infra: add CI job for running tutorial notebooks (#26944)

.github/workflows/run_notebooks.yml

@@ -0,0 +1,56 @@
+name: Run notebooks
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: '0 13 * * *'
+
+env:
+  PYTHON_VERSION: "3.11"
+  POETRY_VERSION: "1.7.1"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    name: "Test docs"
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: run-notebooks
+
+      - name: Install dependencies
+        run: |
+          pip install -e libs/core
+          pip install -e libs/langchain
+          pip install -e libs/community
+          pip install --upgrade langchain-experimental
+          pip install -e libs//partners/anthropic
+          pip install -e libs//partners/chroma
+          pip install -e libs//partners/openai
+          pip install -e libs//partners/mistralai
+          pip install jupyter langgraph click pypdf vcrpy
+
+      - name: Pre-download tiktoken files
+        run: |
+          python docs/scripts/download_tiktoken.py
+
+      - name: Prepare notebooks
+        run: |
+
+          python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells
+
+      - name: Run notebooks
+        env:
+          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+          MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
+        run: |
+          ./docs/scripts/execute_notebooks.sh

docs/cassettes/agents_114ba50d.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/agents_24460239.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/agents_482ce13d.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agents_532d6557.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agents_56d8028b-5dbc-40b2-86f5-ed60631d86a3.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/agents_688b465d.msgpack.zlib

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docs/cassettes/agents_77c2f769.msgpack.zlib

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docs/cassettes/agents_a13462d0-2d02-4474-921e-15a1ba1fa274.msgpack.zlib

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docs/cassettes/agents_a3fb262c.msgpack.zlib

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docs/cassettes/agents_b6a7e925.msgpack.zlib

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docs/cassettes/agents_c96c960b.msgpack.zlib

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docs/cassettes/chatbot_17.msgpack.zlib

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docs/cassettes/chatbot_18.msgpack.zlib

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docs/cassettes/chatbot_20.msgpack.zlib

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docs/cassettes/chatbot_22.msgpack.zlib

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docs/cassettes/chatbot_27.msgpack.zlib

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docs/cassettes/chatbot_31.msgpack.zlib

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docs/cassettes/chatbot_32.msgpack.zlib

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docs/cassettes/chatbot_36.msgpack.zlib

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docs/cassettes/chatbot_40.msgpack.zlib

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docs/cassettes/chatbot_41.msgpack.zlib

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docs/cassettes/chatbot_46.msgpack.zlib

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docs/cassettes/chatbot_48.msgpack.zlib

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docs/cassettes/chatbot_5.msgpack.zlib

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docs/cassettes/chatbot_51.msgpack.zlib

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docs/cassettes/chatbot_53.msgpack.zlib

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docs/cassettes/chatbot_56.msgpack.zlib

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docs/cassettes/chatbot_7.msgpack.zlib

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docs/cassettes/chatbot_9.msgpack.zlib

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docs/cassettes/classification_0bdfcb05.msgpack.zlib

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docs/cassettes/classification_1c12fa00.msgpack.zlib

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docs/cassettes/classification_5509b6a6.msgpack.zlib

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docs/cassettes/classification_9154474c.msgpack.zlib

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docs/cassettes/data_generation_ff823394.msgpack.zlib

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docs/cassettes/extraction_cf7062cc-1d1d-4a37-9122-509d1b87f0a6.msgpack.zlib

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docs/cassettes/rag_ff01d415-7b0f-469d-bfda-b9cb672da611.msgpack.zlib

