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# Description Add a new vector index type `diskann` to Azure Cosmos DB Mongo vCore vector store. Paper of DiskANN can be found here [DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node](https://proceedings.neurips.cc/paper_files/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf). ## Sample Usage ```python from pymongo import MongoClient # INDEX_NAME = "izzy-test-index-2" # NAMESPACE = "izzy_test_db.izzy_test_collection" # DB_NAME, COLLECTION_NAME = NAMESPACE.split(".") client: MongoClient = MongoClient(CONNECTION_STRING) collection = client[DB_NAME][COLLECTION_NAME] model_deployment = os.getenv( "OPENAI_EMBEDDINGS_DEPLOYMENT", "smart-agent-embedding-ada" ) model_name = os.getenv("OPENAI_EMBEDDINGS_MODEL_NAME", "text-embedding-ada-002") vectorstore = AzureCosmosDBVectorSearch.from_documents( docs, openai_embeddings, collection=collection, index_name=INDEX_NAME, ) # Read more about these variables in detail here. https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/vector-search maxDegree = 40 dimensions = 1536 similarity_algorithm = CosmosDBSimilarityType.COS kind = CosmosDBVectorSearchType.VECTOR_DISKANN lBuild = 20 vectorstore.create_index( dimensions=dimensions, similarity=similarity_algorithm, kind=kind , max_degree=maxDegree, l_build=lBuild, ) ``` ## Dependencies No additional dependencies were added --------- Co-authored-by: Yang Qiao (from Dev Box) <yangqiao@microsoft.com> Co-authored-by: Erick Friis <erick@langchain.dev> |
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| .. | ||
| adapters | ||
| caches | ||
| callbacks | ||
| chat | ||
| chat_loaders | ||
| document_loaders | ||
| document_transformers | ||
| graphs | ||
| llms | ||
| memory | ||
| providers | ||
| retrievers | ||
| stores | ||
| text_embedding | ||
| tools | ||
| vectorstores | ||
| llm_caching.ipynb | ||