diff --git a/pipeline/core/builder.py b/pipeline/core/builder.py
index f73b3fb6..8841ad12 100644
--- a/pipeline/core/builder.py
+++ b/pipeline/core/builder.py
@@ -9,6 +9,7 @@
 from tqdm import tqdm
 
 from pipeline.preprocessors import preprocess_markdown
+from pipeline.tools.notebook.convert import convert_notebook
 
 logger = logging.getLogger(__name__)
 
@@ -40,6 +41,7 @@ def __init__(self, src_dir: Path, build_dir: Path) -> None:
         self.copy_extensions: set[str] = {
             ".mdx",
             ".md",
+            ".ipynb",
             ".json",
             ".svg",
             ".png",
@@ -51,6 +53,35 @@ def __init__(self, src_dir: Path, build_dir: Path) -> None:
             ".css",
         }
 
+    def _should_ignore_file(self, file_path: Path) -> bool:
+        """Check if a file should be ignored during build.
+
+        This method filters out cached files, temporary files, and other
+        files that should not be included in the build process.
+
+        Args:
+            file_path: Path to the file to check.
+
+        Returns:
+            True if the file should be ignored, False otherwise.
+        """
+        filename = file_path.name
+
+        # Ignore files starting with .~ (cached/temporary files)
+        if filename.startswith(".~"):
+            return True
+
+        # Ignore files starting with ~ (backup files)
+        if filename.startswith("~"):
+            return True
+
+        # Ignore hidden files starting with . (except specific ones we want)
+        if filename.startswith(".") and filename not in {".gitkeep"}:
+            return True
+
+        # Ignore common temporary file patterns
+        return bool(filename.endswith((".tmp", ".temp")))
+
     def build_all(self) -> None:
         """Build all documentation files from source to build directory.
 
@@ -76,7 +107,8 @@ def build_all(self) -> None:
 
         # Collect all files to process
         all_files = [
-            file_path for file_path in self.src_dir.rglob("*") if file_path.is_file()
+            file_path for file_path in self.src_dir.rglob("*")
+            if file_path.is_file() and not self._should_ignore_file(file_path)
         ]
 
         if not all_files:
@@ -188,6 +220,34 @@ def _process_markdown_file(self, input_path: Path, output_path: Path) -> None:
             logger.exception("Failed to process markdown file %s", input_path)
             raise
 
+    def _process_notebook_file(self, input_path: Path, output_path: Path) -> None:
+        """Process a Jupyter notebook file and convert to markdown.
+
+        This method converts a Jupyter notebook to markdown, applies preprocessing,
+        and writes the processed content to the output path as an .mdx file.
+
+        Args:
+            input_path: Path to the source notebook file.
+            output_path: Path where the processed file should be written.
+        """
+        try:
+            # Convert notebook to markdown
+            markdown_content = convert_notebook(input_path)
+
+            # Apply markdown preprocessing
+            processed_content = self._process_markdown_content(markdown_content, input_path)
+
+            # Convert .ipynb to .mdx
+            output_path = output_path.with_suffix(".mdx")
+
+            # Write the processed content
+            with output_path.open("w", encoding="utf-8") as f:
+                f.write(processed_content)
+
+        except Exception:
+            logger.exception("Failed to process notebook file %s", input_path)
+            raise
+
     def build_file(self, file_path: Path) -> None:
         """Build a single file by copying it to the build directory.
 
@@ -227,6 +287,10 @@ def build_file(self, file_path: Path) -> None:
             if file_path.suffix.lower() in {".md", ".mdx"}:
                 self._process_markdown_file(file_path, output_path)
                 logger.info("Processed markdown: %s", relative_path)
+            # Handle notebook files with conversion to markdown
+            elif file_path.suffix.lower() == ".ipynb":
+                self._process_notebook_file(file_path, output_path)
+                logger.info("Converted notebook: %s", relative_path)
             else:
                 shutil.copy2(file_path, output_path)
                 logger.info("Copied: %s", relative_path)
@@ -269,6 +333,10 @@ def _build_file_with_progress(self, file_path: Path, pbar: tqdm) -> bool:
             if file_path.suffix.lower() in {".md", ".mdx"}:
                 self._process_markdown_file(file_path, output_path)
                 return True
+            # Handle notebook files with conversion to markdown
+            if file_path.suffix.lower() == ".ipynb":
+                self._process_notebook_file(file_path, output_path)
+                return True
             shutil.copy2(file_path, output_path)
             return True
         return False
diff --git a/pipeline/core/watcher.py b/pipeline/core/watcher.py
index 3da8f759..ca2633d6 100644
--- a/pipeline/core/watcher.py
+++ b/pipeline/core/watcher.py
@@ -67,7 +67,8 @@ def on_modified(self, event: FileSystemEvent) -> None:
         src_path = event.src_path
 
         file_path = Path(src_path)
-        if file_path.suffix.lower() in self.builder.copy_extensions:
+        if (file_path.suffix.lower() in self.builder.copy_extensions and
+            not self.builder._should_ignore_file(file_path)):
             logger.info("File changed: %s", file_path)
             # Put file change event in queue for async processing
             self.loop.call_soon_threadsafe(self.event_queue.put_nowait, file_path)
diff --git a/src/docs.json b/src/docs.json
index 5bd9c8bd..b3d35c74 100644
--- a/src/docs.json
+++ b/src/docs.json
@@ -52,6 +52,23 @@
   },
   "navigation": {
     "dropdowns": [
+      {
+        "dropdown": "LangChain v1",
+        "icon": "/images/brand/langchain-pill.svg",
+        "description": "LangChain v1 documentation and guides",
+        "tabs": [
+          {
+            "tab": "Prebuilts",
+            "pages": [
+              "langchain_v1/stuff",
+              "langchain_v1/map_reduce",
+              "langchain_v1/recursive",
+              "langchain_v1/rag_agent",
+              "langchain_v1/data_analysis"
+            ]
+          }
+        ]
+      },
       {
         "dropdown": "LangGraph Platform",
         "icon": "/images/brand/langgraph-platform-pill.svg",
@@ -316,4 +333,4 @@
       }
     ]
   }
-}
\ No newline at end of file
+}
diff --git a/src/langchain_v1/data_analysis.ipynb b/src/langchain_v1/data_analysis.ipynb
new file mode 100644
index 00000000..1837d183
--- /dev/null
+++ b/src/langchain_v1/data_analysis.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "63e58898-44fa-49e5-abe4-c88b552ae014",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: \"Data analysis\"\n",
+    "icon: \"chart-line\"\n",
+    "---\n",
+    "\n",
+    "**Data Analysis Agents** use language models to write and run Python code for exploring and visualizing data. This guide shows how to analyze a CSV file using a LangChain agent with code execution capabilities.\n",
+    "\n",
+    "## 1. Configure the sandbox"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f847013-ae15-46a4-92d4-88890d6ee3c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# todo: move to langchain_daytona\n",
+    "from langchain.sandboxes import DaytonaSandboxManager\n",
+    "import csv\n",
+    "\n",
+    "filename = \"sales_data.csv\"\n",
+    "data = [\n",
+    "    [\"Date\", \"Product\", \"Units Sold\", \"Revenue\"],\n",
+    "    [\"2025-08-01\", \"Widget A\", 10, 250],\n",
+    "    [\"2025-08-02\", \"Widget B\", 5, 125],\n",
+    "    [\"2025-08-03\", \"Widget A\", 7, 175],\n",
+    "    [\"2025-08-04\", \"Widget C\", 3, 90],\n",
+    "    [\"2025-08-05\", \"Widget B\", 8, 200]\n",
+    "]\n",
+    "\n",
+    "with open(filename, mode=\"w\", newline=\"\") as file:\n",
+    "    writer = csv.writer(file)\n",
+    "    writer.writerows(data)\n",
+    "\n",
+    "\n",
+    "# TODO: Fix life cycle management for the sandbox\n",
+    "sandbox_manager = DaytonaSandboxManager()\n",
+    "sandbox = sandbox_manager.create()\n",
+    "print(sandbox.get_capabilities())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1310eef7-a2e4-48a1-96cf-a00785909446",
+   "metadata": {},
+   "source": [
+    "## 2. Configure the agent\n",
+    "\n",
+    "We initialize a language model and create a DataAnalysisAgent, passing it:\n",
+    "* the sandbox\n",
+    "* the model\n",
+    "* available tools (run_code, exec)\n",
+    "* any files to attach"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "701e58bd-0570-48eb-9720-49b5113a4e0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uuid\n",
+    "from langchain.agents.data_analysis import create_data_analysis_agent\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "thread_id = uuid.uuid4()\n",
+    "\n",
+    "\n",
+    "model = init_chat_model('anthropic:claude-3-7-sonnet-latest', max_tokens=32_000)\n",
+    "\n",
+    "data_analysis_agent = create_data_analysis_agent(\n",
+    "    model,\n",
+    "    sandbox,\n",
+    "    tools=['run_code', 'exec'],\n",
+    "    files=[{\n",
+    "        \"source\": \"sales_data.csv\",\n",
+    "        \"destination\": \"./data/sales_data.csv\",\n",
+    "    }]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42d7f12b-f6d8-4e24-baa3-c5bcb5f3b3fb",
+   "metadata": {},
+   "source": [
+    "## 3. Run the agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8cfe63a-9ae7-4227-9810-3e36d0afde79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "## Sales Data Analysis Summary\n",
+      "\n",
+      "I've created a comprehensive analysis of the sales data with a beautiful dashboard visualization. Here are the key findings:\n",
+      "\n",
+      "### Overall Summary Statistics:\n",
