diff --git a/docs/docs/additional_resources/tutorials.mdx b/docs/docs/additional_resources/tutorials.mdx
index 85c2259ad29..b9aa3ac256e 100644
--- a/docs/docs/additional_resources/tutorials.mdx
+++ b/docs/docs/additional_resources/tutorials.mdx
@@ -1,15 +1,18 @@
 # Tutorials
 
-Below are links to tutorials and courses on LangChain. For written guides on common use cases for LangChain, check out the [use cases guides](/docs/use_cases/qa_structured/sql).
+Below are links to tutorials and courses on LangChain. For written guides on common use cases for LangChain, check out the [use cases guides](/docs/use_cases).
 
 ⛓ icon marks a new addition [last update 2023-09-21]
 
 ---------------------
 
+### [LangChain on Wikipedia](https://en.wikipedia.org/wiki/LangChain)
+
 ### DeepLearning.AI courses
- by [Harrison Chase](https://github.com/hwchase17) and [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
+ by [Harrison Chase](https://en.wikipedia.org/wiki/LangChain) and [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
 - [LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain)
 - [LangChain Chat with Your Data](https://learn.deeplearning.ai/langchain-chat-with-your-data)
+- ⛓ [Functions, Tools and Agents with LangChain](https://learn.deeplearning.ai/functions-tools-agents-langchain)
 
 ### Handbook
 [LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
diff --git a/docs/docs/expression_language/index.mdx b/docs/docs/expression_language/index.mdx
index 902cbe00a2a..c93884b6b7c 100644
--- a/docs/docs/expression_language/index.mdx
+++ b/docs/docs/expression_language/index.mdx
@@ -28,3 +28,6 @@ Input and output schemas give every LCEL chain Pydantic and JSONSchema schemas i
 **Seamless LangSmith tracing integration**
 As your chains get more and more complex, it becomes increasingly important to understand what exactly is happening at every step.
 With LCEL, **all** steps are automatically logged to [LangSmith](/docs/langsmith/) for maximum observability and debuggability.
+
+**Seamless LangServe deployment integration**
+Any chain created with LCEL can be easily deployed using LangServe.
\ No newline at end of file
diff --git a/docs/docs/get_started/introduction.mdx b/docs/docs/get_started/introduction.mdx
index 4b5a49bf383..4b436f9a05a 100644
--- a/docs/docs/get_started/introduction.mdx
+++ b/docs/docs/get_started/introduction.mdx
@@ -10,9 +10,9 @@ sidebar_position: 0
 
 This framework consists of several parts.
 - **LangChain Libraries**: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.
-- **[LangChain Templates](https://github.com/langchain-ai/langchain/tree/master/templates)**: A collection of easily deployable reference architectures for a wide variety of tasks.
-- **[LangServe](https://github.com/langchain-ai/langserve)**: A library for deploying LangChain chains as a REST API.
-- **[LangSmith](https://smith.langchain.com/)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
+- **[LangChain Templates](/docs/templates)**: A collection of easily deployable reference architectures for a wide variety of tasks.
+- **[LangServe](/docs/langserve)**: A library for deploying LangChain chains as a REST API.
+- **[LangSmith](/docs/langsmith)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
 
 ![LangChain Diagram](/img/langchain_stack.png)
 
