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docs:update to MongoDB Docs (#27531)
**Description:** Update to MongoDB docs --------- Co-authored-by: Erick Friis <erick@langchain.dev>
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Prakul
GitHub
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031d0e4725
@ -9,7 +9,7 @@
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"\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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">[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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">[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, full text search (BM25), and hybrid search on your MongoDB document data.\n",
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"\n",
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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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@ -23,7 +23,7 @@
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"\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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"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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"To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of cluster on a cloud of your choice 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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"You'll need to install `langchain-mongodb` and `pymongo` to use this integration."
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]
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@ -104,7 +104,7 @@
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"# | echo: false\n",
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"from langchain_openai import OpenAIEmbeddings\n",
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"\n",
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"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")"
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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@ -114,7 +114,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch\n",
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"from langchain_mongodb import MongoDBAtlasVectorSearch\n",
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"from pymongo import MongoClient\n",
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"\n",
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"# initialize MongoDB python client\n",
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@ -131,7 +131,31 @@
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" embedding=embeddings,\n",
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" index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,\n",
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" relevance_score_fn=\"cosine\",\n",
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")"
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")\n",
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"\n",
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"# Create vector search index on the collection\n",
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"# Since we are using the default OpenAI embedding model (ada-v2) we need to specify the dimensions as 1536\n",
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"vector_store.create_vector_search_index(dimensions=1536)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aec0bb58",
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"metadata": {},
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"source": [
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"[OPTIONAL] Alternative to the `vector_store.create_vector_search_index` command above, you can also create the vector search index using the Atlas UI with the following index definition:\n",
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"```json\n",
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"{\n",
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" \"fields\":[\n",
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" {\n",
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" \"type\": \"vector\",\n",
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" \"path\": \"embedding\",\n",
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" \"numDimensions\": 1536,\n",
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" \"similarity\": \"cosine\"\n",
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" }\n",
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" ]\n",
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"}\n",
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"```"
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]
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},
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{
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@ -367,6 +391,21 @@
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"id": "dacac7b8",
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"metadata": {},
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"source": [
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"\n",
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"\n",
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"To enable pre-filtering you need to update the index definition to include a filter field. In this example, we will use the `source` field as the filter field.\n",
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"\n",
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"This can be done programmatically using the `MongoDBAtlasVectorSearch.create_vector_search_index` method.\n",
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"\n",
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"```python\n",
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"vectorstore.create_vector_search_index(\n",
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" dimensions=1536,\n",
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" filters=[{\"type\":\"filter\", \"path\":\"source\"}],\n",
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" update=True\n",
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")\n",
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"```\n",
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"\n",
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"Alternatively, you can also update the index using the Atlas UI with the following index definition:\n",
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"```json\n",
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"{\n",
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" \"fields\":[\n",
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@ -384,16 +423,6 @@
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"}\n",
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"```\n",
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"\n",
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"You can also update the index programmatically using the `MongoDBAtlasVectorSearch.create_index` method.\n",
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"\n",
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"```python\n",
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"vectorstore.create_index(\n",
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" dimensions=1536,\n",
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" filters=[{\"type\":\"filter\", \"path\":\"source\"}],\n",
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" update=True\n",
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")\n",
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"```\n",
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"\n",
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"And then you can run a query with filter as follows:\n",
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"\n",
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"```python\n",
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@ -410,7 +439,7 @@
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"source": [
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"#### Other search methods\n",
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"\n",
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"There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `AstraDBVectorStore` check out the [API reference](https://python.langchain.com/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html)."
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"There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `MongoDBAtlasVectorStore` check out the [API reference](https://api.python.langchain.com/en/latest/vectorstores/langchain_mongodb.vectorstores.MongoDBAtlasVectorSearch.html)."
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]
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},
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{
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@ -470,7 +499,7 @@
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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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">* More documentation can be found at [MongoDB's LangChain Docs](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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@ -509,3 +538,4 @@
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"nbformat": 4,
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"nbformat_minor": 5
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}
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