From f3aa4563042305d767b6adbaccc49db632d01939 Mon Sep 17 00:00:00 2001
From: berkecanrizai <63911408+berkecanrizai@users.noreply.github.com>
Date: Mon, 13 Jan 2025 17:15:04 +0300
Subject: [PATCH] add example notebook with langgraph (#7320)

Co-authored-by: Szymon Dudycz <szymond@pathway.com>
Co-authored-by: saksham65 <112824661+Saksham65@users.noreply.github.com>
GitOrigin-RevId: 92f6acf4548f1702ac7e09978c6d898023c249fb
---
 .../pathway_deploy_langgraph_agents.ipynb     | 1034 +++++++++++++++++
 .../pathway_langgraph_agentic_rag.ipynb       |  921 +++++++++++++++
 2 files changed, 1955 insertions(+)
 create mode 100644 cookbooks/self-rag-agents/pathway_deploy_langgraph_agents.ipynb
 create mode 100644 cookbooks/self-rag-agents/pathway_langgraph_agentic_rag.ipynb

diff --git a/cookbooks/self-rag-agents/pathway_deploy_langgraph_agents.ipynb b/cookbooks/self-rag-agents/pathway_deploy_langgraph_agents.ipynb
new file mode 100644
index 0000000..d2d168d
--- /dev/null
+++ b/cookbooks/self-rag-agents/pathway_deploy_langgraph_agents.ipynb
@@ -0,0 +1,1034 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b3e94de1-ca3d-4956-b212-4a558b68062a",
+   "metadata": {},
+   "source": [
+    "# Power and Deploy LangGraph Agents with Pathway: A Complete Guide\n",
+    "This notebook demonstrates how to use Pathway to power and deploy LangGraph Agents.\n",
+    "\n",
+    "In this notebook, you will learn:\n",
+    "- How to build a Pathway RAG app that indexes documents from your data sources\n",
+    "- How to create a Hybrid Index that is powered by semantic search and BM25 index\n",
+    "- How to use the Pathway LangChain Retriever in a LangGraph Agent\n",
+    "- How to use Pathway to serve LangGraph Agents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d27aa0d3-9b11-4678-8a83-b3865b26f30c",
+   "metadata": {},
+   "source": [
+    "# Setup\n",
+    "\n",
+    "Install the required packages and set your OpenAI API key. \n",
+    "OpenAI is used for embeddings during the indexing stage and to power the LangGraph agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "209efe9f-b9d3-4760-8863-0a6052617ebd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -U \"pathway[all]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b44f689e-0a62-4bbd-94e8-5bf31ece9cd9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -U langgraph langchain-community langchainhub langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ff9da9f-66b8-4675-8c45-afa10e891462",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import json\n",
+    "from typing import Iterable, Literal, List\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "# needed for the OpenAI embedder and the LLM we will use below, you can change the embedding provider, see the documentation:\n",
+    "# https://pathway.com/developers/api-docs/pathway-xpacks-llm/embedders\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "571d4836-4d08-4ee5-97fa-37132ccc8ef9",
+   "metadata": {},
+   "source": [
+    "Lets define a `DATA_PATH` folder that will store the text files/documents for indexing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54979252-13d3-4b94-b2ad-ec7b012aa320",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# folder that we will gather our docs in\n",
+    "DATA_PATH = \"./data\"\n",
+    "\n",
+    "os.makedirs(DATA_PATH, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55c2fb67-c562-47f7-a08e-6a673b4b612b",
+   "metadata": {},
+   "source": [
+    "## Load & save a webpage\n",
+    "\n",
+    "Define utility functions to read content from a webpage and save it locally into `DATA_PATH`.\n",
+    "  1. `load_page_content`: Reads the raw text from a webpage using using a `WebBaseLoader` from `langchain_community.document_loaders`.\n",
+    "  2.  `ingest_webpage`: Saves the loaded content as a text file into the `DATA_PATH`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64538684-5b2b-460a-b3a5-1fe737fd8269",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from urllib.parse import urlparse\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "\n",
+    "\n",
+    "def load_page_content(url: str) -> str:\n",
+    "    \"\"\"Load web page content with Langchain utilities.\"\"\"\n",
+    "    return WebBaseLoader(url).load()[0].page_content\n",
+    "\n",
+    "\n",
+    "def ingest_webpage(url: str) -> None:\n",
+    "    \"\"\"Save a webpage to local `DATA_PATH` folder.\"\"\"\n",
+    "    text_content = load_page_content(url)\n",
+    "\n",
+    "    parsed_url = urlparse(url)\n",
+    "    file_name = parsed_url.hostname + parsed_url.path.replace(\"/\", \"_\") + \".txt\"\n",