+      "- **Total Revenue**: $840\n",
+      "- **Total Units Sold**: 33\n",
+      "- **Average Price Per Unit**: $25.45\n",
+      "- **Best Selling Product**: Widget A (17 units)\n",
+      "- **Highest Revenue Product**: Widget A ($425)\n",
+      "\n",
+      "### Product-Specific Analysis:\n",
+      "\n",
+      "**Widget A**:\n",
+      "- Total Units Sold: 17 units\n",
+      "- Total Revenue: $425\n",
+      "- Average Price Per Unit: $25.00\n",
+      "- Sales Trend: Decreasing or Stable (from 10 to 7 units)\n",
+      "\n",
+      "**Widget B**:\n",
+      "- Total Units Sold: 13 units\n",
+      "- Total Revenue: $325\n",
+      "- Average Price Per Unit: $25.00\n",
+      "- Sales Trend: Increasing (from 5 to 8 units)\n",
+      "\n",
+      "**Widget C**:\n",
+      "- Total Units Sold: 3 units\n",
+      "- Total Revenue: $90\n",
+      "- Average Price Per Unit: $30.00\n",
+      "- Only appeared in sales on one day (2025-08-04)\n",
+      "\n",
+      "### Key Insights:\n",
+      "1. Widget A is the most popular product in terms of both units sold and revenue generated.\n",
+      "2. Widget C has the highest price per unit at $30.00, compared to $25.00 for Widgets A and B.\n",
+      "3. The data shows a strong positive correlation between units sold and revenue (as shown in the scatter plot).\n",
+      "4. Widget B is showing an upward sales trend, which might indicate growing popularity.\n",
+      "5. Sales are distributed throughout the 5-day period with different products having stronger performance on different days.\n",
+      "\n",
+      "The dashboard visualization includes:\n",
+      "- Bar charts showing units sold and revenue by product\n",
+      "- A time series of daily sales\n",
+      "- A pie chart showing the distribution of sales across products\n",
+      "- A horizontal bar chart showing average price per unit\n",
+      "- A scatter plot showing the correlation between units sold and revenue\n",
+      "\n",
+      "This comprehensive analysis provides a clear picture of the sales performance across different products and over time, highlighting key trends and patterns in the data.\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = data_analysis_agent.invoke(\n",
+    "    {\n",
+    "        \"messages\": [{\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": \"Make a beautiful plot of provided data using matplotlib and add some summarys stats while you're at it.\",\n",
+    "        }]\n",
+    "    },\n",
+    "    config={\"configurable\": {\"thread_id\": thread_id}}\n",
+    ")\n",
+    "print(response['messages'][-1].content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "672a4876-7e4f-40bb-9b12-22f854f178e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = data_analysis_agent.invoke(\n",
+    "    {\n",
+    "        \"messages\": [{\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": \"Did you save the generated plot to a file? if not please do and put it in sales_analysis.png\",\n",
+    "        }]\n",
+    "    },\n",
+    "    config={\"configurable\": {\"thread_id\": thread_id}}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "42efc694-a341-41b0-ba13-2825eb4e5044",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = sandbox.download_file('./sales_analysis.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d7730439-caf2-4619-9659-09de43f3ce69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d895f0a7-769b-4f6c-8ac0-060c1be9578c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b60d930-e74e-41ae-9cf5-8d3386dda63c",
+   "metadata": {},
+   "source": [
+    "<Warning title=\"Sandbox Security\">\n",
+    "\n",
+    "The sandboxed environment allows for arbitrary code execution. To ensure safety:\n",
+    "\n",
+    "* **Never place secrets (e.g., API keys, credentials) or sensitive files inside the sandbox**.\n",
+    "  The agent operates on untrusted input, and **prompt injection attacks can exfiltrate anything inside the sandbox**.\n",
+    "\n",
+    "* The sandbox **does protect your local/production machine** from harmful code (e.g., file deletion, network access), as code execution is fully isolated.\n",
+    "</Warning>"
+   ]
+  }
+ ],
+ "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": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/langchain_v1/extraction.md b/src/langchain_v1/extraction.md
new file mode 100644
index 00000000..50768761
--- /dev/null
+++ b/src/langchain_v1/extraction.md
@@ -0,0 +1,170 @@
+# Document extraction
+
+This guide shows you how to extract information from documents using LangChain's **prebuilt** extraction functionality. The extraction chain can produce either text summaries or structured data from one or more documents.
+
+## Prerequisites
+
+Before you start this tutorial, ensure you have the following:
+
+- An [Anthropic](https://console.anthropic.com/settings/keys) API key
+
+## 1. Install dependencies
+
+If you haven't already, install LangGraph and LangChain:
+
+```bash
+pip install -U langgraph "langchain[anthropic]"
+```
+
+<Tip>
+    LangChain is installed so the extractor can call the [model](https://python.langchain.com/docs/integrations/chat/).
+</Tip>
+
+## 2. Set up documents
+
+First, create some documents to extract information from:
+
+```python
+from langchain_core.documents import Document
+
+documents = [
+    Document(
+        id="1",
+        page_content="""Bobby Luka was 10 years old.
+Synthetic fuels—produced from captured carbon and green hydrogen—are gaining traction in aviation. The EU's "ReFuelEU" mandate requires increasing blends of sustainable aviation fuel (SAF) starting in 2025. Airbus and Rolls-Royce have completed long-haul test flights powered entirely by synthetic kerosene.""",
+        metadata={"source": "synthetic_fuel_aviation"},
+    ),
+    Document(
+        id="2",
+        page_content="""
+AI is accelerating early-stage drug discovery, especially in target identification and molecule generation. Platforms like BenevolentAI and Insilico Medicine have generated preclinical candidates using generative models trained on biological and chemical data.""",
+        metadata={"source": "ai_drug_discovery"},
+    ),
+    Document(
+        id="3",
+        page_content="""Jack Johnson was 23 years old and blonde.
+Bobby Luka's hair is brown.""",
+        metadata={"source": "people_info"},
+    ),
+]
+```
+
+## 3. Configure a model
+
+Configure an LLM for extraction using [init_chat_model](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html):
+
+```python
+from langchain.chat_models import init_chat_model
+
+model = init_chat_model(
+    "anthropic:claude-3-5-sonnet-latest",
+    temperature=0
+)
+```
+
+## 4. Extract a basic summary
+
+Create an extractor to produce text summaries from documents:
+
+```python
+from langchain.chains.summarization import create_summarizer
+
+# Create a basic summarizer
+summarizer = create_summarizer(
+    model,
+    initial_prompt="Produce a concise summary of the following document in 2-3 sentences."
+).compile(name="TextSummarizer")
+
+# Extract summary
+result = summarizer.invoke({"documents": documents})
+print(result["result"])
+```
+
+## 5. Extract structured summaries
+
+To produce structured responses with a specific format, use the `response_format` parameter with a Pydantic model:
+
+```python
+from pydantic import BaseModel
+from langchain.chains.summarization import create_summarizer
+
+class Summary(BaseModel):
+    """Structured summary with title and key points."""
+    
+    title: str
+    key_points: list[str]
+
+# Create structured summarizer
+structured_summarizer = create_summarizer(
+    model,
+    initial_prompt="Extract the main topics and create a structured summary with a title and up to 3 key points.",
+    response_format=Summary
+).compile(name="StructuredSummarizer")
+
+# Extract structured summary
+result = structured_summarizer.invoke({"documents": documents})
+
+# Access structured fields
+print(f"Title: {result['result'].title}")
+print("Key points:")
+for point in result['result'].key_points:
+    print(f"  • {point}")
+```
+
+## 6. Extract entities with source tracking
+
+Extract specific entities while tracking which documents they came from:
+
+```python
+from typing import Optional
+from pydantic import BaseModel, Field
+
+class Person(BaseModel):
+    """Person entity with source tracking."""
+    
+    name: str
+    age: Optional[str] = None
+    hair_color: Optional[str] = None
+    source_doc_ids: list[str] = Field(
+        default=[],
+        description="The IDs of the documents where the information was found.",
+    )
+
+class PeopleExtraction(BaseModel):
+    """Collection of extracted people."""
+    
+    people: list[Person]
+
+# Create entity extractor
+entity_extractor = create_summarizer(
+    model,
+    initial_prompt="Extract information about people mentioned in the documents. Include the document IDs where each piece of information was found.",
+    response_format=PeopleExtraction
+).compile(name="EntityExtractor")
+
+# Extract entities
+result = entity_extractor.invoke({"documents": documents})
+
+# Display extracted people with sources
+for person in result['result'].people:
+    print(f"Name: {person.name}")
+    if person.age:
+        print(f"  Age: {person.age}")
+    if person.hair_color:
+        print(f"  Hair: {person.hair_color}")
+    print(f"  Sources: {', '.join(person.source_doc_ids)}")
+    print()
+```
+
+## Custom prompts
+
+Customize extraction behavior with specific prompts:
+
+```python
+custom_extractor = create_summarizer(
+    model,
+    initial_prompt="Focus on extracting technical information and key innovations mentioned in the documents."
+).compile()
+```
+
+For more advanced extraction patterns and customization, see the [extraction how-to guides](../how-tos/extraction/).