@@ -0,0 +1 @@
+eNrtVV9v3EQQV8UXWa2QkND5znfn8zVGeYii0iIaFdSChNrK2qzH9jb2rru7zuUa3QOhrzyYTwAkSqqoBR4QL1CJN3jgC4RPw6x91z8hqFV5hJNO55vZmfnNzm9+PjjZBW2EkpceC2lBM27xj/nq4ETD/RqMfXhcgs1Vcnj1yq3DWouzt3NrKxMNBqwSfVMKm/cLJjOeMyH7XJUDIVN1tK2S+a8nObAE0z88/cSA9jYykLb50Z1u47xqPvD7w/5wdPn7Dc6hst4VyVUiZNY8yR6IqkcSSAtm4bhzNz+wqioEZw7j4J5R8nRTSQkt5uZ0B6DyWCF24ZEGU2Eb8MWxsczW5uAI88Ifv5+UYAzL4NsbH67AfXmyUVjv5i5vzvr5eJ1GQTCm75GSrY8mayPf93v52ButXeA4Iw6WMR6CsFoV3kZRqJm3qSHBRgUrTHNkdQ2/XHjsWle++ebdxxwdGOHZeQV/7/HnC8NvaJEJ2Xz90znvFttzF90chr5/ceGtdp6u8NNz/it7lTLwArJjHBvyozmpdwVXWh4lOIzm6fta9MhoSm5CRUb+KCDDMBqOo0lIrm7dOvxUsOYUp0oypbICnnDGc/B4V6N5JJXXWr7bXHZ9HWRm8+YwmExa2nyOM9PIgT8v/bZPl+ykEfX70/54jfYoXg7gaGPYq4Rurym2ogQaybooenSbWZ7HGI/kjbFqKjIa7VPDWQFxXcX3jXgAMVbIMtA0GroJP/dKm2ts38SFQIqiO1w5EzWTsYSysvPn0QF6XbrV6TbXM0O8PbdgaDTy16bDychf9KiQSEjJIUZeZ8YBw6XAtbMQMxHjxul5DJJtF5DQyJGnR5XOYo6g2k4TYZbOFPmFXpOrWWxtEddiFWBxi7FDATpO6uUNJWzeViuUzNyGYIKgBZsrbZeGYYAADTCN93cOw0zpHVO5tIarCmKHSchd0ba3QjKOjVUat+vl6MXin8Vk/VVigl+0moEuygGm9iqtcAIDJwrG/q8y/0WVmf4rlbkcBC+rzFvX92nHsjhnJkelmfhBMGLpcDyGcDiZhjANJuOQh5NJyCGFYcr4MJhylvjjURqMp9Pt0OfBOISQJ3w7BNSokkmRIkPdygncg9v0Ga3R25HY4BNaLP5s4s9HrfEWCozjIr2LQsdxJ3GdcV6ICltDxDXHFcOInRnTnX4suYbPt1+r1rUawW11QW9as0v6quaWp3r0TcvYVUREP1M1YRoIk4QZI5yIWpIqTVpdQRJ7TJoZuIkSy8yO6RNUA2JzwFOOkM5RCUAmEpUSDTh8QNUj7VbsWWIV6TK0MausffJBSuZYO1HyHUt2pJq1/u5oj9yrjSWGzdHI7LmDKwQagBhwLHTFS7YnyrrEDAlxUvJCOoeFCwP9O/LjZf2I7K+gLMgdudmBResStjNutMERdS+XqrbxLtPCya9jBF1Fu/l3Ie76Vxcb4w2WyIqIpl63DnSBn7uvnWqBbwzYY5itPXN38RcRsNwq

docs/docs/tutorials/data_generation.ipynb

@@ -56,11 +56,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain langchain_experimental langchain-openai\n",
-    "# Set env var OPENAI_API_KEY or load from a .env file:\n",
-    "# import dotenv\n",
-    "# dotenv.load_dotenv()\n",
+    "%pip install --upgrade --quiet  langchain langchain_experimental langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "391ae036-36a2-4d2a-8e1b-5a536b268b45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
     "\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b47bb31-b4c7-42ff-9253-2c94747db750",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "from langchain.prompts import FewShotPromptTemplate, PromptTemplate\n",
     "from langchain_experimental.tabular_synthetic_data.openai import (\n",
     "    OPENAI_TEMPLATE,\n",
@@ -639,7 +658,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/graph.ipynb

@@ -72,7 +72,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")\n",
     "\n",
     "# Uncomment the below to use LangSmith. Not required.\n",
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()\n",
@@ -329,7 +330,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/llm_chain.ipynb

@@ -155,7 +155,9 @@
    "cell_type": "markdown",
    "id": "f83373db",
    "metadata": {},
-   "source": "If we've enabled LangSmith, we can see that this run is logged to LangSmith, and can see the [LangSmith trace](https://smith.langchain.com/public/88baa0b2-7c1a-4d09-ba30-a47985dde2ea/r)"
+   "source": [
+    "If we've enabled LangSmith, we can see that this run is logged to LangSmith, and can see the [LangSmith trace](https://smith.langchain.com/public/88baa0b2-7c1a-4d09-ba30-a47985dde2ea/r)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -534,27 +536,16 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "85174643",
+   "cell_type": "markdown",
+   "id": "96a19287-f3d5-42be-8338-5a5d749101b0",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Ciao'"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
+    "```python\n",
     "from langserve import RemoteRunnable\n",
     "\n",
     "remote_chain = RemoteRunnable(\"http://localhost:8000/chain/\")\n",
-    "remote_chain.invoke({\"language\": \"italian\", \"text\": \"hi\"})"
+    "remote_chain.invoke({\"language\": \"italian\", \"text\": \"hi\"})\n",
+    "```"
    ]
   },
   {
@@ -616,7 +607,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/pdf_qa.ipynb

