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docs:Update reference to langchain-mongodb (#22705)
**Description**: Update reference to langchain-mongodb
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"source": [
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"# MongoDB Atlas\n",
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"\n",
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">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS, Azure, and GCP. It now has support for native Vector Search on your MongoDB document data.\n",
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"This notebook covers how to MongoDB Atlas vector search in LangChain, using the `langchain-mongodb` package.\n",
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"\n",
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"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
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">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS, Azure, and GCP. It supports native Vector Search and full text search (BM25) on your MongoDB document data.\n",
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"\n",
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"This notebook shows how to use [MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with an approximate nearest neighbor algorithm (`Hierarchical Navigable Small Worlds`). It uses the [$vectorSearch MQL Stage](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/). \n",
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"\n",
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"\n",
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"To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of clusters available. To get started head over to Atlas here: [quick start](https://www.mongodb.com/docs/atlas/getting-started/).\n",
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"\n",
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" "
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">[MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) allows to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with an approximate nearest neighbor algorithm (`Hierarchical Navigable Small Worlds`). It uses the [$vectorSearch MQL Stage](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/). "
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]
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},
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{
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"cell_type": "markdown",
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"id": "5abfec15",
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"id": "359b8e9b",
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"metadata": {},
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"source": [
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"> Note: \n",
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"> \n",
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">* More documentation can be found at [LangChain-MongoDB site](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/)\n",
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">* This feature is Generally Available and ready for production deployments.\n",
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">* The langchain version 0.0.305 ([release notes](https://github.com/langchain-ai/langchain/releases/tag/v0.0.305)) introduces the support for $vectorSearch MQL stage, which is available with MongoDB Atlas 6.0.11 and 7.0.2. Users utilizing earlier versions of MongoDB Atlas need to pin their LangChain version to <=0.0.304\n",
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"> "
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"## Prerequisites\n",
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">*An Atlas cluster running MongoDB version 6.0.11, 7.0.2, or later (including RCs).\n",
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"\n",
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">*An OpenAI API Key. You must have a paid OpenAI account with credits available for API requests.\n",
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"\n",
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"You'll need to install `langchain-mongodb` to use this integration"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d899e588",
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"metadata": {},
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"source": [
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"## Setting up MongoDB Atlas Cluster\n",
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"To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of clusters available. To get started head over to Atlas here: [quick start](https://www.mongodb.com/docs/atlas/getting-started/)."
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]
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},
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{
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@ -37,6 +41,7 @@
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"id": "1b5ce18d",
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"metadata": {},
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"source": [
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"## Usage\n",
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"In the notebook we will demonstrate how to perform `Retrieval Augmented Generation` (RAG) using MongoDB Atlas, OpenAI and Langchain. We will be performing Similarity Search, Similarity Search with Metadata Pre-Filtering, and Question Answering over the PDF document for [GPT 4 technical report](https://arxiv.org/pdf/2303.08774.pdf) that came out in March 2023 and hence is not part of the OpenAI's Large Language Model(LLM)'s parametric memory, which had a knowledge cutoff of September 2021."
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]
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet langchain pypdf pymongo langchain-openai tiktoken"
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"%pip install --upgrade --quiet langchain langchain-mongodb pypdf pymongo langchain-openai tiktoken"
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]
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},
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{
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@ -411,6 +416,18 @@
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"source": [
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"GPT-4 requires significantly more compute than earlier GPT models. On a dataset derived from OpenAI's internal codebase, GPT-4 requires 100p (petaflops) of compute to reach the lowest loss, while the smaller models require 1-10n (nanoflops)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "0ac44802",
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"metadata": {},
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"source": [
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"# Other Notes\n",
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">* More documentation can be found at [LangChain-MongoDB](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/) site\n",
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">* This feature is Generally Available and ready for production deployments.\n",
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">* The langchain version 0.0.305 ([release notes](https://github.com/langchain-ai/langchain/releases/tag/v0.0.305)) introduces the support for $vectorSearch MQL stage, which is available with MongoDB Atlas 6.0.11 and 7.0.2. Users utilizing earlier versions of MongoDB Atlas need to pin their LangChain version to <=0.0.304\n",
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"> "
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]
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}
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],
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"metadata": {
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