MongoDBAtlasEmbeddingRetriever

Retrieve documents from the MongoDBAtlasDocumentStore by embedding similarity. This retriever is only compatible with the MongoDBAtlasDocumentStore.

Basic Information

• Type: haystack_integrations.mongodb_atlas.src.haystack_integrations.components.retrievers.mongodb_atlas.embedding_retriever.MongoDBAtlasEmbeddingRetriever
• Components it can connect with:
  • TextEmbedders: MongoDBAtlasEmbeddingRetriever can receive the query embedding from a text embedder, such as SentenceTransformersTextEmbedder.
  • Rankers: MongoDBAtlasEmbeddingRetriever can send the retrieved documents to a Ranker.

Inputs

Parameter	Type	Default	Description
query_embedding	List[float]		Embedding of the query.
filters	Optional[Dict[str, Any]]	None	Filters applied to the retrieved Documents. The way runtime filters are applied depends on the filter_policy configured for the retrieve. For more details, see the Init Parameters section below.
top_k	Optional[int]	None	Maximum number of Documents to return.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		List of Documents most similar to the given query_embedding.

Overview

MongoDBAtlasEmbeddingRetriever retrieves documents from the MongoDBAtlasDocumentStore by comparing the similarity between the query embedding and the document embeddings. It must receive a query embedding from a text embedder, such as SentenceTransformersTextEmbedder.

Usage Example

This is a document search pipeline that uses MongoDBAtlasEmbeddingRetriever to retrieve documents from the MongoDBAtlasDocumentStore by comparing the similarity between the query embedding and the document embeddings. It uses MistralTextEmbedder to embed the query and TransformersSimilarityRanker to rank the documents.

components:
  MistralTextEmbedder:
    type: haystack_integrations.components.embedders.mistral.text_embedder.MistralTextEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - MISTRAL_API_KEY
        strict: false
      model: mistral-embed

  MongoDBAtlasEmbeddingRetriever:
    type: haystack_integrations.components.retrievers.mongodb_atlas.embedding_retriever.MongoDBAtlasEmbeddingRetriever
    init_parameters:
      filters:
      top_k: 10
      filter_policy: replace
      document_store:
        type: haystack_integrations.document_stores.mongodb_atlas.document_store.MongoDBAtlasDocumentStore
        init_parameters:
          mongo_connection_string:
            type: env_var
            env_vars:
            - MONGO_CONNECTION_STRING
            strict: false
          database_name: my-db
          collection_name: my-collection
          vector_search_index: vector-search
          full_text_search_index: full-text-search
          embedding_field: embedding
          content_field: content

  TransformersSimilarityRanker:
    type: haystack.components.rankers.transformers_similarity.TransformersSimilarityRanker
    init_parameters:
      model: cross-encoder/ms-marco-MiniLM-L-6-v2
      device:
      token:
        type: env_var
        env_vars:
        - HF_API_TOKEN
        - HF_TOKEN
        strict: false
      top_k: 10
      query_prefix: ''
      document_prefix: ''
      meta_fields_to_embed:
      embedding_separator: \n
      scale_score: true
      calibration_factor: 1
      score_threshold:
      model_kwargs:
      tokenizer_kwargs:
      batch_size: 16

connections:
- sender: MistralTextEmbedder.embedding
  receiver: MongoDBAtlasEmbeddingRetriever.query_embedding

- sender: MongoDBAtlasEmbeddingRetriever.documents
  receiver: TransformersSimilarityRanker.documents

max_runs_per_component: 100

metadata: {}

inputs:
  query:
  - MistralTextEmbedder.text
  - TransformersSimilarityRanker.query

outputs:
  documents: TransformersSimilarityRanker.documents

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
document_store	MongoDBAtlasDocumentStore		An instance of MongoDBAtlasDocumentStore.
filters	Optional[Dict[str, Any]]	None	Filters applied to the retrieved Documents. Make sure that the fields used in the filters are included in the configuration of the vector_search_index. You must configure them manually in the Web UI of MongoDB Atlas.
top_k	int	10	Maximum number of Documents to return.
filter_policy	Union[str, FilterPolicy]	FilterPolicy.REPLACE	Policy to determine how filters are applied if they're configured for the component but also passed at runtime. Possible values: MERGE and REPLACE. MERGE: If both filter types target the same field, the runtime filter takes precedence. Logical filters are combined unly if they have the same operator (AND, OR). Comparison filters are combined using the default logical operator (defaults to AND).

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]		Embedding of the query.
filters	Optional[Dict[str, Any]]	None	Filters applied to the retrieved Documents. The way runtime filters are applied depends on the filter_policy configured for the retriever.
top_k	Optional[int]	None	Maximum number of Documents to return. Overrides the value specified at initialization.