---
title: "MongoDBAtlasFullTextRetriever"
id: mongodbatlasfulltextretriever
slug: "/mongodbatlasfulltextretriever"
description: "This is a full-text search Retriever compatible with the MongoDB Atlas Document Store."
---

# MongoDBAtlasFullTextRetriever

This is a full-text search Retriever compatible with the MongoDB Atlas Document Store.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | 1. Before a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx) in a RAG pipeline 2. The last component in the semantic search pipeline 3. Before an [ExtractiveReader](../readers/extractivereader.mdx) in an extractive QA pipeline |
| **Mandatory init variables**           | `document_store`: An instance of a [MongoDBAtlasDocumentStore](../../document-stores/mongodbatlasdocumentstore.mdx)                                                                                                                   |
| **Mandatory run variables**            | `query`: A query string to search for. If the query contains multiple terms, Atlas Search evaluates each term separately for matches.                                                                             |
| **Output variables**                   | `documents`: A list of documents                                                                                                                                                                                  |
| **API reference**                      | [MongoDB Atlas](/reference/integrations-mongodb-atlas)                                                                                                                                                                   |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mongodb_atlas                                                                                                                   |

</div>

The `MongoDBAtlasFullTextRetriever` is a full-text search Retriever compatible with the [`MongoDBAtlasDocumentStore`](../../document-stores/mongodbatlasdocumentstore.mdx). The full-text search is dependent on the `full_text_search_index` used in the [`MongoDBAtlasDocumentStore`](../../document-stores/mongodbatlasdocumentstore.mdx).

### Parameters

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

When running the component, you can specify more optional parameters such as `fuzzy` or `synonyms`, `match_criteria`, `score`. Check out our [MongoDB Atlas](/reference/integrations-mongodb-atlas) API Reference for more details on all parameters.

## Usage

### Installation

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

```shell
pip install mongodb-atlas-haystack
```

### On its own

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

```python
from haystack_integrations.document_stores.mongodb_atlas import (
    MongoDBAtlasDocumentStore,
)
from haystack_integrations.components.retrievers.mongodb_atlas import (
    MongoDBAtlasFullTextRetriever,
)

store = MongoDBAtlasDocumentStore(
    database_name="your_existing_db",
    collection_name="your_existing_collection",
    vector_search_index="your_existing_index",
    full_text_search_index="your_existing_index",
)
retriever = MongoDBAtlasFullTextRetriever(document_store=store)

results = retriever.run(query="Your search query")
print(results["documents"])
```

### In a Pipeline

Here's a Hybrid Retrieval pipeline example that makes use of both available MongoDB Atlas Retrievers:

```python
from haystack import Pipeline, Document
from haystack.document_stores.types import DuplicatePolicy
from haystack.components.writers import DocumentWriter
from haystack.components.embedders import (
    SentenceTransformersDocumentEmbedder,
    SentenceTransformersTextEmbedder,
)
from haystack.components.joiners import DocumentJoiner

from haystack_integrations.document_stores.mongodb_atlas import (
    MongoDBAtlasDocumentStore,
)
from haystack_integrations.components.retrievers.mongodb_atlas import (
    MongoDBAtlasEmbeddingRetriever,
    MongoDBAtlasFullTextRetriever,
)

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."),
    Document(content="Python is a programming language popular for data science."),
    Document(
        content="MongoDB Atlas offers full-text search and vector search capabilities.",
    ),
]

document_store = MongoDBAtlasDocumentStore(
    database_name="haystack_test",
    collection_name="test_collection",
    vector_search_index="test_vector_search_index",
    full_text_search_index="test_full_text_search_index",
)

## Clean out any old data so this example is repeatable
print(f"Clearing collection {document_store.collection_name} …")
document_store.collection.delete_many({})

ingest_pipe = Pipeline()

doc_embedder = SentenceTransformersDocumentEmbedder(model="intfloat/e5-base-v2")
ingest_pipe.add_component(instance=doc_embedder, name="doc_embedder")

doc_writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP)
ingest_pipe.add_component(instance=doc_writer, name="doc_writer")
ingest_pipe.connect("doc_embedder.documents", "doc_writer.documents")

print(f"Running ingestion on {len(documents)} in-memory docs …")
ingest_pipe.run({"doc_embedder": {"documents": documents}})

query_pipe = Pipeline()

text_embedder = SentenceTransformersTextEmbedder(model="intfloat/e5-base-v2")
query_pipe.add_component(instance=text_embedder, name="text_embedder")

embed_retriever = MongoDBAtlasEmbeddingRetriever(document_store=document_store, top_k=3)
query_pipe.add_component(instance=embed_retriever, name="embedding_retriever")
query_pipe.connect("text_embedder", "embedding_retriever")

## (c) full-text retriever
ft_retriever = MongoDBAtlasFullTextRetriever(document_store=document_store, top_k=3)
query_pipe.add_component(instance=ft_retriever, name="full_text_retriever")

joiner = DocumentJoiner(join_mode="reciprocal_rank_fusion", top_k=3)
query_pipe.add_component(instance=joiner, name="joiner")

query_pipe.connect("embedding_retriever", "joiner")
query_pipe.connect("full_text_retriever", "joiner")

question = "Where does Mark live?"
print(f"Running hybrid retrieval for query: '{question}'")
output = query_pipe.run(
    {
        "text_embedder": {"text": question},
        "full_text_retriever": {"query": question},
    },
)

print("\nFinal fused documents:")
for doc in output["joiner"]["documents"]:
    print(f"- {doc.content}")
```
