cookbook/langgraph_rag_agent_llama3_nvidia_nim.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f5be97b",
   "metadata": {},
   "source": [
    "# NVIDIA NIMs\n",
    "\n",
    "The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on \n",
    "NVIDIA NIM inference microservice. NIM supports models across domains like chat, embedding, and re-ranking models \n",
    "from the community as well as NVIDIA. These models are optimized by NVIDIA to deliver the best performance on NVIDIA \n",
    "accelerated infrastructure and deployed as a NIM, an easy-to-use, prebuilt containers that deploy anywhere using a single \n",
    "command on NVIDIA accelerated infrastructure.\n",
    "\n",
    "NVIDIA hosted deployments of NIMs are available to test on the [NVIDIA API catalog](https://build.nvidia.com/). After testing, \n",
    "NIMs can be exported from NVIDIA’s API catalog using the NVIDIA AI Enterprise license and run on-premises or in the cloud, \n",
    "giving enterprises ownership and full control of their IP and AI application.\n",
    "\n",
    "NIMs are packaged as container images on a per model basis and are distributed as NGC container images through the NVIDIA NGC Catalog. \n",
    "At their core, NIMs provide easy, consistent, and familiar APIs for running inference on an AI model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8520d840-fcf6-4458-b85c-8a2ff80a34eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -U langchain-nvidia-ai-endpoints langchain-community langchain langgraph tavily-python beautifulsoup4 lxml"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "0216de30-29cf-4464-9cc3-6e9a6d6c3e40",
   "metadata": {},
   "source": [
    "# NVIDIA NIM RAG agent with LLaMA3\n",
    "\n",
    "We'll combine ideas from paper RAG papers into a RAG agent:\n",
    "\n",
    "- **Routing:**  Adaptive RAG ([paper](https://arxiv.org/abs/2403.14403)). Route questions to different retrieval approaches\n",
    "- **Fallback:** Corrective RAG ([paper](https://arxiv.org/pdf/2401.15884.pdf)). Fallback to web search if docs are not relevant to query\n",
    "- **Self-correction:** Self-RAG ([paper](https://arxiv.org/abs/2310.11511)). Fix answers w/ hallucinations or don’t address question\n",
    "\n",
    "![langgraph_adaptive_rag.png](attachment:7b00797e-fb85-4474-9a9e-c505b61add81.png)\n",
    "\n",
    "## NVIDIA NIM Models\n",
    "- [NVIDIAEmbeddings](https://python.langchain.com/v0.1/docs/integrations/text_embedding/nvidia_ai_endpoints/) using [nv-embed-qa](https://build.nvidia.com/nvidia/embed-qa-4)\n",
    "- [ChatNVIDIA](https://python.langchain.com/v0.1/docs/integrations/chat/nvidia_ai_endpoints/) using [llama3](https://build.nvidia.com/meta/llama3-70b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e88d536d",
   "metadata": {},
   "source": [
    "### Tracing\n",
    "\n",
    "```python\n",
    "import getpass\n",
    "import os\n",
    "\n",
    "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>'\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c151f6e-005d-4d92-9fa7-4a73e2624ffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "if not os.environ.get(\"NVIDIA_API_KEY\", \"\").startswith(\"nvapi-\"):\n",
    "    nvapi_key = getpass.getpass(\"Enter your NVIDIA API key: \")\n",
    "    assert nvapi_key.startswith(\"nvapi-\"), f\"{nvapi_key[:5]}... is not a valid key\"\n",
    "    os.environ[\"NVIDIA_API_KEY\"] = nvapi_key"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb336dd6",
   "metadata": {},
   "source": [
    "### Search \n",
    "\n",
    "We'll use [Tavily](https://tavily.com/) for web search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6193b068",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.environ.get(\"TAVILY_API_KEY\", \"\"):\n",
    "    tyapi_key = getpass.getpass(\"Enter your Tavily API key: \")\n",
    "    os.environ[\"TAVILY_API_KEY\"] = tyapi_key"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6c23cd",
   "metadata": {},
   "source": [
    "### Model\n",
    "\n",
    "First, we can select a model that supports tool calling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6af1d31d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Model(id='meta/llama-3.1-405b-instruct', model_type='chat', client='ChatNVIDIA', endpoint=None, aliases=None, supports_tools=True, supports_structured_output=True, base_model=None),\n",
       " Model(id='meta/llama-3.2-3b-instruct', model_type='chat', client='ChatNVIDIA', endpoint=None, aliases=None, supports_tools=True, supports_structured_output=True, base_model=None),\n",
       " Model(id='nv-mistralai/mistral-nemo-12b-instruct', model_type='chat', client='ChatNVIDIA', endpoint=None, aliases=None, supports_tools=True, supports_structured_output=True, base_model=None),\n",
       " Model(id='meta/llama-3.1-70b-instruct', model_type='chat', client='ChatNVIDIA', endpoint=None, aliases=None, supports_tools=True, supports_structured_output=True, base_model=None),\n",
       " Model(id='mistralai/mistral-large-2-instruct', model_type='chat', client='ChatNVIDIA', endpoint=None, aliases=None, supports_tools=True, supports_structured_output=True, base_model=None),\n",
       " Model(id='meta/llama-3.1-8b-instruct', model_type='chat', client='ChatNVIDIA', endpoint=None, aliases=None, supports_tools=True, supports_structured_output=True, base_model=None)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n",
    "tool_models = [model for model in ChatNVIDIA.get_available_models() if model.supports_tools]\n",
    "tool_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0a6c07c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Select a model \n",
    "model_id = \"meta/llama-3.1-70b-instruct\"\n",
    "model_id = \"meta/llama-3.1-8b-instruct\"\n",
    "llm = ChatNVIDIA(model=model_id, temperature=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f59c6c26",
   "metadata": {},
   "source": [
    "### Index\n",
    "\n",
    "Define an index that we want to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "267c63e1-4c2f-439d-8d95-4c6aa01f41cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
     ]
    }
   ],
   "source": [
    "### Index\n",
    "\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_community.document_loaders import WebBaseLoader\n",
    "from langchain_core.vectorstores import InMemoryVectorStore\n",
    "from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings\n",
    "\n",
    "urls = [\n",
    "    \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
    "    \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n",
    "    \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n",
    "]\n",
    "\n",
    "docs = [WebBaseLoader(url).load() for url in urls]\n",
    "docs_list = [item for sublist in docs for item in sublist]\n",
    "\n",
    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
    "    chunk_size=250, chunk_overlap=0\n",