\ No newline at end of file
diff --git a/src/langchain_v1/map_reduce.ipynb b/src/langchain_v1/map_reduce.ipynb
new file mode 100644
index 00000000..65cf5dbc
--- /dev/null
+++ b/src/langchain_v1/map_reduce.ipynb
@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4b43aef4-1930-47d6-a112-73a2cfbae0a1",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: \"Map reduce\"\n",
+    "icon: \"rhombus\"\n",
+    "---\n",
+    "\n",
+    "**Map Reduce** chains enable efficient parallel processing of multiple documents by dividing the task into two stages:\n",
+    "\n",
+    "1. **Map:** Each document is processed independently and concurrently—similar to having multiple readers analyze different books at the same time.\n",
+    "2. **Reduce (optional):** The individual outputs are then aggregated into a single, cohesive result.\n",
+    "\n",
+    "This method is particularly valuable in two scenarios:\n",
+    "\n",
+    "* When processing **many large documents** that, together, would exceed the context window of a language model.\n",
+    "* When documents are **independent** and can be processed in parallel to improve efficiency.\n",
+    "\n",
+    "By splitting the workload, Map Reduce helps scale processing while maintaining performance and coherence.\n",
+    "\n",
+    "```mermaid\n",
+    "graph TD\n",
+    "    A[Input: Individual Documents] --> B1[Map: Process Doc 1]\n",
+    "    A --> B2[Map: Process Doc 2]\n",
+    "    A --> B3[Map: Process Doc 3]\n",
+    "    A --> Bn[Map: Process Doc N]\n",
+    "\n",
+    "    B1 --> C[Intermediate Results]\n",
+    "    B2 --> C\n",
+    "    B3 --> C\n",
+    "    Bn --> C\n",
+    "\n",
+    "    C -- Optional --> D[Reduce: Aggregate Results]\n",
+    "    D --> E[Final Output]\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ea9f2d4b-5d65-4227-95ef-095c16fcbaf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.documents import Document\n",
+    "\n",
+    "documents = [\n",
+    "    Document(\n",
+    "        page_content=(\n",
+    "            \"Richard Feynman was born on May 11, 1918, in Queens, New York. He showed an early \"\n",
+    "            \"interest in science, especially radios and engineering. As a teenager, he repaired \"\n",
+    "            \"radios as a hobby and even earned some money doing it.\\n\\n\"\n",
+    "            \"He attended the Massachusetts Institute of Technology (MIT) for his undergraduate \"\n",
+    "            \"studies and later earned his PhD in physics from Princeton University in 1942. At \"\n",
+    "            \"Princeton, he impressed many with his quick mind and problem-solving skills.\\n\\n\"\n",
+    "            \"After completing his PhD, he joined the Los Alamos Laboratory as part of the Manhattan Project.\"\n",
+    "        ),\n",
+    "        metadata={\"source\": \"early_life\"},\n",
+    "        id=\"1\"\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=(\n",
+    "            \"During World War II, Feynman worked on the Manhattan Project, the top-secret effort \"\n",
+    "            \"to build the first atomic bomb. He was based at Los Alamos Laboratory in New Mexico.\\n\\n\"\n",
+    "            \"There, he worked under physicist Hans Bethe and was known for his creativity and sense \"\n",
+    "            \"of humor. One of his habits was picking locks and cracking safes—not to steal secrets, \"\n",
+    "            \"but to prove how insecure they were.\\n\\n\"\n",
+    "            \"Feynman’s contributions helped the U.S. develop nuclear weapons, which were used in \"\n",
+    "            \"1945 to end the war.\"\n",
+    "        ),\n",
+    "        metadata={\"source\": \"manhattan_project\"},\n",
+    "        id=\"2\"\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=(\n",
+    "            \"After the war, Feynman became a professor at Cornell University and later at the \"\n",
+    "            \"California Institute of Technology (Caltech). In 1965, he won the Nobel Prize in \"\n",
+    "            \"Physics for his work on quantum electrodynamics, shared with Julian Schwinger and \"\n",
+    "            \"Sin-Itiro Tomonaga.\\n\\n\"\n",
+    "            \"He became famous for his lectures, especially the Feynman Lectures on Physics, which \"\n",
+    "            \"are still used today. In 1986, he served on the Rogers Commission that investigated \"\n",
+    "            \"the Space Shuttle Challenger disaster.\\n\\n\"\n",
+    "            \"Feynman died on February 15, 1988, in Los Angeles, California, after a long battle \"\n",
+    "            \"with cancer.\"\n",
+    "        ),\n",
+    "        metadata={\"source\": \"later_career\"},\n",
+    "        id=\"3\"\n",
+    "    ),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3785b5fb-e29b-4f95-97c0-3c3a76aad09b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'indexes': [0], 'result': \"Based on the document, the following locations are mentioned:\\n\\n1. **Queens, New York** - Richard Feynman's birthplace\\n2. **Massachusetts Institute of Technology (MIT)** - Where he attended undergraduate studies\\n3. **Princeton University** - Where he earned his PhD in physics in 1942\\n4. **Los Alamos Laboratory** - Where he worked after completing his PhD as part of the Manhattan Project\\n\\nThese locations trace Feynman's early life journey from his birth in New York through his education in Massachusetts and New Jersey, to his work on the Manhattan Project in New Mexico.\"}\n",
+      "--\n",
+      "{'indexes': [1], 'result': \"Based on the document, the following locations are mentioned:\\n\\n1. **Los Alamos Laboratory** - Where Feynman was based during his work on the Manhattan Project\\n2. **New Mexico** - The state where Los Alamos Laboratory is located\\n3. **U.S. (United States)** - The country that developed the nuclear weapons\\n\\nThese are the only specific locations mentioned in this document about Feynman's work during World War II on the Manhattan Project.\"}\n",
+      "--\n",
+      "{'indexes': [2], 'result': 'Based on the document, the following locations are mentioned:\\n\\n1. **Cornell University** - Where Feynman became a professor after the war\\n2. **California Institute of Technology (Caltech)** - Where he later became a professor\\n3. **Los Angeles, California** - Where Feynman died on February 15, 1988\\n\\nThese are the only specific locations explicitly mentioned in this document.'}\n",
+      "--\n",
+      "Based on the documents, here are the locations associated with Richard Feynman's life and career:\n",
+      "\n",
+      "**Early Life and Education:**\n",
+      "- **Queens, New York** - Birthplace\n",
+      "- **Massachusetts Institute of Technology (MIT)** - Undergraduate studies\n",
+      "- **Princeton University** - PhD in physics (1942)\n",
+      "\n",
+      "**Manhattan Project:**\n",
+      "- **Los Alamos Laboratory, New Mexico** - Worked on nuclear weapons development during World War II\n",
+      "\n",
+      "**Academic Career:**\n",
+      "- **Cornell University** - Professor position after the war\n",
+      "- **California Institute of Technology (Caltech)** - Later professor position\n",
+      "\n",
+      "**Death:**\n",
+      "- **Los Angeles, California** - Died February 15, 1988\n",
+      "\n",
+      "These locations trace Feynman's journey from his New York origins through his education in the Northeast, his wartime service in New Mexico, his post-war academic positions at prestigious universities, and his final years in California.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.chains import create_map_reduce_chain\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "model = init_chat_model(\"claude-opus-4-20250514\", max_tokens=32_000)\n",
+    "\n",
+    "chain = (\n",
+    "    create_map_reduce_chain(\n",
+    "        model, \n",
+    "        map_prompt=\"Which locations are mentioned in the document?\",\n",
+    "    )\n",
+    "    .compile(name='location-extractor')\n",
+    ")\n",
+    "\n",
+    "\n",
+    "response = chain.invoke({\"documents\": documents})\n",
+    "for mapped_result in response['map_results']:\n",
+    "    print(mapped_result)\n",
+    "    print('--')\n",
+    "\n",
+    "\n",
+    "# And examine the default reduce result\n",
+    "print(response['result'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "064470e6-f6a3-4ea4-a979-800dc079ef10",
+   "metadata": {},
+   "source": [
+    "# Rate Limiting\n",
+    "\n",
+    "You may need to rate limit the requests to the LLM when issuing requests in parallel.\n",
+    "\n",
+    "Please see the documentation in **CHAT MODELS** for information on how to add a rate limiter."
+   ]
+  }
+ ],
+ "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/langchain_v1/rag_agent.md b/src/langchain_v1/rag_agent.md
new file mode 100644
index 00000000..a609e420
--- /dev/null
+++ b/src/langchain_v1/rag_agent.md
@@ -0,0 +1,325 @@
+---
+title: RAG
+---
+
+**Retrieval-Augmented Generation (RAG)** is a method for enhancing the responses of language models by injecting external knowledge at generation time. Instead of relying solely on what the model "knows" (from training), RAG enables the model to query external sources—like search engines, databases, APIs, or custom document stores—to access the most relevant and up-to-date information.
+
+RAG can be implemented in multiple ways, depending on your system's needs:
+
+| Architecture            | Description                                                                | Control   | Flexibility | Latency        | Example Use Case                                   |
+|-------------------------|----------------------------------------------------------------------------|-----------|-------------|----------------|----------------------------------------------------|
+| **Agentic RAG**         | An LLM-powered agent decides *when* and *how* to retrieve during reasoning | ❌ Low     | ✅ High      | ⏳ Variable     | Research assistants with access to multiple tools  |
+| **Bounded Agentic RAG** | Agentic RAG with a fixed number of reasoning/retrieval loops               | ✅ Medium  | ✅ Medium    | ⏱️ Predictable | Smart assistants with predictable runtime/behavior |
+| **2-Step RAG**          | Retrieval always happens before generation. Simple and predictable         | ✅ High    | ❌ Low       | ⚡ Fast         | FAQs, documentation bots                           |
+| **Hybrid**              | Combines characteristics of both approaches with validation steps          | ⚖️ Medium | ⚖️ Medium   | ⏳ Variable     | Domain-specific Q&A with quality validation        |
+
+<Info>
+**Latency**: Latency is generally more **predictable** in both **Bounded Agentic RAG** and **2-Step RAG**, as the maximum number of LLM calls is known and capped. This predictability assumes that LLM inference time is the dominant factor. However, real-world latency may also be affected by the performance of retrieval steps—such as API response times, network delays, or database queries—which can vary based on the tools and infrastructure in use.
+</Info>
+
+## Agentic RAG
+
+**Agentic Retrieval-Augmented Generation (RAG)** combines the strengths of Retrieval-Augmented Generation with agent-based reasoning. Instead of retrieving documents before answering, an agent (powered by an LLM) reasons step-by-step and decides **when** and **how** to retrieve information during the interaction.
+
+<Tip>
+
+The only thing an agent needs to enable RAG behavior is access to one or more **tools** that can fetch external knowledge — such as documentation loaders, web APIs, or database queries. This tool-based architecture makes Agentic RAG modular, flexible, and ideal for evolving knowledge environments.
