You can integrate MongoDB Vector Search with LlamaIndex to implement retrieval-augmented generation (RAG) in your LLM application. This tutorial demonstrates how to start using MongoDB Vector Search with LlamaIndex to perform semantic search on your data and build a RAG implementation. Specifically, you perform the following actions:
Set up the environment.
Store custom data in MongoDB.
Create a MongoDB Vector Search index on your data.
Run the following vector search queries:
Semantic search.
Semantic search with metadata pre-filtering.
Implement RAG by using MongoDB Vector Search to answer questions on your data.
Background
LlamaIndex is an open-source framework designed to simplify how you connect custom data sources to LLMs. It provides several tools such as data connectors, indexes, and query engines to help you load and prepare vector embeddings for RAG applications.
By integrating MongoDB Vector Search with LlamaIndex, you can use MongoDB as a vector database and use MongoDB Vector Search to implement RAG by retrieving semantically similar documents from your data. To learn more about RAG, see Retrieval-Augmented Generation (RAG) with MongoDB.
Procedure
Run Vector Search Queries
After MongoDB builds your index, return to your notebook and run vector search queries on your data. The following examples demonstrate different queries that you can run on your vectorized data.
This example performs a basic semantic search for the string MongoDB Atlas security and returns a list of documents ranked by relevance score. It also specifies the following:
MongoDB Vector Search as a retriever to perform semantic search.
The
similarity_top_kparameter to return only the three most relevant documents.
retriever = vector_store_index.as_retriever(similarity_top_k=3) nodes = retriever.retrieve("MongoDB acquisition") for node in nodes: print(node)
Node ID: 479446ef-8a32-410d-a5e0-8650bd10d78d Text: MongoDB completed the redemption of 2026 Convertible Notes, eliminating all debt from the balance sheet. Additionally, in conjunction with the acquisition of Voyage, MongoDB is announcing a stock buyback program of $200 million, to offset the dilutive impact of the acquisition consideration. Score: 0.914 Node ID: 453137d9-8902-4fae-8d81-5f5d9b0836eb Text: "Looking ahead, we remain incredibly excited about our long-term growth opportunity. MongoDB removes the constraints of legacy databases, enabling businesses to innovate at AI speed with our flexible document model and seamless scalability. Following the Voyage AI acquisition, we combine real-time data, sophisticated embedding and retrieval mod... Score: 0.914 Node ID: f3c35db6-43e5-4da7-a297-d9b009b9d300 Text: Lombard Odier, a Swiss private bank, partnered with MongoDB to migrate and modernize its legacy banking technology systems on MongoDB with generative AI. The initiative enabled the bank to migrate code 50-60 times quicker and move applications from a legacy relational database to MongoDB 20 times faster than previous migrations. Score: 0.912
You can pre-filter your data by using an MQL match expression that compares the indexed field with another value in your collection. You must index any metadata fields that you want to filter by as the filter type. To learn more, see How to Index Fields for Vector Search.
Note
You specified the metadata.page_label field as a filter when you created the index for this tutorial.
This example performs a semantic search for the string MongoDB Atlas security and returns a list of documents ranked by relevance score. It also specifies the following:
MongoDB Vector Search as a retriever to perform semantic search.
The
similarity_top_kparameter to return only the three most relevant documents.A filter on the
metadata.page_labelfield so that MongoDB Vector Search searches for documents appearing on page two only.
# Specify metadata filters metadata_filters = MetadataFilters( filters=[ExactMatchFilter(key="metadata.page_label", value="2")] ) retriever = vector_store_index.as_retriever(similarity_top_k=3, filters=metadata_filters) nodes = retriever.retrieve("MongoDB acquisition") for node in nodes: print(node)
Node ID: 479446ef-8a32-410d-a5e0-8650bd10d78d Text: MongoDB completed the redemption of 2026 Convertible Notes, eliminating all debt from the balance sheet. Additionally, in conjunction with the acquisition of Voyage, MongoDB is announcing a stock buyback program of $200 million, to offset the dilutive impact of the acquisition consideration. Score: 0.914 Node ID: f3c35db6-43e5-4da7-a297-d9b009b9d300 Text: Lombard Odier, a Swiss private bank, partnered with MongoDB to migrate and modernize its legacy banking technology systems on MongoDB with generative AI. The initiative enabled the bank to migrate code 50-60 times quicker and move applications from a legacy relational database to MongoDB 20 times faster than previous migrations. Score: 0.912 Node ID: 82a2a0c0-80b9-4a9e-a848-529b4ff8f301 Text: Fourth Quarter Fiscal 2025 and Recent Business Highlights MongoDB acquired Voyage AI, a pioneer in state-of-the-art embedding and reranking models that power next-generation AI applications. Integrating Voyage AI's technology with MongoDB will enable organizations to easily build trustworthy, AI-powered applications by offering highly accurate... Score: 0.911
Answer Questions on Your Data
This section demonstrates how to implement RAG in your application with MongoDB Vector Search and LlamaIndex. Now that you've learned how to run vector search queries to retrieve semantically similar documents, run the following code to use MongoDB Vector Search to retrieve documents and a LlamaIndex query engine to then answer questions based on those documents.
