diff --git "a/langgraph/source/examples/rag/langgraph_self_rag_local.ipynb" "b/langgraph/source/examples/rag/langgraph_self_rag_local.ipynb"
new file mode 100644--- /dev/null
+++ "b/langgraph/source/examples/rag/langgraph_self_rag_local.ipynb"
@@ -0,0 +1,755 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "345488d8",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
+  {
+   "attachments": {
+    "5fca0a3e-d13d-4bfa-95ea-58203640cc7a.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "848ba742-7443-4123-8115-061da9823309",
+   "metadata": {},
+   "source": [
+    "# Self-RAG using local LLMs\n",
+    "\n",
+    "Self-RAG is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents and generations. \n",
+    "\n",
+    "In the [paper](https://arxiv.org/abs/2310.11511), a few decisions are made:\n",
+    "\n",
+    "1. Should I retrieve from retriever, `R` -\n",
+    "\n",
+    "* Input: `x (question)` OR `x (question)`, `y (generation)`\n",
+    "* Decides when to retrieve `D` chunks with `R`\n",
+    "* Output: `yes, no, continue`\n",
+    "\n",
+    "2. Are the retrieved passages `D` relevant to the question `x` -\n",
+    "\n",
+    "* * Input: (`x (question)`, `d (chunk)`) for `d` in `D`\n",
+    "* `d` provides useful information to solve `x`\n",
+    "* Output: `relevant, irrelevant`\n",
+    "\n",
+    "3. Are the LLM generation from each chunk in `D` is relevant to the chunk (hallucinations, etc)  -\n",
+    "\n",
+    "* Input: `x (question)`, `d (chunk)`,  `y (generation)` for `d` in `D`\n",
+    "* All of the verification-worthy statements in `y (generation)` are supported by `d`\n",
+    "* Output: `{fully supported, partially supported, no support`\n",
+    "\n",
+    "4. The LLM generation from each chunk in `D` is a useful response to `x (question)` -\n",
+    "\n",
+    "* Input: `x (question)`, `y (generation)` for `d` in `D`\n",
+    "* `y (generation)` is a useful response to `x (question)`.\n",
+    "* Output: `{5, 4, 3, 2, 1}`\n",
+    "\n",
+    "We will implement some of these ideas from scratch using [LangGraph](https://langchain-ai.github.io/langgraph/).\n",
+    "\n",
+    "![Screenshot 2024-04-01 at 12.42.59 PM.png](attachment:5fca0a3e-d13d-4bfa-95ea-58203640cc7a.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ed0a85a-a33b-40a6-99fa-2444bf57a6cc",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First let's install our required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7f9cc6d-a70c-433a-b0ad-ea47c5a0717e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%capture --no-stderr\n",
+    "%pip install -U langchain-nomic langchain_community tiktoken langchainhub chromadb langchain langgraph nomic[local]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71c540ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(key: str):\n",
+    "    if key not in os.environ:\n",
+    "        os.environ[key] = getpass.getpass(f\"{key}:\")\n",
+    "\n",
+    "\n",
+    "_set_env(\"NOMIC_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05e8cf60",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccc6b6bd-a2fa-4a43-83b7-704b8b6fb855",
+   "metadata": {},
+   "source": [
+    "### LLMs\n",
+    "\n",
+    "#### Local Embeddings\n",
+    "\n",
+    "You can use `GPT4AllEmbeddings()` from Nomic, which can access use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n",
+    "\n",
+    "\n",
+    "Follow the documentation [here](https://docs.gpt4all.io/gpt4all_python_embedding.html#supported-embedding-models).\n",
+    "\n",
+    "#### Local LLM\n",
+    "\n",
+    "(1) Download [Ollama app](https://ollama.ai/).\n",
+    "\n",
+    "(2) Download a `Mistral` model from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available.\n",
+    "```\n",
+    "ollama pull mistral\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bedffc73-6b10-42c8-8768-2085c8ed3398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ollama model name\n",
+    "local_llm = \"mistral\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba68a46d-b617-4fdc-9113-fabdcf736feb",
+   "metadata": {},
+   "source": [
+    "## Create Index\n",
+    "\n",
+    "Let's index 3 blog posts."