+
+</Tip>
+
+```mermaid
+graph LR
+    A[User Input / Question] --> B["Agent (LLM)"]
+    B --> C{Need external info?}
+    C -- Yes --> D["Search using tool(s)"]
+    D --> H{Enough to answer?}
+    H -- No --> B
+    H -- Yes --> I[Generate final answer]
+    C -- No --> I
+    I --> J[Return to user]
+
+    %% Dark-mode friendly styling
+    classDef startend fill:#2e7d32,stroke:#1b5e20,stroke-width:2px,color:#fff
+    classDef decision fill:#f9a825,stroke:#f57f17,stroke-width:2px,color:#000
+    classDef process fill:#1976d2,stroke:#0d47a1,stroke-width:1.5px,color:#fff
+
+    class A,J startend
+    class B,D,I process
+    class C,H decision
+```
+
+```python
+from langchain.chat_models import init_chat_model
+from langgraph.prebuilt import create_react_agent
+
+model = init_chat_model('claude-sonnet-4-0', max_tokens=32_000)
+
+agent = create_react_agent(
+    model=model,
+    # Include tools that include retrieval tools
+    tools=tools,  # [!code highlight] 
+    # Customize the prompt with instructions on how to retrieve
+    # the data.
+    prompt=system_prompt,
+)
+```
+
+### Bounded Agentic RAG
+
+Agentic RAG systems can be configured with a limit on how many reasoning/retrieval loops the agent may perform. This provides a useful balance between **flexibility** and **predictability**.
+
+A common pattern is **1-loop Agentic RAG**:
+
+* The agent decides whether to retrieve.
+* If it does retrieve, it may rewrite the query.
+* After at most one retrieval step, it generates the final answer.
+
+This setup enables paraphrasing and tool use without allowing open-ended loops. If the LLM supports it, tool calls may run in parallel.
+
+```python
+agent = create_react_agent(
+    model=model,
+    tools=tools,
+    prompt=system_prompt,
+    max_iterations=1,  # Limits agent to one loop
+)
+```
+
+<Expandable title="Extended example: Agentic RAG for LangGraph's llms.txt">
+
+This example implements an **Agentic RAG system** to assist users in querying LangGraph documentation. The agent begins by loading [llms.txt](https://llmstxt.org/), which lists available documentation URLs, and can then dynamically use a `fetch_documentation` tool to retrieve and process the relevant content based on the user’s question.
+
+```python
+import requests
+from langchain.chat_models import init_chat_model
+from langchain_core.tools import tool
+from langgraph.prebuilt import create_react_agent
+from markdownify import markdownify
+
+ALLOWED_DOMAINS = ["https://langchain-ai.github.io/"]
+LLMS_TXT = 'https://langchain-ai.github.io/langgraph/llms.txt'
+
+
+@tool
+def fetch_documentation(url: str) -> str:  # [!code highlight]
+    """Fetch and convert documentation from a URL"""
+    if not any(url.startswith(domain) for domain in ALLOWED_DOMAINS):
+        return (
+            "Error: URL not allowed. "
+            f"Must start with one of: {', '.join(ALLOWED_DOMAINS)}"
+        )
+    response = requests.get(url, timeout=10.0)
+    response.raise_for_status()
+    return markdownify(response.text)
+
+
+# We will fetch the content of llms.txt, so this can 
+# be done ahead of time without requiring an LLM request.
+llms_txt_content = requests.get(LLMS_TXT).text
+
+# System prompt for the agent
+system_prompt = f"""
+You are an expert Python developer and technical assistant. 
+Your primary role is to help users with questions about LangGraph and related tools.
+
+Instructions:
+
+1. If a user asks a question you're unsure about — or one that likely involves API usage, 
+   behavior, or configuration — you MUST use the `fetch_documentation` tool to consult the relevant docs.
+2. When citing documentation, summarize clearly and include relevant context from the content.
+3. Do not use any URLs outside of the allowed domain.
+4. If a documentation fetch fails, tell the user and proceed with your best expert understanding.
+
+You can access official documentation from the following approved sources:
+
+{llms_txt_content}
+
+You MUST consult the documentation to get up to date documentation 
+before answering a user's question about LangGraph.
+
+Your answers should be clear, concise, and technically accurate.
+"""
+
+tools = [fetch_documentation]
+
+model = init_chat_model('claude-sonnet-4-0', max_tokens=32_000)
+
+agent = create_react_agent(
+    model=model,
+    tools=tools,  # [!code highlight]
+    prompt=system_prompt,  # [!code highlight]
+    name="Agentic RAG",
+)
+
+response = agent.invoke({
+    'messages': [{
+        'role': 'user',
+        'content': (
+            "Write a short example of a langgraph agent using the "
+            "prebuilt create react agent. the agent should be able "
+            "to loook up stock pricing information."
+        )
+    }]
+})
+
+print(response['messages'][-1].content)
+```
+</Expandable>
+
+## 2-step workflow
+
+In **2-Step RAG**, the retrieval step is always executed before the generation step. This architecture is straightforward and predictable, making it suitable for many applications where the retrieval of relevant documents is a clear prerequisite for generating an answer.
+
+```mermaid
+graph LR
+    A[User Question] --> B["Retrieve Relevant Documents"]
+    B --> C["Generate Answer"]
+    C --> D[Return Answer to User]
+
+    %% Styling
+    classDef startend fill:#2e7d32,stroke:#1b5e20,stroke-width:2px,color:#fff
+    classDef process fill:#1976d2,stroke:#0d47a1,stroke-width:1.5px,color:#fff
+
+    class A,D startend
+    class B,C process
+```
+
+<Expandable title="Extended example: 2-Step RAG for LangGraph GitHub issues">
+
+This example demonstrates a simple 2-step RAG system that retrieves open GitHub issues from the LangGraph repository and generates an answer based on the retrieved content.
+
+```python
+from typing import TypedDict, NotRequired
+
+import requests
+from langchain.chat_models import init_chat_model
+from langgraph.graph import StateGraph, END
+
+
+class GraphState(TypedDict):
+    question: str
+    retrieved_content: NotRequired[str]
+    answer: NotRequired[str]
+
+
+llm = init_chat_model('claude-sonnet-4-0', max_tokens=32000)
+
+
+def retrieval_step(state: GraphState):  # [!code highlight]
+    """Retrieve open issues from the LangGraph GitHub repository."""
+    headers = {
+        "Accept": "application/vnd.github+json",
+        "User-Agent": "langgraph-rag-example",
+    }
+
+    url = "https://api.github.com/repos/langchain-ai/langgraph/issues"
+    params = {
+        "state": "open",
+        "per_page": 50,
+    }
+    response = requests.get(url, headers=headers, params=params)
+    response.raise_for_status()
+
+    items = response.json()
+    base_url = "https://github.com/langchain-ai/langgraph/issues/"
+    # Filter out PRs (issues with "pull_request" key are actually PRs)
+    issues = [f"- {issue['title']} {base_url}{issue['number']}" for issue in items if
+              "pull_request" not in issue]
+    retrieved = "\n".join(issues) if issues else "No issues found."
+
+    return {
+        "retrieved_content": retrieved
+    }
+
+
+def generate_response(state: GraphState):  # [!code highlight]
+    """Generate an answer based on the retrieved content and the user's question."""
+    prompt = [
+        {
+            "role": "system",
+            "content": (
+                "You are a helpful assistant. Use the following GitHub issue data to answer the user's question. "
+                "When relevant also include urls to the issues in the response.\n\n---\n\n"
+                f"Retrieved GitHub Issues:\n{state['retrieved_content']}"
+            )
+        },
+        {
+            "role": "user",
+            "content": state["question"]
+        }
+    ]
+    response = llm.invoke(prompt)
+    return {
+        "question": state["question"],
+        "retrieved_content": state["retrieved_content"],
+        "answer": response.content
+    }
+
+
+builder = StateGraph(GraphState)
+builder.add_node("retrieval", retrieval_step)  # [!code highlight]
+builder.add_node("generation", generate_response)  # [!code highlight]
+builder.set_entry_point("retrieval")
+builder.add_edge("retrieval", "generation")
+builder.add_edge("generation", END)
+
+graph = builder.compile(name="2-step rag")
+
+response = graph.invoke({
+    "question": "What are the themes in the recent issues?",
+})
+
+print(response['answer'])
+```
+
+</Expandable>
+
+## Hybrid RAG
+
+Hybrid RAG combines characteristics of both 2-Step and Agentic RAG. It introduces intermediate steps such as query preprocessing, retrieval validation, and post-generation checks. These systems offer more flexibility than fixed pipelines while maintaining some control over execution.
+
+Typical components include:
+
+* **Query enhancement**: Modify the input question to improve retrieval quality. This can involve rewriting unclear queries, generating multiple variations, or expanding queries with additional context.
+* **Retrieval validation**: Evaluate whether retrieved documents are relevant and sufficient. If not, the system may refine the query and retrieve again.
+* **Answer validation**: Check the generated answer for accuracy, completeness, and alignment with source content. If needed, the system can regenerate or revise the answer.
+
+The architecture often supports multiple iterations between these steps:
+
+```mermaid
+graph LR
+    A[User Question] --> B[Query Enhancement]
+    B --> C[Retrieve Documents]
+    C --> D{Sufficient Info?}
+    D -- No --> E[Refine Query]
+    E --> C
+    D -- Yes --> F[Generate Answer]
+    F --> G{Answer Quality OK?}
+    G -- No --> H{Try Different Approach?}
+    H -- Yes --> E
+    H -- No --> I[Return Best Answer]
+    G -- Yes --> I
+    I --> J[Return to User]
+
+    classDef startend fill:#2e7d32,stroke:#1b5e20,stroke-width:2px,color:#fff
+    classDef decision fill:#f9a825,stroke:#f57f17,stroke-width:2px,color:#000
+    classDef process fill:#1976d2,stroke:#0d47a1,stroke-width:1.5px,color:#fff
+
+    class A,J startend
+    class B,C,E,F,I process
+    class D,G,H decision
+```
+
+This architecture is suitable for:
+
+* Applications with ambiguous or underspecified queries
+* Systems that require validation or quality control steps
+* Workflows involving multiple sources or iterative refinement
+
+**Example** [Agentic RAG with Self-Correction](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag)
+
+## Building a knowledge base
+
+A key component of RAG systems is a **knowledge base**—a repository of documents or data that the retrieval step can query.
+
+If you want to build a custom knowledge base, you can use LangChain's document loaders and vector stores to create one from your own data.