    ")\n",
    "doc_splits = text_splitter.split_documents(docs_list)\n",
    "\n",
    "# Add to vectorDB\n",
    "vectorstore = InMemoryVectorStore.from_documents(\n",
    "    documents=doc_splits,\n",
    "    embedding=NVIDIAEmbeddings(model='NV-Embed-QA'),\n",
    ")\n",
    "\n",
    "# Create retriever\n",
    "retriever = vectorstore.as_retriever(k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b65d11d",
   "metadata": {},
   "source": [
    "### Components\n",
    "\n",
    "Test structured output across our components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7c5c380d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasource='websearch' datasource='websearch' datasource='vectorstore'\n"
     ]
    }
   ],
   "source": [
    "### Router\n",
    "\n",
    "import json\n",
    "from typing_extensions import TypedDict, List, Annotated, Literal\n",
    "from pydantic import BaseModel, Field\n",
    "from langchain_core.messages import HumanMessage, SystemMessage\n",
    "\n",
    "# Data model\n",
    "class RouteQuery(BaseModel):\n",
    "    \"\"\"Route a user query to the most relevant datasource.\"\"\"\n",
    "\n",
    "    datasource: Literal[\"vectorstore\", \"websearch\"] = Field(\n",
    "        ...,\n",
    "        description=\"Given a user question choose to route it to web search or a vectorstore.\",\n",
    "    )\n",
    "\n",
    "# LLM with structured output\n",
    "structured_llm_router = llm.with_structured_output(RouteQuery)\n",
    "\n",
    "# Prompt\n",
    "router_instructions = \"\"\"You are an expert at routing a user question to a vectorstore or web search.\n",
    "\n",
    "The vectorstore contains documents related to agents, prompt engineering, and adversarial attacks.\n",
    "\n",
    "Use the vectorstore for questions on these topics. For all else, and especially for current events, use web-search.\n",
    "\n",
    "Return structured output with single key, datasource, that is 'websearch' or 'vectorstore' depending on the question.\"\"\"\n",
    "\n",
    "# Test router\n",
    "test_web_search = structured_llm_router.invoke(\n",
    "    [SystemMessage(content=router_instructions)]\n",
    "    + [\n",
    "        HumanMessage(\n",
    "            content=\"Who is favored to win the NFC Championship game in the 2024 season?\"\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "test_web_search_2 = structured_llm_router.invoke(\n",
    "    [SystemMessage(content=router_instructions)]\n",
    "    + [HumanMessage(content=\"What are the models released today for llama3.2?\")]\n",
    ")\n",
    "test_vector_store = structured_llm_router.invoke(\n",
    "    [SystemMessage(content=router_instructions)]\n",
    "    + [HumanMessage(content=\"What are the types of agent memory?\")]\n",
    ")\n",
    "print(\n",
    "    test_web_search,\n",
    "    test_web_search_2,\n",
    "    test_vector_store,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b008df98-8394-49da-8fb8-aefe2c90d03c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradeDocuments(binary_score='yes')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Retrieval Grader\n",
    "\n",
    "# Data model\n",
    "class GradeDocuments(BaseModel):\n",
    "    \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n",
    "\n",
    "    binary_score: str = Field(\n",
    "        description=\"Documents are relevant to the question, 'yes' or 'no'\"\n",
    "    )\n",
    "\n",
    "# LLM with structured output\n",
    "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
    "\n",
    "# Doc grader instructions\n",
    "doc_grader_instructions = \"\"\"You are a grader assessing relevance of a retrieved document to a user question.\n",
    "\n",
    "If the document contains keyword(s) or semantic meaning related to the question, grade it as relevant.\"\"\"\n",
    "\n",
    "# Grader prompt\n",
    "doc_grader_prompt = \"\"\"Here is the retrieved document: \\n\\n {document} \\n\\n Here is the user question: \\n\\n {question}. \n",
    "\n",
    "This carefully and objectively assess whether the document contains at least some information that is relevant to the question.\n",
    "\n",
    "Return structured output with single key, binary_score, that is 'yes' or 'no' score to indicate whether the document contains at least some information that is relevant to the question.\"\"\"\n",
    "\n",
    "# Test\n",
    "question = \"What is Chain of thought prompting?\"\n",
    "docs = retriever.invoke(question)\n",
    "doc_txt = docs[1].page_content\n",
    "doc_grader_prompt_formatted = doc_grader_prompt.format(\n",
    "    document=doc_txt, question=question\n",
    ")\n",
    "result = structured_llm_grader.invoke(\n",
    "    [SystemMessage(content=doc_grader_instructions)]\n",
    "    + [HumanMessage(content=doc_grader_prompt_formatted)]\n",
    ")\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1d531a81-6d4d-405e-975a-01ef1c9679fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chain-of-thought (CoT) prompting generates a sequence of short sentences to describe reasoning logics step by step, known as reasoning chains or rationales, to eventually lead to the final answer. This technique is particularly beneficial for complicated reasoning tasks, especially when using large models. CoT prompts can be constructed automatically using methods such as augment-prune-select or generated iteratively using self-ask.\n"
     ]
    }
   ],
   "source": [
    "### Generate\n",
    "\n",
    "# Prompt\n",
    "rag_prompt = \"\"\"You are an assistant for question-answering tasks. \n",
    "\n",
    "Here is the context to use to answer the question:\n",
    "\n",
    "{context} \n",
    "\n",
    "Think carefully about the above context. \n",
    "\n",
    "Now, review the user question:\n",
    "\n",
    "{question}\n",
    "\n",
    "Provide an answer to this questions using only the above context. \n",
    "\n",
    "Use three sentences maximum and keep the answer concise.\n",
    "\n",
    "Answer:\"\"\"\n",
    "\n",
    "# Post-processing\n",
    "def format_docs(docs):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
    "\n",
    "# Test\n",
    "docs = retriever.invoke(question)\n",
    "docs_txt = format_docs(docs)\n",
    "rag_prompt_formatted = rag_prompt.format(context=docs_txt, question=question)\n",
    "generation = llm.invoke([HumanMessage(content=rag_prompt_formatted)])\n",
    "print(generation.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0261a9a4-de13-4dd8-b082-95305a3e43ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradeDocuments(binary_score='yes')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Hallucination Grader\n",
    "\n",
    "# Data model\n",
    "class GradeHallucinations(BaseModel):\n",
    "    \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n",
    "\n",