+   ]
+  },
+  {
+   "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_nomic.embeddings import NomicEmbeddings\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 = Chroma.from_documents(\n",
+    "    documents=doc_splits,\n",
+    "    collection_name=\"rag-chroma\",\n",
+    "    embedding=NomicEmbeddings(model=\"nomic-embed-text-v1.5\", inference_mode=\"local\"),\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc60ff95-7e12-4004-b18e-a067a2dcc201",
+   "metadata": {},
+   "source": [
+    "## LLMs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3aad0c60-3208-48fb-af82-0024630b4da1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'score': 'yes'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Retrieval Grader\n",
+    "\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "    Here is the retrieved document: \\n\\n {document} \\n\\n\n",
+    "    Here is the user question: {question} \\n\n",
+    "    If the document contains keywords related to the user question, grade it as relevant. \\n\n",
+    "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
+    "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \\n\n",
+    "    Provide the binary score as a JSON with a single key 'score' and no premable or explanation.\"\"\",\n",
+    "    input_variables=[\"question\", \"document\"],\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = prompt | llm | JsonOutputParser()\n",
+    "question = \"agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e5e45953-248d-492f-af28-d5e80c664c95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " In an LLM-powered autonomous agent system, the Large Language Model (LLM) functions as the agent's brain. The agent has key components including memory, planning, and reflection mechanisms. The memory component is a long-term memory module that records a comprehensive list of agents’ experience in natural language. It includes a memory stream, which is an external database for storing past experiences. The reflection mechanism synthesizes memories into higher-level inferences over time and guides the agent's future behavior.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "\n",
+    "\n",
+    "# Post-processing\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "56297862-df87-42a7-ba9d-310926dfb328",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'score': 'yes'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Hallucination Grader\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 preamble or explanation.\"\"\",\n",
+    "    input_variables=[\"generation\", \"documents\"],\n",
+    ")\n",
+    "\n",
+    "hallucination_grader = prompt | llm | JsonOutputParser()\n",
+    "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e1dd9174-2df6-45b1-8e69-13381f579c39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'score': 'yes'}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Answer Grader\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 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 preamble or explanation.\"\"\",\n",
+    "    input_variables=[\"generation\", \"question\"],\n",
+    ")\n",
+    "\n",
+    "answer_grader = prompt | llm | JsonOutputParser()\n",
+    "answer_grader.invoke({\"question\": question, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5216d92b-1ca1-4bcf-a34f-c99ec2766b54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' What is agent memory and how can it be effectively utilized in vector database retrieval?'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "re_write_prompt = PromptTemplate(\n",
+    "    template=\"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
+    "     for vectorstore retrieval. Look at the initial and formulate an improved question. \\n\n",
+    "     Here is the initial question: \\n\\n {question}. Improved question with no preamble: \\n \"\"\",\n",
+    "    input_variables=[\"generation\", \"question\"],\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d3339b9-5f30-4d54-bfc9-9091c6035955",
+   "metadata": {},
+   "source": [
+    "# Graph \n",
+    "\n",
+    "Capture the flow in as a graph.\n",
+    "\n",
+    "## Graph state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "90fb1dc6-c482-483a-8441-39965c401beb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        documents: list of documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    question: str\n",
+    "    generation: str\n",
+    "    documents: List[str]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5324ea49-5745-47b5-a0a5-bf58c8babe46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### 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",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # RAG generation\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
+    "\n",
+    "\n",
+    "def grade_documents(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the retrieved documents are relevant to the question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates documents key with only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Score each doc\n",
+    "    filtered_docs = []\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke(\n",
+    "            {\"question\": question, \"document\": d.page_content}\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",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_question}\n",
+    "\n",
+    "\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 graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    state[\"question\"]\n",
+    "    filtered_documents = state[\"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(\n",
+    "            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n",
+    "        )\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_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",
+    "\n",
+    "    score = hallucination_grader.invoke(\n",
+    "        {\"documents\": documents, \"generation\": generation}\n",
+    "    )\n",
+    "    grade = score[\"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",
+    "        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
+    "        grade = score[\"score\"]\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
+    "            return \"useful\"\n",
+    "        else:\n",
+    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
+    "            return \"not useful\"\n",
+    "    else:\n",
+    "        print(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
+   ]
+  },
+  {
+   "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": 15,
+   "id": "5605dee4-b2df-46ae-a640-cc2ed90c21a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, StateGraph, START\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)  # generate\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.add_edge(START, \"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_and_question,\n",
+    "    {\n",
+    "        \"not supported\": \"generate\",\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "26a64f7d-0c14-4e31-a67f-63021dee626e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: GENERATE---\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK HALLUCINATIONS---\n",
+      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
+      "---GRADE GENERATION vs QUESTION---\n",
+      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "(' In a LLM-powered autonomous agent system, memory is a key component that '\n",
+      " 'enables agents to store and retrieve information. There are different types '\n",
+      " 'of memory in human brains, such as sensory memory which retains impressions '\n",
+      " 'of sensory information for a few seconds, and long-term memory which records '\n",
+      " \"experiences for extended periods (Lil'Log, 2023). In the context of LLM \"\n",
+      " 'agents, memory is often implemented as an external database or memory stream '\n",
+      " \"(Lil'Log, 2023). The agent can consult this memory to inform its behavior \"\n",
+      " 'based on relevance, recency, and importance. Additionally, reflection '\n",
+      " 'mechanisms synthesize memories into higher-level inferences over time and '\n",
+      " \"guide the agent's future behavior (Lil'Log, 2023).\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"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(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a27907-2611-4791-910b-0d66c59f5cf5",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/4163a342-5260-4852-8602-bda3f95177e7/r"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}