diff --git a/docs/docs/integrations/document_loaders/docusaurus.ipynb b/docs/docs/integrations/document_loaders/docusaurus.ipynb
new file mode 100644
index 00000000000..ca953cb6684
--- /dev/null
+++ b/docs/docs/integrations/document_loaders/docusaurus.ipynb
@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Docusaurus\n",
+    "> [Docusaurus](https://docusaurus.io/) is a static-site generator which provides out-of-the-box documentation features.\n",
+    "\n",
+    "By utilizing the existing `SitemapLoader`, this loader scans and loads all pages from a given Docusaurus application and returns the main documentation content of each page as a Document."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import DocusaurusLoader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Install necessary dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -U beautifulsoup4 lxml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fixes a bug with asyncio and jupyter\n",
+    "import nest_asyncio\n",
+    "\n",
+    "nest_asyncio.apply()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fetching pages: 100%|##########| 939/939 [01:19<00:00, 11.85it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = DocusaurusLoader(\"https://python.langchain.com\")\n",
+    "\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> `SitemapLoader` also provides the ability to utilize and tweak concurrency which can help optimize the time it takes to load the source documentation. Refer to the [sitemap docs](/docs/integrations/document_loaders/sitemap) for more info."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(page_content=\"\\n\\n\\n\\n\\nCookbook | 🦜️🔗 Langchain\\n\\n\\n\\n\\n\\n\\nSkip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKCookbookThe page you're looking for has been moved to the cookbook section of the repo as a notebook.CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.\\n\\n\\n\\n\", metadata={'source': 'https://python.langchain.com/cookbook', 'loc': 'https://python.langchain.com/cookbook', 'changefreq': 'weekly', 'priority': '0.5'})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filtering sitemap URLs\n",
+    "\n",
+    "Sitemaps can contain thousands of URLs and ften you don't need every single one of them. You can filter the URLs by passing a list of strings or regex patterns to the `url_filter` parameter.  Only URLs that match one of the patterns will be loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fetching pages: 100%|##########| 1/1 [00:00<00:00,  5.21it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = DocusaurusLoader(\n",
+    "    \"https://python.langchain.com\",\n",
+    "    filter_urls=[\"https://python.langchain.com/docs/integrations/document_loaders/sitemap\"],\n",
+    ")\n",
+    "documents = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(page_content='\\n\\n\\n\\n\\nSitemap | 🦜️🔗 Langchain\\n\\n\\n\\n\\n\\n\\nSkip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersSitemapOn this pageSitemapExtends from the WebBaseLoader, SitemapLoader loads a sitemap from a given URL, and then scrape and load all pages in the sitemap, returning each page as a Document.The scraping is done concurrently.  There are reasonable limits to concurrent requests, defaulting to 2 per second.  If you aren\\'t concerned about being a good citizen, or you control the scrapped server, or don\\'t care about load. Note, while this will speed up the scraping process, but it may cause the server to block you.  Be careful!pip install nest_asyncio    Requirement already satisfied: nest_asyncio in /Users/tasp/Code/projects/langchain/.venv/lib/python3.10/site-packages (1.5.6)        [notice] A new release of pip available: 22.3.1 -> 23.0.1    [notice] To update, run: pip install --upgrade pip# fixes a bug with asyncio and jupyterimport nest_asyncionest_asyncio.apply()from langchain.document_loaders.sitemap import SitemapLoadersitemap_loader = SitemapLoader(web_path=\"https://langchain.readthedocs.io/sitemap.xml\")docs = sitemap_loader.load()You can change the requests_per_second parameter to increase the max concurrent requests. and use requests_kwargs to pass kwargs when send requests.sitemap_loader.requests_per_second = 2# Optional: avoid `[SSL: CERTIFICATE_VERIFY_FAILED]` issuesitemap_loader.requests_kwargs = {\"verify\": False}docs[0]    Document(page_content=\\'\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nWelcome to LangChain — 🦜🔗 LangChain 0.0.123\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nSkip to main content\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nCtrl+K\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n🦜🔗 LangChain 0.0.123\\\\n\\\\n\\\\n\\\\nGetting Started\\\\n\\\\nQuickstart Guide\\\\n\\\\nModules\\\\n\\\\nPrompt Templates\\\\nGetting Started\\\\nKey Concepts\\\\nHow-To Guides\\\\nCreate a custom prompt template\\\\nCreate a custom example selector\\\\nProvide few shot examples to a prompt\\\\nPrompt Serialization\\\\nExample Selectors\\\\nOutput Parsers\\\\n\\\\n\\\\nReference\\\\nPromptTemplates\\\\nExample Selector\\\\n\\\\n\\\\n\\\\n\\\\nLLMs\\\\nGetting Started\\\\nKey Concepts\\\\nHow-To Guides\\\\nGeneric Functionality\\\\nCustom LLM\\\\nFake LLM\\\\nLLM Caching\\\\nLLM Serialization\\\\nToken Usage Tracking\\\\n\\\\n\\\\nIntegrations\\\\nAI21\\\\nAleph Alpha\\\\nAnthropic\\\\nAzure OpenAI LLM Example\\\\nBanana\\\\nCerebriumAI LLM Example\\\\nCohere\\\\nDeepInfra LLM Example\\\\nForefrontAI LLM Example\\\\nGooseAI LLM Example\\\\nHugging Face Hub\\\\nManifest\\\\nModal\\\\nOpenAI\\\\nPetals LLM Example\\\\nPromptLayer OpenAI\\\\nSageMakerEndpoint\\\\nSelf-Hosted Models via Runhouse\\\\nStochasticAI\\\\nWriter\\\\n\\\\n\\\\nAsync API for LLM\\\\nStreaming with LLMs\\\\n\\\\n\\\\nReference\\\\n\\\\n\\\\nDocument Loaders\\\\nKey Concepts\\\\nHow To Guides\\\\nCoNLL-U\\\\nAirbyte JSON\\\\nAZLyrics\\\\nBlackboard\\\\nCollege Confidential\\\\nCopy Paste\\\\nCSV Loader\\\\nDirectory Loader\\\\nEmail\\\\nEverNote\\\\nFacebook Chat\\\\nFigma\\\\nGCS Directory\\\\nGCS File Storage\\\\nGitBook\\\\nGoogle Drive\\\\nGutenberg\\\\nHacker News\\\\nHTML\\\\niFixit\\\\nImages\\\\nIMSDb\\\\nMarkdown\\\\nNotebook\\\\nNotion\\\\nObsidian\\\\nPDF\\\\nPowerPoint\\\\nReadTheDocs Documentation\\\\nRoam\\\\ns3 Directory\\\\ns3 File\\\\nSubtitle Files\\\\nTelegram\\\\nUnstructured File Loader\\\\nURL\\\\nWeb Base\\\\nWord Documents\\\\nYouTube\\\\n\\\\n\\\\n\\\\n\\\\nUtils\\\\nKey Concepts\\\\nGeneric Utilities\\\\nBash\\\\nBing Search\\\\nGoogle Search\\\\nGoogle Serper API\\\\nIFTTT WebHooks\\\\nPython REPL\\\\nRequests\\\\nSearxNG Search API\\\\nSerpAPI\\\\nWolfram Alpha\\\\nZapier Natural Language Actions API\\\\n\\\\n\\\\nReference\\\\nPython REPL\\\\nSerpAPI\\\\nSearxNG Search\\\\nDocstore\\\\nText Splitter\\\\nEmbeddings\\\\nVectorStores\\\\n\\\\n\\\\n\\\\n\\\\nIndexes\\\\nGetting Started\\\\nKey Concepts\\\\nHow To Guides\\\\nEmbeddings\\\\nHypothetical Document Embeddings\\\\nText Splitter\\\\nVectorStores\\\\nAtlasDB\\\\nChroma\\\\nDeep Lake\\\\nElasticSearch\\\\nFAISS\\\\nMilvus\\\\nOpenSearch\\\\nPGVector\\\\nPinecone\\\\nQdrant\\\\nRedis\\\\nWeaviate\\\\nChatGPT Plugin Retriever\\\\nVectorStore Retriever\\\\nAnalyze Document\\\\nChat Index\\\\nGraph QA\\\\nQuestion Answering with Sources\\\\nQuestion Answering\\\\nSummarization\\\\nRetrieval Question/Answering\\\\nRetrieval Question Answering with Sources\\\\nVector DB Text Generation\\\\n\\\\n\\\\n\\\\n\\\\nChains\\\\nGetting Started\\\\nHow-To Guides\\\\nGeneric Chains\\\\nLoading from LangChainHub\\\\nLLM Chain\\\\nSequential Chains\\\\nSerialization\\\\nTransformation Chain\\\\n\\\\n\\\\nUtility Chains\\\\nAPI Chains\\\\nSelf-Critique Chain with Constitutional AI\\\\nBashChain\\\\nLLMCheckerChain\\\\nLLM Math\\\\nLLMRequestsChain\\\\nLLMSummarizationCheckerChain\\\\nModeration\\\\nPAL\\\\nSQLite example\\\\n\\\\n\\\\nAsync API for Chain\\\\n\\\\n\\\\nKey Concepts\\\\nReference\\\\n\\\\n\\\\nAgents\\\\nGetting Started\\\\nKey Concepts\\\\nHow-To Guides\\\\nAgents and Vectorstores\\\\nAsync API for Agent\\\\nConversation Agent (for Chat Models)\\\\nChatGPT Plugins\\\\nCustom Agent\\\\nDefining Custom Tools\\\\nHuman as a tool\\\\nIntermediate Steps\\\\nLoading from LangChainHub\\\\nMax Iterations\\\\nMulti Input Tools\\\\nSearch Tools\\\\nSerialization\\\\nAdding SharedMemory to an Agent and its Tools\\\\nCSV Agent\\\\nJSON Agent\\\\nOpenAPI Agent\\\\nPandas Dataframe Agent\\\\nPython Agent\\\\nSQL Database Agent\\\\nVectorstore Agent\\\\nMRKL\\\\nMRKL Chat\\\\nReAct\\\\nSelf Ask With Search\\\\n\\\\n\\\\nReference\\\\n\\\\n\\\\nMemory\\\\nGetting Started\\\\nKey Concepts\\\\nHow-To Guides\\\\nConversationBufferMemory\\\\nConversationBufferWindowMemory\\\\nEntity Memory\\\\nConversation Knowledge Graph Memory\\\\nConversationSummaryMemory\\\\nConversationSummaryBufferMemory\\\\nConversationTokenBufferMemory\\\\nAdding Memory To an LLMChain\\\\nAdding Memory to a Multi-Input Chain\\\\nAdding Memory to an Agent\\\\nChatGPT Clone\\\\nConversation Agent\\\\nConversational Memory Customization\\\\nCustom Memory\\\\nMultiple Memory\\\\n\\\\n\\\\n\\\\n\\\\nChat\\\\nGetting Started\\\\nKey Concepts\\\\nHow-To Guides\\\\nAgent\\\\nChat Vector DB\\\\nFew Shot Examples\\\\nMemory\\\\nPromptLayer ChatOpenAI\\\\nStreaming\\\\nRetrieval Question/Answering\\\\nRetrieval Question Answering with Sources\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nUse Cases\\\\n\\\\nAgents\\\\nChatbots\\\\nGenerate Examples\\\\nData Augmented Generation\\\\nQuestion Answering\\\\nSummarization\\\\nQuerying Tabular Data\\\\nExtraction\\\\nEvaluation\\\\nAgent Benchmarking: Search + Calculator\\\\nAgent VectorDB Question Answering Benchmarking\\\\nBenchmarking Template\\\\nData Augmented Question Answering\\\\nUsing Hugging Face Datasets\\\\nLLM Math\\\\nQuestion Answering Benchmarking: Paul Graham Essay\\\\nQuestion Answering Benchmarking: State of the Union Address\\\\nQA Generation\\\\nQuestion Answering\\\\nSQL Question Answering Benchmarking: Chinook\\\\n\\\\n\\\\nModel Comparison\\\\n\\\\nReference\\\\n\\\\nInstallation\\\\nIntegrations\\\\nAPI References\\\\nPrompts\\\\nPromptTemplates\\\\nExample Selector\\\\n\\\\n\\\\nUtilities\\\\nPython REPL\\\\nSerpAPI\\\\nSearxNG Search\\\\nDocstore\\\\nText Splitter\\\\nEmbeddings\\\\nVectorStores\\\\n\\\\n\\\\nChains\\\\nAgents\\\\n\\\\n\\\\n\\\\nEcosystem\\\\n\\\\nLangChain Ecosystem\\\\nAI21 Labs\\\\nAtlasDB\\\\nBanana\\\\nCerebriumAI\\\\nChroma\\\\nCohere\\\\nDeepInfra\\\\nDeep Lake\\\\nForefrontAI\\\\nGoogle Search Wrapper\\\\nGoogle Serper Wrapper\\\\nGooseAI\\\\nGraphsignal\\\\nHazy Research\\\\nHelicone\\\\nHugging Face\\\\nMilvus\\\\nModal\\\\nNLPCloud\\\\nOpenAI\\\\nOpenSearch\\\\nPetals\\\\nPGVector\\\\nPinecone\\\\nPromptLayer\\\\nQdrant\\\\nRunhouse\\\\nSearxNG Search API\\\\nSerpAPI\\\\nStochasticAI\\\\nUnstructured\\\\nWeights & Biases\\\\nWeaviate\\\\nWolfram Alpha Wrapper\\\\nWriter\\\\n\\\\n\\\\n\\\\nAdditional Resources\\\\n\\\\nLangChainHub\\\\nGlossary\\\\nLangChain Gallery\\\\nDeployments\\\\nTracing\\\\nDiscord\\\\nProduction Support\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n.rst\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n.pdf\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nWelcome to LangChain\\\\n\\\\n\\\\n\\\\n\\\\n Contents \\\\n\\\\n\\\\n\\\\nGetting Started\\\\nModules\\\\nUse Cases\\\\nReference Docs\\\\nLangChain Ecosystem\\\\nAdditional Resources\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nWelcome to LangChain#\\\\nLarge language models (LLMs) are emerging as a transformative technology, enabling\\\\ndevelopers to build applications that they previously could not.\\\\nBut using these LLMs in isolation is often not enough to\\\\ncreate a truly powerful app - the real power comes when you are able to\\\\ncombine them with other sources of computation or knowledge.\\\\nThis library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:\\\\n❓ Question Answering over specific documents\\\\n\\\\nDocumentation\\\\nEnd-to-end Example: Question Answering over Notion Database\\\\n\\\\n💬 Chatbots\\\\n\\\\nDocumentation\\\\nEnd-to-end Example: Chat-LangChain\\\\n\\\\n🤖 Agents\\\\n\\\\nDocumentation\\\\nEnd-to-end Example: GPT+WolframAlpha\\\\n\\\\n\\\\nGetting Started#\\\\nCheckout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application.\\\\n\\\\nGetting Started Documentation\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nModules#\\\\nThere are several main modules that LangChain provides support for.\\\\nFor each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides.\\\\nThese modules are, in increasing order of complexity:\\\\n\\\\nPrompts: This includes prompt management, prompt optimization, and prompt serialization.\\\\nLLMs: This includes a generic interface for all LLMs, and common utilities for working with LLMs.\\\\nDocument Loaders: This includes a standard interface for loading documents, as well as specific integrations to all types of text data sources.\\\\nUtils: Language models are often more powerful when interacting with other sources of knowledge or computation. This can include Python REPLs, embeddings, search engines, and more. LangChain provides a large collection of common utils to use in your application.\\\\nChains: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.\\\\nIndexes: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.\\\\nAgents: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.\\\\nMemory: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.\\\\nChat: Chat models are a variation on Language Models that expose a different API - rather than working with raw text, they work with messages. LangChain provides a standard interface for working with them and doing all the same things as above.