This example does the following:
- Instantiates MongoDB Vector Search as a vector index retriever, a specific type of retriever for vector stores. It includes the
similarity_top_kparameter so that MongoDB Vector Search retrieves only the 5 most relevant documents.
Instantiates the
RetrieverQueryEnginequery engine to answer questions on your data. When prompted, the query engine performs the following actions:Uses MongoDB Vector Search as a retriever to query for semantically similar documents based on the prompt.
Calls the LLM that you specified when you set up your environment to generate a context-aware response based on the retrieved documents.
Prompts the LLM with a sample query about Atlas security recommendations.
Returns the LLM's response and the documents used as context. The generated response might vary.
# Instantiate MongoDB Vector Search as a retriever vector_store_retriever = VectorIndexRetriever(index=vector_store_index, similarity_top_k=5) # Pass the retriever into the query engine query_engine = RetrieverQueryEngine(retriever=vector_store_retriever) # Prompt the LLM response = query_engine.query("What was MongoDB's latest acquisition?") print(response) print("\nSource documents: ") pprint.pprint(response.source_nodes)
MongoDB's latest acquisition was Voyage AI, a pioneer in embedding and reranking models for next-generation AI applications. Source documents: [NodeWithScore(node=TextNode(id_='82a2a0c0-80b9-4a9e-a848-529b4ff8f301', embedding=None, metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='2171a7d3-482c-4f83-beee-8c37e0ebc747', node_type='4', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='ef623ef7400aa6e120f821b455b2ddce99b94c57365e7552b676abaa3eb23640'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='8cfe6680-8dec-486e-92c5-89ac1733b6c8', node_type='1', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='b6c412af868c29d67a6b030f266cd0e680f4a578a34c209c1818ff9a366c9d44'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='479446ef-8a32-410d-a5e0-8650bd10d78d', node_type='1', metadata={}, hash='b805543bf0ef0efc25492098daa9bd9c037043fb7228fb0c3270de235e668341')}, metadata_template='{key}: {value}', metadata_separator='\n', text="Fourth Quarter Fiscal 2025 and Recent Business Highlights\nMongoDB acquired Voyage AI, a pioneer in state-of-the-art embedding and reranking models that power next-generation\nAI applications. Integrating Voyage AI's technology with MongoDB will enable organizations to easily build trustworthy,\nAI-powered applications by offering highly accurate and relevant information retrieval deeply integrated with operational\ndata.", mimetype='text/plain', start_char_idx=1678, end_char_idx=2101, metadata_seperator='\n', text_template='{metadata_str}\n\n{content}'), score=0.9279670119285583), NodeWithScore(node=TextNode(id_='453137d9-8902-4fae-8d81-5f5d9b0836eb', embedding=None, metadata={'page_label': '1', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='62b7cace-30c0-4687-9d87-e178547ae357', node_type='4', metadata={'page_label': '1', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='cb1dbd172c17e53682296ccc966ebdbb5605acb4fbf3872286e3a202c1d3650d'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='b6ae7c13-5bec-47f5-887f-835fc7bae374', node_type='1', metadata={'page_label': '1', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='a4835102686cdf03d1106946237d50031d00a0861eea892e38b928dd5e44e295'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='3d4034d3-bac5-4985-8926-9213f8a87318', node_type='1', metadata={}, hash='f103b351f2bda28ec3d2f1bb4f40d93ac1698ea5f7630a5297688a4caa419389')}, metadata_template='{key}: {value}', metadata_separator='\n', text='"Looking ahead, we remain incredibly excited about our long-term growth opportunity. MongoDB removes the constraints of legacy databases,\nenabling businesses to innovate at AI speed with our flexible document model and seamless scalability. Following the Voyage AI acquisition, we\ncombine real-time data, sophisticated embedding and retrieval models and semantic search directly in the database, simplifying the development of\ntrustworthy AI-powered