+    "\n",
+    "    with open(os.path.join(DATA_PATH, file_name), \"w\", encoding=\"utf-8\") as f:\n",
+    "        f.write(text_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bf15463-7da1-4d04-9f6d-423975e377cb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Download the Self-RAG paper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fff7b382-2e25-415b-a3ee-06df976b3dbd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ingest_webpage(\"https://arxiv.org/html/2310.11511\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62376b16-e5bc-463d-bf84-bec69a7c2035",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Build the Pathway indexing pipeline\n",
+    "\n",
+    "Set up Pathway to read and index the documents saved under the `DATA_PATH`.\n",
+    "\n",
+    "\n",
+    "1. [Connectors](https://pathway.com/developers/user-guide/connect/pathway-connectors): Use Pathway’s file reader to ingest all text files under the `DATA_PATH`.\n",
+    "2. [Parsers](https://pathway.com/developers/api-docs/pathway-xpacks-llm/parsers): Utilize the ParseUnstructured to parse the documents. This parser supports multiple file types, including PDF, DOCX, and PPTX.\n",
+    "3. [Text Splitters](https://pathway.com/developers/api-docs/pathway-xpacks-llm/splitters): Split the document content into chunks.\n",
+    "4. [Embedders](https://pathway.com/developers/api-docs/pathway-xpacks-llm/embedders): Use OpenAI API for embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27c3d42a-30e1-47f8-bf3c-bd44d4a490f0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# host and port of the RAG app\n",
+    "pathway_host: str = \"0.0.0.0\"\n",
+    "pathway_port: int = 8000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "094ec4e1-e972-441b-9c47-ff0ce3c8d2ff",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pathway as pw\n",
+    "from pathway.stdlib.indexing import BruteForceKnnFactory, HybridIndexFactory\n",
+    "from pathway.stdlib.indexing.bm25 import TantivyBM25Factory\n",
+    "from pathway.udfs import DiskCache\n",
+    "from pathway.xpacks.llm import embedders, llms, parsers, splitters\n",
+    "from pathway.xpacks.llm.document_store import DocumentStore\n",
+    "from pathway.xpacks.llm.question_answering import BaseRAGQuestionAnswerer, RAGClient\n",
+    "\n",
+    "\n",
+    "# read the text files under the data folder, we can also read from Google Drive, Sharepoint, etc.\n",
+    "# See connectors documentation: https://pathway.com/developers/user-guide/connect/pathway-connectors to learn more\n",
+    "folder = pw.io.fs.read(\n",
+    "    path=f\"{DATA_PATH}/*.txt\",\n",
+    "    format=\"binary\",\n",
+    "    with_metadata=True,\n",
+    ")\n",
+    "\n",
+    "# list of data sources to be indexed\n",
+    "sources = [folder]\n",
+    "\n",
+    "# define the document processing steps\n",
+    "parser = parsers.ParseUnstructured()\n",
+    "\n",
+    "text_splitter = splitters.TokenCountSplitter(min_tokens=150, max_tokens=450)\n",
+    "\n",
+    "embedder = embedders.OpenAIEmbedder(cache_strategy=DiskCache())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3426ef8-90ee-4023-8094-5ce007d64b0e",
+   "metadata": {},
+   "source": [
+    "Hybrid index combines semantic search and keyword based BM25 search.\n",
+    "\n",
+    "[`HybridIndexFactory`](https://pathway.com/developers/api-docs/indexing#pathway.stdlib.indexing.HybridIndexFactory) combines different indexes to build an hybrid index:\n",
+    "  1. [BM25](https://pathway.com/developers/api-docs/indexing#pathway.stdlib.indexing.TantivyBM25) (via TantivyBM25Factory) → Keyword based BM25 search.\n",
+    "  2. [BruteForceKnn](https://pathway.com/developers/api-docs/indexing#pathway.stdlib.indexing.BruteForceKnn) → Vector-based semantic search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3f7c59b-925b-4ef3-88a8-89749479925e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "index = HybridIndexFactory(\n",
+    "    [\n",
+    "        TantivyBM25Factory(),\n",
+    "        BruteForceKnnFactory(embedder=embedder),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b55a446-cf97-4d47-b1ca-374bcf0e8a7b",
+   "metadata": {},
+   "source": [
+    "[DocumentStore](https://pathway.com/developers/api-docs/pathway-xpacks-llm/document_store#pathway.xpacks.llm.document_store.DocumentStore) manages document ingestion, parsing, splitting, and indexing.\n",
+    "\n",
+    "[BaseRAGQuestionAnswerer](https://pathway.com/developers/api-docs/pathway-xpacks-llm/question_answering#pathway.xpacks.llm.question_answering.BaseRAGQuestionAnswerer) creates a Pathway “RAG” application that:\n",