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nUse Cases#\\\\nThe above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports.\\\\n\\\\nAgents: Agents are systems that use a language model to interact with other tools. These can be used to do more grounded question/answering, interact with APIs, or even take actions.\\\\nChatbots: Since language models are good at producing text, that makes them ideal for creating chatbots.\\\\nData Augmented Generation: Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.\\\\nQuestion Answering: Answering questions over specific documents, only utilizing the information in those documents to construct an answer. A type of Data Augmented Generation.\\\\nSummarization: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.\\\\nQuerying Tabular Data: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page.\\\\nEvaluation: Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.\\\\nGenerate similar examples: Generating similar examples to a given input. This is a common use case for many applications, and LangChain provides some prompts/chains for assisting in this.\\\\nCompare models: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nReference Docs#\\\\nAll of LangChain’s reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain.\\\\n\\\\nReference Documentation\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nLangChain Ecosystem#\\\\nGuides for how other companies/products can be used with LangChain\\\\n\\\\nLangChain Ecosystem\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nAdditional Resources#\\\\nAdditional collection of resources we think may be useful as you develop your application!\\\\n\\\\nLangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents.\\\\nGlossary: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not!\\\\nGallery: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.\\\\nDeployments: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.\\\\nDiscord: Join us on our Discord to discuss all things LangChain!\\\\nTracing: A guide on using tracing in LangChain to visualize the execution of chains and agents.\\\\nProduction Support: As you move your LangChains into production, we’d love to offer more comprehensive support. Please fill out this form and we’ll set up a dedicated support Slack channel.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nnext\\\\nQuickstart Guide\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n Contents\\\\n  \\\\n\\\\n\\\\nGetting Started\\\\nModules\\\\nUse Cases\\\\nReference Docs\\\\nLangChain Ecosystem\\\\nAdditional Resources\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nBy Harrison Chase\\\\n\\\\n\\\\n\\\\n\\\\n    \\\\n      © Copyright 2023, Harrison Chase.\\\\n      \\\\n\\\\n\\\\n\\\\n\\\\n  Last updated on Mar 24, 2023.\\\\n  \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\', lookup_str=\\'\\', metadata={\\'source\\': \\'https://python.langchain.com/en/stable/\\', \\'loc\\': \\'https://python.langchain.com/en/stable/\\', \\'lastmod\\': \\'2023-03-24T19:30:54.647430+00:00\\', \\'changefreq\\': \\'weekly\\', \\'priority\\': \\'1\\'}, lookup_index=0)Filtering sitemap URLs\\u200bSitemaps can be massive files, with thousands of URLs.  Often you don\\'t need every single one of them.  You can filter the URLs by passing a list of strings or regex patterns to the url_filter parameter.  Only URLs that match one of the patterns will be loaded.loader = SitemapLoader(    \"https://langchain.readthedocs.io/sitemap.xml\",    filter_urls=[\"https://python.langchain.com/en/latest/\"],)documents = loader.load()documents[0]    Document(page_content=\\'\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nWelcome to LangChain — 🦜🔗 LangChain 0.0.123\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nSkip to main content\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nCtrl+K\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n🦜🔗 LangChain 0.0.123\\\\n\\\\n\\\\n\\\\nGetting Started\\\\n\\\\nQuickstart Guide\\\\n\\\\nModules\\\\n\\\\nModels\\\\nLLMs\\\\nGetting Started\\\\nGeneric Functionality\\\\nHow to use the async API for LLMs\\\\nHow to write a custom LLM wrapper\\\\nHow (and why) to use the fake LLM\\\\nHow to cache LLM calls\\\\nHow to serialize LLM classes\\\\nHow to stream LLM responses\\\\nHow to track token usage\\\\n\\\\n\\\\nIntegrations\\\\nAI21\\\\nAleph Alpha\\\\nAnthropic\\\\nAzure OpenAI LLM Example\\\\nBanana\\\\nCerebriumAI LLM Example\\\\nCohere\\\\nDeepInfra LLM Example\\\\nForefrontAI LLM Example\\\\nGooseAI LLM Example\\\\nHugging Face Hub\\\\nManifest\\\\nModal\\\\nOpenAI\\\\nPetals LLM Example\\\\nPromptLayer OpenAI\\\\nSageMakerEndpoint\\\\nSelf-Hosted Models via Runhouse\\\\nStochasticAI\\\\nWriter\\\\n\\\\n\\\\nReference\\\\n\\\\n\\\\nChat Models\\\\nGetting Started\\\\nHow-To Guides\\\\nHow to use few shot examples\\\\nHow to stream responses\\\\n\\\\n\\\\nIntegrations\\\\nAzure\\\\nOpenAI\\\\nPromptLayer ChatOpenAI\\\\n\\\\n\\\\n\\\\n\\\\nText Embedding Models\\\\nAzureOpenAI\\\\nCohere\\\\nFake Embeddings\\\\nHugging Face Hub\\\\nInstructEmbeddings\\\\nOpenAI\\\\nSageMaker Endpoint Embeddings\\\\nSelf Hosted Embeddings\\\\nTensorflowHub\\\\n\\\\n\\\\n\\\\n\\\\nPrompts\\\\nPrompt Templates\\\\nGetting Started\\\\nHow-To Guides\\\\nHow to create a custom prompt template\\\\nHow to create a prompt template that uses few shot examples\\\\nHow to work with partial Prompt Templates\\\\nHow to serialize prompts\\\\n\\\\n\\\\nReference\\\\nPromptTemplates\\\\nExample Selector\\\\n\\\\n\\\\n\\\\n\\\\nChat Prompt Template\\\\nExample Selectors\\\\nHow to create a custom example selector\\\\nLengthBased ExampleSelector\\\\nMaximal Marginal Relevance ExampleSelector\\\\nNGram Overlap ExampleSelector\\\\nSimilarity ExampleSelector\\\\n\\\\n\\\\nOutput Parsers\\\\nOutput Parsers\\\\nCommaSeparatedListOutputParser\\\\nOutputFixingParser\\\\nPydanticOutputParser\\\\nRetryOutputParser\\\\nStructured Output Parser\\\\n\\\\n\\\\n\\\\n\\\\nIndexes\\\\nGetting Started\\\\nDocument Loaders\\\\nCoNLL-U\\\\nAirbyte JSON\\\\nAZLyrics\\\\nBlackboard\\\\nCollege Confidential\\\\nCopy Paste\\\\nCSV Loader\\\\nDirectory Loader\\\\nEmail\\\\nEverNote\\\\nFacebook Chat\\\\nFigma\\\\nGCS Directory\\\\nGCS File Storage\\\\nGitBook\\\\nGoogle Drive\\\\nGutenberg\\\\nHacker News\\\\nHTML\\\\niFixit\\\\nImages\\\\nIMSDb\\\\nMarkdown\\\\nNotebook\\\\nNotion\\\\nObsidian\\\\nPDF\\\\nPowerPoint\\\\nReadTheDocs Documentation\\\\nRoam\\\\ns3 Directory\\\\ns3 File\\\\nSubtitle Files\\\\nTelegram\\\\nUnstructured File Loader\\\\nURL\\\\nWeb Base\\\\nWord Documents\\\\nYouTube\\\\n\\\\n\\\\nText Splitters\\\\nGetting Started\\\\nCharacter Text Splitter\\\\nHuggingFace Length Function\\\\nLatex Text Splitter\\\\nMarkdown Text Splitter\\\\nNLTK Text Splitter\\\\nPython Code Text Splitter\\\\nRecursiveCharacterTextSplitter\\\\nSpacy Text Splitter\\\\ntiktoken (OpenAI) Length Function\\\\nTiktokenText Splitter\\\\n\\\\n\\\\nVectorstores\\\\nGetting Started\\\\nAtlasDB\\\\nChroma\\\\nDeep Lake\\\\nElasticSearch\\\\nFAISS\\\\nMilvus\\\\nOpenSearch\\\\nPGVector\\\\nPinecone\\\\nQdrant\\\\nRedis\\\\nWeaviate\\\\n\\\\n\\\\nRetrievers\\\\nChatGPT Plugin Retriever\\\\nVectorStore Retriever\\\\n\\\\n\\\\n\\\\n\\\\nMemory\\\\nGetting Started\\\\nHow-To Guides\\\\nConversationBufferMemory\\\\nConversationBufferWindowMemory\\\\nEntity Memory\\\\nConversation Knowledge Graph Memory\\\\nConversationSummaryMemory\\\\nConversationSummaryBufferMemory\\\\nConversationTokenBufferMemory\\\\nHow to add Memory to an LLMChain\\\\nHow to add memory to a Multi-Input Chain\\\\nHow to add Memory to an Agent\\\\nHow to customize conversational memory\\\\nHow to create a custom Memory class\\\\nHow to use multiple memroy classes in the same chain\\\\n\\\\n\\\\n\\\\n\\\\nChains\\\\nGetting Started\\\\nHow-To Guides\\\\nAsync API for Chain\\\\nLoading from LangChainHub\\\\nLLM Chain\\\\nSequential Chains\\\\nSerialization\\\\nTransformation Chain\\\\nAnalyze Document\\\\nChat Index\\\\nGraph QA\\\\nHypothetical Document Embeddings\\\\nQuestion Answering with Sources\\\\nQuestion Answering\\\\nSummarization\\\\nRetrieval Question/Answering\\\\nRetrieval Question Answering with Sources\\\\nVector DB Text Generation\\\\nAPI Chains\\\\nSelf-Critique Chain with Constitutional AI\\\\nBashChain\\\\nLLMCheckerChain\\\\nLLM Math\\\\nLLMRequestsChain\\\\nLLMSummarizationCheckerChain\\\\nModeration\\\\nPAL\\\\nSQLite example\\\\n\\\\n\\\\nReference\\\\n\\\\n\\\\nAgents\\\\nGetting Started\\\\nTools\\\\nGetting Started\\\\nDefining Custom Tools\\\\nMulti Input Tools\\\\nBash\\\\nBing Search\\\\nChatGPT Plugins\\\\nGoogle Search\\\\nGoogle Serper API\\\\nHuman as a tool\\\\nIFTTT WebHooks\\\\nPython REPL\\\\nRequests\\\\nSearch Tools\\\\nSearxNG Search API\\\\nSerpAPI\\\\nWolfram Alpha\\\\nZapier Natural Language Actions API\\\\n\\\\n\\\\nAgents\\\\nAgent Types\\\\nCustom Agent\\\\nConversation Agent (for Chat Models)\\\\nConversation Agent\\\\nMRKL\\\\nMRKL Chat\\\\nReAct\\\\nSelf Ask With Search\\\\n\\\\n\\\\nToolkits\\\\nCSV Agent\\\\nJSON Agent\\\\nOpenAPI Agent\\\\nPandas Dataframe Agent\\\\nPython Agent\\\\nSQL Database Agent\\\\nVectorstore Agent\\\\n\\\\n\\\\nAgent Executors\\\\nHow to combine agents and vectorstores\\\\nHow to use the async API for Agents\\\\nHow to create ChatGPT Clone\\\\nHow to access intermediate steps\\\\nHow to cap the max number of iterations\\\\nHow to add SharedMemory to an Agent and its Tools\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nUse Cases\\\\n\\\\nPersonal Assistants\\\\nQuestion Answering over Docs\\\\nChatbots\\\\nQuerying Tabular Data\\\\nInteracting with APIs\\\\nSummarization\\\\nExtraction\\\\nEvaluation\\\\nAgent Benchmarking: Search + Calculator\\\\nAgent VectorDB Question Answering Benchmarking\\\\nBenchmarking Template\\\\nData Augmented Question Answering\\\\nUsing Hugging Face Datasets\\\\nLLM Math\\\\nQuestion Answering Benchmarking: Paul Graham Essay\\\\nQuestion Answering Benchmarking: State of the Union Address\\\\nQA Generation\\\\nQuestion Answering\\\\nSQL Question Answering Benchmarking: Chinook\\\\n\\\\n\\\\n\\\\nReference\\\\n\\\\nInstallation\\\\nIntegrations\\\\nAPI References\\\\nPrompts\\\\nPromptTemplates\\\\nExample Selector\\\\n\\\\n\\\\nUtilities\\\\nPython REPL\\\\nSerpAPI\\\\nSearxNG Search\\\\nDocstore\\\\nText Splitter\\\\nEmbeddings\\\\nVectorStores\\\\n\\\\n\\\\nChains\\\\nAgents\\\\n\\\\n\\\\n\\\\nEcosystem\\\\n\\\\nLangChain Ecosystem\\\\nAI21 Labs\\\\nAtlasDB\\\\nBanana\\\\nCerebriumAI\\\\nChroma\\\\nCohere\\\\nDeepInfra\\\\nDeep Lake\\\\nForefrontAI\\\\nGoogle Search Wrapper\\\\nGoogle Serper Wrapper\\\\nGooseAI\\\\nGraphsignal\\\\nHazy Research\\\\nHelicone\\\\nHugging Face\\\\nMilvus\\\\nModal\\\\nNLPCloud\\\\nOpenAI\\\\nOpenSearch\\\\nPetals\\\\nPGVector\\\\nPinecone\\\\nPromptLayer\\\\nQdrant\\\\nRunhouse\\\\nSearxNG Search API\\\\nSerpAPI\\\\nStochasticAI\\\\nUnstructured\\\\nWeights & Biases\\\\nWeaviate\\\\nWolfram Alpha Wrapper\\\\nWriter\\\\n\\\\n\\\\n\\\\nAdditional Resources\\\\n\\\\nLangChainHub\\\\nGlossary\\\\nLangChain Gallery\\\\nDeployments\\\\nTracing\\\\nDiscord\\\\nProduction Support\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n.rst\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n.pdf\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nWelcome to LangChain\\\\n\\\\n\\\\n\\\\n\\\\n Contents \\\\n\\\\n\\\\n\\\\nGetting Started\\\\nModules\\\\nUse Cases\\\\nReference Docs\\\\nLangChain Ecosystem\\\\nAdditional Resources\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nWelcome to LangChain#\\\\nLangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also:\\\\n\\\\nBe data-aware: connect a language model to other sources of data\\\\nBe agentic: allow a language model to interact with its environment\\\\n\\\\nThe LangChain framework is designed with the above principles in mind.\\\\nThis is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see here. For the JavaScript documentation, see here.\\\\n\\\\nGetting Started#\\\\nCheckout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application.\\\\n\\\\nGetting Started Documentation\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nModules#\\\\nThere are several main modules that LangChain provides support for.\\\\nFor each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides.\\\\nThese modules are, in increasing order of complexity:\\\\n\\\\nModels: The various model types and model integrations LangChain supports.\\\\nPrompts: This includes prompt management, prompt optimization, and prompt serialization.\\\\nMemory: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.\\\\nIndexes: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.\\\\nChains: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.\\\\nAgents: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nUse Cases#\\\\nThe above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports.\\\\n\\\\nPersonal Assistants: The main LangChain use case. Personal assistants need to take actions, remember interactions, and have knowledge about your data.\\\\nQuestion Answering: The second big LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.\\\\nChatbots: Since language models are good at producing text, that makes them ideal for creating chatbots.\\\\nQuerying Tabular Data: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page.\\\\nInteracting with APIs: Enabling LLMs to interact with APIs is extremely powerful in order to give them more up-to-date information and allow them to take actions.\\\\nExtraction: Extract structured information from text.