@@ -133,8 +133,6 @@
     "# | output: false\n",
     "# | echo: false\n",
     "\n",
-    "%pip install langchain_anthropic\n",
-    "\n",
     "import getpass\n",
     "import os\n",
     "\n",
@@ -152,7 +150,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install langchain_chroma langchain_openai"
+    "%pip install langchain_openai"
    ]
   },
   {
@@ -177,13 +175,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_chroma import Chroma\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "\n",
     "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
     "splits = text_splitter.split_documents(docs)\n",
-    "vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
+    "vectorstore = InMemoryVectorStore.from_documents(\n",
+    "    documents=splits, embedding=OpenAIEmbeddings()\n",
+    ")\n",
     "\n",
     "retriever = vectorstore.as_retriever()"
    ]
@@ -329,7 +329,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -343,9 +343,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/tutorials/qa_chat_history.ipynb

@@ -52,7 +52,7 @@
     "\n",
     "### Dependencies\n",
     "\n",
-    "We'll use OpenAI embeddings and a Chroma vector store in this walkthrough, but everything shown here works with any [Embeddings](/docs/concepts#embedding-models), and [VectorStore](/docs/concepts#vectorstores) or [Retriever](/docs/concepts#retrievers). \n",
+    "We'll use OpenAI embeddings and a simple in-memory vector store in this walkthrough, but everything shown here works with any [Embeddings](/docs/concepts#embedding-models), and [VectorStore](/docs/concepts#vectorstores) or [Retriever](/docs/concepts#retrievers). \n",
     "\n",
     "We'll use the following packages:"
    ]
@@ -65,7 +65,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install --upgrade --quiet  langchain langchain-community langchainhub langchain-chroma beautifulsoup4"
+    "%pip install --upgrade --quiet langchain langchain-community beautifulsoup4"
    ]
   },
   {
@@ -103,19 +103,13 @@
     "\n",
     "Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. The best way to do this is with [LangSmith](https://smith.langchain.com).\n",
     "\n",
-    "Note that LangSmith is not needed, but it is helpful. If you do want to use LangSmith, after you sign up at the link above, make sure to set your environment variables to start logging traces:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "07411adb-3722-4f65-ab7f-8f6f57663d11",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "Note that LangSmith is not needed, but it is helpful. If you do want to use LangSmith, after you sign up at the link above, make sure to set your environment variables to start logging traces:\n",
+    "\n",
+    "```python\n",
     "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "if not os.environ.get(\"LANGCHAIN_API_KEY\"):\n",
-    "    os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
+    "if not os.environ.get(\"LANGSMITH_API_KEY\"):\n",
+    "    os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass()\n",
+    "```"
    ]
   },
   {
@@ -161,12 +155,11 @@
    "outputs": [],
    "source": [
     "import bs4\n",
-    "from langchain import hub\n",
     "from langchain.chains import create_retrieval_chain\n",
     "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.document_loaders import WebBaseLoader\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "\n",
@@ -183,7 +176,9 @@
     "\n",
     "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
     "splits = text_splitter.split_documents(docs)\n",
-    "vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
+    "vectorstore = InMemoryVectorStore.from_documents(\n",
+    "    documents=splits, embedding=OpenAIEmbeddings()\n",
+    ")\n",
     "retriever = vectorstore.as_retriever()\n",
     "\n",
     "\n",
@@ -570,12 +565,12 @@
     "import bs4\n",
     "from langchain.chains import create_history_aware_retriever, create_retrieval_chain\n",
     "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.chat_message_histories import ChatMessageHistory\n",
     "from langchain_community.document_loaders import WebBaseLoader\n",
     "from langchain_core.chat_history import BaseChatMessageHistory\n",
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
     "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "\n",
@@ -595,7 +590,9 @@
     "\n",
     "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
     "splits = text_splitter.split_documents(docs)\n",
-    "vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
+    "vectorstore = InMemoryVectorStore.from_documents(\n",
+    "    documents=splits, embedding=OpenAIEmbeddings()\n",
+    ")\n",
     "retriever = vectorstore.as_retriever()\n",
     "\n",
     "\n",
@@ -1011,8 +1008,8 @@
    "source": [
     "import bs4\n",
     "from langchain.tools.retriever import create_retriever_tool\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
     "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "from langgraph.checkpoint.memory import MemorySaver\n",
@@ -1035,7 +1032,9 @@
     "\n",
     "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
     "splits = text_splitter.split_documents(docs)\n",
-    "vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
+    "vectorstore = InMemoryVectorStore.from_documents(\n",
+    "    documents=splits, embedding=OpenAIEmbeddings()\n",
+    ")\n",
     "retriever = vectorstore.as_retriever()\n",
     "\n",
     "\n",
@@ -1095,7 +1094,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/query_analysis.ipynb