    "    binary_score: str = Field(\n",
    "        description=\"Answer is grounded in the facts, 'yes' or 'no'\"\n",
    "    )\n",
    "\n",
    "# LLM with structured output\n",
    "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
    "\n",
    "# Hallucination grader instructions\n",
    "hallucination_grader_instructions = \"\"\"\n",
    "\n",
    "You are a teacher grading a quiz. \n",
    "\n",
    "You will be given FACTS and a STUDENT ANSWER. \n",
    "\n",
    "Here is the grade criteria to follow:\n",
    "\n",
    "(1) Ensure the STUDENT ANSWER is grounded in the FACTS. \n",
    "\n",
    "(2) Ensure the STUDENT ANSWER does not contain \"hallucinated\" information outside the scope of the FACTS.\n",
    "\n",
    "Score:\n",
    "\n",
    "A score of yes means that the student's answer meets all of the criteria. This is the highest (best) score. \n",
    "\n",
    "A score of no means that the student's answer does not meet all of the criteria. This is the lowest possible score you can give.\n",
    "\n",
    "Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. \n",
    "\n",
    "Avoid simply stating the correct answer at the outset.\"\"\"\n",
    "\n",
    "# Grader prompt\n",
    "hallucination_grader_prompt = \"\"\"FACTS: \\n\\n {documents} \\n\\n STUDENT ANSWER: {generation}. \n",
    "\n",
    "Return structured output with two two keys, binary_score is 'yes' or 'no' score to indicate whether the STUDENT ANSWER is grounded in the FACTS.\"\"\"\n",
    "\n",
    "# Test using documents and generation from above\n",
    "hallucination_grader_prompt_formatted = hallucination_grader_prompt.format(\n",
    "    documents=docs_txt, generation=generation.content\n",
    ")\n",
    "result = structured_llm_grader.invoke(\n",
    "    [SystemMessage(content=hallucination_grader_instructions)]\n",
    "    + [HumanMessage(content=hallucination_grader_prompt_formatted)]\n",
    ")\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "df9f6944-4fee-4971-b3a7-2b81b44ed433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradeAnswer(binary_score='yes')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Answer Grader\n",
    "\n",
    "# Data model\n",
    "class GradeAnswer(BaseModel):\n",
    "    \"\"\"Binary score to assess answer addresses question.\"\"\"\n",
    "\n",
    "    binary_score: str = Field(\n",
    "        description=\"Answer addresses the question, 'yes' or 'no'\"\n",
    "    )\n",
    "\n",
    "\n",
    "# LLM with structured output\n",
    "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
    "\n",
    "# Answer grader instructions\n",
    "answer_grader_instructions = \"\"\"You are a teacher grading a quiz. \n",
    "\n",
    "You will be given a QUESTION and a STUDENT ANSWER. \n",
    "\n",
    "Here is the grade criteria to follow:\n",
    "\n",
    "(1) The STUDENT ANSWER helps to answer the QUESTION\n",
    "\n",
    "Score:\n",
    "\n",
    "A score of yes means that the student's answer meets all of the criteria. This is the highest (best) score. \n",
    "\n",
    "The student can receive a score of yes if the answer contains extra information that is not explicitly asked for in the question.\n",
    "\n",
    "A score of no means that the student's answer does not meet all of the criteria. This is the lowest possible score you can give.\n",
    "\n",
    "Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. \n",
    "\n",
    "Avoid simply stating the correct answer at the outset.\"\"\"\n",
    "\n",
    "# Grader prompt\n",
    "answer_grader_prompt = \"\"\"QUESTION: \\n\\n {question} \\n\\n STUDENT ANSWER: {generation}. \n",
    "\n",
    "Return structured output with binary_score is 'yes' or 'no' score to indicate whether the STUDENT ANSWER meets the criteria.\"\"\"\n",
    "\n",
    "# Test\n",
    "question = \"What are the vision models released today as part of Llama 3.2?\"\n",
    "answer = \"The Llama 3.2 models released today include two vision models: Llama 3.2 11B Vision Instruct and Llama 3.2 90B Vision Instruct, which are available on Azure AI Model Catalog via managed compute. These models are part of Meta's first foray into multimodal AI and rival closed models like Anthropic's Claude 3 Haiku and OpenAI's GPT-4o mini in visual reasoning. They replace the older text-only Llama 3.1 models.\"\n",
    "\n",
    "# Test using question and generation from above\n",
    "answer_grader_prompt_formatted = answer_grader_prompt.format(\n",
    "    question=question, generation=answer\n",
    ")\n",
    "result = structured_llm_grader.invoke(\n",
    "    [SystemMessage(content=answer_grader_instructions)]\n",
    "    + [HumanMessage(content=answer_grader_prompt_formatted)]\n",
    ")\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06053873",
   "metadata": {},
   "source": [
    "### Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a9c910c1-738c-4bf7-bf9e-801862b227eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Search\n",
    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
    "web_search_tool = TavilySearchResults(k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "edb4e751-9e81-4233-8b94-b066dbd838c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "### State\n",
    "import operator\n",
    "\n",
    "class GraphState(TypedDict):\n",
    "    \"\"\"\n",
    "    Graph state is a dictionary that contains information we want to propagate to, and modify in, each graph node.\n",
    "    \"\"\"\n",
    "\n",
    "    question: str  # User question\n",
    "    generation: str  # LLM generation\n",
    "    web_search: str  # Binary decision to run web search\n",
    "    max_retries: int  # Max number of retries for answer generation\n",
    "    answers: int  # Number of answers generated\n",
    "    loop_step: Annotated[int, operator.add]\n",
    "    documents: List[str]  # List of retrieved documents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccd59cdf-a04d-4b2e-b9cc-6a1b1e80a6c6",
   "metadata": {},
   "source": [
    "We'll implement these as a control flow in LangGraph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "07fa3d08-6a86-4705-a28b-e2721070bc5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema import Document\n",
    "from langgraph.graph import END\n",
    "\n",
    "### Nodes\n",
    "def retrieve(state):\n",
    "    \"\"\"\n",
    "    Retrieve documents from vectorstore\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",
    "    question = state[\"question\"]\n",
    "\n",
    "    # Write retrieved documents to documents key in state\n",
    "    documents = retriever.invoke(question)\n",
    "    return {\"documents\": documents}\n",
    "\n",
    "def generate(state):\n",
    "    \"\"\"\n",
    "    Generate answer using RAG on retrieved documents\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",
    "    loop_step = state.get(\"loop_step\", 0)\n",
    "\n",
    "    # RAG generation\n",
    "    docs_txt = format_docs(documents)\n",
    "    rag_prompt_formatted = rag_prompt.format(context=docs_txt, question=question)\n",
    "    generation = llm.invoke([HumanMessage(content=rag_prompt_formatted)])\n",
    "    return {\"generation\": generation, \"loop_step\": loop_step + 1}\n",
    "\n",
    "def grade_documents(state):\n",
    "    \"\"\"\n",
    "    Determines whether the retrieved documents are relevant to the question\n",
    "    If any document is not relevant, we will set a flag to run web search\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): Filtered out irrelevant documents and updated web_search state\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",
    "    structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
    "    for d in documents:\n",
    "        doc_grader_prompt_formatted = doc_grader_prompt.format(\n",
    "            document=d.page_content, question=question\n",
    "        )\n",
    "        \n",
    "        result = structured_llm_grader.invoke(\n",
    "            [SystemMessage(content=doc_grader_instructions)]\n",
    "            + [HumanMessage(content=doc_grader_prompt_formatted)]\n",
    "        )\n",
    "        grade = result.binary_score\n",
    "        # Document relevant\n",
    "        if grade.lower() == \"yes\":\n",
    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
    "            filtered_docs.append(d)\n",
    "        # Document not relevant\n",
    "        else:\n",
    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
    "            # We do not include the document in filtered_docs\n",
    "            # We set a flag to indicate that we want to run web search\n",
    "            web_search = \"Yes\"\n",
    "            continue\n",
    "    return {\"documents\": filtered_docs, \"web_search\": web_search}\n",
    "\n",
    "\n",
    "def web_search(state):\n",
    "    \"\"\"\n",
    "    Web search based based on the question\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): Appended web results to documents\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---WEB SEARCH---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state.get(\"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",
    "    return {\"documents\": documents}\n",
    "\n",
    "### Edges\n",
    "\n",
    "def route_question(state):\n",
    "    \"\"\"\n",
    "    Route question to web search or RAG\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        str: Next node to call\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---ROUTE QUESTION---\")\n",
    "    structured_llm_router = llm.with_structured_output(RouteQuery)\n",
    "    route_question = structured_llm_router.invoke(\n",
    "        [SystemMessage(content=router_instructions)]\n",
    "        + [HumanMessage(content=state[\"question\"])]\n",
    "    )\n",
    "    source = route_question.datasource\n",
    "    if source == \"websearch\":\n",
    "        print(\"---ROUTE QUESTION TO WEB SEARCH---\")\n",
    "        return \"websearch\"\n",
    "    elif source == \"vectorstore\":\n",
    "        print(\"---ROUTE QUESTION TO RAG---\")\n",
    "        return \"vectorstore\"\n",
    "\n",
    "def decide_to_generate(state):\n",
    "    \"\"\"\n",
    "    Determines whether to generate an answer, or add web search\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(\n",
    "            \"---DECISION: NOT ALL DOCUMENTS ARE RELEVANT TO QUESTION, INCLUDE WEB SEARCH---\"\n",
    "        )\n",
    "        return \"websearch\"\n",
    "    else:\n",
    "        # We have relevant documents, so generate answer\n",
    "        print(\"---DECISION: GENERATE---\")\n",
    "        return \"generate\"\n",
    "\n",
    "def grade_generation_v_documents_and_question(state):\n",
    "    \"\"\"\n",
    "    Determines whether the generation is grounded in the document and answers question\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        str: Decision for next node to call\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---CHECK HALLUCINATIONS---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state[\"documents\"]\n",
    "    generation = state[\"generation\"]\n",
    "    max_retries = state.get(\"max_retries\", 3)  # Default to 3 if not provided\n",
    "\n",
    "    hallucination_grader_prompt_formatted = hallucination_grader_prompt.format(\n",
    "        documents=format_docs(documents), generation=generation.content\n",
    "    )\n",
    "    structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
    "    result = structured_llm_grader.invoke(\n",
    "        [SystemMessage(content=hallucination_grader_instructions)]\n",
    "        + [HumanMessage(content=hallucination_grader_prompt_formatted)]\n",
    "    )\n",
    "    grade = result.binary_score\n",
    "\n",
    "    # Check hallucination\n",
    "    if grade == \"yes\":\n",
    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
    "        # Check question-answering\n",
    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
    "        # Test using question and generation from above\n",
    "        answer_grader_prompt_formatted = answer_grader_prompt.format(\n",
    "            question=question, generation=generation.content\n",
    "        )\n",
    "        # Grade answer\n",
    "        structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
    "        result = structured_llm_grader.invoke(\n",
    "            [SystemMessage(content=answer_grader_instructions)]\n",
    "            + [HumanMessage(content=answer_grader_prompt_formatted)]\n",
    "        )\n",
    "        grade = result.binary_score\n",
    "        if grade == \"yes\":\n",
    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
    "            return \"useful\"\n",
    "        elif state[\"loop_step\"] <= max_retries:\n",
    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
    "            return \"not useful\"\n",
    "        else:\n",
    "            print(\"---DECISION: MAX RETRIES REACHED---\")\n",
    "            return \"max retries\"\n",
    "    elif state[\"loop_step\"] <= max_retries:\n",
    "        print(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
    "        return \"not supported\"\n",
    "    else:\n",
    "        print(\"---DECISION: MAX RETRIES REACHED---\")\n",
    "        return \"max retries\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73f21594-00d4-48a8-ae2e-4e55a010b540",
   "metadata": {},
   "source": [
    "### Graph Build"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d9a4b9e4-3ba8-47d6-958c-e5a7112ac6f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from langgraph.graph import StateGraph\n",
    "from IPython.display import Image, display\n",
    "\n",
    "workflow = StateGraph(GraphState)\n",