+    "  * Indexes the documents (via `document_store`)\n",
+    "  * Exposes a standard question-answering interface\n",
+    "  * Enables us to create endpoint for the agent with Pathway."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7db0ab59-a641-43cc-901b-cc2713f03a99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = llms.OpenAIChat(model=\"gpt-4o-mini\", cache_strategy=DiskCache())\n",
+    "\n",
+    "document_store = DocumentStore(\n",
+    "    docs=sources, parser=parser, splitter=text_splitter, retriever_factory=index\n",
+    ")\n",
+    "\n",
+    "# create the RAG app that will power the index, and serve the agent endpoint\n",
+    "rag_app = BaseRAGQuestionAnswerer(\n",
+    "    llm=llm,\n",
+    "    indexer=document_store,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a027e277-fba8-4198-863b-2168ae6f8505",
+   "metadata": {},
+   "source": [
+    "# Create the LangGraph agent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf15141f-9612-4d0d-a21b-11bd8530686a",
+   "metadata": {},
+   "source": [
+    "### Create the Langchain Retriever\n",
+    "\n",
+    "You can now query Pathway and access up-to-date documents for your RAG applications from LangChain using [PathwayVectorClient](https://api.python.langchain.com/en/latest/vectorstores/langchain_community.vectorstores.pathway.PathwayVectorClient.html)\n",
+    "\n",
+    "The Langchain Retriever is a wrapper around the Pathway client, you will use it in `retrieve` part stage of the Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fefffab6-d5a3-4925-a52d-5a5c574421ea",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.vectorstores import PathwayVectorClient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51d365aa-5526-4ec8-932d-e65e83ee355d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "vectorstore_client = PathwayVectorClient(pathway_host, pathway_port)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59624377-8481-4407-8ee3-0f014bdff30e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# this will not be able to fetch data until the RAG app is running\n",
+    "retriever = vectorstore_client.as_retriever()"
+   ]
+  },
+  {
+   "attachments": {
+    "76895b7a-fcc5-4758-9fbb-510b17fdeda4.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "5894115c-0323-4098-83be-da49d949a53c",
+   "metadata": {},
+   "source": [
+    "### Self-RAG\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",
+    "![image.png](attachment:76895b7a-fcc5-4758-9fbb-510b17fdeda4.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1def042d-abcc-47af-8926-a9d1e2e8154f",
+   "metadata": {},
+   "source": [
+    "### Define the LangGraph Workflow\n",
+    "\n",
+    "\n",
+    "The LangGraph agent operates as a multi-step workflow.\n",
+    "\n",
+    "- It retrieves documents, grades their relevance, generates an answer, and ensures factual accuracy (e.g., no hallucinations).\n",
+    "- You can also implement self-reflection and query optimization using LangGraph nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c4c9be8-396c-4ebc-a9e4-d4e11504c05c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\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",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
+    "    If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n",
+    "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\"\n",
+    "grade_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Retrieved document: \\n\\n {document} \\n\\n User question: {question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = grade_prompt | structured_llm_grader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "499173a5-8df0-4fe6-bb46-552ce496b8ba",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "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 = ChatOpenAI(model_name=\"gpt-3.5-turbo\", 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()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9055d3b-8e9b-494f-bf4f-d02c153639c7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "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",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \\n \n",
+    "     Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts.\"\"\"\n",
+    "hallucination_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Set of facts: \\n\\n {documents} \\n\\n LLM generation: {generation}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "hallucination_grader = hallucination_prompt | structured_llm_grader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f92d351d-5ee1-48cc-ad8f-9b57ceed18ed",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "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 function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing whether an answer addresses / resolves a question \\n \n",
