensory Memory: Input data from sensors\\n'\n",
      " '* Short-Term Memory: Active processing of information, temporary storage for '\n",
      " 'complex tasks\\n'\n",
      " '* Long-Term Memory: Stored knowledge and experiences that can be accessed '\n",
      " 'and used to learn new tasks or make decisions.')\n"
     ]
    }
   ],
   "source": [
    "# Run\n",
    "inputs = {\"keys\": {\"question\": \"Explain how the different types of agent memory work?\"}}\n",
    "for output in app.stream(inputs):\n",
    "    for key, value in output.items():\n",
    "        # Node\n",
    "        pprint.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.pprint(\"\\n---\\n\")\n",
    "\n",
    "# Final generation\n",
    "pprint.pprint(value['keys']['generation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d40a430-be4a-4d9c-98f9-46c5eb3066e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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