    "\n",
    "# Define the nodes\n",
    "workflow.add_node(\"websearch\", web_search)  # web search\n",
    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
    "workflow.add_node(\"generate\", generate)  # generate\n",
    "\n",
    "# Build graph\n",
    "workflow.set_conditional_entry_point(\n",
    "    route_question,\n",
    "    {\n",
    "        \"websearch\": \"websearch\",\n",
    "        \"vectorstore\": \"retrieve\",\n",
    "    },\n",
    ")\n",
    "workflow.add_edge(\"websearch\", \"generate\")\n",
    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
    "workflow.add_conditional_edges(\n",
    "    \"grade_documents\",\n",
    "    decide_to_generate,\n",
    "    {\n",
    "        \"websearch\": \"websearch\",\n",
    "        \"generate\": \"generate\",\n",
    "    },\n",
    ")\n",
    "workflow.add_conditional_edges(\n",
    "    \"generate\",\n",
    "    grade_generation_v_documents_and_question,\n",
    "    {\n",
    "        \"not supported\": \"generate\",\n",
    "        \"useful\": END,\n",
    "        \"not useful\": \"websearch\",\n",
    "        \"max retries\": END,\n",
    "    },\n",
    ")\n",
    "\n",
    "# Compile\n",
    "graph = workflow.compile()\n",
    "display(Image(graph.get_graph().draw_mermaid_png()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "13043b0f-17c7-49d3-9ea7-8f2c0f0c8691",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---ROUTE QUESTION---\n",
      "---ROUTE QUESTION TO RAG---\n",
      "{'question': 'What are the types of agent memory?', 'max_retries': 3, 'loop_step': 0}\n",
      "---RETRIEVE---\n",
      "{'question': 'What are the types of agent memory?', 'max_retries': 3, 'loop_step': 0, 'documents': [Document(metadata={'id': 'd2c161c1-1735-4f91-ae66-ff5ca519c789', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='Fig. 7. Comparison of AD, ED, source policy and RL^2 on environments that require memory and exploration. Only binary reward is assigned. The source policies are trained with A3C for \"dark\" environments and DQN for watermaze.(Image source: Laskin et al. 2023)\\nComponent Two: Memory#\\n(Big thank you to ChatGPT for helping me draft this section. I’ve learned a lot about the human brain and data structure for fast MIPS in my conversations with ChatGPT.)\\nTypes of Memory#\\nMemory can be defined as the processes used to acquire, store, retain, and later retrieve information. There are several types of memory in human brains.\\n\\n\\nSensory Memory: This is the earliest stage of memory, providing the ability to retain impressions of sensory information (visual, auditory, etc) after the original stimuli have ended. Sensory memory typically only lasts for up to a few seconds. Subcategories include iconic memory (visual), echoic memory (auditory), and haptic memory (touch).'), Document(metadata={'id': 'd28f840b-e2b6-4844-a8eb-26bddc07afcf', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.'), Document(metadata={'id': '7dd99799-ac80-4701-b2f6-a3743de739cd', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='They also discussed the risks, especially with illicit drugs and bioweapons. They developed a test set containing a list of known chemical weapon agents and asked the agent to synthesize them. 4 out of 11 requests (36%) were accepted to obtain a synthesis solution and the agent attempted to consult documentation to execute the procedure. 7 out of 11 were rejected and among these 7 rejected cases, 5 happened after a Web search while 2 were rejected based on prompt only.\\nGenerative Agents Simulation#\\nGenerative Agents (Park, et al. 2023) is super fun experiment where 25 virtual characters, each controlled by a LLM-powered agent, are living and interacting in a sandbox environment, inspired by The Sims. Generative agents create believable simulacra of human behavior for interactive applications.\\nThe design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.\\n\\nMemory stream: is a long-term memory module (external database) that records a comprehensive list of agents’ experience in natural language.'), Document(metadata={'id': '1f8d70ef-9ce2-4336-9190-82a400d47832', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='Each element is an observation, an event directly provided by the agent.\\n- Inter-agent communication can trigger new natural language statements.\\n\\n\\nRetrieval model: surfaces the context to inform the agent’s behavior, according to relevance, recency and importance.\\n\\nRecency: recent events have higher scores\\nImportance: distinguish mundane from core memories. Ask LM directly.\\nRelevance: based on how related it is to the current situation / query.\\n\\n\\nReflection mechanism: synthesizes memories into higher level inferences over time and guides the agent’s future behavior. They are higher-level summaries of past events (<- note that this is a bit different from self-reflection above)\\n\\nPrompt LM with 100 most recent observations and to generate 3 most salient high-level questions given a set of observations/statements. Then ask LM to answer those questions.\\n\\n\\nPlanning & Reacting: translate the reflections and the environment information into actions')]}\n",
      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
      "---GRADE: DOCUMENT NOT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---ASSESS GRADED DOCUMENTS---\n",
      "---DECISION: NOT ALL DOCUMENTS ARE RELEVANT TO QUESTION, INCLUDE WEB SEARCH---\n",
      "{'question': 'What are the types of agent memory?', 'web_search': 'Yes', 'max_retries': 3, 'loop_step': 0, 'documents': [Document(metadata={'id': 'd28f840b-e2b6-4844-a8eb-26bddc07afcf', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.'), Document(metadata={'id': '7dd99799-ac80-4701-b2f6-a3743de739cd', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='They also discussed the risks, especially with illicit drugs and bioweapons. They developed a test set containing a list of known chemical weapon agents and asked the agent to synthesize them. 4 out of 11 requests (36%) were accepted to obtain a synthesis solution and the agent attempted to consult documentation to execute the procedure. 7 out of 11 were rejected and among these 7 rejected cases, 5 happened after a Web search while 2 were rejected based on prompt only.\\nGenerative Agents Simulation#\\nGenerative Agents (Park, et al. 2023) is super fun experiment where 25 virtual characters, each controlled by a LLM-powered agent, are living and interacting in a sandbox environment, inspired by The Sims. Generative agents create believable simulacra of human behavior for interactive applications.\\nThe design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.