Azure Cognitive Search vector store.
Semantic Cache for Azure CosmosDB NoSql and Mongo vCore API.
Utility functions for working with vectors and vectorstores.
Vector Store for CosmosDB NoSql ā DEPRECATED.
This module has moved to langchain_azure_cosmosdb.
Install and import directly from there instead::
pip install langchain-azure-cosmosdb
from langchain_azure_cosmosdb import AzureCosmosDBNoSqlVectorSearch
Vector Store for Azure DocumentDB (with MongoDB compatibility) ā DEPRECATED.
This module has moved to langchain_azure_cosmosdb.
Install and import directly from there instead::
pip install langchain-azure-cosmosdb
from langchain_azure_cosmosdb import AzureDocumentDBVectorSearch
Vector store implementation for Azure Cognitive Search.
This module provides the AzureSearch vector store and retriever classes for integration with Azure Cognitive Search.
Vector store stores embedded data and performs vector search.
One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors, and then query the store and retrieve the data that are 'most similar' to the embedded query.
Class hierarchy:
VectorStore --> <name> # Examples: AzureSearch, FAISS, Milvus
BaseRetriever --> VectorStoreRetriever --> <name>Retriever # Example: AzureAISearchRetriever
Main helpers:
Embeddings, Document