\ No newline at end of file
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diff --git a/src/langchain_v1/recursive.ipynb b/src/langchain_v1/recursive.ipynb
new file mode 100644
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--- /dev/null
+++ b/src/langchain_v1/recursive.ipynb
@@ -0,0 +1,321 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "57ad9ff9-68b6-47ef-842e-4254177b8028",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: \"Recursive\"\n",
+    "icon: \"arrows-spin\"\n",
+    "---\n",
+    "\n",
+    "\n",
+    "<Warning>\n",
+    "This chain will not be released! \n",
+    "    \n",
+    "It seems pretty unimportant these days and replceable with a simple map reduce given that context windows are so large.\n",
+    "\n",
+    "Likely it'll appear for some speciality use cases and in these cases users will probably can optimize the graph on their own.\n",
+    "</Warning>\n",
+    "\n",
+    "\n",
+    "\n",
+    "```mermaid\n",
+    "graph TD\n",
+    "    %% Level 0 - Original Docs\n",
+    "    A1[Doc1] --> B1[Sum1]\n",
+    "    A2[Doc2] --> B2[Sum2]\n",
+    "    A3[Doc3] --> B3[Sum3]\n",
+    "    A4[Doc4] --> B4[Sum4]\n",
+    "    A5[Doc5] --> B5[Sum5]\n",
+    "    A6[Doc6] --> B6[Sum6]\n",
+    "    A7[Doc7] --> B7[Sum7]\n",
+    "    A8[Doc8] --> B8[Sum8]\n",
+    "\n",
+    "    %% Level 1 - First Combines\n",
+    "    B1 --> C1[CombSum1]\n",
+    "    B2 --> C1\n",
+    "    B3 --> C2[CombSum2]\n",
+    "    B4 --> C2\n",
+    "    B5 --> C3[CombSum3]\n",
+    "    B6 --> C3\n",
+    "    B7 --> C4[CombSum4]\n",
+    "    B8 --> C4\n",
+    "\n",
+    "    %% Level 2 - Mega Combines\n",
+    "    C1 --> D1[MegaSum1]\n",
+    "    C2 --> D1\n",
+    "    C3 --> D2[MegaSum2]\n",
+    "    C4 --> D2\n",
+    "\n",
+    "    %% Level 3 - Final Summary\n",
+    "    D1 --> E[FINAL_SUMMARY]\n",
+    "    D2 --> E\n",
+    "```\n",
+    "\n",
+    "\n",
+    "## Example dataset\n",
+    "\n",
+    "\n",
+    "This text is sourced from [Project Gutenberg](https://www.gutenberg.org/ebooks/2600) and is in the public domain. Redistribution is permitted, but the following attribution must be preserved:\n",
+    "\n",
+    "> This eBook is for the use of anyone anywhere at no cost and with\n",
+    "> almost no restrictions whatsoever. You may copy it, give it away or\n",
+    "> re-use it under the terms of the Project Gutenberg License included\n",
+    "> with this eBook or online at [www.gutenberg.org](https://www.gutenberg.org).\n",
+    ">\n",
+    "> Public domain text provided by Project Gutenberg:\n",
+    "> [https://www.gutenberg.org/ebooks/2600](https://www.gutenberg.org/ebooks/2600)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad27b7f1-a866-4785-8ffe-593dc8ea20c0",
+   "metadata": {},
+   "source": [
+    "## 🛠️ Step 1: Download the Text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b7b505b7-0118-43a6-8c56-0c3a81636533",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File 'war_and_peace_gutenberg.txt' already exists. Skipping download.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import requests\n",
+    "\n",
+    "# URL of the plain text file from Project Gutenberg\n",
+    "url = \"https://www.gutenberg.org/cache/epub/1184/pg1184.txt\"\n",
+    "output_path = Path(\"war_and_peace_gutenberg.txt\")\n",
+    "\n",
+    "# Check if file already exists\n",
+    "if output_path.exists():\n",
+    "    print(f\"File '{output_path}' already exists. Skipping download.\")\n",
+    "else:\n",
+    "    response = requests.get(url)\n",
+    "    if response.status_code == 200:\n",
+    "        output_path.write_text(response.text + attribution, encoding=\"utf-8\")\n",
+    "        print(f\"Downloaded and saved to '{output_path}' with attribution.\")\n",
+    "    else:\n",
+    "        print(f\"Failed to download. Status code: {response.status_code}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84220f8a-b877-4785-a01a-35ffcbf62f0d",
+   "metadata": {},
+   "source": [
+    "## 🧱 Step 2: Split Text into Chunks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b41fb753-2184-4dd4-930f-60f997cc2535",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chunks created: 27\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
+    "\n",
+    "text = output_path.read_text()\n",
+    "\n",
+    "splitter = RecursiveCharacterTextSplitter(\n",
+    "    chunk_size=100_000,\n",
+    "    chunk_overlap=500,\n",
+    ")\n",
+    "\n",
+    "texts = splitter.split_text(text)\n",
+    "print(f\"Chunks created: {len(texts)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8aaaacc2-bc99-4ce6-83a3-e71f393a5a0e",
+   "metadata": {},
+   "source": [
+    "## 🧾 Step 3: Convert to Document Format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c9e60652-9f13-45aa-bc33-1a75c200aed5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "documents = [Document(page_content=chunk) for chunk in texts]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a395680c-9fd6-4245-bd63-3fd56a0e5c3d",
+   "metadata": {},
+   "source": [
+    "## 🔄 Step 4: Define Output Schema (Optional)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e5185b45-df3c-4426-beae-001281a07932",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "class Person(BaseModel):\n",
+    "    name: str\n",
+    "    age: str | None = None\n",
+    "    hair_color: str | None = None\n",
+    "    source_doc_ids: list[str] = Field(\n",
+    "        default=[],\n",
+    "        description=\"The IDs of the documents where the information was found.\"\n",
+    "    )\n",
+    "\n",
+    "class PeopleRoot(BaseModel):\n",
+    "    people: list[Person]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fe4b01b-c514-46a5-b860-2766afaf5316",
+   "metadata": {},
+   "source": [
+    "## 🤖 Step 5: Build Recursive Summarizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "aae93f68-f7de-49c0-a9ce-f884b29ff7cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import create_recursive_document_chain\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "# Choose model ID (adjust to what your setup supports)\n",
+    "model = init_chat_model(\"claude-opus-4-20250514\")\n",
+    "\n",
+    "summarizer = create_recursive_document_chain(\n",
+    "    model,\n",
+    "    map_prompt=\"Produce a summary in bullet points with up to 3 bullets.\",\n",
+    ").compile(name=\"RecursiveSummarizer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d523c7a8-3e49-48d5-9b5b-7b154bca323f",
+   "metadata": {},
+   "source": [
+    "## 🚀 Step 6: Run Summarization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ffaa45f3-4531-48c2-b229-fc51bdee328c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Output parser received a `max_tokens` stop reason. The output is likely incomplete—please increase `max_tokens` or shorten your prompt.\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/eugene/.cache/uv/archive-v0/H7PJAEZVghiAsX_gNYVSD/lib/python3.12/site-packages/langchain_core/output_parsers/openai_tools.py\", line 336, in parse_result\n",
+      "    pydantic_objects.append(name_dict[res[\"type\"]](**res[\"args\"]))\n",
+      "                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/eugene/.cache/uv/archive-v0/H7PJAEZVghiAsX_gNYVSD/lib/python3.12/site-packages/pydantic/main.py\", line 253, in __init__\n",
+      "    validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)\n",
+      "                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "pydantic_core._pydantic_core.ValidationError: 1 validation error for PeopleRoot\n",
+      "people\n",
+      "  Field required [type=missing, input_value={}, input_type=dict]\n",
+      "    For further information visit https://errors.pydantic.dev/2.11/v/missing\n"
+     ]
+    },
+    {
+     "ename": "ValidationError",
+     "evalue": "1 validation error for PeopleRoot\npeople\n  Field required [type=missing, input_value={}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.11/v/missing",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mValidationError\u001b[39m                           Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m output = \u001b[43msummarizer\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdocuments\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[32;43m8\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      2\u001b[39m \u001b[38;5;28mprint\u001b[39m(output)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/docs/.venv/lib/python3.12/site-packages/langgraph/pregel/main.py:3015\u001b[39m, in \u001b[36mPregel.invoke\u001b[39m\u001b[34m(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, **kwargs)\u001b[39m\n\u001b[32m   3012\u001b[39m chunks: \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any] | Any] = []\n\u001b[32m   3013\u001b[39m interrupts: \u001b[38;5;28mlist\u001b[39m[Interrupt] = []\n\u001b[32m-> \u001b[39m\u001b[32m3015\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   3016\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m   3017\u001b[39m \u001b[43m    \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3018\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3019\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mupdates\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m   3020\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m   3021\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3022\u001b[39m \u001b[43m    \u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3023\u001b[39m \u001b[43m    \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3024\u001b[39m \u001b[43m    \u001b[49m\u001b[43minterrupt_before\u001b[49m\u001b[43m=\u001b[49m\u001b[43minterrupt_before\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3025\u001b[39m \u001b[43m    \u001b[49m\u001b[43minterrupt_after\u001b[49m\u001b[43m=\u001b[49m\u001b[43minterrupt_after\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3026\u001b[39m \u001b[43m    \u001b[49m\u001b[43mdurability\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdurability\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3027\u001b[39m \u001b[43m    \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3028\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m   3029\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\n\u001b[32m   3030\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m:\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/docs/.venv/lib/python3.12/site-packages/langgraph/pregel/main.py:2642\u001b[39m, in \u001b[36mPregel.stream\u001b[39m\u001b[34m(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, subgraphs, debug, **kwargs)\u001b[39m\n\u001b[32m   2640\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m task \u001b[38;5;129;01min\u001b[39;00m loop.match_cached_writes():\n\u001b[32m   2641\u001b[39m     loop.output_writes(task.id, task.writes, cached=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m-> \u001b[39m\u001b[32m2642\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrunner\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtick\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   2643\u001b[39m \u001b[43m    \u001b[49m\u001b[43m[\u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mloop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwrites\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   2644\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstep_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   2645\u001b[39m \u001b[43m    \u001b[49m\u001b[43mget_waiter\u001b[49m\u001b[43m=\u001b[49m\u001b[43mget_waiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   2646\u001b[39m \u001b[43m    \u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m=\u001b[49m\u001b[43mloop\u001b[49m\u001b[43m.