AzureDocumentDBEmbeddingRetriever

Retrieve documents from an AzureDocumentDBDocumentStore using cosmosSearch vector similarity. Use this component in query pipelines to find semantically similar documents based on dense embeddings stored in Azure DocumentDB. Azure DocumentDB is Azure Cosmos DB for MongoDB (vCore), so this component is also relevant if you're searching for "Cosmos DB" or "vCore".

Embedding Models in Pipelines and Indexes

The embedding model you use to embed documents in your index must be the same as the embedding model you use to embed the query in your pipeline.

This means the embedders for your indexes and pipelines must match. For example, if you use CohereDocumentEmbedder to embed your documents, you should use CohereTextEmbedder with the same model to embed your queries.

Key Features

• Performs cosmosSearch vector similarity search against documents stored in Azure DocumentDB.
• Supports cosine (COS), Euclidean (L2), and inner product (IP) similarity metrics defined on the vector index.
• Configurable filter policy to merge or replace filters at query time.
• Supports both synchronous and asynchronous execution.
• Authenticates using Microsoft Entra ID (DefaultAzureCredential) by default.

Configuration

Add Workspace-Level Integration

1. Click your profile icon and choose Settings.
2. Go to Workspace>Integrations.
3. Find the provider you want to connect and click Connect next to them.
4. Enter the API key and any other required details.
5. Click Connect. You can use this integration in pipelines and indexes in the current workspace.

Add Organization-Level Integration

1. Click your profile icon and choose Settings.
2. Go to Organization>Integrations.
3. Find the provider you want to connect and click Connect next to them.
4. Enter the API key and any other required details.
5. Click Connect. You can use this integration in pipelines and indexes in all workspaces in the current organization.

1. Set the AZURE_DOCUMENTDB_CLUSTER_NAME environment variable with your Azure DocumentDB cluster name, or provide a mongo_connection_string for local development.
2. First, configure an AzureDocumentDBDocumentStore in your pipeline with an existing database and collection, and create a cosmosSearch vector index.
3. Drag the AzureDocumentDBEmbeddingRetriever component onto the canvas from the Component Library.
4. Connect an embedder component to provide query_embedding as input.
5. Connect the retriever output to downstream components such as PromptBuilder.

Connections

AzureDocumentDBEmbeddingRetriever receives a query_embedding (list of floats) from a text embedder such as SentenceTransformersTextEmbedder. It outputs a list of Document objects you can connect to PromptBuilder or other downstream components.

Source Code

To check this component's source code, open embedding_retriever.py in the Haystack Core Integrations repository.

Usage Examples

Basic Configuration

  AzureDocumentDBEmbeddingRetriever:
    type: haystack_integrations.components.retrievers.azure_documentdb.embedding_retriever.AzureDocumentDBEmbeddingRetriever
    init_parameters:
      document_store: AzureDocumentDBDocumentStore
      top_k: 5

Using the Component in a Pipeline

# haystack-pipeline
components:
  text_embedder:
    type: haystack_integrations.components.embedders.sentence_transformers.SentenceTransformersTextEmbedder
    init_parameters:
      model: sentence-transformers/all-MiniLM-L6-v2

  document_store:
    type: haystack_integrations.document_stores.azure_documentdb.document_store.AzureDocumentDBDocumentStore
    init_parameters:
      database_name: haystack
      collection_name: documents
      vector_search_index: haystack_vector_index

  retriever:
    type: haystack_integrations.components.retrievers.azure_documentdb.embedding_retriever.AzureDocumentDBEmbeddingRetriever
    init_parameters:
      document_store: document_store
      top_k: 5

connections:
- sender: text_embedder.embedding
  receiver: retriever.query_embedding

inputs:
  query:
  - text_embedder.text

outputs:
  documents: retriever.documents

Parameters

Inputs

Parameter	Type	Description
query_embedding	List[float]	The query embedding vector to search for similar documents.
filters	Optional[Dict[str, Any]]	Filters to apply when retrieving documents.
top_k	Optional[int]	The maximum number of documents to retrieve. Overrides the init-time value.

Outputs

Parameter	Type	Description
documents	List[Document]	A list of the most similar documents from the document store.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
document_store	AzureDocumentDBDocumentStore		The Azure DocumentDB document store to retrieve documents from.
filters	Optional[Dict[str, Any]]	None	Default filters to apply when retrieving documents.
top_k	int	10	The maximum number of documents to retrieve.
filter_policy	FilterPolicy	FilterPolicy.REPLACE	How to handle filters passed at query time. REPLACE replaces init-time filters; MERGE combines them.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
query_embedding	List[float]		The embedding vector to search with.
filters	Optional[Dict[str, Any]]	None	Runtime filters to apply.
top_k	Optional[int]	None	Maximum number of documents to retrieve. Overrides the init-time value.

Related Information

• AzureDocumentDBFullTextRetriever
• Pipeline Components