\\\\nSummarization: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.\\\\nEvaluation: Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nReference Docs#\\\\nAll of LangChain’s reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain.\\\\n\\\\nReference Documentation\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nLangChain Ecosystem#\\\\nGuides for how other companies/products can be used with LangChain\\\\n\\\\nLangChain Ecosystem\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nAdditional Resources#\\\\nAdditional collection of resources we think may be useful as you develop your application!\\\\n\\\\nLangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents.\\\\nGlossary: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not!\\\\nGallery: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.\\\\nDeployments: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.\\\\nTracing: A guide on using tracing in LangChain to visualize the execution of chains and agents.\\\\nModel Laboratory: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.\\\\nDiscord: Join us on our Discord to discuss all things LangChain!\\\\nProduction Support: As you move your LangChains into production, we’d love to offer more comprehensive support. Please fill out this form and we’ll set up a dedicated support Slack channel.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nnext\\\\nQuickstart Guide\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n Contents\\\\n  \\\\n\\\\n\\\\nGetting Started\\\\nModules\\\\nUse Cases\\\\nReference Docs\\\\nLangChain Ecosystem\\\\nAdditional Resources\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nBy Harrison Chase\\\\n\\\\n\\\\n\\\\n\\\\n    \\\\n      © Copyright 2023, Harrison Chase.\\\\n      \\\\n\\\\n\\\\n\\\\n\\\\n  Last updated on Mar 27, 2023.\\\\n  \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\', lookup_str=\\'\\', metadata={\\'source\\': \\'https://python.langchain.com/en/latest/\\', \\'loc\\': \\'https://python.langchain.com/en/latest/\\', \\'lastmod\\': \\'2023-03-27T22:50:49.790324+00:00\\', \\'changefreq\\': \\'daily\\', \\'priority\\': \\'0.9\\'}, lookup_index=0)Add custom scraping rules\\u200bThe SitemapLoader uses beautifulsoup4 for the scraping process, and it scrapes every element on the page by default. The SitemapLoader constructor accepts a custom scraping function. This feature can be helpful to tailor the scraping process to your specific needs; for example, you might want to avoid scraping headers or navigation elements. The following example shows how to develop and use a custom function to avoid navigation and header elements.Import the beautifulsoup4 library and define the custom function.pip install beautifulsoup4from bs4 import BeautifulSoupdef remove_nav_and_header_elements(content: BeautifulSoup) -> str:    # Find all \\'nav\\' and \\'header\\' elements in the BeautifulSoup object    nav_elements = content.find_all(\"nav\")    header_elements = content.find_all(\"header\")    # Remove each \\'nav\\' and \\'header\\' element from the BeautifulSoup object    for element in nav_elements + header_elements:        element.decompose()    return str(content.get_text())Add your custom function to the SitemapLoader object.loader = SitemapLoader(    \"https://langchain.readthedocs.io/sitemap.xml\",    filter_urls=[\"https://python.langchain.com/en/latest/\"],    parsing_function=remove_nav_and_header_elements,)Local Sitemap\\u200bThe sitemap loader can also be used to load local files.sitemap_loader = SitemapLoader(web_path=\"example_data/sitemap.xml\", is_local=True)docs = sitemap_loader.load()    Fetching pages: 100%|####################################################################################################################################| 3/3 [00:00<00:00,  3.91it/s]PreviousRSTNextSlackFiltering sitemap URLsAdd custom scraping rulesLocal SitemapCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.\\n\\n\\n\\n', metadata={'source': 'https://python.langchain.com/docs/integrations/document_loaders/sitemap', 'loc': 'https://python.langchain.com/docs/integrations/document_loaders/sitemap', 'changefreq': 'weekly', 'priority': '0.5'})"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add custom scraping rules\n",
+    "\n",
+    "By default, the parser **removes** all but the main content of the docusaurus page, which is normally the `<article>` tag. You also have the option  to define an **inclusive** list HTML tags by providing them as a list utilizing the `custom_html_tags` parameter. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = DocusaurusLoader(\n",
+    "    \"https://python.langchain.com\",\n",
+    "    filter_urls=[\"https://python.langchain.com/docs/integrations/document_loaders/sitemap\"],\n",
+    "    # This will only include the content that matches these tags, otherwise they will be removed\n",
+    "    custom_html_tags=[\"#content\", \".main\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also define an entirely custom parsing function if you need finer-grained control over the returned content for each page.\n",
+    "\n",
+    "The following example shows how to develop and use a custom function to avoid navigation and header elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bs4 import BeautifulSoup\n",
+    "\n",
+    "\n",
+    "def remove_nav_and_header_elements(content: BeautifulSoup) -> str:\n",
+    "    # Find all 'nav' and 'header' elements in the BeautifulSoup object\n",
+    "    nav_elements = content.find_all(\"nav\")\n",
+    "    header_elements = content.find_all(\"header\")\n",
+    "\n",
+    "    # Remove each 'nav' and 'header' element from the BeautifulSoup object\n",
+    "    for element in nav_elements + header_elements:\n",
+    "        element.decompose()\n",
+    "\n",
+    "    return str(content.get_text())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Add your custom function to the `DocusaurusLoader` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = DocusaurusLoader(\n",
+    "    \"https://python.langchain.com\",\n",
+    "    filter_urls=[\"https://python.langchain.com/docs/integrations/document_loaders/sitemap\"],\n",
+    "    parsing_function=remove_nav_and_header_elements,\n",
+    ")"
+   ]
+  }
+ ],
+ "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/docs/integrations/memory/neo4j_chat_message_history.ipynb b/docs/docs/integrations/memory/neo4j_chat_message_history.ipynb
new file mode 100644
index 00000000000..238beb09982
--- /dev/null
+++ b/docs/docs/integrations/memory/neo4j_chat_message_history.ipynb
@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "91c6a7ef",
+   "metadata": {},
+   "source": [
+    "# Neo4j\n",
+    "\n",
+    "[Neo4j](https://en.wikipedia.org/wiki/Neo4j) is an open-source graph database management system, renowned for its efficient management of highly connected data. Unlike traditional databases that store data in tables, Neo4j uses a graph structure with nodes, edges, and properties to represent and store data. This design allows for high-performance queries on complex data relationships.\n",
+    "\n",
+    "This notebook goes over how to use `Neo4j` to store chat message history."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d15e3302",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.memory import Neo4jChatMessageHistory\n",
+    "\n",
+    "history = Neo4jChatMessageHistory(\n",
+    "    url=\"bolt://localhost:7687\",\n",
+    "    username=\"neo4j\",\n",
+    "    password=\"password\",\n",
+    "    session_id=\"session_id_1\"\n",
+    ")\n",
+    "\n",
+    "history.add_user_message(\"hi!\")\n",
+    "\n",
+    "history.add_ai_message(\"whats up?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64fc465e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "history.messages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8af285f8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/integrations/retrievers/fleet_context.ipynb b/docs/docs/integrations/retrievers/fleet_context.ipynb
index 10f4145b7c1..4a57d3f7953 100644
--- a/docs/docs/integrations/retrievers/fleet_context.ipynb
+++ b/docs/docs/integrations/retrievers/fleet_context.ipynb
@@ -19,7 +19,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install langchain openai pandas faiss-cpu # faiss-gpu for CUDA supported GPU"
+    "!pip install langchain fleet-context openai pandas faiss-cpu # faiss-gpu for CUDA supported GPU"
    ]
   },
   {
@@ -43,13 +43,12 @@
     "\n",
     "\n",
     "def load_fleet_retriever(\n",
-    "    url: str,\n",
+    "    df: pd.DataFrame,\n",
     "    *,\n",
     "    vectorstore_cls: Type[VectorStore] = FAISS,\n",
     "    docstore: Optional[BaseStore] = None,\n",
     "    **kwargs: Any,\n",
     "):\n",
-    "    df = pd.read_parquet(url)\n",
     "    vectorstore = _populate_vectorstore(df, vectorstore_cls)\n",
     "    if docstore is None:\n",
     "        return vectorstore.as_retriever(**kwargs)\n",
@@ -106,7 +105,10 @@
    "source": [
     "## Retriever chunks\n",
     "\n",
-    "As part of their embedding process, the Fleet AI team first chunked long documents before embedding them. This means the vectors correspond to sections of pages in the LangChain docs, not entire pages. By default, when we spin up a retriever from these embeddings, we'll be retrieving these embedded chunks:"
+    "As part of their embedding process, the Fleet AI team first chunked long documents before embedding them. This means the vectors correspond to sections of pages in the LangChain docs, not entire pages. By default, when we spin up a retriever from these embeddings, we'll be retrieving these embedded chunks.",
+    "\n",
+    "\n",
+    "We will be using Fleet Context's `download_embeddings()` to grab Langchain's documentation embeddings. You can view all supported libraries' documentation at https://fleet.so/context."
    ]
   },
   {
@@ -116,9 +118,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "vecstore_retriever = load_fleet_retriever(\n",
-    "    \"https://www.dropbox.com/scl/fi/4rescpkrg9970s3huz47l/libraries_langchain_release.parquet?rlkey=283knw4wamezfwiidgpgptkep&dl=1\",\n",
-    ")"
+    "from context import download_embeddings\n",
+    "\n",
+    "df = download_embeddings(\"langchain\")\n",
+    "vecstore_retriever = load_fleet_retriever(df)"
    ]
   },
   {
diff --git a/docs/docs/integrations/text_embedding/fastembed.ipynb b/docs/docs/integrations/text_embedding/fastembed.ipynb
new file mode 100644
index 00000000000..9d6826f92d2
--- /dev/null
+++ b/docs/docs/integrations/text_embedding/fastembed.ipynb
@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Qdrant FastEmbed\n",
+    "\n",
+    "[FastEmbed](https://qdrant.github.io/fastembed/) is a lightweight, fast, Python library built for embedding generation. \n",
+    "\n",
+    "- Quantized model weights\n",
+    "- ONNX Runtime, no PyTorch dependency\n",
+    "- CPU-first design\n",
+    "- Data-parallelism for encoding of large datasets."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "2a773d8d",
+   "metadata": {},
+   "source": [
+    "## Dependencies\n",
+    "\n",
+    "To use FastEmbed with LangChain, install the `fastembed` Python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91ea14ce-831d-409a-a88f-30353acdabd1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%pip install fastembed"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "426f1156",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3f5dc9d7-65e3-4b5b-9086-3327d016cfe0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.fastembed import FastEmbedEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiating FastEmbed\n",
+    "   \n",
+    "### Parameters\n",
+    "- `model_name: str` (default: \"BAAI/bge-small-en-v1.5\")\n",
+    "    > Name of the FastEmbedding model to use. You can find the list of supported models [here](https://qdrant.github.io/fastembed/examples/Supported_Models/).\n",
+    "\n",
+    "- `max_length: int` (default: 512)\n",
+    "    > The maximum number of tokens. Unknown behavior for values > 512.\n",
+    "\n",
+    "- `cache_dir: Optional[str]`\n",
+    "    > The path to the cache directory. Defaults to `local_cache` in the parent directory.\n",
+    "\n",
+    "- `threads: Optional[int]`\n",
+    "    > The number of threads a single onnxruntime session can use. Defaults to None.\n",
+    "\n",
+    "- `doc_embed_type: Literal[\"default\", \"passage\"]` (default: \"default\")\n",
+    "    > \"default\": Uses FastEmbed's default embedding method.\n",
+    "    \n",
+    "    > \"passage\": Prefixes the text with \"passage\" before embedding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fb585dd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "embeddings = FastEmbedEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "\n",
+    "### Generating document embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "document_embeddings = embeddings.embed_documents([\"This is a document\", \"This is some other document\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generating query embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_embeddings = embeddings.embed_query(\"This is a query\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/modules/agents/agent_types/index.mdx b/docs/docs/modules/agents/agent_types/index.mdx
index 75dae52ff5c..c97947e78d5 100644
--- a/docs/docs/modules/agents/agent_types/index.mdx
+++ b/docs/docs/modules/agents/agent_types/index.mdx
@@ -38,7 +38,7 @@ It uses the ReAct framework to decide which tool to use, and uses memory to reme
 ## [Self-ask with search](/docs/modules/agents/agent_types/self_ask_with_search)
 
