docs:Update reference to langchain-mongodb (#22705)

**Description**: Update reference to langchain-mongodb
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Prakul 2024-06-10 13:35:21 -07:00 committed by GitHub
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@ -7,29 +7,33 @@
"source": [
"# MongoDB Atlas\n",
"\n",
">[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",
"This notebook covers how to MongoDB Atlas vector search in LangChain, using the `langchain-mongodb` package.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
">[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",
"\n",
"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 [$vectorSearch MQL Stage](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/). \n",
"\n",
"\n",
"To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of clusters available. To get started head over to Atlas here: [quick start](https://www.mongodb.com/docs/atlas/getting-started/).\n",
"\n",
" "
">[MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) allows to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with an approximate nearest neighbor algorithm (`Hierarchical Navigable Small Worlds`). It uses the [$vectorSearch MQL Stage](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/). "
]
},
{
"cell_type": "markdown",
"id": "5abfec15",
"id": "359b8e9b",
"metadata": {},
"source": [
"> Note: \n",
"> \n",
">* More documentation can be found at [LangChain-MongoDB site](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/)\n",
">* This feature is Generally Available and ready for production deployments.\n",
">* 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",
"> "
"## Prerequisites\n",
">*An Atlas cluster running MongoDB version 6.0.11, 7.0.2, or later (including RCs).\n",
"\n",
">*An OpenAI API Key. You must have a paid OpenAI account with credits available for API requests.\n",
"\n",
"You'll need to install `langchain-mongodb` to use this integration"
]
},
{
"cell_type": "markdown",
"id": "d899e588",
"metadata": {},
"source": [
"## Setting up MongoDB Atlas Cluster\n",
"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/)."
]
},
{
@ -37,6 +41,7 @@
"id": "1b5ce18d",
"metadata": {},
"source": [
"## Usage\n",
"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."
]
},
@ -76,7 +81,7 @@
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain pypdf pymongo langchain-openai tiktoken"
"%pip install --upgrade --quiet langchain langchain-mongodb pypdf pymongo langchain-openai tiktoken"
]
},
{
@ -411,6 +416,18 @@
"source": [
"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)."
]
},
{
"cell_type": "markdown",
"id": "0ac44802",
"metadata": {},
"source": [
"# Other Notes\n",
">* More documentation can be found at [LangChain-MongoDB](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/) site\n",
">* This feature is Generally Available and ready for production deployments.\n",
">* 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",
"> "
]
}
],
"metadata": {