+    "     Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question.\"\"\"\n",
+    "answer_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"User question: \\n\\n {question} \\n\\n LLM generation: {generation}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "answer_grader = answer_prompt | structured_llm_grader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "286a9a2a-2a62-4004-84a2-fc82e6996cf1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"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 input and try to reason about the underlying semantic intent / meaning.\"\"\"\n",
+    "re_write_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\n",
+    "            \"human\",\n",
+    "            \"Here is the initial question: \\n\\n {question} \\n Formulate an improved question.\",\n",
+    "        ),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb9bd597-a683-433a-879d-7991cfed5cf7",
+   "metadata": {
+    "tags": []
+   },
+   "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": null,
+   "id": "398e5cc2-9c41-465d-a4f4-93c82ed452f2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "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.binary_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.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",
+    "        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
+    "        grade = score.binary_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",
+    "        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b77061d5-fa7f-4dbc-ad5b-cc409b931bf4",
+   "metadata": {},
+   "source": [
+    "### Compose the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5ae35f7-ff5c-40f1-9b0e-1a8bac0dc58f",
+   "metadata": {
+    "tags": []
+   },
+   "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)  # generatae\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": "598a9ef4-cfb2-479a-9a74-b82c5781daca",
+   "metadata": {},
+   "source": [
+    "# Serve the agent\n",
+    "\n",
+    "`BaseRAGQuestionAnswerer` provides `serve_callable` that lets you attach a custom Python function to an endpoint. This way, you can create your own endpoints on the Pathway server in addition to the [built-in ones](https://pathway.com/developers/ai-pipelines/rest-api).\n",
+    "\n",
+    "Use the `serve_callable` method to expose the LangGraph agent as an HTTP endpoint.\n",
+    "The `/agent` endpoint accepts user queries, processes them through the LangGraph pipeline, and returns the final answer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fafc447a-c221-4251-ad40-c56cb77c338c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "@rag_app.serve_callable(route=\"/agent\")\n",
+    "async def call_agent(user_query: str) -> str:\n",
+    "    langgraph_input = {\"question\": user_query}\n",
+    "    result = app.invoke(langgraph_input)\n",
+    "    return result[\"generation\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a71d3943-faf8-4f74-9f9d-a8f914db2acb",
+   "metadata": {},
+   "source": [
+    "The above Pathway endpoint,\n",
+    "* Takes a `user_query` as the JSON input.\n",
+    "* Passes it into the compiled LangGraph pipeline.\n",
+    "* Returns the final answer from the state (result[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e86ac7c-db40-4636-b704-77df7c0d18a6",
+   "metadata": {},
+   "source": [
+    "# Build and Run the Pathway server"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "963e891e-4ccf-4186-a3f2-19997b44bb86",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# This creates and runs the index, also serves the agent endpoint defined above\n",
+    "\n",
+    "rag_app.build_server(pathway_host, pathway_port)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "065f68f6-99b9-4d5e-8abf-796fdf67c050",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "t = rag_app.run_server(threaded=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95b9772b-42ce-422f-bd40-56ba317a8249",
+   "metadata": {},
+   "source": [
+    "List the indexed documents.\n",
+    "\n",
+    "This request may take a while since app is parsing the documents and building the index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bc8ab3c-cfb6-41e6-a102-281caa1bb19e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pathway_client = RAGClient(pathway_host, pathway_port)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aebfab0c-c827-4a47-a5b4-36361c2c68c1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pathway_client.pw_list_documents()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e682805-8d61-4054-aab0-47044d921157",