 This agent utilizes a single tool that should be named `Intermediate Answer`.
-This tool should be able to lookup factual answers to questions. This agent
+This tool should be able to look up factual answers to questions. This agent
 is equivalent to the original [self-ask with search paper](https://ofir.io/self-ask.pdf),
 where a Google search API was provided as the tool.
 
@@ -46,7 +46,7 @@ where a Google search API was provided as the tool.
 
 This agent uses the ReAct framework to interact with a docstore. Two tools must
 be provided: a `Search` tool and a `Lookup` tool (they must be named exactly as so).
-The `Search` tool should search for a document, while the `Lookup` tool should lookup
+The `Search` tool should search for a document, while the `Lookup` tool should look up
 a term in the most recently found document.
 This agent is equivalent to the
 original [ReAct paper](https://arxiv.org/pdf/2210.03629.pdf), specifically the Wikipedia example.
diff --git a/docs/docs/modules/agents/how_to/custom_llm_agent.mdx b/docs/docs/modules/agents/how_to/custom_llm_agent.mdx
index 7d10cf50b84..6e92c54717e 100644
--- a/docs/docs/modules/agents/how_to/custom_llm_agent.mdx
+++ b/docs/docs/modules/agents/how_to/custom_llm_agent.mdx
@@ -1,4 +1,4 @@
-# Custom LLM agent
+# Custom LLM Agent
 
 This notebook goes through how to create your own custom LLM agent.
 
diff --git a/docs/docs/modules/agents/how_to/custom_llm_chat_agent.mdx b/docs/docs/modules/agents/how_to/custom_llm_chat_agent.mdx
index a00f59d11ed..f9d8045dc6e 100644
--- a/docs/docs/modules/agents/how_to/custom_llm_chat_agent.mdx
+++ b/docs/docs/modules/agents/how_to/custom_llm_chat_agent.mdx
@@ -1,13 +1,13 @@
-# Custom LLM Agent (with a ChatModel)
+# Custom LLM Chat Agent
 
-This notebook goes through how to create your own custom agent based on a chat model.
+This notebook explains how to create your own custom agent based on a chat model.
 
-An LLM chat agent consists of three parts:
+An LLM chat agent consists of four key components:
 
-- `PromptTemplate`: This is the prompt template that can be used to instruct the language model on what to do
-- `ChatModel`: This is the language model that powers the agent
-- `stop` sequence: Instructs the LLM to stop generating as soon as this string is found
-- `OutputParser`: This determines how to parse the LLM output into an `AgentAction` or `AgentFinish` object
+- `PromptTemplate`: This is the prompt template that instructs the language model on what to do.
+- `ChatModel`: This is the language model that powers the agent.
+- `stop` sequence: Instructs the LLM to stop generating as soon as this string is found.
+- `OutputParser`: This determines how to parse the LLM output into an `AgentAction` or `AgentFinish` object.
 
 The LLM Agent is used in an `AgentExecutor`. This `AgentExecutor` can largely be thought of as a loop that:
 1. Passes user input and any previous steps to the Agent (in this case, the LLM Agent)
diff --git a/docs/docs/modules/agents/how_to/mrkl.mdx b/docs/docs/modules/agents/how_to/mrkl.mdx
index 6657fedc97e..2269766ee42 100644
--- a/docs/docs/modules/agents/how_to/mrkl.mdx
+++ b/docs/docs/modules/agents/how_to/mrkl.mdx
@@ -3,7 +3,7 @@
 This walkthrough demonstrates how to replicate the [MRKL](https://arxiv.org/pdf/2205.00445.pdf) system using agents.
 
 This uses the example Chinook database.
-To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository.
+To set it up, follow the instructions on https://database.guide/2-sample-databases-sqlite/ and place the `.db` file in a "notebooks" folder at the root of this repository.
 
 ```python
 from langchain.chains import LLMMathChain
@@ -127,7 +127,7 @@ mrkl.run("What is the full name of the artist who recently released an album cal
 
 </CodeOutputBlock>
 
-## With a chat model
+## Using a Chat Model
 
 ```python
 from langchain.chat_models import ChatOpenAI
diff --git a/docs/docs/modules/agents/tools/index.mdx b/docs/docs/modules/agents/tools/index.mdx
index bf04ed6c47a..31e0fca7d41 100644
--- a/docs/docs/modules/agents/tools/index.mdx
+++ b/docs/docs/modules/agents/tools/index.mdx
@@ -4,17 +4,17 @@ sidebar_position: 2
 # Tools
 
 :::info
-Head to [Integrations](/docs/integrations/tools/) for documentation on built-in tool integrations.
+For documentation on built-in tool integrations, visit [Integrations](/docs/integrations/tools/).
 :::
 
 Tools are interfaces that an agent can use to interact with the world.
 
-## Get started
+## Getting Started
 
 Tools are functions that agents can use to interact with the world.
 These tools can be generic utilities (e.g. search), other chains, or even other agents.
 