@@ -74,7 +74,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
     "\n",
     "# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
@@ -596,7 +597,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/rag.ipynb

@@ -144,8 +144,6 @@
     "# | output: false\n",
     "# | echo: false\n",
     "\n",
-    "%pip install --quiet --upgrade langchain-openai\n",
-    "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-4\")"

docs/scripts/download_tiktoken.py

@@ -0,0 +1,4 @@
+import tiktoken
+
+# This will trigger the download and caching of the necessary files
+encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")

docs/scripts/execute_notebooks.sh

@@ -0,0 +1,31 @@
+#!/bin/bash
+
+# Read the list of notebooks to skip from the JSON file
+SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('docs/notebooks_no_execution.json'))))")
+
+# Function to execute a single notebook
+execute_notebook() {
+    file="$1"
+    echo "Starting execution of $file"
+    start_time=$(date +%s)
+    if ! output=$(time poetry run jupyter nbconvert --to notebook --execute $file 2>&1); then
+        end_time=$(date +%s)
+        execution_time=$((end_time - start_time))
+        echo "Error in $file. Execution time: $execution_time seconds"
+        echo "Error details: $output"
+        exit 1
+    fi
+    end_time=$(date +%s)
+    execution_time=$((end_time - start_time))
+    echo "Finished $file. Execution time: $execution_time seconds"
+}
+
+export -f execute_notebook
+
+# Find all notebooks and filter out those in the skip list
+notebooks=$(find docs/docs/tutorials -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
+
+# Execute notebooks sequentially
+for file in $notebooks; do
+    execute_notebook "$file"
+done

