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+---
+layout: integration
+name: MongoDB
+description: Use a MongoDB Atlas with Haystack
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: deepset-ai
+pypi: https://pypi.org/project/farm-haystack
+repo: https://github.com/deepset-ai/haystack
+type: Document Store
+report_issue: https://github.com/deepset-ai/haystack/issues
+logo: /logos/mongodb.png
+toc: true
+---
+
+[MongoDB](https://www.mongodb.com/) is a document database designed for ease of application development and scaling. [MongoDB Atlas](https://www.mongodb.com/atlas) is a multi-cloud database service built by people behind MongoDB. MongoDB Atlas simplifies deploying and managing your databases while offering the versatility you need to build resilient and performant global applications on the cloud providers of your choice.
+
+For a detailed overview of all the available methods and settings for the `MongoDBAtlasDocumentStore`, visit the Haystack [Documentation](https://docs.haystack.deepset.ai/docs/document_store#initialization).
+
+## Installation
+
+```bash
+pip install farm-haystack[mongodb]
+```
+
+## Usage
+
+To use MongoDB Atlas as your data storage for your Haystack LLM pipelines, you must have a running database at MongoDB Atlas. For details, see [Get Started with Atlas](https://www.mongodb.com/docs/atlas/getting-started/).  
+
+Once your database is set, you can initialize a `MongoDBAtlasDocumentStore` for Haystack with required configurations:
+
+```python
+from haystack.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
+
+ds=MongoDBAtlasDocumentStore(
+    mongo_connection_string="mongodb+srv://{mongo_atlas_username}:{mongo_atlas_password}@{mongo_atlas_host}/?{mongo_atlas_params_string}",
+    database_name="database_name",
+    collection_name="collection_name",
+)
+```
+
+### Writing Documents to MongoDBAtlasDocumentStore
+
+To write documents to your `MongoDBAtlasDocumentStore`, create an indexing pipeline, or use the `write_documents()` function.
+For this step, you may make use of the available [FileConverters](https://docs.haystack.deepset.ai/docs/file_converters) and [PreProcessors](https://docs.haystack.deepset.ai/docs/preprocessor), as well as other [Integrations](/integrations) that might help you fetch data from other resources. Below is an example indexing pipeline that indexes your Markdown files into a MongoDB Atlas instance.
+
+#### Indexing Pipeline
+
+```python
+from haystack import Pipeline
+from haystack.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
+from haystack.nodes import MarkdownConverter, PreProcessor
+
+document_store=MongoDBAtlasDocumentStore(
+    mongo_connection_string="mongodb+srv://{mongo_atlas_username}:{mongo_atlas_password}@{mongo_atlas_host}/?{mongo_atlas_params_string}",
+    database_name="database_name",
+    collection_name="collection_name",
+    embedding_dim=1536
+)
+converter = MarkdownConverter()
+preprocessor = PreProcessor()
+
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_node(component=converter, name="PDFConverter", inputs=["File"])
+indexing_pipeline.add_node(component=preprocessor, name="PreProcessor", inputs=["PDFConverter"])
+indexing_pipeline.add_node(component=document_store, name="DocumentStore", inputs=["PreProcessor"])
+
+indexing_pipeline.run(file_paths=["filename.pdf"])
+```
+
+### Query Pipeline
+
+Once you have documents in your `MongoDBAtlasDocumentStore`, it's ready to be used in any Haystack pipeline. For example, below is a pipeline that makes use of a custom prompt that is designed to answer questions for the retrieved documents.
+
+```python
+from haystack import Pipeline
+from haystack.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
+from haystack.nodes import AnswerParser, EmbeddingRetriever, PromptNode, PromptTemplate
+
+document_store=MongoDBAtlasDocumentStore(
+    mongo_connection_string="mongodb+srv://{mongo_atlas_username}:{mongo_atlas_password}@{mongo_atlas_host}/?{mongo_atlas_params_string}",
+    database_name="database_name",
+    collection_name="collection_name"
+)
+              
+retriever = EmbeddingRetriever(document_store = document_store,
+                               embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1")
+prompt_template = PromptTemplate(prompt = """"Answer the following query based on the provided context. If the context does
+                                              not include an answer, reply with 'I don't know'.\n
+                                              Query: {query}\n
+                                              Documents: {join(documents)}
+                                              Answer: 
+                                          """,
+                                          output_parser=AnswerParser())
+prompt_node = PromptNode(model_name_or_path="gpt-4",
+                         api_key="YOUR_OPENAI_KEY",
+                         default_prompt_template=prompt_template)
+
+query_pipeline = Pipeline()
+query_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
+query_pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Retriever"])
+
+query_pipeline.run(query = "What is MongoDB Atlas?", params={"Retriever" : {"top_k": 5}})
+```
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