-Currently, tools can be loaded with the following snippet:
+Currently, tools can be loaded using the following snippet:
 
 ```python
 from langchain.agents import load_tools
diff --git a/docs/docs/modules/agents/tools/toolkits.mdx b/docs/docs/modules/agents/tools/toolkits.mdx
index a09facee720..b8d1997025b 100644
--- a/docs/docs/modules/agents/tools/toolkits.mdx
+++ b/docs/docs/modules/agents/tools/toolkits.mdx
@@ -4,7 +4,7 @@ sidebar_position: 3
 # Toolkits
 
 :::info
-Head to [Integrations](/docs/integrations/toolkits/) for documentation on built-in toolkit integrations.
+For documentation on built-in toolkit integrations, visit [Integrations](/docs/integrations/toolkits/).
 :::
 
-Toolkits are collections of tools that are designed to be used together for specific tasks and have convenience loading methods.
+Toolkits are collections of tools that are designed to be used together for specific tasks and have convenient loading methods.
diff --git a/docs/docs/modules/model_io/chat/index.ipynb b/docs/docs/modules/model_io/chat/index.ipynb
index 56ec08a245c..b7b3fc658b4 100644
--- a/docs/docs/modules/model_io/chat/index.ipynb
+++ b/docs/docs/modules/model_io/chat/index.ipynb
@@ -593,7 +593,7 @@
    "id": "a4a7d783-4ddf-42e7-b143-8050891663c2",
    "metadata": {},
    "source": [
-    "## [LangSmith](https://smith.langchain.com)\n",
+    "## [LangSmith](/docs/langsmith)\n",
     "\n",
     "All `ChatModel`s come with built-in LangSmith tracing. Just set the following environment variables:\n",
     "```bash\n",
diff --git a/docs/docs/modules/model_io/llms/index.ipynb b/docs/docs/modules/model_io/llms/index.ipynb
index 84072797d3d..64c9449e3e6 100644
--- a/docs/docs/modules/model_io/llms/index.ipynb
+++ b/docs/docs/modules/model_io/llms/index.ipynb
@@ -459,7 +459,7 @@
    "id": "09108687-ed15-468b-9ac5-674e75785199",
    "metadata": {},
    "source": [
-    "## [LangSmith](https://smith.langchain.com)\n",
+    "## [LangSmith](/docs/langsmith)\n",
     "\n",
     "All `LLM`s come with built-in LangSmith tracing. Just set the following environment variables:\n",
     "```bash\n",
diff --git a/docs/static/img/langchain_stack.png b/docs/static/img/langchain_stack.png
index 0f74a7a00bd..20777e333eb 100644
Binary files a/docs/static/img/langchain_stack.png and b/docs/static/img/langchain_stack.png differ
diff --git a/libs/langchain/langchain/chat_models/azureml_endpoint.py b/libs/langchain/langchain/chat_models/azureml_endpoint.py
index 53bdc849252..8efa957ad0f 100644
--- a/libs/langchain/langchain/chat_models/azureml_endpoint.py
+++ b/libs/langchain/langchain/chat_models/azureml_endpoint.py
@@ -4,7 +4,7 @@ from typing import Any, Dict, List, Optional, cast
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.chat_models.base import SimpleChatModel
 from langchain.llms.azureml_endpoint import AzureMLEndpointClient, ContentFormatterBase
-from langchain.pydantic_v1 import validator
+from langchain.pydantic_v1 import SecretStr, validator
 from langchain.schema.messages import (
     AIMessage,
     BaseMessage,
@@ -12,7 +12,7 @@ from langchain.schema.messages import (
     HumanMessage,
     SystemMessage,
 )
-from langchain.utils import get_from_dict_or_env
+from langchain.utils import convert_to_secret_str, get_from_dict_or_env
 
 
 class LlamaContentFormatter(ContentFormatterBase):
@@ -94,7 +94,7 @@ class AzureMLChatOnlineEndpoint(SimpleChatModel):
     """URL of pre-existing Endpoint. Should be passed to constructor or specified as 
         env var `AZUREML_ENDPOINT_URL`."""
 
-    endpoint_api_key: str = ""
+    endpoint_api_key: SecretStr = convert_to_secret_str("")
     """Authentication Key for Endpoint. Should be passed to constructor or specified as
         env var `AZUREML_ENDPOINT_API_KEY`."""
 
@@ -112,13 +112,15 @@ class AzureMLChatOnlineEndpoint(SimpleChatModel):
     @classmethod
     def validate_client(cls, field_value: Any, values: Dict) -> AzureMLEndpointClient:
         """Validate that api key and python package exist in environment."""
-        endpoint_key = get_from_dict_or_env(
-            values, "endpoint_api_key", "AZUREML_ENDPOINT_API_KEY"
+        values["endpoint_api_key"] = convert_to_secret_str(
+            get_from_dict_or_env(values, "endpoint_api_key", "AZUREML_ENDPOINT_API_KEY")
         )
         endpoint_url = get_from_dict_or_env(
             values, "endpoint_url", "AZUREML_ENDPOINT_URL"
         )
-        http_client = AzureMLEndpointClient(endpoint_url, endpoint_key)
+        http_client = AzureMLEndpointClient(
+            endpoint_url, values["endpoint_api_key"].get_secret_value()
+        )
         return http_client
 
     @property
diff --git a/libs/langchain/langchain/document_loaders/__init__.py b/libs/langchain/langchain/document_loaders/__init__.py
index 730e2400b67..96cfd9e1b54 100644
--- a/libs/langchain/langchain/document_loaders/__init__.py
+++ b/libs/langchain/langchain/document_loaders/__init__.py
@@ -67,6 +67,7 @@ from langchain.document_loaders.diffbot import DiffbotLoader
 from langchain.document_loaders.directory import DirectoryLoader
 from langchain.document_loaders.discord import DiscordChatLoader
 from langchain.document_loaders.docugami import DocugamiLoader
+from langchain.document_loaders.docusaurus import DocusaurusLoader
 from langchain.document_loaders.dropbox import DropboxLoader
 from langchain.document_loaders.duckdb_loader import DuckDBLoader
 from langchain.document_loaders.email import (
@@ -250,6 +251,7 @@ __all__ = [
     "DirectoryLoader",
     "DiscordChatLoader",
     "DocugamiLoader",
+    "DocusaurusLoader",
     "Docx2txtLoader",
     "DropboxLoader",
     "DuckDBLoader",
diff --git a/libs/langchain/langchain/document_loaders/docusaurus.py b/libs/langchain/langchain/document_loaders/docusaurus.py
new file mode 100644
index 00000000000..3d434156d83
--- /dev/null
+++ b/libs/langchain/langchain/document_loaders/docusaurus.py
@@ -0,0 +1,49 @@
+"""Load Documents from Docusarus Documentation"""
+from typing import Any, List, Optional
+
+from langchain.document_loaders.sitemap import SitemapLoader
+
+
+class DocusaurusLoader(SitemapLoader):
+    """
+    Loader that leverages the SitemapLoader to loop through the generated pages of a
+    Docusaurus Documentation website and extracts the content by looking for specific
+    HTML tags. By default, the parser searches for the main content of the Docusaurus
+    page, which is normally the <article>. You also have the option to define your own
+    custom HTML tags by providing them as a list, for example: ["div", ".main", "a"].
+    """
+
+    def __init__(
+        self,
+        url: str,
+        custom_html_tags: Optional[List[str]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Initialize DocusaurusLoader
+        Args:
+            url: The base URL of the Docusaurus website.
+            custom_html_tags: Optional custom html tags to extract content from pages.
+            kwargs: Additional args to extend the underlying SitemapLoader, for example:
+                filter_urls, blocksize, meta_function, is_local, continue_on_failure
+        """
+        if not kwargs.get("is_local"):
+            url = f"{url}/sitemap.xml"
+
+        self.custom_html_tags = custom_html_tags or ["main article"]
+
+        super().__init__(
+            url,
+            parsing_function=kwargs.get("parsing_function") or self._parsing_function,
+            **kwargs,
+        )
+
+    def _parsing_function(self, content: Any) -> str:
+        """Parses specific elements from a Docusarus page."""
+        relevant_elements = content.select(",".join(self.custom_html_tags))
+
+        for element in relevant_elements:
+            if element not in relevant_elements:
+                element.decompose()
+
+        return str(content.get_text())
diff --git a/libs/langchain/langchain/embeddings/__init__.py b/libs/langchain/langchain/embeddings/__init__.py
index dfbb814fbe2..8ead8ee1c03 100644
--- a/libs/langchain/langchain/embeddings/__init__.py
+++ b/libs/langchain/langchain/embeddings/__init__.py
@@ -32,6 +32,7 @@ from langchain.embeddings.elasticsearch import ElasticsearchEmbeddings
 from langchain.embeddings.embaas import EmbaasEmbeddings
 from langchain.embeddings.ernie import ErnieEmbeddings
 from langchain.embeddings.fake import DeterministicFakeEmbedding, FakeEmbeddings
+from langchain.embeddings.fastembed import FastEmbedEmbeddings
 from langchain.embeddings.google_palm import GooglePalmEmbeddings
 from langchain.embeddings.gpt4all import GPT4AllEmbeddings
 from langchain.embeddings.gradient_ai import GradientEmbeddings
@@ -77,6 +78,7 @@ __all__ = [
     "ClarifaiEmbeddings",
     "CohereEmbeddings",
     "ElasticsearchEmbeddings",
+    "FastEmbedEmbeddings",
     "HuggingFaceEmbeddings",
     "HuggingFaceInferenceAPIEmbeddings",
     "GradientEmbeddings",
diff --git a/libs/langchain/langchain/embeddings/clarifai.py b/libs/langchain/langchain/embeddings/clarifai.py
index 8af1f6b749b..a33c267a57f 100644
--- a/libs/langchain/langchain/embeddings/clarifai.py
+++ b/libs/langchain/langchain/embeddings/clarifai.py
@@ -20,7 +20,7 @@ class ClarifaiEmbeddings(BaseModel, Embeddings):
 
             from langchain.embeddings import ClarifaiEmbeddings
             clarifai = ClarifaiEmbeddings(
-                model="embed-english-light-v2.0", clarifai_api_key="my-api-key"
+                model="embed-english-light-v3.0", clarifai_api_key="my-api-key"
             )
     """
 
diff --git a/libs/langchain/langchain/embeddings/cohere.py b/libs/langchain/langchain/embeddings/cohere.py
index 0531390ce2e..73435eaa932 100644
--- a/libs/langchain/langchain/embeddings/cohere.py
+++ b/libs/langchain/langchain/embeddings/cohere.py
@@ -17,7 +17,7 @@ class CohereEmbeddings(BaseModel, Embeddings):
 
             from langchain.embeddings import CohereEmbeddings
             cohere = CohereEmbeddings(
-                model="embed-english-light-v2.0", cohere_api_key="my-api-key"
+                model="embed-english-light-v3.0", cohere_api_key="my-api-key"
             )
     """
 
diff --git a/libs/langchain/langchain/embeddings/fastembed.py b/libs/langchain/langchain/embeddings/fastembed.py
new file mode 100644
index 00000000000..cbc2c9ff16b
--- /dev/null
+++ b/libs/langchain/langchain/embeddings/fastembed.py
@@ -0,0 +1,108 @@
+from typing import Any, Dict, List, Literal, Optional
+
+import numpy as np
+
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
+from langchain.schema.embeddings import Embeddings
+
+
+class FastEmbedEmbeddings(BaseModel, Embeddings):
+    """Qdrant FastEmbedding models.
+    FastEmbed is a lightweight, fast, Python library built for embedding generation.
+    See more documentation at:
+    * https://github.com/qdrant/fastembed/
+    * https://qdrant.github.io/fastembed/
+
+    To use this class, you must install the `fastembed` Python package.
+
+    `pip install fastembed`
+    Example:
+        from langchain.embeddings import FastEmbedEmbeddings
+        fastembed = FastEmbedEmbeddings()
+    """
+
+    model_name: str = "BAAI/bge-small-en-v1.5"
+    """Name of the FastEmbedding model to use
+    Defaults to "BAAI/bge-small-en-v1.5"
+    Find the list of supported models at
+    https://qdrant.github.io/fastembed/examples/Supported_Models/
+    """
+
+    max_length: int = 512
+    """The maximum number of tokens. Defaults to 512.
+    Unknown behavior for values > 512.
+    """
+
+    cache_dir: Optional[str]
+    """The path to the cache directory.
+    Defaults to `local_cache` in the parent directory
+    """
+
+    threads: Optional[int]
+    """The number of threads single onnxruntime session can use.
+    Defaults to None
+    """
+
+    doc_embed_type: Literal["default", "passage"] = "default"
+    """Type of embedding to use for documents
+    "default": Uses FastEmbed's default embedding method
+    "passage": Prefixes the text with "passage" before embedding.
+    """
+
+    _model: Any  # : :meta private:
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that FastEmbed has been installed."""
+        try:
+            from fastembed.embedding import FlagEmbedding
+
+            model_name = values.get("model_name")
+            max_length = values.get("max_length")
+            cache_dir = values.get("cache_dir")
+            threads = values.get("threads")
+            values["_model"] = FlagEmbedding(
+                model_name=model_name,
+                max_length=max_length,
+                cache_dir=cache_dir,
+                threads=threads,
+            )
+        except ImportError as ie:
+            raise ImportError(
+                "Could not import 'fastembed' Python package. "
+                "Please install it with `pip install fastembed`."
+            ) from ie
+        return values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Generate embeddings for documents using FastEmbed.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings: List[np.ndarray]
+        if self.doc_embed_type == "passage":
+            embeddings = self._model.passage_embed(texts)
+        else:
+            embeddings = self._model.embed(texts)
+        return [e.tolist() for e in embeddings]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Generate query embeddings using FastEmbed.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        query_embeddings: np.ndarray = next(self._model.query_embed(text))
+        return query_embeddings.tolist()
diff --git a/libs/langchain/langchain/memory/chat_message_histories/__init__.py b/libs/langchain/langchain/memory/chat_message_histories/__init__.py
index c0b7c544a9b..a1497e8a122 100644
--- a/libs/langchain/langchain/memory/chat_message_histories/__init__.py
+++ b/libs/langchain/langchain/memory/chat_message_histories/__init__.py
@@ -13,6 +13,7 @@ from langchain.memory.chat_message_histories.firestore import (
 from langchain.memory.chat_message_histories.in_memory import ChatMessageHistory
 from langchain.memory.chat_message_histories.momento import MomentoChatMessageHistory
 from langchain.memory.chat_message_histories.mongodb import MongoDBChatMessageHistory
+from langchain.memory.chat_message_histories.neo4j import Neo4jChatMessageHistory
 from langchain.memory.chat_message_histories.postgres import PostgresChatMessageHistory
 from langchain.memory.chat_message_histories.redis import RedisChatMessageHistory
 from langchain.memory.chat_message_histories.rocksetdb import RocksetChatMessageHistory
@@ -48,4 +49,5 @@ __all__ = [
     "XataChatMessageHistory",
     "ZepChatMessageHistory",
     "UpstashRedisChatMessageHistory",
+    "Neo4jChatMessageHistory",
 ]
diff --git a/libs/langchain/langchain/memory/chat_message_histories/neo4j.py b/libs/langchain/langchain/memory/chat_message_histories/neo4j.py
new file mode 100644
index 00000000000..dfbf75cc304
--- /dev/null
+++ b/libs/langchain/langchain/memory/chat_message_histories/neo4j.py
@@ -0,0 +1,112 @@
+from typing import List, Optional, Union
+
+from langchain.schema import BaseChatMessageHistory
+from langchain.schema.messages import BaseMessage, messages_from_dict
+from langchain.utils import get_from_env
+
+
+class Neo4jChatMessageHistory(BaseChatMessageHistory):
+    """Chat message history stored in a Neo4j database."""
+
+    def __init__(
+        self,
+        session_id: Union[str, int],
+        url: Optional[str] = None,
+        username: Optional[str] = None,
+        password: Optional[str] = None,
+        database: str = "neo4j",
+        node_label: str = "Session",
+        window: int = 3,
+    ):
+        try:
+            import neo4j
+        except ImportError:
+            raise ValueError(
+                "Could not import neo4j python package. "
+                "Please install it with `pip install neo4j`."
+            )
+
+        # Make sure session id is not null
+        if not session_id:
+            raise ValueError("Please ensure that the session_id parameter is provided")
+
+        url = get_from_env("url", "NEO4J_URI", url)
+        username = get_from_env("username", "NEO4J_USERNAME", username)
+        password = get_from_env("password", "NEO4J_PASSWORD", password)
+        database = get_from_env("database", "NEO4J_DATABASE", database)
+
+        self._driver = neo4j.GraphDatabase.driver(url, auth=(username, password))
+        self._database = database
+        self._session_id = session_id
+        self._node_label = node_label
+        self._window = window
+
+        # Verify connection
+        try:
+            self._driver.verify_connectivity()
+        except neo4j.exceptions.ServiceUnavailable:
+            raise ValueError(
+                "Could not connect to Neo4j database. "
+                "Please ensure that the url is correct"
+            )
+        except neo4j.exceptions.AuthError:
+            raise ValueError(
+                "Could not connect to Neo4j database. "
+                "Please ensure that the username and password are correct"
+            )
+        # Create session node
+        self._driver.execute_query(
+            f"MERGE (s:`{self._node_label}` {{id:$session_id}})",
+            {"session_id": self._session_id},
+        ).summary
+
+    @property
+    def messages(self) -> List[BaseMessage]:  # type: ignore
+        """Retrieve the messages from Neo4j"""
+        query = (
+            f"MATCH (s:`{self._node_label}`)-[:LAST_MESSAGE]->(last_message) "
+            "WHERE s.id = $session_id MATCH p=(last_message)<-[:NEXT*0.."
+            f"{self._window*2}]-() WITH p, length(p) AS length "
+            "ORDER BY length DESC LIMIT 1 UNWIND reverse(nodes(p)) AS node "
+            "RETURN {data:{content: node.content}, type:node.type} AS result"
+        )
+        records, _, _ = self._driver.execute_query(
+            query, {"session_id": self._session_id}
+        )
+
+        messages = messages_from_dict([el["result"] for el in records])
+        return messages
+
+    def add_message(self, message: BaseMessage) -> None:
+        """Append the message to the record in Neo4j"""
+        query = (
+            f"MATCH (s:`{self._node_label}`) WHERE s.id = $session_id "
+            "OPTIONAL MATCH (s)-[lm:LAST_MESSAGE]->(last_message) "
+            "CREATE (s)-[:LAST_MESSAGE]->(new:Message) "
+            "SET new += {type:$type, content:$content} "
+            "WITH new, lm, last_message WHERE last_message IS NOT NULL "
+            "CREATE (last_message)-[:NEXT]->(new) "
+            "DELETE lm"
+        )
+        self._driver.execute_query(