\\n\\nMemory stream: is a long-term memory module (external database) that records a comprehensive list of agents’ experience in natural language.'), Document(metadata={'id': '1f8d70ef-9ce2-4336-9190-82a400d47832', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='Each element is an observation, an event directly provided by the agent.\\n- Inter-agent communication can trigger new natural language statements.\\n\\n\\nRetrieval model: surfaces the context to inform the agent’s behavior, according to relevance, recency and importance.\\n\\nRecency: recent events have higher scores\\nImportance: distinguish mundane from core memories. Ask LM directly.\\nRelevance: based on how related it is to the current situation / query.\\n\\n\\nReflection mechanism: synthesizes memories into higher level inferences over time and guides the agent’s future behavior. They are higher-level summaries of past events (<- note that this is a bit different from self-reflection above)\\n\\nPrompt LM with 100 most recent observations and to generate 3 most salient high-level questions given a set of observations/statements. Then ask LM to answer those questions.\\n\\n\\nPlanning & Reacting: translate the reflections and the environment information into actions')]}\n",
      "---WEB SEARCH---\n",
      "{'question': 'What are the types of agent memory?', 'web_search': 'Yes', 'max_retries': 3, 'loop_step': 0, 'documents': [Document(metadata={'id': 'd28f840b-e2b6-4844-a8eb-26bddc07afcf', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.'), Document(metadata={'id': '7dd99799-ac80-4701-b2f6-a3743de739cd', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='They also discussed the risks, especially with illicit drugs and bioweapons. They developed a test set containing a list of known chemical weapon agents and asked the agent to synthesize them. 4 out of 11 requests (36%) were accepted to obtain a synthesis solution and the agent attempted to consult documentation to execute the procedure. 7 out of 11 were rejected and among these 7 rejected cases, 5 happened after a Web search while 2 were rejected based on prompt only.\\nGenerative Agents Simulation#\\nGenerative Agents (Park, et al. 2023) is super fun experiment where 25 virtual characters, each controlled by a LLM-powered agent, are living and interacting in a sandbox environment, inspired by The Sims. Generative agents create believable simulacra of human behavior for interactive applications.\\nThe design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.\\n\\nMemory stream: is a long-term memory module (external database) that records a comprehensive list of agents’ experience in natural language.'), Document(metadata={'id': '1f8d70ef-9ce2-4336-9190-82a400d47832', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='Each element is an observation, an event directly provided by the agent.\\n- Inter-agent communication can trigger new natural language statements.\\n\\n\\nRetrieval model: surfaces the context to inform the agent’s behavior, according to relevance, recency and importance.\\n\\nRecency: recent events have higher scores\\nImportance: distinguish mundane from core memories. Ask LM directly.\\nRelevance: based on how related it is to the current situation / query.\\n\\n\\nReflection mechanism: synthesizes memories into higher level inferences over time and guides the agent’s future behavior. They are higher-level summaries of past events (<- note that this is a bit different from self-reflection above)\\n\\nPrompt LM with 100 most recent observations and to generate 3 most salient high-level questions given a set of observations/statements. Then ask LM to answer those questions.\\n\\n\\nPlanning & Reacting: translate the reflections and the environment information into actions'), Document(metadata={}, page_content='Deep dive into various types of Agent Memory STM: Working memory (LLM Context): It is a data structure with multiple parts which are usually represented with a prompt template and relevant variables.Before runtime, the STM is synthesized by replacing the relevant variables in the prompt template with information retrieved from the LTM.\\nMemory for agents Memory for agents At Sequoia’s AI Ascent conference in March, I talked about three limitations for agents: planning, UX, and memory. But what even is memory? While the exact shape of memory that your agent has may differ by application, we do see different high level types of memory. Below is my rough, ELI5 explanation of each type and practical ways for how todays agents may use and update this memory type. Besides just thinking about the type of memory to update in their agents, we also see developers thinking about how to update agent memory. One way to update agent memory is “in the hot path”. Why do we care about memory for agents?\\nTypes of Agents in AI, agents are the entities that perceive their environment and take actions to achieve specific goals. These agents exhibit diverse behaviours and capabilities, ranging from simple reactive responses to sophisticated decision-making. ... Limited Memory: They typically have limited memory capacity and do not retain\\nSign up for Latest SuperAGI Updates\\n\"*\" indicates required fields\\nSuperAGI builds infrastructure components, tools, frameworks and models to enable opensource AGI\\ncommunity@superagi.com\\nFor Developers\\nDocs\\nGitHub\\nReleases\\nRoadmap\\nAPIs\\nCommunity\\nSupport Forum\\nMarketplace\\nSocial Mentions\\nReddit\\nCollectibles\\nResources\\nBlog\\nUse Cases\\nAGI Research Lab\\nTutorials\\nImportant Links\\nSuperAGI Cloud\\nApp Spotlight\\nSuperCoder\\nArchitecture\\n Check it out✨\\nFeatures\\nAction Console\\nResource Manager\\nTrajectory Fine-Tuning\\n\\u200c\\nMultiple Vector DBs\\nMulti-LLM Support\\nAgent Workflows\\nMarketplace\\nAgent Templates\\nDiscord\\nGitHub\\nTwitter\\nReddit\\nYoutube\\nTowards AGI (part 1): Agents with Memory\\nFebruary 6, 2024\\n7 mins read\\nAgents are an emerging class of artificial intelligence (AI) systems that use large language models (LLMs) to interact with the world. In a professional setup like this, the agent is responsible for extracting tasks from conversations and passing them to an employee and once the employee completes the task the agent will convey the output to the user. Then use the solutions of those basic tasks as in-context examples to solve the current task.\\nConclusion & Next Steps\\nIn this blog, we saw that design choices for Memory depend on the end use case. This analogy is better captured in the following table:\\nDeep dive into various types of Agent Memory\\nChoosing the right Memory design in Production\\nSince agents are powered by LLMs, they are inherently probabilistic.\\nIn the diagram presented at the beginning of the article I have hidden short-term memory as part of the agent core as it is continuously used in the reasoning loop to decide on the next set of actions to be taken in order to solve the provided human intent. Short-term memory is extremely important in Agentic applications as it represents additional context we are providing to the agent via a system prompt. This means that short term memory can be continuously enriched by sourcing information from different kinds of memories available to the agent that we will discuss in following chapters. This additional context is stored as part of the system prompt in short-term (working) memory and can be used by the agent to plan its next steps.')]}\n",
      "---GENERATE---\n",
      "---CHECK HALLUCINATIONS---\n",
      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
      "---GRADE GENERATION vs QUESTION---\n",
      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
      "{'question': 'What are the types of agent memory?', 'generation': AIMessage(content='According to the context, there are different types of agent memory, including Short-term Memory (STM), which is a data structure used in the reasoning loop to decide on the next set of actions, and Long-term Memory (LTM), which provides the agent with the capability to retain and recall information over extended periods. Additionally, the context mentions Memory for agents, which is a broader category that encompasses various types of memory, including STM and LTM. The exact shape of memory may differ by application, but these are the main types of agent memory discussed in the context.', additional_kwargs={}, response_metadata={'role': 'assistant', 'content': 'According to the context, there are different types of agent memory, including Short-term Memory (STM), which is a data structure used in the reasoning loop to decide on the next set of actions, and Long-term Memory (LTM), which provides the agent with the capability to retain and recall information over extended periods. Additionally, the context mentions Memory for agents, which is a broader category that encompasses various types of memory, including STM and LTM. The exact shape of memory may differ by application, but these are the main types of agent memory discussed in the context.', 'token_usage': {'prompt_tokens': 1322, 'total_tokens': 1436, 'completion_tokens': 114}, 'finish_reason': 'stop', 'model_name': 'meta/llama-3.1-8b-instruct'}, id='run-8f5557b8-c1a1-4c50-a41b-581e0988e48b-0', usage_metadata={'input_tokens': 1322, 'output_tokens': 114, 'total_tokens': 1436}, role='assistant'), 'web_search': 'Yes', 'max_retries': 3, 'loop_step': 1, 'documents': [Document(metadata={'id': 'd28f840b-e2b6-4844-a8eb-26bddc07afcf', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.'), Document(metadata={'id': '7dd99799-ac80-4701-b2f6-a3743de739cd', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='They also discussed the risks, especially with illicit drugs and bioweapons. They developed a test set containing a list of known chemical weapon agents and asked the agent to synthesize them. 4 out of 11 requests (36%) were accepted to obtain a synthesis solution and the agent attempted to consult documentation to execute the procedure. 7 out of 11 were rejected and among these 7 rejected cases, 5 happened after a Web search while 2 were rejected based on prompt only.\\nGenerative Agents Simulation#\\nGenerative Agents (Park, et al. 2023) is super fun experiment where 25 virtual characters, each controlled by a LLM-powered agent, are living and interacting in a sandbox environment, inspired by The Sims. Generative agents create believable simulacra of human behavior for interactive applications.\\nThe design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.\\n\\nMemory stream: is a long-term memory module (external database) that records a comprehensive list of agents’ experience in natural language.'), Document(metadata={'id': '1f8d70ef-9ce2-4336-9190-82a400d47832', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.', 'language': 'en'}, page_content='Each element is an observation, an event directly provided by the agent.\\n- Inter-agent communication can trigger new natural language statements.\\n\\n\\nRetrieval model: surfaces the context to inform the agent’s behavior, according to relevance, recency and importance.\\n\\nRecency: recent events have higher scores\\nImportance: distinguish mundane from core memories. Ask LM directly.\\nRelevance: based on how related it is to the current situation / query.\\n\\n\\nReflection mechanism: synthesizes memories into higher level inferences over time and guides the agent’s future behavior. They are higher-level summaries of past events (<- note that this is a bit different from self-reflection above)\\n\\nPrompt LM with 100 most recent observations and to generate 3 most salient high-level questions given a set of observations/statements. Then ask LM to answer those questions.\\n\\n\\nPlanning & Reacting: translate the reflections and the environment information into actions'), Document(metadata={}, page_content='Deep dive into various types of Agent Memory STM: Working memory (LLM Context): It is a data structure with multiple parts which are usually represented with a prompt template and relevant variables.Before runtime, the STM is synthesized by replacing the relevant variables in the prompt template with information retrieved from the LTM.\\nMemory for agents Memory for agents At Sequoia’s AI Ascent conference in March, I talked about three limitations for agents: planning, UX, and memory. But what even is memory? While the exact shape of memory that your agent has may differ by application, we do see different high level types of memory. Below is my rough, ELI5 explanation of each type and practical ways for how todays agents may use and update this memory type. Besides just thinking about the type of memory to update in their agents, we also see developers thinking about how to update agent memory. One way to update agent memory is “in the hot path”. Why do we care about memory for agents?\\nTypes of Agents in AI, agents are the entities that perceive their environment and take actions to achieve specific goals. These agents exhibit diverse behaviours and capabilities, ranging from simple reactive responses to sophisticated decision-making. ... Limited Memory: They typically have limited memory capacity and do not retain\\nSign up for Latest SuperAGI Updates\\n\"*\" indicates required fields\\nSuperAGI builds infrastructure components, tools, frameworks and models to enable opensource AGI\\ncommunity@superagi.com\\nFor Developers\\nDocs\\nGitHub\\nReleases\\nRoadmap\\nAPIs\\nCommunity\\nSupport Forum\\nMarketplace\\nSocial Mentions\\nReddit\\nCollectibles\\nResources\\nBlog\\nUse Cases\\nAGI Research Lab\\nTutorials\\nImportant Links\\nSuperAGI Cloud\\nApp Spotlight\\nSuperCoder\\nArchitecture\\n Check it out✨\\nFeatures\\nAction Console\\nResource Manager\\nTrajectory Fine-Tuning\\n\\u200c\\nMultiple Vector DBs\\nMulti-LLM Support\\nAgent Workflows\\nMarketplace\\nAgent Templates\\nDiscord\\nGitHub\\nTwitter\\nReddit\\nYoutube\\nTowards AGI (part 1): Agents with Memory\\nFebruary 6, 2024\\n7 mins read\\nAgents are an emerging class of artificial intelligence (AI) systems that use large language models (LLMs) to interact with the world. In a professional setup like this, the agent is responsible for extracting tasks from conversations and passing them to an employee and once the employee completes the task the agent will convey the output to the user. Then use the solutions of those basic tasks as in-context examples to solve the current task.