apps."', mimetype='text/plain', start_char_idx=1062, end_char_idx=1519, metadata_seperator='\n', text_template='{metadata_str}\n\n{content}'), score=0.921961784362793), NodeWithScore(node=TextNode(id_='85dd431c-2d4c-4336-ab39-e87a97b30c59', embedding=None, metadata={'page_label': '4', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='311532cc-f526-4fc3-adb6-49e76afdd580', node_type='4', metadata={'page_label': '4', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='37f0ad7fcb7f204226ea7c6c475360e2db55bb77447f1742a164efb9c1da5dc0'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='6175bcb6-9e2a-4196-85f7-0585bcbbdd3b', node_type='1', metadata={}, hash='0e92e55a50f8b6dbfe7bcaedb0ccc42345a185048efcd440e3ee1935875e7cbf')}, metadata_template='{key}: {value}', metadata_separator='\n', text="Headquartered in New York, MongoDB's mission is to empower innovators to create, transform, and disrupt industries with software and data.\nMongoDB's unified, intelligent data platform was built to power the next generation of applications, and MongoDB is the most widely available, globally\ndistributed database on the market.", mimetype='text/plain', start_char_idx=0, end_char_idx=327, metadata_seperator='\n', text_template='{metadata_str}\n\n{content}'), score=0.9217028021812439), NodeWithScore(node=TextNode(id_='f3c35db6-43e5-4da7-a297-d9b009b9d300', embedding=None, metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='2171a7d3-482c-4f83-beee-8c37e0ebc747', node_type='4', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='ef623ef7400aa6e120f821b455b2ddce99b94c57365e7552b676abaa3eb23640'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='3008736c-29f0-4b41-ac0f-efdb469319b9', node_type='1', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='cd3647350e6d7fcd89e2303fe1995b8f91b633c5f33e14b3b4c18a16738ea86f'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='c9bef874-77ee-40bc-a1fe-ca42d1477cb3', node_type='1', metadata={}, hash='c7d7af8a1b43b587a9c47b27f57e7cb8bc35bd90390a078db21e3f5253ee7cc1')}, metadata_template='{key}: {value}', metadata_separator='\n', text='Lombard Odier, a Swiss private bank, partnered with MongoDB to migrate and modernize its legacy banking technology\nsystems on MongoDB with generative AI. The initiative enabled the bank to migrate code 50-60 times quicker and move\napplications from a legacy relational database to MongoDB 20 times faster than previous migrations.', mimetype='text/plain', start_char_idx=2618, end_char_idx=2951, metadata_seperator='\n', text_template='{metadata_str}\n\n{content}'), score=0.9197831153869629), NodeWithScore(node=TextNode(id_='479446ef-8a32-410d-a5e0-8650bd10d78d', embedding=None, metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='2171a7d3-482c-4f83-beee-8c37e0ebc747', node_type='4', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='ef623ef7400aa6e120f821b455b2ddce99b94c57365e7552b676abaa3eb23640'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='82a2a0c0-80b9-4a9e-a848-529b4ff8f301', node_type='1', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='688872b911c388c239669970f562d4014aaec4753903e75f4bdfcf1eb1daf5ab'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='3008736c-29f0-4b41-ac0f-efdb469319b9', node_type='1', metadata={}, hash='a854a9bf103e429ce78b45603df9e2341e5d0692aa95e544e6c82616be29b28e')}, metadata_template='{key}: {value}', metadata_separator='\n', text='MongoDB completed the redemption of 2026 Convertible Notes, eliminating all debt from the balance sheet. Additionally, in\nconjunction with the acquisition of Voyage, MongoDB is announcing a stock buyback program of $200 million, to offset the\ndilutive impact of the acquisition consideration.', mimetype='text/plain', start_char_idx=2102, end_char_idx=2396, metadata_seperator='\n', text_template='{metadata_str}\n\n{content}'), score=0.9183852672576904)]
This example does the following:
Defines a metadata filter on the
metadata.page_labelfield so that MongoDB Vector Search searches for documents appearing on page two only.Instantiates MongoDB Vector Search as a vector index retriever, a specific type of retriever for vector stores. It includes the metadata filters that you defined and the
similarity_top_kparameter so that MongoDB Vector Search retrieves only the 5 most relevant documents from page two.