+            query,
+            {
+                "type": message.type,
+                "content": message.content,
+                "session_id": self._session_id,
+            },
+        ).summary
+
+    def clear(self) -> None:
+        """Clear session memory from Neo4j"""
+        query = (
+            f"MATCH (s:`{self._node_label}`)-[:LAST_MESSAGE]->(last_message) "
+            "WHERE s.id = $session_id MATCH p=(last_message)<-[:NEXT]-() "
+            "WITH p, length(p) AS length ORDER BY length DESC LIMIT 1 "
+            "UNWIND nodes(p) as node DETACH DELETE node;"
+        )
+        self._driver.execute_query(query, {"session_id": self._session_id}).summary
+
+    def __del__(self) -> None:
+        if self._driver:
+            self._driver.close()
diff --git a/libs/langchain/langchain/schema/runnable/base.py b/libs/langchain/langchain/schema/runnable/base.py
index c9919adc530..4308a202534 100644
--- a/libs/langchain/langchain/schema/runnable/base.py
+++ b/libs/langchain/langchain/schema/runnable/base.py
@@ -1217,16 +1217,97 @@ class RunnableSerializable(Serializable, Runnable[Input, Output]):
 
 
 class RunnableSequence(RunnableSerializable[Input, Output]):
-    """
-    A sequence of runnables, where the output of each is the input of the next.
+    """A sequence of runnables, where the output of each is the input of the next.
+
+    RunnableSequence is the most important composition operator in LangChain as it is
+    used in virtually every chain.
+
+    A RunnableSequence can be instantiated directly or more commonly by using the `|`
+    operator where either the left or right operands (or both) must be a Runnable.
+
+    Any RunnableSequence automatically supports sync, async, batch.
+
+    The default implementations of `batch` and `abatch` utilize threadpools and
+    asyncio gather and will be faster than naive invocation of invoke or ainvoke
+    for IO bound runnables.
+
+    Batching is implemented by invoking the batch method on each component of the
+    RunnableSequence in order.
+
+    A RunnableSequence preserves the streaming properties of its components, so if all
+    components of the sequence implement a `transform` method -- which
+    is the method that implements the logic to map a streaming input to a streaming
+    output -- then the sequence will be able to stream input to output!
+
+    If any component of the sequence does not implement transform then the
+    streaming will only begin after this component is run. If there are
+    multiple blocking components, streaming begins after the last one.
+
+    Please note: RunnableLambdas do not support `transform` by default! So if
+        you need to use a RunnableLambdas be careful about where you place them in a
+        RunnableSequence (if you need to use the .stream()/.astream() methods).
+
+        If you need arbitrary logic and need streaming, you can subclass
+        Runnable, and implement `transform` for whatever logic you need.
+
+    Here is a simple example that uses simple functions to illustrate the use of
+    RunnableSequence:
+
+        .. code-block:: python
+
+            from langchain.schema.runnable import RunnableLambda
+
+            def add_one(x: int) -> int:
+                return x + 1
+
+            def mul_two(x: int) -> int:
+                return x * 2
+
+            runnable_1 = RunnableLambda(add_one)
+            runnable_2 = RunnableLambda(mul_two)
+            sequence = runnable_1 | runnable_2
+            # Or equivalently:
+            # sequence = RunnableSequence(first=runnable_1, last=runnable_2)
+            sequence.invoke(1)
+            await runnable.ainvoke(1)
+
+            sequence.batch([1, 2, 3])
+            await sequence.abatch([1, 2, 3])
+
+    Here's an example that uses streams JSON output generated by an LLM:
+
+        .. code-block:: python
+
+            from langchain.output_parsers.json import SimpleJsonOutputParser
+            from langchain.chat_models.openai import ChatOpenAI
+
+            prompt = PromptTemplate.from_template(
+                'In JSON format, give me a list of {topic} and their '
+                'corresponding names in French, Spanish and in a '
+                'Cat Language.'
+            )
+
+            model = ChatOpenAI()
+            chain = prompt | model | SimpleJsonOutputParser()
+
+            async for chunk in chain.astream({'topic': 'colors'}):
+                print('-')
+                print(chunk, sep='', flush=True)
     """
 
+    # The steps are broken into first, middle and last, solely for type checking
+    # purposes. It allows specifying the `Input` on the first type, the `Output` of
+    # the last type.
     first: Runnable[Input, Any]
+    """The first runnable in the sequence."""
     middle: List[Runnable[Any, Any]] = Field(default_factory=list)
+    """The middle runnables in the sequence."""
     last: Runnable[Any, Output]
+    """The last runnable in the sequence."""
 
     @property
     def steps(self) -> List[Runnable[Any, Any]]:
+        """All the runnables that make up the sequence in order."""
         return [self.first] + self.middle + [self.last]
 
     @classmethod
diff --git a/libs/langchain/tests/integration_tests/document_loaders/test_docusaurus.py b/libs/langchain/tests/integration_tests/document_loaders/test_docusaurus.py
new file mode 100644
index 00000000000..53323ae9e4e
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/document_loaders/test_docusaurus.py
@@ -0,0 +1,43 @@
+from pathlib import Path
+
+from langchain.document_loaders import DocusaurusLoader
+
+DOCS_URL = str(Path(__file__).parent.parent / "examples/docusaurus-sitemap.xml")
+
+
+def test_docusarus() -> None:
+    """Test sitemap loader."""
+    loader = DocusaurusLoader(DOCS_URL, is_local=True)
+    documents = loader.load()
+    assert len(documents) > 1
+    assert "🦜️🔗 Langchain" in documents[0].page_content
+
+
+def test_filter_docusaurus_sitemap() -> None:
+    """Test sitemap loader."""
+    loader = DocusaurusLoader(
+        DOCS_URL,
+        is_local=True,
+        filter_urls=[
+            "https://python.langchain.com/docs/integrations/document_loaders/sitemap"
+        ],
+    )
+    documents = loader.load()
+    assert len(documents) == 1
+    assert "SitemapLoader" in documents[0].page_content
+
+
+def test_docusarus_metadata() -> None:
+    def sitemap_metadata_one(meta: dict, _content: None) -> dict:
+        return {**meta, "mykey": "Super Important Metadata"}
+
+    """Test sitemap loader."""
+    loader = DocusaurusLoader(
+        DOCS_URL,
+        is_local=True,
+        meta_function=sitemap_metadata_one,
+    )
+    documents = loader.load()
+    assert len(documents) > 1
+    assert "mykey" in documents[0].metadata
+    assert "Super Important Metadata" in documents[0].metadata["mykey"]
diff --git a/libs/langchain/tests/integration_tests/embeddings/test_fastembed.py b/libs/langchain/tests/integration_tests/embeddings/test_fastembed.py
new file mode 100644
index 00000000000..84e4ea67fe8
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/embeddings/test_fastembed.py
@@ -0,0 +1,76 @@
+"""Test FastEmbed embeddings."""
+import pytest
+
+from langchain.embeddings.fastembed import FastEmbedEmbeddings
+
+
+@pytest.mark.parametrize(
+    "model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
+)
+@pytest.mark.parametrize("max_length", [50, 512])
+@pytest.mark.parametrize("doc_embed_type", ["default", "passage"])
+@pytest.mark.parametrize("threads", [0, 10])
+def test_fastembed_embedding_documents(
+    model_name: str, max_length: int, doc_embed_type: str, threads: int
+) -> None:
+    """Test fastembed embeddings for documents."""
+    documents = ["foo bar", "bar foo"]
+    embedding = FastEmbedEmbeddings(
+        model_name=model_name,
+        max_length=max_length,
+        doc_embed_type=doc_embed_type,
+        threads=threads,
+    )
+    output = embedding.embed_documents(documents)
+    assert len(output) == 2
+    assert len(output[0]) == 384
+
+
+@pytest.mark.parametrize(
+    "model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
+)
+@pytest.mark.parametrize("max_length", [50, 512])
+def test_fastembed_embedding_query(model_name: str, max_length: int) -> None:
+    """Test fastembed embeddings for query."""
+    document = "foo bar"
+    embedding = FastEmbedEmbeddings(model_name=model_name, max_length=max_length)
+    output = embedding.embed_query(document)
+    assert len(output) == 384
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
+)
+@pytest.mark.parametrize("max_length", [50, 512])
+@pytest.mark.parametrize("doc_embed_type", ["default", "passage"])
+@pytest.mark.parametrize("threads", [0, 10])
+async def test_fastembed_async_embedding_documents(
+    model_name: str, max_length: int, doc_embed_type: str, threads: int
+) -> None:
+    """Test fastembed embeddings for documents."""
+    documents = ["foo bar", "bar foo"]
+    embedding = FastEmbedEmbeddings(
+        model_name=model_name,
+        max_length=max_length,
+        doc_embed_type=doc_embed_type,
+        threads=threads,
+    )
+    output = await embedding.aembed_documents(documents)
+    assert len(output) == 2
+    assert len(output[0]) == 384
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
+)
+@pytest.mark.parametrize("max_length", [50, 512])
+async def test_fastembed_async_embedding_query(
+    model_name: str, max_length: int
+) -> None:
+    """Test fastembed embeddings for query."""
+    document = "foo bar"
+    embedding = FastEmbedEmbeddings(model_name=model_name, max_length=max_length)
+    output = await embedding.aembed_query(document)
+    assert len(output) == 384
diff --git a/libs/langchain/tests/integration_tests/examples/docusaurus-sitemap.xml b/libs/langchain/tests/integration_tests/examples/docusaurus-sitemap.xml
new file mode 100644
index 00000000000..eebae785b88
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/examples/docusaurus-sitemap.xml
@@ -0,0 +1,42 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"
+  xmlns:news="http://www.google.com/schemas/sitemap-news/0.9"
+  xmlns:xhtml="http://www.w3.org/1999/xhtml"
+  xmlns:image="http://www.google.com/schemas/sitemap-image/1.1"
+  xmlns:video="http://www.google.com/schemas/sitemap-video/1.1">
+  <url>
+    <loc>https://python.langchain.com/docs/integrations/document_loaders/sitemap</loc>
+    <changefreq>weekly</changefreq>
+    <priority>0.5</priority>
+  </url>
+  <url>
+    <loc>https://python.langchain.com/cookbook</loc>
+    <changefreq>weekly</changefreq>
+    <priority>0.5</priority>
+  </url>
+  <url>
+    <loc>https://python.langchain.com/docs/additional_resources</loc>
+    <changefreq>weekly</changefreq>
+    <priority>0.5</priority>
+  </url>
+  <url>
+    <loc>https://python.langchain.com/docs/modules/chains/how_to/</loc>
+    <changefreq>weekly</changefreq>
+    <priority>0.5</priority>
+  </url>
+  <url>
+    <loc>https://python.langchain.com/docs/use_cases/question_answering/local_retrieval_qa</loc>
+    <changefreq>weekly</changefreq>
+    <priority>0.5</priority>
+  </url>
+  <url>
+    <loc>https://python.langchain.com/docs/use_cases/summarization</loc>
+    <changefreq>weekly</changefreq>
+    <priority>0.5</priority>
+  </url>
+  <url>
+    <loc>https://python.langchain.com/</loc>
+    <changefreq>weekly</changefreq>
+    <priority>0.5</priority>
+  </url>
+</urlset>
\ No newline at end of file
diff --git a/libs/langchain/tests/integration_tests/memory/test_neo4j.py b/libs/langchain/tests/integration_tests/memory/test_neo4j.py
new file mode 100644
index 00000000000..d14e2c81f25
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/memory/test_neo4j.py
@@ -0,0 +1,30 @@
+import json
+
+from langchain.memory import ConversationBufferMemory
+from langchain.memory.chat_message_histories import Neo4jChatMessageHistory
+from langchain.schema.messages import _message_to_dict
+
+
+def test_memory_with_message_store() -> None:
+    """Test the memory with a message store."""
+    # setup MongoDB as a message store
+    message_history = Neo4jChatMessageHistory(session_id="test-session")
+    memory = ConversationBufferMemory(
+        memory_key="baz", chat_memory=message_history, return_messages=True
+    )
+
+    # add some messages
+    memory.chat_memory.add_ai_message("This is me, the AI")
+    memory.chat_memory.add_user_message("This is me, the human")
+
+    # get the message history from the memory store and turn it into a json
+    messages = memory.chat_memory.messages
+    messages_json = json.dumps([_message_to_dict(msg) for msg in messages])
+
+    assert "This is me, the AI" in messages_json
+    assert "This is me, the human" in messages_json
+
+    # remove the record from Azure Cosmos DB, so the next test run won't pick it up
+    memory.chat_memory.clear()
+
+    assert memory.chat_memory.messages == []
diff --git a/libs/langchain/tests/unit_tests/chat_models/test_azureml_endpoint.py b/libs/langchain/tests/unit_tests/chat_models/test_azureml_endpoint.py
new file mode 100644
index 00000000000..de1055fc3d5
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/chat_models/test_azureml_endpoint.py
@@ -0,0 +1,65 @@
+"""Test AzureML chat endpoint."""
+
+import os
+
+import pytest
+from pytest import CaptureFixture, FixtureRequest
+
+from langchain.chat_models.azureml_endpoint import AzureMLChatOnlineEndpoint
+from langchain.pydantic_v1 import SecretStr
+
+
+@pytest.fixture(scope="class")
+def api_passed_via_environment_fixture() -> AzureMLChatOnlineEndpoint:
+    """Fixture to create an AzureMLChatOnlineEndpoint instance
+    with API key passed from environment"""
+    os.environ["AZUREML_ENDPOINT_API_KEY"] = "my-api-key"
+    azure_chat = AzureMLChatOnlineEndpoint(
+        endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score"
+    )
+    del os.environ["AZUREML_ENDPOINT_API_KEY"]
+    return azure_chat
+
+
+@pytest.fixture(scope="class")
+def api_passed_via_constructor_fixture() -> AzureMLChatOnlineEndpoint:
+    """Fixture to create an AzureMLChatOnlineEndpoint instance
+    with API key passed from constructor"""
+    azure_chat = AzureMLChatOnlineEndpoint(
+        endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score",
+        endpoint_api_key="my-api-key",
+    )
+    return azure_chat
+
+
+@pytest.mark.parametrize(
+    "fixture_name",
+    ["api_passed_via_constructor_fixture", "api_passed_via_environment_fixture"],
+)
+class TestAzureMLChatOnlineEndpoint:
+    def test_api_key_is_secret_string(
+        self, fixture_name: str, request: FixtureRequest
+    ) -> None:
+        """Test that the API key is a SecretStr instance"""
+        azure_chat = request.getfixturevalue(fixture_name)
+        assert isinstance(azure_chat.endpoint_api_key, SecretStr)
+
+    def test_api_key_masked(
+        self, fixture_name: str, request: FixtureRequest, capsys: CaptureFixture
+    ) -> None:
+        """Test that the API key is masked"""
+        azure_chat = request.getfixturevalue(fixture_name)
+        print(azure_chat.endpoint_api_key, end="")
+        captured = capsys.readouterr()
+        assert (
+            (str(azure_chat.endpoint_api_key) == "**********")
+            and (repr(azure_chat.endpoint_api_key) == "SecretStr('**********')")
+            and (captured.out == "**********")
+        )
+
+    def test_api_key_is_readable(
+        self, fixture_name: str, request: FixtureRequest
+    ) -> None:
+        """Test that the real secret value of the API key can be read"""
+        azure_chat = request.getfixturevalue(fixture_name)
+        assert azure_chat.endpoint_api_key.get_secret_value() == "my-api-key"
diff --git a/libs/langchain/tests/unit_tests/document_loaders/test_imports.py b/libs/langchain/tests/unit_tests/document_loaders/test_imports.py
index 18d3c31f537..9f35b895e46 100644
--- a/libs/langchain/tests/unit_tests/document_loaders/test_imports.py
+++ b/libs/langchain/tests/unit_tests/document_loaders/test_imports.py
@@ -47,6 +47,7 @@ EXPECTED_ALL = [
     "DirectoryLoader",
     "DiscordChatLoader",
     "DocugamiLoader",
+    "DocusaurusLoader",
     "Docx2txtLoader",
     "DropboxLoader",
     "DuckDBLoader",
diff --git a/libs/langchain/tests/unit_tests/embeddings/test_imports.py b/libs/langchain/tests/unit_tests/embeddings/test_imports.py
index 6fe7a85cff6..a8884d23b1c 100644
--- a/libs/langchain/tests/unit_tests/embeddings/test_imports.py
+++ b/libs/langchain/tests/unit_tests/embeddings/test_imports.py
@@ -7,6 +7,7 @@ EXPECTED_ALL = [
     "ClarifaiEmbeddings",
     "CohereEmbeddings",
     "ElasticsearchEmbeddings",
+    "FastEmbedEmbeddings",
     "HuggingFaceEmbeddings",
     "HuggingFaceInferenceAPIEmbeddings",
     "GradientEmbeddings",
diff --git a/templates/docs/INDEX.md b/templates/docs/INDEX.md
index f9d24400a1d..ab2a1519aa2 100644
--- a/templates/docs/INDEX.md
+++ b/templates/docs/INDEX.md
@@ -19,7 +19,7 @@ These templates cover advanced retrieval techniques, which can be used for chat
 
 - [Reranking](../rag-pinecone-rerank): This retrieval technique uses Cohere's reranking endpoint to rerank documents from an initial retrieval step.
 - [Anthropic Iterative Search](../anthropic-iterative-search): This retrieval technique uses iterative prompting to determine what to retrieve and whether the retriever documents are good enough.
-- [Neo4j Parent Document Retrieval](../neo4j-parent): This retrieval technique stores embeddings for smaller chunks, but then returns larger chunks to pass to the model for generation.
+- **Parent Document Retrieval** using [Neo4j](../neo4j-parent) or [MongoDB](../mongo-parent-document-retrieval): This retrieval technique stores embeddings for smaller chunks, but then returns larger chunks to pass to the model for generation.
 - [Semi-Structured RAG](../rag-semi-structured): The template shows how to do retrieval over semi-structured data (e.g. data that involves both text and tables).
 - [Temporal RAG](../rag-timescale-hybrid-search-time): The template shows how to do hybrid search over data with a time-based component using [Timescale Vector](https://www.timescale.com/ai?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral).
 
diff --git a/templates/rag-timescale-conversation/LICENSE b/templates/rag-timescale-conversation/LICENSE
new file mode 100644
index 00000000000..426b6509034
--- /dev/null
+++ b/templates/rag-timescale-conversation/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/templates/rag-timescale-conversation/README.md b/templates/rag-timescale-conversation/README.md