+   "metadata": {},
+   "source": [
+    "# Run with a sample question\n",
+    "\n",
+    "Test the `/agent` endpoint by sending a sample question. The server runs the entire RAG pipeline and returns the answer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4676f4c8-26bd-4f36-aadf-80771b001b59",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "question: str = (\n",
+    "    \"How does self-RAG work? Explain the steps involved in the implementation.\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b84144d8-e266-4a13-a515-a55fcdc02f11",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "\n",
+    "# send request to the endpoint we defined above, put the expected fields as payload\n",
+    "response = requests.post(\n",
+    "    pathway_client.url + \"/agent\",\n",
+    "    json={\"user_query\": question},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5689309-1780-4725-8d3f-364841303def",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "response.json()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35051a4c-1c70-4631-9005-5a06520640e1",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1. **Data Ingestion:** Ingest a webpage (the Self-RAG arxiv page).\n",
+    "2. **Indexing:** Pathway reads these text documents, parses, splits, and indexes them using a hybrid BM25 + semantic index.\n",
+    "3. **LangGraph Agent:** Define a multi-step agent that:\n",
+    "      * Re-writes the query if needed.\n",
+    "      * Retrieves documents via the Pathway VectorStore.\n",
+    "      * Grades those docs for relevance.\n",
+    "      * Generates an answer.\n",
+    "      * Grades that answer for hallucination and for how well it addresses the user’s question.\n",
+    "      * Loops as necessary until it gets a satisfactory final response.\n",
+    "4. **Serving:** Pathway’s built-in server is launched, hosting an `/agent` endpoint for queries.\n",
+    "\n",
+    "\n",
+    "Thus, whenever you POST a `\"user_query\"` to the `/agent` endpoint, the agent pipeline runs, and returns the final answer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "512f527d-400e-44af-add3-30a1b5a3c844",
+   "metadata": {},
+   "source": [
+    "> This notebook was inspired by this [LangGraph cookbook](https://github.com/langchain-ai/langgraph/blob/e3ca7bb3e9d34b09633852f4d08d55f6dcd4364b/examples/rag/langgraph_self_rag.ipynb)"
+   ]
+  }
+ ],
+ "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": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cookbooks/self-rag-agents/pathway_langgraph_agentic_rag.ipynb b/cookbooks/self-rag-agents/pathway_langgraph_agentic_rag.ipynb
new file mode 100644
index 0000000..003c47f
--- /dev/null
+++ b/cookbooks/self-rag-agents/pathway_langgraph_agentic_rag.ipynb
@@ -0,0 +1,921 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9b009d03-9465-4b1a-b64d-50a05c0dfbc5",
+   "metadata": {},
+   "source": [
+    "# LangGraph Agents Powered by Pathway: A Complete Guide\n",
+    "\n",
+    "This notebook demonstrates how to use Pathway VectorStore as a LangChain Retriever to power LangGraph Agents that are synced with your documents.\n",
+    "\n",
+    "In this notebook, you will learn:\n",
+    "- How to build a Pathway vector store that indexes documents from your data sources\n",
+    "- How to create a vector index that enables semantic search through your documents\n",
+    "- How to use the Pathway LangChain Retriever in a LangGraph Agent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "727099e5-e1d8-4f20-8240-d74ec1299ddf",
+   "metadata": {},
+   "source": [
+    "# Setup\n",
+    "\n",
+    "Install the required packages and set your OpenAI API key. \n",
+    "OpenAI is used for embeddings during the indexing stage and to power the LangGraph agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bdea4ea-882e-4043-8cda-4671690b33a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -U \"pathway[all]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88ec0f0e-7fa2-457f-a3d7-49a158653e24",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -U langgraph langchain-community langchainhub langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ff9da9f-66b8-4675-8c45-afa10e891462",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import json\n",
+    "from typing import Iterable, Literal, List\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "# needed for the OpenAI embedder and the LLM we will use below, you can change the embedding provider, see the documentation:\n",