docs/scripts/prepare_notebooks_for_ci.py

@@ -0,0 +1,181 @@
+"""Preprocess notebooks for CI. Currently adds VCR cassettes and optionally removes pip install cells."""
+
+import json
+import logging
+import os
+
+import click
+import nbformat
+
+logger = logging.getLogger(__name__)
+NOTEBOOK_DIRS = ("docs/docs/tutorials",)
+DOCS_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+CASSETTES_PATH = os.path.join(DOCS_PATH, "cassettes")
+
+# TODO: populate if needed
+NOTEBOOKS_NO_CASSETTES = [
+    "docs/docs/tutorials/retrievers.ipynb",  # TODO: fix non-determinism
+]
+
+NOTEBOOKS_NO_EXECUTION = [
+    "docs/docs/tutorials/graph.ipynb",  # Requires local graph db running
+    "docs/docs/tutorials/local_rag.ipynb",  # Local LLMs
+    "docs/docs/tutorials/query_analysis.ipynb",  # Requires youtube_transcript_api
+    "docs/docs/tutorials/sql_qa.ipynb",  # Requires Chinook db locally
+    "docs/docs/tutorials/summarization.ipynb",  # TODO: source of non-determinism somewhere, fix or add to no cassettes
+]
+
+
+def comment_install_cells(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
+    for cell in notebook.cells:
+        if cell.cell_type != "code":
+            continue
+
+        if "pip install" in cell.source:
+            # Comment out the lines in cells containing "pip install"
+            cell.source = "\n".join(
+                f"# {line}" if line.strip() else line
+                for line in cell.source.splitlines()
+            )
+
+    return notebook
+
+
+def is_magic_command(code: str) -> bool:
+    return code.strip().startswith("%") or code.strip().startswith("!")
+
+
+def is_comment(code: str) -> bool:
+    return code.strip().startswith("#")
+
+
+def add_vcr_to_notebook(
+    notebook: nbformat.NotebookNode, cassette_prefix: str
+) -> nbformat.NotebookNode:
+    """Inject `with vcr.cassette` into each code cell of the notebook."""
+
+    # Inject VCR context manager into each code cell
+    for idx, cell in enumerate(notebook.cells):
+        if cell.cell_type != "code":
+            continue
+
+        lines = cell.source.splitlines()
+        # skip if empty cell
+        if not lines:
+            continue
+
+        are_magic_lines = [is_magic_command(line) for line in lines]
+
+        # skip if all magic
+        if all(are_magic_lines):
+            continue
+
+        if any(are_magic_lines):
+            raise ValueError(
+                "Cannot process code cells with mixed magic and non-magic code."
+            )
+
+        # skip if just comments
+        if all(is_comment(line) or not line.strip() for line in lines):
+            continue
+
+        cell_id = cell.get("id", idx)
+        cassette_name = f"{cassette_prefix}_{cell_id}.msgpack.zlib"
+        cell.source = (
+            f"with custom_vcr.use_cassette('{cassette_name}', filter_headers=['x-api-key', 'authorization'], record_mode='once', serializer='advanced_compressed'):\n"
+            + "\n".join(f"    {line}" for line in lines)
+        )
+
+    # Add import statement
+    vcr_import_lines = [
+        "import nest_asyncio",
+        "nest_asyncio.apply()",
+        "import vcr",
+        "import msgpack",
+        "import base64",
+        "import zlib",
+        "custom_vcr = vcr.VCR()",
+        "",
+        "def compress_data(data, compression_level=9):",
+        "    packed = msgpack.packb(data, use_bin_type=True)",
+        "    compressed = zlib.compress(packed, level=compression_level)",
+        "    return base64.b64encode(compressed).decode('utf-8')",
+        "",
+        "def decompress_data(compressed_string):",
+        "    decoded = base64.b64decode(compressed_string)",
+        "    decompressed = zlib.decompress(decoded)",
+        "    return msgpack.unpackb(decompressed, raw=False)",
+        "",
+        "class AdvancedCompressedSerializer:",
+        "    def serialize(self, cassette_dict):",
+        "        return compress_data(cassette_dict)",
+        "",
+        "    def deserialize(self, cassette_string):",
+        "        return decompress_data(cassette_string)",
+        "",
+        "custom_vcr.register_serializer('advanced_compressed', AdvancedCompressedSerializer())",
+        "custom_vcr.serializer = 'advanced_compressed'",
+    ]
+    import_cell = nbformat.v4.new_code_cell(source="\n".join(vcr_import_lines))
+    import_cell.pop("id", None)
+    notebook.cells.insert(0, import_cell)
+    return notebook
+
+
+def process_notebooks(should_comment_install_cells: bool) -> None:
+    for directory in NOTEBOOK_DIRS:
+        for root, _, files in os.walk(directory):
+            for file in files:
+                if not file.endswith(".ipynb") or "ipynb_checkpoints" in root:
+                    continue
+
+                notebook_path = os.path.join(root, file)
+                try:
+                    notebook = nbformat.read(notebook_path, as_version=4)
+
+                    if should_comment_install_cells:
+                        notebook = comment_install_cells(notebook)
+
+                    base_filename = os.path.splitext(os.path.basename(file))[0]
+                    cassette_prefix = os.path.join(CASSETTES_PATH, base_filename)
+                    if notebook_path not in NOTEBOOKS_NO_CASSETTES:
+                        notebook = add_vcr_to_notebook(
+                            notebook, cassette_prefix=cassette_prefix
+                        )
+
+                    if notebook_path in NOTEBOOKS_NO_EXECUTION:
+                        # Add a cell at the beginning to indicate that this notebook should not be executed
+                        warning_cell = nbformat.v4.new_markdown_cell(
+                            source="**Warning:** This notebook is not meant to be executed automatically."
+                        )
+                        notebook.cells.insert(0, warning_cell)
+
+                        # Add a special tag to the first code cell
+                        if notebook.cells and notebook.cells[1].cell_type == "code":
+                            notebook.cells[1].metadata["tags"] = notebook.cells[
+                                1
+                            ].metadata.get("tags", []) + ["no_execution"]
+
+                    nbformat.write(notebook, notebook_path)
+                    logger.info(f"Processed: {notebook_path}")
+                except Exception as e:
+                    logger.error(f"Error processing {notebook_path}: {e}")
+
+    with open(os.path.join(DOCS_PATH, "notebooks_no_execution.json"), "w") as f:
+        json.dump(NOTEBOOKS_NO_EXECUTION, f)
+
+
+@click.command()
+@click.option(
+    "--comment-install-cells",
+    is_flag=True,
+    default=False,
+    help="Whether to comment out install cells",
+)
+def main(comment_install_cells):
+    process_notebooks(should_comment_install_cells=comment_install_cells)
+    logger.info("All notebooks processed successfully.")
+
+
+if __name__ == "__main__":
+    main()