new file mode 100644
index 00000000000..6a7fdebd68c
--- /dev/null
+++ b/templates/rag-timescale-conversation/README.md
@@ -0,0 +1,80 @@
+
+# rag-timescale-conversation
+
+This template is used for [conversational](https://python.langchain.com/docs/expression_language/cookbook/retrieval#conversational-retrieval-chain) [retrieval](https://python.langchain.com/docs/use_cases/question_answering/), which is one of the most popular LLM use-cases.
+
+It passes both a conversation history and retrieved documents into an LLM for synthesis.
+
+## Environment Setup
+
+This template uses Timescale Vector as a vectorstore and requires that `TIMESCALES_SERVICE_URL`. Signup for a 90-day trial [here](https://console.cloud.timescale.com/signup?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral) if you don't yet have an account.
+
+To load the sample dataset, set `LOAD_SAMPLE_DATA=1`. To load your own dataset see the section below.
+
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
+
+## Usage
+
+To use this package, you should first have the LangChain CLI installed:
+
+```shell
+pip install -U "langchain-cli[serve]"
+```
+
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package rag-timescale-conversation
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add rag-timescale-conversation
+```
+
+And add the following code to your `server.py` file:
+```python
+from rag_timescale_conversation import chain as rag_timescale_conversation_chain
+
+add_routes(app, rag_timescale_conversation_chain, path="/rag-timescale_conversation")
+```
+
+(Optional) Let's now configure LangSmith.
+LangSmith will help us trace, monitor and debug LangChain applications.
+LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
+If you don't have access, you can skip this section
+
+```shell
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+If you are inside this directory, then you can spin up a LangServe instance directly by:
+
+```shell
+langchain serve
+```
+
+This will start the FastAPI app with a server is running locally at
+[http://localhost:8000](http://localhost:8000)
+
+We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
+We can access the playground at [http://127.0.0.1:8000/rag-timescale-conversation/playground](http://127.0.0.1:8000/rag-timescale-conversation/playground)
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/rag-timescale-conversation")
+```
+
+See the `rag_conversation.ipynb` notebook for example usage.
+
+## Loading your own dataset
+
+To load your own dataset you will have to create a `load_dataset` function. You can see an example, in the
+`load_ts_git_dataset` function defined in the `load_sample_dataset.py` file. You can then run this as a
+standalone function (e.g. in a bash script) or add it to chain.py (but then you should run it just once).
\ No newline at end of file
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+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.8.1,<4.0"
+content-hash = "078747db3e381f1480af6917fb388512c05682d97bfe4c650cb94a9bd3db4dc1"
diff --git a/templates/rag-timescale-conversation/pyproject.toml b/templates/rag-timescale-conversation/pyproject.toml
new file mode 100644
index 00000000000..2b207ac771e
--- /dev/null
+++ b/templates/rag-timescale-conversation/pyproject.toml
@@ -0,0 +1,31 @@
+[tool.poetry]
+name = "rag-timescale-conversation"
+version = "0.1.0"
+description = ""
+authors = [
+    "Lance Martin <lance@langchain.dev>",
+]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = ">=0.0.325"
+openai = ">=0.28.1"
+tiktoken = ">=0.5.1"
+pinecone-client = ">=2.2.4"
+beautifulsoup4 = "^4.12.2"
+python-dotenv = "^1.0.0"
+timescale-vector = "^0.0.3"
+
+[tool.poetry.group.dev.dependencies]
+langchain-cli = ">=0.0.15"
+
+[tool.langserve]
+export_module = "rag_timescale_conversation"
+export_attr = "chain"
+
+[build-system]
+requires = [
+    "poetry-core",
+]
+build-backend = "poetry.core.masonry.api"
diff --git a/templates/rag-timescale-conversation/rag_conversation.ipynb b/templates/rag-timescale-conversation/rag_conversation.ipynb
new file mode 100644
index 00000000000..4203689a153
--- /dev/null
+++ b/templates/rag-timescale-conversation/rag_conversation.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "424a9d8d",
+   "metadata": {},
+   "source": [
+    "## Run Template\n",
+    "\n",
+    "In `server.py`, set -\n",
+    "```\n",
+    "add_routes(app, chain_rag_timescale_conv, path=\"/rag_timescale_conversation\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5f521923",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langserve.client import RemoteRunnable\n",
+    "\n",
+    "rag_app = RemoteRunnable(\"http://0.0.0.0:8000/rag_timescale_conversation\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "563a58dd",
+   "metadata": {},
+   "source": [
+    "First, setup the history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "14541994",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question = \"My name is Sven Klemm\"\n",
+    "answer = rag_app.invoke(\n",
+    "    {\n",
+    "        \"question\": question,\n",
+    "        \"chat_history\": [],\n",
+    "    }\n",
+    ")\n",
+    "chat_history = [(question, answer)]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "63e76c4d",
+   "metadata": {},
+   "source": [
+    "Next, use the history for a question"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b2d8f735",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The person named Sven Klemm made the following commits:\\n\\n1. Commit \"a31c9b9f8cdfe8643499b710dc983e5c5d6457e4\" on \"Mon May 22 11:34:06 2023 +0200\" with the change summary \"Increase number of sqlsmith loops in nightly CI\". The change details are \"To improve coverage with sqlsmith we run it for longer in the scheduled nightly run.\"\\n\\n2. Commit \"e4ba2bcf560568ae68f3775c058f0a8d7f7c0501\" on \"Wed Nov 9 09:29:36 2022 +0100\" with the change summary \"Remove debian 9 from packages tests.\" The change details are \"Debian 9 is EOL since July 2022 so we won\\'t build packages for it anymore and can remove it from CI.\"'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "answer = rag_app.invoke(\n",
+    "    {\n",
+    "        \"question\": \"What commits did the person with my name make?\",\n",
+    "        \"chat_history\": chat_history,\n",
+    "    }\n",
+    ")\n",
+    "answer"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "bd62df23",
+   "metadata": {},
+   "source": [
+    "## Filter by time\n",
+    "\n",
+    "You can also use timed filters. For example, the sample dataset doesn't include any commits before 2010, so this should return no matches."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b0a598b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The context does not provide any information about any commits made by a person named Sven Klemm.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "answer = rag_app.invoke(\n",
+    "    {\n",
+    "        \"question\": \"What commits did the person with my name make?\",\n",
+    "        \"chat_history\": chat_history,\n",
+    "        \"end_date\": \"2016-01-01 00:00:00\",\n",
+    "    }\n",
+    ")\n",
+    "answer\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "25851869",
+   "metadata": {},
+   "source": [
+    "However, there is data from 2022, which can be used"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4aef5219",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The person named Sven Klemm made the following commits:\\n\\n1. \"e4ba2bcf560568ae68f3775c058f0a8d7f7c0501\" with the change summary \"Remove debian 9 from packages tests.\" The details of this change are that \"Debian 9 is EOL since July 2022 so we won\\'t build packages for it anymore and can remove it from CI.\"\\n\\n2. \"2f237e6e57e5ac66c126233d66969a1f674ffaa4\" with the change summary \"Add Enterprise Linux 9 packages to RPM package test\". The change details for this commit are not provided.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "answer = rag_app.invoke(\n",
+    "    {\n",
+    "        \"question\": \"What commits did the person with my name make?\",\n",
+    "        \"chat_history\": chat_history,\n",
+    "        \"start_date\": \"2020-01-01 00:00:00\",\n",
+    "        \"end_date\": \"2023-01-01 00:00:00\",\n",
+    "    }\n",
+    ")\n",
+    "answer"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "6ad86fbd",
+   "metadata": {},
+   "source": [
+    "## Filter by metadata\n",
+    "\n",
+    "You can also filter by metadata using this chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7ac9365f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The person named Sven Klemm made a commit with the ID \"5cd2c038796fb302190b080c90e5acddbef4b8d1\". The change summary for this commit is \"Simplify windows-build-and-test-ignored.yaml\" and the change details are \"Remove code not needed for the skip workflow of the windows test.\" The commit was made on \"Sat Mar 4 10:18:34 2023 +0100\".'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "answer = rag_app.invoke(\n",
+    "    {\n",
+    "        \"question\": \"What commits did the person with my name make?\",\n",
+    "        \"chat_history\": chat_history,\n",
+    "        \"metadata_filter\": {\"commit_hash\": \" 5cd2c038796fb302190b080c90e5acddbef4b8d1\"},\n",
+    "    }\n",
+    ")\n",
+    "answer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cde5da5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/templates/rag-timescale-conversation/rag_timescale_conversation/__init__.py b/templates/rag-timescale-conversation/rag_timescale_conversation/__init__.py
new file mode 100644
index 00000000000..1638a54ce2d
--- /dev/null
+++ b/templates/rag-timescale-conversation/rag_timescale_conversation/__init__.py
@@ -0,0 +1,3 @@
+from rag_timescale_conversation.chain import chain
+
+__all__ = ["chain"]
diff --git a/templates/rag-timescale-conversation/rag_timescale_conversation/chain.py b/templates/rag-timescale-conversation/rag_timescale_conversation/chain.py
new file mode 100644
index 00000000000..6ff6c5b0427
--- /dev/null
+++ b/templates/rag-timescale-conversation/rag_timescale_conversation/chain.py
@@ -0,0 +1,164 @@
+import os
+from datetime import datetime, timedelta
+from operator import itemgetter
+from typing import List, Optional, Tuple
+
+from dotenv import find_dotenv, load_dotenv
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.prompts.prompt import PromptTemplate
+from langchain.schema import AIMessage, HumanMessage, format_document
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import (
+    RunnableBranch,
+    RunnableLambda,
+    RunnableMap,
+    RunnablePassthrough,
+)
+from langchain.vectorstores.timescalevector import TimescaleVector
+from pydantic import BaseModel, Field
+
+from .load_sample_dataset import load_ts_git_dataset
+
+load_dotenv(find_dotenv())
+
+if os.environ.get("TIMESCALE_SERVICE_URL", None) is None:
+    raise Exception("Missing `TIMESCALE_SERVICE_URL` environment variable.")
+
+SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"]
+LOAD_SAMPLE_DATA = os.environ.get("LOAD_SAMPLE_DATA", False)
+COLLECTION_NAME = os.environ.get("COLLECTION_NAME", "timescale_commits")
+OPENAI_MODEL = os.environ.get("OPENAI_MODEL", "gpt-4")
+
+partition_interval = timedelta(days=7)
+if LOAD_SAMPLE_DATA:
+    load_ts_git_dataset(
+        SERVICE_URL,
+        collection_name=COLLECTION_NAME,
+        num_records=500,
+        partition_interval=partition_interval,
+    )
+
+embeddings = OpenAIEmbeddings()
+vectorstore = TimescaleVector(
+    embedding=embeddings,
+    collection_name=COLLECTION_NAME,
+    service_url=SERVICE_URL,
+    time_partition_interval=partition_interval,
+)
+retriever = vectorstore.as_retriever()
+
+# Condense a chat history and follow-up question into a standalone question
+_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
+Chat History:
+{chat_history}
+Follow Up Input: {question}
+Standalone question:"""  # noqa: E501
+CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+
+# RAG answer synthesis prompt
+template = """Answer the question based only on the following context:
+<context>
+{context}
+</context>"""
+ANSWER_PROMPT = ChatPromptTemplate.from_messages(
+    [
+        ("system", template),
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("user", "{question}"),
+    ]
+)
+
+# Conversational Retrieval Chain
+DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
+
+
+def _combine_documents(
+    docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"
+):
+    doc_strings = [format_document(doc, document_prompt) for doc in docs]
+    return document_separator.join(doc_strings)
+
+
+def _format_chat_history(chat_history: List[Tuple[str, str]]) -> List:
+    buffer = []
+    for human, ai in chat_history:
+        buffer.append(HumanMessage(content=human))
+        buffer.append(AIMessage(content=ai))
+    return buffer
+
+
+# User input
+class ChatHistory(BaseModel):
+    chat_history: List[Tuple[str, str]] = Field(..., extra={"widget": {"type": "chat"}})
+    question: str
+    start_date: Optional[datetime]
+    end_date: Optional[datetime]
+    metadata_filter: Optional[dict]
+
+
+_search_query = RunnableBranch(
+    # If input includes chat_history, we condense it with the follow-up question
+    (
+        RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config(
+            run_name="HasChatHistoryCheck"
+        ),  # Condense follow-up question and chat into a standalone_question
+        RunnablePassthrough.assign(
+            retriever_query=RunnablePassthrough.assign(
+                chat_history=lambda x: _format_chat_history(x["chat_history"])
+            )
+            | CONDENSE_QUESTION_PROMPT
+            | ChatOpenAI(temperature=0, model=OPENAI_MODEL)
+            | StrOutputParser()
+        ),
+    ),
+    # Else, we have no chat history, so just pass through the question
+    RunnablePassthrough.assign(retriever_query=lambda x: x["question"]),
+)
+
+
+def get_retriever_with_metadata(x):
+    start_dt = x.get("start_date", None)
+    end_dt = x.get("end_date", None)
+    metadata_filter = x.get("metadata_filter", None)
+    opt = {}
+
+    if start_dt is not None:
+        opt["start_date"] = start_dt
+    if end_dt is not None:
+        opt["end_date"] = end_dt
+    if metadata_filter is not None:
+        opt["filter"] = metadata_filter
+    v = vectorstore.as_retriever(search_kwargs=opt)
+    return RunnableLambda(itemgetter("retriever_query")) | v
+
+
+_retriever = RunnableLambda(get_retriever_with_metadata)
+
+_inputs = RunnableMap(
+    {
+        "question": lambda x: x["question"],
+        "chat_history": lambda x: _format_chat_history(x["chat_history"]),
+        "start_date": lambda x: x.get("start_date", None),
+        "end_date": lambda x: x.get("end_date", None),
+        "context": _search_query | _retriever | _combine_documents,
+    }
+)
+
+_datetime_to_string = RunnablePassthrough.assign(
+    start_date=lambda x: x.get("start_date", None).isoformat()
+    if x.get("start_date", None) is not None
+    else None,
+    end_date=lambda x: x.get("end_date", None).isoformat()
+    if x.get("end_date", None) is not None
+    else None,
+).with_types(input_type=ChatHistory)
+
+chain = (
+    _datetime_to_string
+    | _inputs
+    | ANSWER_PROMPT
+    | ChatOpenAI(model=OPENAI_MODEL)
+    | StrOutputParser()
+)
diff --git a/templates/rag-timescale-conversation/rag_timescale_conversation/load_sample_dataset.py b/templates/rag-timescale-conversation/rag_timescale_conversation/load_sample_dataset.py
new file mode 100644
index 00000000000..7b200b12581
--- /dev/null
+++ b/templates/rag-timescale-conversation/rag_timescale_conversation/load_sample_dataset.py
@@ -0,0 +1,84 @@
+import os
+import tempfile
+from datetime import datetime, timedelta
+
+import requests
+from langchain.document_loaders import JSONLoader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores.timescalevector import TimescaleVector
+from timescale_vector import client
+
+
+def parse_date(date_string: str) -> datetime:
+    if date_string is None:
+        return None
+    time_format = "%a %b %d %H:%M:%S %Y %z"
+    return datetime.strptime(date_string, time_format)
+
+
+def extract_metadata(record: dict, metadata: dict) -> dict:
+    dt = parse_date(record["date"])
+    metadata["id"] = str(client.uuid_from_time(dt))
+    if dt is not None:
+        metadata["date"] = dt.isoformat()
+    else:
+        metadata["date"] = None
+    metadata["author"] = record["author"]
+    metadata["commit_hash"] = record["commit"]
+    return metadata
+
+
+def load_ts_git_dataset(
+    service_url,
+    collection_name="timescale_commits",
+    num_records: int = 500,
+    partition_interval=timedelta(days=7),
+):
+    json_url = "https://s3.amazonaws.com/assets.timescale.com/ai/ts_git_log.json"
+    tmp_file = "ts_git_log.json"
+
+    temp_dir = tempfile.gettempdir()
+    json_file_path = os.path.join(temp_dir, tmp_file)
+
+    if not os.path.exists(json_file_path):
+        response = requests.get(json_url)
+        if response.status_code == 200:
+            with open(json_file_path, "w") as json_file:
+                json_file.write(response.text)
+        else:
+            print(f"Failed to download JSON file. Status code: {response.status_code}")
+
+    loader = JSONLoader(
+        file_path=json_file_path,
+        jq_schema=".commit_history[]",
+        text_content=False,
+        metadata_func=extract_metadata,
+    )
+
+    documents = loader.load()
+
+    # Remove documents with None dates
+    documents = [doc for doc in documents if doc.metadata["date"] is not None]
+
+    if num_records > 0:
+        documents = documents[:num_records]
+
+    # Split the documents into chunks for embedding
+    text_splitter = CharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200,
+    )
+    docs = text_splitter.split_documents(documents)
+
+    embeddings = OpenAIEmbeddings()
+
+    # Create a Timescale Vector instance from the collection of documents
+    TimescaleVector.from_documents(
+        embedding=embeddings,
+        ids=[doc.metadata["id"] for doc in docs],
+        documents=docs,
+        collection_name=collection_name,
+        service_url=service_url,
+        time_partition_interval=partition_interval,
+    )
diff --git a/templates/rag-timescale-conversation/tests/__init__.py b/templates/rag-timescale-conversation/tests/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d