\u001b[49m\u001b[43maccept_push\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   2647\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m   2648\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# emit output\u001b[39;49;00m\n\u001b[32m   2649\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01myield from\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_output\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   2650\u001b[39m \u001b[43m        \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubgraphs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqueue\u001b[49m\u001b[43m.\u001b[49m\u001b[43mEmpty\u001b[49m\n\u001b[32m   2651\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2652\u001b[39m loop.after_tick()\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/docs/.venv/lib/python3.12/site-packages/langgraph/pregel/_runner.py:253\u001b[39m, in \u001b[36mPregelRunner.tick\u001b[39m\u001b[34m(self, tasks, reraise, timeout, retry_policy, get_waiter, schedule_task)\u001b[39m\n\u001b[32m    251\u001b[39m \u001b[38;5;66;03m# panic on failure or timeout\u001b[39;00m\n\u001b[32m    252\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m253\u001b[39m     \u001b[43m_panic_or_proceed\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    254\u001b[39m \u001b[43m        \u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdone\u001b[49m\u001b[43m.\u001b[49m\u001b[43munion\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m.\u001b[49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    255\u001b[39m \u001b[43m        \u001b[49m\u001b[43mpanic\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreraise\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    256\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    257\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m    258\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m tb := exc.__traceback__:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/docs/.venv/lib/python3.12/site-packages/langgraph/pregel/_runner.py:511\u001b[39m, in \u001b[36m_panic_or_proceed\u001b[39m\u001b[34m(futs, timeout_exc_cls, panic)\u001b[39m\n\u001b[32m    509\u001b[39m                 interrupts.append(exc)\n\u001b[32m    510\u001b[39m             \u001b[38;5;28;01melif\u001b[39;00m fut \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m SKIP_RERAISE_SET:\n\u001b[32m--> \u001b[39m\u001b[32m511\u001b[39m                 \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[32m    512\u001b[39m \u001b[38;5;66;03m# raise combined interrupts\u001b[39;00m\n\u001b[32m    513\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m interrupts:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/docs/.venv/lib/python3.12/site-packages/langgraph/pregel/_executor.py:81\u001b[39m, in \u001b[36mBackgroundExecutor.done\u001b[39m\u001b[34m(self, task)\u001b[39m\n\u001b[32m     79\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Remove the task from the tasks dict when it's done.\"\"\"\u001b[39;00m\n\u001b[32m     80\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m     \u001b[43mtask\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     82\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m GraphBubbleUp:\n\u001b[32m     83\u001b[39m     \u001b[38;5;66;03m# This exception is an interruption signal, not an error\u001b[39;00m\n\u001b[32m     84\u001b[39m     \u001b[38;5;66;03m# so we don't want to re-raise it on exit\u001b[39;00m\n\u001b[32m     85\u001b[39m     \u001b[38;5;28mself\u001b[39m.tasks.pop(task)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.12.8-linux-x86_64-gnu/lib/python3.12/concurrent/futures/_base.py:449\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m    447\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[32m    448\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state == FINISHED:\n\u001b[32m--> \u001b[39m\u001b[32m449\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    451\u001b[39m \u001b[38;5;28mself\u001b[39m._condition.wait(timeout)\n\u001b[32m    453\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.12.8-linux-x86_64-gnu/lib/python3.12/concurrent/futures/_base.py:401\u001b[39m, in \u001b[36mFuture.__get_result\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    399\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception:\n\u001b[32m    400\u001b[39m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m401\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception\n\u001b[32m    402\u001b[39m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m    403\u001b[39m         \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[32m    404\u001b[39m         \u001b[38;5;28mself\u001b[39m = \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.12.8-linux-x86_64-gnu/lib/python3.12/concurrent/futures/thread.py:59\u001b[39m, in \u001b[36m_WorkItem.run\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     56\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m     58\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m59\u001b[39m     result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     60\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m     61\u001b[39m     \u001b[38;5;28mself\u001b[39m.future.set_exception(exc)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/docs/.venv/lib/python3.12/site-packages/langgraph/pregel/_retry.py:42\u001b[39m, in \u001b[36mrun_with_retry\u001b[39m\u001b[34m(task, retry_policy, configurable)\u001b[39m\n\u001b[32m     40\u001b[39m     task.writes.clear()\n\u001b[32m     41\u001b[39m     \u001b[38;5;66;03m# run the task\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m42\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtask\u001b[49m\u001b[43m.\u001b[49m\u001b[43mproc\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m.\u001b[49m\u001b[43minput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     43\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ParentCommand \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m     44\u001b[39m     ns: \u001b[38;5;28mstr\u001b[39m = config[CONF][CONFIG_KEY_CHECKPOINT_NS]\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/docs/.venv/lib/python3.12/site-packages/langgraph/_internal/_runnable.py:657\u001b[39m, in \u001b[36mRunnableSeq.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m    655\u001b[39m     \u001b[38;5;66;03m# run in context\u001b[39;00m\n\u001b[32m    656\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m set_config_context(config, run) \u001b[38;5;28;01mas\u001b[39;00m context:\n\u001b[32m--> \u001b[39m\u001b[32m657\u001b[39m         \u001b[38;5;28minput\u001b[39m = \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    658\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    659\u001b[39m     \u001b[38;5;28minput\u001b[39m = step.invoke(\u001b[38;5;28minput\u001b[39m, config)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/docs/.venv/lib/python3.12/site-packages/langgraph/_internal/_runnable.py:401\u001b[39m, in \u001b[36mRunnableCallable.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m    399\u001b[39m         run_manager.on_chain_end(ret)\n\u001b[32m    400\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m401\u001b[39m     ret = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    402\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.recurse \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ret, Runnable):\n\u001b[32m    403\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m ret.invoke(\u001b[38;5;28minput\u001b[39m, config)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/langchain/libs/langchain_v1/langchain/chains/documents/recursive.py:329\u001b[39m, in \u001b[36m_RecursiveSummarizer.create_map_node.<locals>._map_node\u001b[39m\u001b[34m(state, runtime, config)\u001b[39m\n\u001b[32m    325\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_map_node\u001b[39m(\n\u001b[32m    326\u001b[39m     state: MapState, runtime: Runtime, config: RunnableConfig\n\u001b[32m    327\u001b[39m ) -> \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m]]:\n\u001b[32m    328\u001b[39m     prompt = \u001b[38;5;28mself\u001b[39m._get_map_prompt(state, runtime)\n\u001b[32m--> \u001b[39m\u001b[32m329\u001b[39m     response = cast(\u001b[33m\"\u001b[39m\u001b[33mAIMessage\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m    330\u001b[39m     result = response \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.response_format \u001b[38;5;28;01melse\u001b[39;00m response.text()\n\u001b[32m    331\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[33m\"\u001b[39m\u001b[33msummaries\u001b[39m\u001b[33m\"\u001b[39m: [\u001b[38;5;28mstr\u001b[39m(result)]}\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.cache/uv/archive-v0/H7PJAEZVghiAsX_gNYVSD/lib/python3.12/site-packages/langchain_core/runnables/base.py:3046\u001b[39m, in \u001b[36mRunnableSequence.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m   3044\u001b[39m                 input_ = context.run(step.invoke, input_, config, **kwargs)\n\u001b[32m   3045\u001b[39m             \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3046\u001b[39m                 input_ = \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   3047\u001b[39m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[32m   3048\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.cache/uv/archive-v0/H7PJAEZVghiAsX_gNYVSD/lib/python3.12/site-packages/langchain_core/output_parsers/base.py:196\u001b[39m, in \u001b[36mBaseOutputParser.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m    188\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m    189\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m    190\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m    193\u001b[39m     **kwargs: Any,\n\u001b[32m    194\u001b[39m ) -> T:\n\u001b[32m    195\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, BaseMessage):\n\u001b[32m--> \u001b[39m\u001b[32m196\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    197\u001b[39m \u001b[43m            \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43minner_input\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mparse_result\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    198\u001b[39m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatGeneration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[43m=\u001b[49m\u001b[43minner_input\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m    199\u001b[39m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    200\u001b[39m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    201\u001b[39m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    202\u001b[39m \u001b[43m            \u001b[49m\u001b[43mrun_type\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mparser\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    203\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    204\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._call_with_config(\n\u001b[32m    205\u001b[39m         \u001b[38;5;28;01mlambda\u001b[39;00m inner_input: \u001b[38;5;28mself\u001b[39m.parse_result([Generation(text=inner_input)]),\n\u001b[32m    206\u001b[39m         \u001b[38;5;28minput\u001b[39m,\n\u001b[32m    207\u001b[39m         config,\n\u001b[32m    208\u001b[39m         run_type=\u001b[33m\"\u001b[39m\u001b[33mparser\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    209\u001b[39m     )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.cache/uv/archive-v0/H7PJAEZVghiAsX_gNYVSD/lib/python3.12/site-packages/langchain_core/runnables/base.py:1939\u001b[39m, in \u001b[36mRunnable._call_with_config\u001b[39m\u001b[34m(self, func, input_, config, run_type, serialized, **kwargs)\u001b[39m\n\u001b[32m   1935\u001b[39m     child_config = patch_config(config, callbacks=run_manager.get_child())\n\u001b[32m   1936\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m set_config_context(child_config) \u001b[38;5;28;01mas\u001b[39;00m context:\n\u001b[32m   1937\u001b[39m         output = cast(\n\u001b[32m   1938\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mOutput\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m-> \u001b[39m\u001b[32m1939\u001b[39m             \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1940\u001b[39m \u001b[43m                \u001b[49m\u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[32m   1941\u001b[39m \u001b[43m                \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1942\u001b[39m \u001b[43m                \u001b[49m\u001b[43minput_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1943\u001b[39m \u001b[43m                \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1944\u001b[39m \u001b[43m                \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1945\u001b[39m \u001b[43m                \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1946\u001b[39m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m   1947\u001b[39m         )\n\u001b[32m   1948\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m   1949\u001b[39m     run_manager.on_chain_error(e)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.cache/uv/archive-v0/H7PJAEZVghiAsX_gNYVSD/lib/python3.12/site-packages/langchain_core/runnables/config.py:429\u001b[39m, in \u001b[36mcall_func_with_variable_args\u001b[39m\u001b[34m(func, input, config, run_manager, **kwargs)\u001b[39m\n\u001b[32m    427\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[32m    428\u001b[39m     kwargs[\u001b[33m\"\u001b[39m\u001b[33mrun_manager\u001b[39m\u001b[33m\"\u001b[39m] = run_manager\n\u001b[32m--> \u001b[39m\u001b[32m429\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.cache/uv/archive-v0/H7PJAEZVghiAsX_gNYVSD/lib/python3.12/site-packages/langchain_core/output_parsers/base.py:197\u001b[39m, in \u001b[36mBaseOutputParser.invoke.<locals>.<lambda>\u001b[39m\u001b[34m(inner_input)\u001b[39m\n\u001b[32m    188\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m    189\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m    190\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m    193\u001b[39m     **kwargs: Any,\n\u001b[32m    194\u001b[39m ) -> T:\n\u001b[32m    195\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, BaseMessage):\n\u001b[32m    196\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._call_with_config(\n\u001b[32m--> \u001b[39m\u001b[32m197\u001b[39m             \u001b[38;5;28;01mlambda\u001b[39;00m inner_input: \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mparse_result\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    198\u001b[39m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatGeneration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[43m=\u001b[49m\u001b[43minner_input\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m    199\u001b[39m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m    200\u001b[39m             \u001b[38;5;28minput\u001b[39m,\n\u001b[32m    201\u001b[39m             config,\n\u001b[32m    202\u001b[39m             run_type=\u001b[33m\"\u001b[39m\u001b[33mparser\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    203\u001b[39m         )\n\u001b[32m    204\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._call_with_config(\n\u001b[32m    205\u001b[39m         \u001b[38;5;28;01mlambda\u001b[39;00m inner_input: \u001b[38;5;28mself\u001b[39m.parse_result([Generation(text=inner_input)]),\n\u001b[32m    206\u001b[39m         \u001b[38;5;28minput\u001b[39m,\n\u001b[32m    207\u001b[39m         config,\n\u001b[32m    208\u001b[39m         run_type=\u001b[33m\"\u001b[39m\u001b[33mparser\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    209\u001b[39m     )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.cache/uv/archive-v0/H7PJAEZVghiAsX_gNYVSD/lib/python3.12/site-packages/langchain_core/output_parsers/openai_tools.py:336\u001b[39m, in \u001b[36mPydanticToolsParser.parse_result\u001b[39m\u001b[34m(self, result, partial)\u001b[39m\n\u001b[32m    334\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[32m    335\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m336\u001b[39m     pydantic_objects.append(\u001b[43mname_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[43mres\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtype\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mres\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43margs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m    337\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (ValidationError, \u001b[38;5;167;01mValueError\u001b[39;00m):\n\u001b[32m    338\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m partial:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.cache/uv/archive-v0/H7PJAEZVghiAsX_gNYVSD/lib/python3.12/site-packages/pydantic/main.py:253\u001b[39m, in \u001b[36mBaseModel.__init__\u001b[39m\u001b[34m(self, **data)\u001b[39m\n\u001b[32m    251\u001b[39m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[32m    252\u001b[39m __tracebackhide__ = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m253\u001b[39m validated_self = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mself_instance\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m    254\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m validated_self:\n\u001b[32m    255\u001b[39m     warnings.warn(\n\u001b[32m    256\u001b[39m         \u001b[33m'\u001b[39m\u001b[33mA custom validator is returning a value other than `self`.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m'\u001b[39m\n\u001b[32m    257\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mReturning anything other than `self` from a top level model validator isn\u001b[39m\u001b[33m'\u001b[39m\u001b[33mt supported when validating via `__init__`.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    258\u001b[39m         \u001b[33m'\u001b[39m\u001b[33mSee the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m    259\u001b[39m         stacklevel=\u001b[32m2\u001b[39m,\n\u001b[32m    260\u001b[39m     )\n",
+      "\u001b[31mValidationError\u001b[39m: 1 validation error for PeopleRoot\npeople\n  Field required [type=missing, input_value={}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.11/v/missing",
+      "During task with name 'map_summarize' and id '8137de8e-dab2-7a83-871c-1b1c8975ef4d'"
+     ]
+    }
+   ],
+   "source": [
+    "output = summarizer.invoke({\"documents\": documents[:8]})\n",
+    "print(output)"
+   ]
+  }
+ ],
+ "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/langchain_v1/stuff.ipynb b/src/langchain_v1/stuff.ipynb
new file mode 100644
index 00000000..134c875f
--- /dev/null
+++ b/src/langchain_v1/stuff.ipynb
@@ -0,0 +1,452 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "32610a5b-23ea-4880-8013-001b114b555a",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: \"Stuff chain\"\n",
+    "icon: \"book\"\n",
+    "---\n",
+    "\n",
+    "The **Stuff Documents** chain processes all documents in a **single pass** by **concatenating their content** and inserting it into the **context window** of the language model (LLM). This approach is ideal when:\n",
+    "\n",
+    "- The combined size of all documents **fits within the model’s context window**.\n",
+    "- You want **joint analysis**, such as summarizing or reasoning across the entire content.\n",
+    "- You may optionally apply **refinement**, where previous outputs are reprocessed along with new documents.\n",
+    "\n",
+    "```mermaid\n",
+    "graph TD\n",
+    "    A[Documents] --> B[Concatenate all]\n",
+    "    C[Optional question] --> E[Prompt construction: all stuffed in context window]\n",
+    "    D[Optional response format] --> E\n",
+    "    B --> E\n",
+    "    E --> F[LLM Processes input]\n",
+    "    F --> H[\"Extraction / Summary / Answer\"]\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed5cf593-14c8-4073-adaa-c708daf25ab7",
+   "metadata": {},
+   "source": [
+    "# 1. Set up data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "425ca669-9caa-4bea-a1e7-fab8e95210d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import create_stuff_documents_chain\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "from langchain.documents import Document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3488cf7a-1e7b-4d0a-83bc-307f10ce0344",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "documents = [\n",
+    "    Document(\n",
+    "        page_content=(\n",
+    "            \"Richard Feynman was born on May 11, 1918, in Queens, New York. He showed an early \"\n",
+    "            \"interest in science, especially radios and engineering. As a teenager, he repaired \"\n",
+    "            \"radios as a hobby and even earned some money doing it.\\n\\n\"\n",
+    "            \"He attended the Massachusetts Institute of Technology (MIT) for his undergraduate \"\n",
+    "            \"studies and later earned his PhD in physics from Princeton University in 1942. At \"\n",
+    "            \"Princeton, he impressed many with his quick mind and problem-solving skills.\\n\\n\"\n",
+    "            \"After completing his PhD, he joined the Los Alamos Laboratory as part of the Manhattan Project.\"\n",
+    "        ),\n",
+    "        metadata={\"source\": \"early_life\"},\n",
+    "        id=\"1\"\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=(\n",
+    "            \"During World War II, Feynman worked on the Manhattan Project, the top-secret effort \"\n",
+    "            \"to build the first atomic bomb. He was based at Los Alamos Laboratory in New Mexico.\\n\\n\"\n",
+    "            \"There, he worked under physicist Hans Bethe and was known for his creativity and sense \"\n",
+    "            \"of humor. One of his habits was picking locks and cracking safes—not to steal secrets, \"\n",
+    "            \"but to prove how insecure they were.\\n\\n\"\n",
+    "            \"Feynman’s contributions helped the U.S. develop nuclear weapons, which were used in \"\n",
+    "            \"1945 to end the war.\"\n",
+    "        ),\n",
+    "        metadata={\"source\": \"manhattan_project\"},\n",
+    "        id=\"2\"\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=(\n",
+    "            \"After the war, Feynman became a professor at Cornell University and later at the \"\n",
+    "            \"California Institute of Technology (Caltech). In 1965, he won the Nobel Prize in \"\n",
+    "            \"Physics for his work on quantum electrodynamics, shared with Julian Schwinger and \"\n",
+    "            \"Sin-Itiro Tomonaga.\\n\\n\"\n",
+    "            \"He became famous for his lectures, especially the Feynman Lectures on Physics, which \"\n",
+    "            \"are still used today. In 1986, he served on the Rogers Commission that investigated \"\n",
+    "            \"the Space Shuttle Challenger disaster.\\n\\n\"\n",
+    "            \"Feynman died on February 15, 1988, in Los Angeles, California, after a long battle \"\n",
+    "            \"with cancer.\"\n",