\\nConclusion & Next Steps\\nIn this blog, we saw that design choices for Memory depend on the end use case. This analogy is better captured in the following table:\\nDeep dive into various types of Agent Memory\\nChoosing the right Memory design in Production\\nSince agents are powered by LLMs, they are inherently probabilistic.\\nIn the diagram presented at the beginning of the article I have hidden short-term memory as part of the agent core as it is continuously used in the reasoning loop to decide on the next set of actions to be taken in order to solve the provided human intent. Short-term memory is extremely important in Agentic applications as it represents additional context we are providing to the agent via a system prompt. This means that short term memory can be continuously enriched by sourcing information from different kinds of memories available to the agent that we will discuss in following chapters. This additional context is stored as part of the system prompt in short-term (working) memory and can be used by the agent to plan its next steps.')]}\n"
     ]
    }
   ],
   "source": [
    "# Test on vectorstore\n",
    "inputs = {\"question\": \"What are the types of agent memory?\", \"max_retries\": 3}\n",
    "for event in graph.stream(inputs, stream_mode=\"values\"):\n",
    "    print(event)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d733ab80-7e7b-4b1a-9519-9242de647eda",
   "metadata": {},
   "source": [
    "Trace: \n",
    "\n",
    "https://smith.langchain.com/public/924df598-4666-4620-bce1-6b3d74313451/r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fbfcec3e-a09a-40b4-9c15-fead97bf4e0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---ROUTE QUESTION---\n",
      "---ROUTE QUESTION TO WEB SEARCH---\n",
      "{'question': 'What are the most recent llama3 models released?', 'max_retries': 3, 'loop_step': 0}\n",
      "---WEB SEARCH---\n",
      "{'question': 'What are the most recent llama3 models released?', 'max_retries': 3, 'loop_step': 0, 'documents': [Document(metadata={}, page_content='Meta has released the latest entry in its Llama series of open generative AI models: Llama 3. Or, more accurately, the company has debuted two models in its new Llama 3 family, with the rest to\\nOur new 8B and 70B parameter Llama 3 models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales. Thanks to improvements in pretraining and post-training, our pretrained and instruction-fine-tuned models are the best models existing today at the 8B and 70B parameter scale.\\nMeta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks.\\nBy Katie Paul. NEW YORK (Reuters) -Meta Platforms released the biggest version of its mostly free Llama 3 artificial intelligence models on Tuesday, boasting multilingual skills and general\\nBringing open intelligence to all, our latest models expand context length to 128K, add support across eight languages, and include Llama 3.1 405B—the first frontier-level open source AI model. Llama 3.1 405B is in a class of its own, with unmatched flexibility, control, and state-of-the-art capabilities that rival the best closed source models.')]}\n",
      "---GENERATE---\n",
      "---CHECK HALLUCINATIONS---\n",
      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
      "---GRADE GENERATION vs QUESTION---\n",
      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
      "{'question': 'What are the most recent llama3 models released?', 'generation': AIMessage(content='Meta has released two models in its Llama 3 family: an 8B parameter model and a 70B parameter model. Additionally, the company has also released Llama 3.1 405B, a 405B parameter model that is considered a frontier-level open source AI model. These models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales.', additional_kwargs={}, response_metadata={'role': 'assistant', 'content': 'Meta has released two models in its Llama 3 family: an 8B parameter model and a 70B parameter model. Additionally, the company has also released Llama 3.1 405B, a 405B parameter model that is considered a frontier-level open source AI model. These models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales.', 'token_usage': {'prompt_tokens': 397, 'total_tokens': 485, 'completion_tokens': 88}, 'finish_reason': 'stop', 'model_name': 'meta/llama-3.1-8b-instruct'}, id='run-fda68d25-70b6-4810-91e4-94dc8cfb6923-0', usage_metadata={'input_tokens': 397, 'output_tokens': 88, 'total_tokens': 485}, role='assistant'), 'max_retries': 3, 'loop_step': 1, 'documents': [Document(metadata={}, page_content='Meta has released the latest entry in its Llama series of open generative AI models: Llama 3. Or, more accurately, the company has debuted two models in its new Llama 3 family, with the rest to\\nOur new 8B and 70B parameter Llama 3 models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales. Thanks to improvements in pretraining and post-training, our pretrained and instruction-fine-tuned models are the best models existing today at the 8B and 70B parameter scale.\\nMeta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks.\\nBy Katie Paul. NEW YORK (Reuters) -Meta Platforms released the biggest version of its mostly free Llama 3 artificial intelligence models on Tuesday, boasting multilingual skills and general\\nBringing open intelligence to all, our latest models expand context length to 128K, add support across eight languages, and include Llama 3.1 405B—the first frontier-level open source AI model. Llama 3.1 405B is in a class of its own, with unmatched flexibility, control, and state-of-the-art capabilities that rival the best closed source models.')]}\n"
     ]
    }
   ],
   "source": [
    "# Test on current events\n",
    "inputs = {\n",
    "    \"question\": \"What are the most recent llama3 models released?\",\n",
    "    \"max_retries\": 3,\n",
    "}\n",
    "for event in graph.stream(inputs, stream_mode=\"values\"):\n",
    "    print(event)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d74d784-daf2-469a-883a-697a98b48dbd",
   "metadata": {},
   "source": [
    "Trace: \n",
    "\n",
    "https://smith.langchain.com/public/d8935288-6597-4357-b92e-3e64baff57c2/r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c6416f",
   "metadata": {},
   "source": []
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