
Name	MongoDBAtlasEmbeddingRetriever
Source	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mongodb_atlas
Most common position in a pipeline	1. After a Text Embedder and before a `PromptBuilder` in a RAG pipeline 2. The last component in the semantic search pipeline 3. After a Text Embedder and before an `ExtractiveReader` in an extractive QA pipeline
Mandatory input variables	“query_embedding”: A list of floats
Output variables	“documents”: A list of documents

The MongoDBAtlasEmbeddingRetriever is an embedding-based Retriever compatible with the MongoDBAtlasDocumentStore. It compares the query and Document embeddings and fetches the Documents most relevant to the query from the Document Store based on the outcome.

Parameters

When using the MongoDBAtlasEmbeddingRetriever in your NLP system, ensure the query and Document embeddings are available. You can do so by adding a Document Embedder to your indexing Pipeline and a Text Embedder to your query Pipeline.

In addition to the query_embedding, the MongoDBAtlasEmbeddingRetriever accepts other optional parameters, including top_k (the maximum number of Documents to retrieve) and filters to narrow down the search space.

Usage

Installation

To start using MongoDB Atlas with Haystack, install the package with:

pip install mongodb-atlas-haystack

On its own

The Retriever needs an instance of MongoDBAtlasDocumentStore and indexed Documents to run.

from haystack_integrations.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
from haystack_integrations.components.embedders.mongodb_atlas import MongoDBAtlasEmbeddingRetriever

document_store = MongoDBAtlasDocumentStore()

retriever = MongoDBAtlasEmbeddingRetriever(document_store=document_store)

# example run query
retriever.run(query_embedding=[0.1]*384)

In a Pipeline

from haystack import Pipeline, Document
from haystack.document_stores.types import DuplicatePolicy
from haystack.components.writers import DocumentWriter
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
from haystack_integrations.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
from haystack_integrations.components.embedders.mongodb_atlas import MongoDBAtlasEmbeddingRetriever

# Create some example documents
documents = [
    Document(content="My name is Jean and I live in Paris."),
    Document(content="My name is Mark and I live in Berlin."),
    Document(content="My name is Giorgio and I live in Rome."),
]

# We support many different databases. Here we load a simple and lightweight in-memory document store.
document_store = MongoDBAtlasDocumentStore()

# Define some more components
doc_writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP)
doc_embedder = SentenceTransformersDocumentEmbedder(model="intfloat/e5-base-v2")
query_embedder = SentenceTransformersTextEmbedder(model="intfloat/e5-base-v2")

# Pipeline that ingests document for retrieval
ingestion_pipe = Pipeline()
ingestion_pipe.add_component(instance=doc_embedder, name="doc_embedder")
ingestion_pipe.add_component(instance=doc_writer, name="doc_writer")

ingestion_pipe.connect("doc_embedder.documents", "doc_writer.documents")
ingestion_pipe.run({"doc_embedder": {"documents": documents}})

# Build a RAG pipeline with a Retriever to get relevant documents to 
# the query and a OpenAIGenerator interacting with LLMs using a custom prompt.
prompt_template = """
Given these documents, answer the question.\nDocuments:
{% for doc in documents %}
    {{ doc.content }}
{% endfor %}

\nQuestion: {{question}}
\nAnswer:
"""
rag_pipeline = Pipeline()
rag_pipeline.add_component(instance=query_embedder, name="query_embedder")
rag_pipeline.add_component(instance=MongoDBAtlasEmbeddingRetriever(document_store=document_store), name="retriever")
rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name="prompt_builder")
rag_pipeline.add_component(instance=OpenAIGenerator(), name="llm")
rag_pipeline.connect("query_embedder", "retriever.query_embedding")
rag_pipeline.connect("embedding_retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")

# Ask a question on the data you just added.
question = "Where does Mark live?"
result = rag_pipeline.run(
    {
        "query_embedder": {"text": question},
        "prompt_builder": {"question": question},
    }
)

# For details, like which documents were used to generate the answer, look into the GeneratedAnswer object
print(result["answer_builder"]["answers"])