+    "        ),\n",
+    "        metadata={\"source\": \"later_career\"},\n",
+    "        id=\"3\"\n",
+    "    ),\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1a42b30-c290-419a-a0d7-60a1517eb0d0",
+   "metadata": {},
+   "source": [
+    "## Configure for summarization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a0581ab0-634a-447e-b8cb-bfe1ef1dde67",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are 3 key sentences summarizing Richard Feynman's life:\n",
+      "\n",
+      "1. Richard Feynman was a brilliant physicist born in 1918 in Queens, New York, who showed early aptitude for science and engineering, eventually earning his PhD from Princeton University in 1942.\n",
+      "\n",
+      "2. During World War II, he worked on the Manhattan Project at Los Alamos Laboratory, contributing to the development of the atomic bomb while becoming known for his creativity, humor, and habit of picking locks to expose security flaws.\n",
+      "\n",
+      "3. After the war, he became a renowned professor at Cornell and Caltech, won the 1965 Nobel Prize in Physics for his work on quantum electrodynamics, created the famous Feynman Lectures on Physics, and served on the commission investigating the Challenger disaster before his death in 1988.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = init_chat_model(\"claude-opus-4-20250514\", max_tokens=32_000)\n",
+    "\n",
+    "summarizer = (\n",
+    "    create_stuff_documents_chain(\n",
+    "        model, \n",
+    "        prompt=\"Summarize the contents into 3 key sentences.\"\n",
+    "    )\n",
+    "    .compile(name='summarize')\n",
+    ")\n",
+    "\n",
+    "print(summarizer.invoke({\"documents\": documents})['result'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aab1b929-ddb9-4ceb-a74c-1b2bb830ef31",
+   "metadata": {},
+   "source": [
+    "### Structured summarization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9c96df90-0a3e-43cc-8606-a394b1cb6381",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Title: Richard Feynman: The Life and Legacy of a Nobel Prize-Winning Physicist\n",
+      " * Born May 11, 1918 in Queens, New York, showing early interest in science and engineering, repairing radios as a teenager (1)\n",
+      " * Educated at MIT for undergraduate studies and earned PhD in physics from Princeton University in 1942 (1)\n",
+      " * Worked on the Manhattan Project at Los Alamos Laboratory during WWII under Hans Bethe, contributing to the development of the atomic bomb (1, 2)\n",
+      " * Known for his creativity, humor, and unconventional habits like picking locks and cracking safes at Los Alamos (2)\n",
+      " * Became professor at Cornell University and later at Caltech after the war (3)\n",
+      " * Won the 1965 Nobel Prize in Physics for quantum electrodynamics work, shared with Julian Schwinger and Sin-Itiro Tomonaga (3)\n",
+      " * Created the famous Feynman Lectures on Physics, which remain widely used educational resources (3)\n",
+      " * Served on the Rogers Commission investigating the Space Shuttle Challenger disaster in 1986 (3)\n",
+      " * Died February 15, 1988 in Los Angeles, California after battling cancer (3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "from typing import Optional, List\n",
+    "\n",
+    "class BulletPoint(BaseModel):\n",
+    "    \"\"\"Represents a single key idea with supporting document references.\"\"\"\n",
+    "    content: str = Field(description=\"A concise bullet point summarizing a key idea.\")\n",
+    "    source_doc_ids: List[str] = Field(default_factory=list, description=\"Document IDs supporting this bullet point.\")\n",
+    "\n",
+    "class StructuredSummary(BaseModel):\n",
+    "    \"\"\"Structured summary including a title and supporting bullet points.\"\"\"\n",
+    "    title: str = Field(description=\"A concise title summarizing the main theme.\")\n",
+    "    bullet_points: List[BulletPoint] = Field(description=\"List of bullet points with supporting document references.\")\n",
+    "\n",
+    "model = init_chat_model(\"claude-opus-4-20250514\", max_tokens=32_000)\n",
+    "\n",
+    "chain = (\n",
+    "    create_stuff_documents_chain(\n",
+    "        model,\n",
+    "        response_format=StructuredSummary,\n",
+    "    )\n",
+    "    .compile(name='structured_summary_extractor')\n",
+    ")\n",
+    "\n",
+    "structured_summary = chain.invoke({\"documents\": documents})['result']\n",
+    "\n",
+    "print(f\"Title: {structured_summary.title}\")\n",
+    "\n",
+    "for bullet_point in structured_summary.bullet_points:\n",
+    "    if not bullet_point.source_doc_ids:\n",
+    "        continue\n",
+    "    sources = ', '.join(bullet_point.source_doc_ids)\n",
+    "    print(f\" * {bullet_point.content} ({sources})\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "781bb94a-d043-4d47-a02b-619e7826fd93",
+   "metadata": {},
+   "source": [
+    "## Configure for extraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "273f6c13-04d6-4ddb-b339-044a36e90129",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracted facts that have dates:\n",
+      " *  on 1918-05-11: Born in Queens, New York (sources: 1)\n",
+      " *  on 1942: PhD from Princeton University (sources: 1)\n",
+      " *  on 1965: Won Nobel Prize in Physics (sources: 3)\n",
+      " *  on 1986: Served on Rogers Commission (sources: 3)\n",
+      " *  on 1988-02-15: Died in Los Angeles, California (sources: 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "from typing import Optional, List\n",
+    "\n",
+    "class Fact(BaseModel):\n",
+    "    \"\"\"Fact about Richard Feynman.\"\"\"\n",
+    "    content: str = Field(description=\"The content of the fact. No more than a few words.\")\n",
+    "    iso_8601_date: Optional[str] = Field(default=None, description=\"The date associated with the fact if available. Formatted as YYYY-MM-DD.\")\n",
+    "    source_doc_ids: List[str] = Field(default_factory=list, description=\"The document or documents in which the fact appeared.\")\n",
+    "\n",
+    "class Data(BaseModel):\n",
+    "    \"\"\"Facts to extract\"\"\"\n",
+    "    facts: List[Fact]\n",
+    "\n",
+    "model = init_chat_model(\"claude-opus-4-20250514\", max_tokens=32_000)\n",
+    "\n",
+    "chain = (\n",
+    "    create_stuff_documents_chain(\n",
+    "        model,\n",
+    "        response_format=Data,\n",
+    "    )\n",
+    "    .compile(name='fact_extractor')\n",
+    ")\n",
+    "\n",
+    "fact_data = chain.invoke({\"documents\": documents})['result']\n",
+    "\n",
+    "print(\"Extracted facts that have dates:\")\n",
+    "for fact in fact_data.facts:\n",
+    "    if not fact.source_doc_ids:\n",
+    "        continue\n",
+    "    if not fact.iso_8601_date:\n",
+    "        continue\n",
+    "    date_str = f\" on {fact.iso_8601_date}\" if fact.iso_8601_date else \"\"\n",
+    "    sources = ', '.join(fact.source_doc_ids)\n",
+    "    print(f\" * {date_str}: {fact.content} (sources: {sources})\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73aa5880-6851-42e0-96ca-e9d6db994c9e",
+   "metadata": {},
+   "source": [
+    "## Use for Q&A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dc9f950d-20bc-495e-b312-961f5a4e9b48",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Based on the documents, here are the key locations where Richard Feynman spent significant time:\n",
+      "\n",
+      "1. Queens, New York - His birthplace\n",
+      "2. Massachusetts Institute of Technology (MIT) - Undergraduate studies\n",
+      "3. Princeton University - PhD studies\n",
+      "4. Los Alamos Laboratory, New Mexico - Worked on the Manhattan Project during World War II\n",
+      "5. Cornell University - Professor\n",
+      "6. California Institute of Technology (Caltech) - Professor\n",
+      "7. Los Angeles, California - Where he died in 1988\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "from typing import Optional, List\n",
+    "\n",
+    "model = init_chat_model(\"claude-3-5-haiku-latest\", max_tokens=5_000)\n",
+    "\n",
+    "chain = (\n",
+    "    create_stuff_documents_chain(model)\n",
+    "    .compile(name='Q&A')\n",
+    ")\n",
+    "\n",
+    "response = chain.invoke({\n",
+    "    \"documents\": documents,\n",
+    "    # A question can be provided during run time. \n",
+    "    \"question\": \"Identify locations where Richard Feynman spent significant time\"\n",
+    "})\n",
+    "print(response['result'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0efd196d-1503-46e0-b331-6515ac743b26",
+   "metadata": {},
+   "source": [
+    "## Refinment\n",
+    "\n",
+    "Refinment can be achieved using LangGraph's persistence layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "11fb5dda-7bf9-429f-a068-c02964373d32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " * Became professor at Caltech (sources: 3)\n",
+      " * Gave famous Feynman Lectures on Physics (sources: 3)\n",
+      " * Served on Rogers Commission investigating Challenger disaster (sources: 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
+    "import uuid\n",
+    "\n",
+    "model = init_chat_model(\"claude-3-5-haiku-latest\", max_tokens=5_000)\n",
+    "\n",
+    "checkpointer = InMemorySaver()\n",
+    "\n",
+    "class Fact(BaseModel):\n",
+    "    \"\"\"Fact.\"\"\"\n",
+    "    content: str = Field(description=\"The content of the fact. No more than a few words.\")\n",
+    "    source_doc_ids: List[str] = Field(default_factory=list, description=\"The document or documents in which the fact appeared.\")\n",
+    "\n",
+    "class Data(BaseModel):\n",
+    "    \"\"\"Facts to extract\"\"\"\n",
+    "    facts: List[Fact]\n",
+    "\n",
+    "chain = (\n",
+    "    create_stuff_documents_chain(\n",
+    "        model, \n",
+    "        prompt=\"Extract any facts about Richard Feynman's time at Caltech.\",\n",
+    "        response_format=Data\n",
+    "    )\n",
+    "    .compile(name='Q&A', checkpointer=checkpointer)\n",
+    ")\n",
+    "\n",
+    "thread_id = uuid.uuid4()\n",
+    "\n",
+    "response = chain.invoke(\n",
+    "    {\n",
+    "        \"documents\": documents,\n",
+    "    },\n",
+    "    {\n",
+    "        'configurable': {\n",
+    "            'thread_id': thread_id,\n",
+    "        }\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "for fact in response['result'].facts:\n",
+    "    sources = ', '.join(fact.source_doc_ids)\n",
+    "    print(f\" * {fact.content} (sources: {sources})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "48e395bd-8ed6-4fdd-ba4a-52cfaf52ad52",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " * Became professor at Caltech (sources: 3)\n",
+      " * Gave famous Feynman Lectures on Physics (sources: 3)\n",
+      " * Served on Rogers Commission investigating Challenger disaster (sources: 3)\n",
+      " * Loved eating ice cream at Caltech (sources: 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = chain.invoke(\n",
+    "    {\n",
+    "        \"documents\": [\n",
+    "            Document(id=4, page_content=\"Richard Feynman used to eat a ton of icecream while at Caltech\")\n",
+    "        ],\n",
+    "    },\n",
+    "    {\n",
+    "        'configurable': {\n",
+    "            'thread_id': thread_id,\n",
+    "        }\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "\n",
+    "for fact in response['result'].facts:\n",
+    "    sources = ', '.join(fact.source_doc_ids)\n",
+    "    print(f\" * {fact.content} (sources: {sources})\")"
+   ]
+  }
+ ],
+ "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}