+    "# https://pathway.com/developers/api-docs/pathway-xpacks-llm/embedders\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b04f68d-2ca4-43e5-9c8d-c8392dd65edb",
+   "metadata": {},
+   "source": [
+    "Lets define a `DATA_PATH` folder that will store the text files/documents for indexing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54979252-13d3-4b94-b2ad-ec7b012aa320",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# folder that we will gather our docs in\n",
+    "DATA_PATH = \"./data\"\n",
+    "\n",
+    "os.makedirs(DATA_PATH, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8169934b-9a2c-433e-b034-8a97ead8d25b",
+   "metadata": {},
+   "source": [
+    "## Load & save a webpage\n",
+    "\n",
+    "Define utility functions to read content from a webpage and save it locally into `DATA_PATH`.\n",
+    "  1. `load_page_content`: Reads the raw text from a webpage using using a `WebBaseLoader` from `langchain_community.document_loaders`.\n",
+    "  2.  `ingest_webpage`: Saves the loaded content as a text file into the `DATA_PATH`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64538684-5b2b-460a-b3a5-1fe737fd8269",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from urllib.parse import urlparse\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "\n",
+    "\n",
+    "def load_page_content(url: str) -> str:\n",
+    "    \"\"\"Load web page content with Langchain utilities.\"\"\"\n",
+    "    return WebBaseLoader(url).load()[0].page_content\n",
+    "\n",
+    "\n",
+    "def ingest_webpage(url: str) -> None:\n",
+    "    \"\"\"Save a webpage to local `DATA_PATH` folder.\"\"\"\n",
+    "    text_content = load_page_content(url)\n",
+    "\n",
+    "    parsed_url = urlparse(url)\n",
+    "    file_name = parsed_url.hostname + parsed_url.path.replace(\"/\", \"_\") + \".txt\"\n",
+    "\n",
+    "    with open(os.path.join(DATA_PATH, file_name), \"w\", encoding=\"utf-8\") as f:\n",
+    "        f.write(text_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bf15463-7da1-4d04-9f6d-423975e377cb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Download the Self-RAG paper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fff7b382-2e25-415b-a3ee-06df976b3dbd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ingest_webpage(\"https://arxiv.org/html/2310.11511\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7e2f21e-2259-49f8-b295-0f442e35c337",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Build the Pathway indexing pipeline\n",
+    "\n",
+    "Set up Pathway to read and index the documents saved under the `DATA_PATH`.\n",
+    "\n",
+    "\n",
+    "1. [Connectors](https://pathway.com/developers/user-guide/connect/pathway-connectors): Use Pathway’s file reader to ingest all text files under the `DATA_PATH`.\n",
+    "2. [Parsers](https://pathway.com/developers/api-docs/pathway-xpacks-llm/parsers): Utilize the ParseUnstructured to parse the documents. This parser supports multiple file types, including PDF, DOCX, and PPTX.\n",
+    "3. [Text Splitters](https://pathway.com/developers/api-docs/pathway-xpacks-llm/splitters): Split the document content into chunks.\n",
+    "4. [Embedders](https://pathway.com/developers/api-docs/pathway-xpacks-llm/embedders): Use OpenAI API for embeddings.\n",
+    "5. [VectorStore](https://pathway.com/developers/api-docs/pathway-xpacks-llm/vectorstore): Orchestrates all the above modules."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27c3d42a-30e1-47f8-bf3c-bd44d4a490f0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# host and port of the RAG app\n",
+    "pathway_host: str = \"0.0.0.0\"\n",
+    "pathway_port: int = 8000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c68a48a-8263-4b52-8c5a-206baefd22d1",
+   "metadata": {},
+   "source": [
+    "Create the vector store that will power the index of our RAG application. \n",
+    "\n",
+    "We use the modules under the Pathway Xpacks to create a suitable vector store."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "094ec4e1-e972-441b-9c47-ff0ce3c8d2ff",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pathway as pw\n",
+    "\n",
+    "from pathway.xpacks.llm.vector_store import VectorStoreServer, VectorStoreClient\n",
+    "from pathway.xpacks.llm import (\n",
+    "    embedders,\n",
+    "    llms,\n",
+    "    parsers,\n",
+    "    splitters,\n",
+    ")\n",
+    "from pathway.udfs import DiskCache\n",
+    "\n",
+    "# read the text files under the data folder, we can also read from Google Drive, Sharepoint, etc.\n",
+    "# See connectors documentation: https://pathway.com/developers/user-guide/connect/pathway-connectors to learn more\n",
+    "folder = pw.io.fs.read(\n",
+    "    path=f\"{DATA_PATH}/*.txt\",\n",
+    "    format=\"binary\",\n",
+    "    with_metadata=True,\n",
+    ")\n",
+    "\n",
+    "# list of data sources to be indexed\n",
+    "sources = [folder]\n",
+    "\n",
+    "# define the document processing steps\n",
+    "parser = parsers.ParseUnstructured()\n",
+    "\n",
+    "text_splitter = splitters.TokenCountSplitter(min_tokens=150, max_tokens=450)\n",
+    "\n",
+    "embedder = embedders.OpenAIEmbedder(cache_strategy=DiskCache())\n",
+    "\n",
+    "vector_server = VectorStoreServer(\n",
+    "    *sources,\n",
+    "    embedder=embedder,\n",
+    "    splitter=text_splitter,\n",
+    "    parser=parser,\n",
+    ")\n",
+    "\n",
+    "# deploy the vector store locally\n",
+    "t = vector_server.run_server(pathway_host, pathway_port, threaded=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34cc8823-e184-47c0-aa09-7b4035817e7d",
+   "metadata": {},
+   "source": [
+    "[VectorStore](https://pathway.com/developers/api-docs/pathway-xpacks-llm/vectorstore) manages document ingestion, parsing, splitting, and indexing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2742133-a7ef-49a8-82d1-093c2d2ff2d2",
+   "metadata": {},
+   "source": [
+    "List the indexed documents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bc8ab3c-cfb6-41e6-a102-281caa1bb19e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pathway_client = VectorStoreClient(pathway_host, pathway_port)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aebfab0c-c827-4a47-a5b4-36361c2c68c1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pathway_client.get_input_files()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba734b24-63bd-4b22-a3cb-fed2393a60f4",
+   "metadata": {},
+   "source": [
+    "# Create the LangGraph agent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf15141f-9612-4d0d-a21b-11bd8530686a",
+   "metadata": {},
+   "source": [
+    "### Create the Langchain Retriever\n",
+    "\n",
+    "You can now query Pathway and access up-to-date documents for your RAG applications from LangChain using [PathwayVectorClient](https://api.python.langchain.com/en/latest/vectorstores/langchain_community.vectorstores.pathway.PathwayVectorClient.html)\n",
+    "\n",
+    "The Langchain Retriever is a wrapper around the Pathway client, you will use it in `retrieve` part stage of the Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fefffab6-d5a3-4925-a52d-5a5c574421ea",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.vectorstores import PathwayVectorClient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51d365aa-5526-4ec8-932d-e65e83ee355d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "vectorstore_client = PathwayVectorClient(pathway_host, pathway_port)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59624377-8481-4407-8ee3-0f014bdff30e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "retriever = vectorstore_client.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4623f53b-013b-4bc5-8c7c-e994a2e35c57",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# test the retriever\n",
+    "# retriever.invoke({\"question\": \"evaluation\"})"
+   ]
+  },
+  {
+   "attachments": {
+    "ac58a69e-7bf3-481f-baea-1b9d6c6eba00.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "7a3c29d5-7f69-4186-8bfd-d03b8cbee280",
+   "metadata": {},
+   "source": [
+    "### Self-RAG\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/).![image.png](attachment:ac58a69e-7bf3-481f-baea-1b9d6c6eba00.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4244331f-1cdc-4548-b577-f758d1cd2bf9",
+   "metadata": {},
+   "source": [
+    "### Define the LangGraph Workflow\n",
+    "\n",
+    "\n",
+    "The LangGraph agent operates as a multi-step workflow.\n",
+    "\n",
+    "- It retrieves documents, grades their relevance, generates an answer, and ensures factual accuracy (e.g., no hallucinations).\n",
+    "- You can also implement self-reflection and query optimization using LangGraph nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c4c9be8-396c-4ebc-a9e4-d4e11504c05c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\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",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
+    "    If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n",
+    "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\"\n",
+    "grade_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Retrieved document: \\n\\n {document} \\n\\n User question: {question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = grade_prompt | structured_llm_grader\n",
+    "\n",
+    "\n",
+    "question = \"self rag performance\"\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": null,
+   "id": "499173a5-8df0-4fe6-bb46-552ce496b8ba",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "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 = ChatOpenAI(model_name=\"gpt-3.5-turbo\", 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": null,
+   "id": "d9055d3b-8e9b-494f-bf4f-d02c153639c7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "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",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \\n \n",
+    "     Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts.\"\"\"\n",
+    "hallucination_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Set of facts: \\n\\n {documents} \\n\\n LLM generation: {generation}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "hallucination_grader = hallucination_prompt | structured_llm_grader\n",
+    "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f92d351d-5ee1-48cc-ad8f-9b57ceed18ed",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "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 function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing whether an answer addresses / resolves a question \\n \n",
+    "     Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question.\"\"\"\n",
+    "answer_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"User question: \\n\\n {question} \\n\\n LLM generation: {generation}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "answer_grader = answer_prompt | structured_llm_grader\n",
+    "answer_grader.invoke({\"question\": question, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "286a9a2a-2a62-4004-84a2-fc82e6996cf1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"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 input and try to reason about the underlying semantic intent / meaning.\"\"\"\n",
+    "re_write_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\n",
+    "            \"human\",\n",
+    "            \"Here is the initial question: \\n\\n {question} \\n Formulate an improved question.\",\n",
+    "        ),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb9bd597-a683-433a-879d-7991cfed5cf7",
+   "metadata": {
+    "tags": []
+   },
+   "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": null,
+   "id": "398e5cc2-9c41-465d-a4f4-93c82ed452f2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "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.binary_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.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",
+    "        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
+    "        grade = score.binary_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",
+    "        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b77061d5-fa7f-4dbc-ad5b-cc409b931bf4",
+   "metadata": {},
+   "source": [
+    "### Compose the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5ae35f7-ff5c-40f1-9b0e-1a8bac0dc58f",
+   "metadata": {
+    "tags": []
+   },
+   "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)  # generatae\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": "074bd11c-0252-46cc-8903-5fa29c327e96",
+   "metadata": {},
+   "source": [
+    "# Run with a sample question\n",
+    "\n",
+    "Test the `/agent` endpoint by sending a sample question. The server runs the entire RAG pipeline and returns the answer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4676f4c8-26bd-4f36-aadf-80771b001b59",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "inputs = {\n",
+    "    \"question\": \"How does self-RAG work? Explain the steps involved in the implementation.\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b84144d8-e266-4a13-a515-a55fcdc02f11",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "result = app.invoke(inputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5689309-1780-4725-8d3f-364841303def",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "result[\"generation\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94575479-3639-46de-83d6-6633fc22c00d",
+   "metadata": {},
+   "source": [
+    "> This notebook was inspired by this [LangGraph cookbook](https://github.com/langchain-ai/langgraph/blob/e3ca7bb3e9d34b09633852f4d08d55f6dcd4364b/examples/rag/langgraph_self_rag.ipynb)"
+   ]
+  }
+ ],
+ "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": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}