Instantiates the
RetrieverQueryEnginequery engine to answer questions on your data. When prompted, the query engine performs the following actions:Uses MongoDB Vector Search as a retriever to query for semantically similar documents based on the prompt.
Calls the LLM that you specified when you set up your environment to generate a context-aware response based on the retrieved documents.
Prompts the LLM with a sample query about Atlas security recommendations.
Returns the LLM's response and the documents used as context. The generated response might vary.
# Specify metadata filters metadata_filters = MetadataFilters( filters=[ExactMatchFilter(key="metadata.page_label", value="2")] ) # Instantiate MongoDB Vector Search as a retriever vector_store_retriever = VectorIndexRetriever(index=vector_store_index, filters=metadata_filters, similarity_top_k=5) # Pass the retriever into the query engine query_engine = RetrieverQueryEngine(retriever=vector_store_retriever) # Prompt the LLM response = query_engine.query("What was MongoDB's latest acquisition?") print(response) print("\nSource documents: ") pprint.pprint(response.source_nodes)
MongoDB's latest acquisition was Voyage AI, a pioneer in embedding and reranking models that power next-generation AI applications. Source documents: [NodeWithScore(node=TextNode(id_='82a2a0c0-80b9-4a9e-a848-529b4ff8f301', embedding=None, metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='2171a7d3-482c-4f83-beee-8c37e0ebc747', node_type='4', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='ef623ef7400aa6e120f821b455b2ddce99b94c57365e7552b676abaa3eb23640'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='8cfe6680-8dec-486e-92c5-89ac1733b6c8', node_type='1', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='b6c412af868c29d67a6b030f266cd0e680f4a578a34c209c1818ff9a366c9d44'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='479446ef-8a32-410d-a5e0-8650bd10d78d', node_type='1', metadata={}, hash='b805543bf0ef0efc25492098daa9bd9c037043fb7228fb0c3270de235e668341')}, metadata_template='{key}: {value}', metadata_separator='\n', text="Fourth Quarter Fiscal 2025 and Recent Business Highlights\nMongoDB acquired Voyage AI, a pioneer in state-of-the-art embedding and reranking models that power next-generation\nAI applications. Integrating Voyage AI's technology with MongoDB will enable organizations to easily build trustworthy,\nAI-powered applications by offering highly accurate and relevant information retrieval deeply integrated with operational\ndata.", mimetype='text/plain', start_char_idx=1678, end_char_idx=2101, metadata_seperator='\n', text_template='{metadata_str}\n\n{content}'), score=0.9280173778533936), NodeWithScore(node=TextNode(id_='f3c35db6-43e5-4da7-a297-d9b009b9d300', embedding=None, metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='2171a7d3-482c-4f83-beee-8c37e0ebc747', node_type='4', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='ef623ef7400aa6e120f821b455b2ddce99b94c57365e7552b676abaa3eb23640'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='3008736c-29f0-4b41-ac0f-efdb469319b9', node_type='1', metadata={'page_label': '2', 'file_name': 'mongodb-earnings-report.pdf', 'file_path': 'data/mongodb-earnings-report.pdf', 'file_type': 'application/pdf', 'file_size': 150863, 'creation_date': '2025-05-28', 'last_modified_date': '2025-05-28'}, hash='cd3647350e6d7fcd89e2303fe1995b8f91b633c5f33e14b3b4c18a16738ea86f'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='c9bef874-77ee-40bc-a1fe-ca42d1477cb3', node_type='1', metadata={}, hash='c7d7af8a1b43b587a9c47b27f57e7cb8bc35bd90390a078db21e3f5253ee7cc1')}, metadata_template='{key}: {value}', metadata_separator='\n', text='Lombard Odier, a Swiss private bank, partnered with MongoDB to migrate and modernize its legacy banking technology\nsystems on MongoDB with generative AI. 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Next Steps
To explore LlamaIndex's full library of tools for RAG applications, which includes data connectors, indexes, and query engines, see LlamaHub.
To extend the application in this tutorial to have back-and-forth conversations, see Chat Engine.
MongoDB also provides the following developer resources: