Comece a usar a integração com o LlamaIndex

Você pode integrar o MongoDB Vector Search ao LlamaIndex para implementar a geração aumentada de recuperação (RAG) em seu aplicação LLM. Este tutorial demonstra como começar a usar o MongoDB Vector Search com LlamaIndex para executar pesquisa semântica em seus dados e criar uma implementação de RAG. Especificamente, você executa as seguintes ações:

Configure o ambiente.
Armazene dados personalizados no MongoDB.
Crie um índice do MongoDB Vector Search em seus dados.
Execute as seguintes query de pesquisa vetorial:
- Pesquisa semântica.
- Pesquisa semântica com pré-filtragem de metadados.
Implemente o RAG usando o MongoDB Vector Search para responder a perguntas sobre seus dados.

Trabalhe com uma versão executável deste tutorial como um notebook Python.

Plano de fundo

O LlamaIndex é uma estrutura de código aberto projetada para simplificar a forma como você conecta conjuntos de dados personalizados aos LLMs . Ele fornece várias ferramentas, como conectores de dados, índices e mecanismos de query para ajudá-lo a carregar e preparar incorporações vetoriais para aplicativos RAG .

Ao integrar o MongoDB pesquisa vetorial ao LlamaIndex, você pode usar o MongoDB como um banco de dados vetorial e usar o MongoDB pesquisa vetorial para implementar o RAG, recuperando documentos semanticamente semelhantes de seus dados. Para saber mais sobre RAG, consulte Geração Aumentada de Recuperação (RAG) com MongoDB.

Procedimento

Incorporando provedor

Executar queries no Vector Search

Depois que o MongoDB criar seu índice, retorne ao seu bloco de anotações e execute consultas de pesquisa vetorial em seus dados. Os exemplos seguintes demonstram diferentes queries que você pode executar em seus dados vetorizados.

Este exemplo executa uma pesquisa semântica básica para a string MongoDB Atlas security e retorna uma lista de documentos classificados por pontuação de relevância. Ele também especifica o seguinte:

MongoDB Vector Search como um recuperador para realizar pesquisas semânticas.
O parâmetro similarity_top_k para retornar apenas os três documentos mais relevantes.

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

Você pode pré-filtrar seus dados usando uma expressão de correspondência MQL que compara o campo indexado com outro valor em sua coleção. Você deve indexar todos os campos de metadados que deseja filtrar como o tipo filter . Para saber mais, consulte Como indexar campos da pesquisa vetorial.

Observação

Você especificou o campo metadata.page_label como um filtro quando criou o índice para este tutorial.

Este exemplo executa uma pesquisa semântica para a string MongoDB Atlas security e retorna uma lista de documentos classificados por pontuação de relevância. Ele também especifica o seguinte:

MongoDB Vector Search como um recuperador para realizar pesquisas semânticas.
O parâmetro similarity_top_k para retornar apenas os três documentos mais relevantes.
Um filtro no campo metadata.page_label para que o MongoDB Vector Search pesquise documentos que aparecem somente na página dois.

# 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

Responda a perguntas sobre seus dados

Esta seção demonstra como implementar RAG em seu aplicação com o MongoDB Vector Search e o LlamaIndex. Agora que você aprenderam a executar queries de pesquisa vetorial para recuperar documentos semanticamente semelhantes, execute o código a seguir para usar o MongoDB Vector Search para recuperar documentos e um mecanismo de query LlamaIndex para responder a perguntas com base nesses documentos.

Este exemplo faz o seguinte:

Instancia o MongoDB Vector Search como um recuperador de índice de vetor, um tipo específico de recuperador para armazenamentos de vetores. Inclui o parâmetro similarity_top_k para que o MongoDB Vector Search recupere somente os documentos mais relevantes do 5.

Instancia o mecanismo de query do RetrieverQueryEngine para responder a perguntas sobre seus dados. Quando solicitado, o mecanismo de query executa a seguinte ação:
- Usa o MongoDB Vector Search como um recuperador para fazer query de documentos semanticamente semelhantes com base no prompt.
- Chama o LLM que você especificou ao configurar seu ambiente para gerar uma resposta sensível ao contexto com base nos documentos recuperados.
Solicita ao LLM um exemplo de query sobre as recomendações de segurança do Atlas.
Retorna a resposta do LLM e os documentos usados como contexto. A resposta gerada pode variar.

# 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)]

Este exemplo faz o seguinte:

Define um filtro de metadados no campo metadata.page_label para que o MongoDB Vector Search pesquise documentos que aparecem somente na página dois.
Instancia o MongoDB Vector Search como um recuperador de índice de vetor, um tipo específico de recuperador para armazenamentos de vetores. Ele inclui os filtros de metadados que você definiu e o parâmetro similarity_top_k para que o MongoDB Vector Search recupere somente os documentos 5 mais relevantes da página dois.

Instancia o mecanismo de query do RetrieverQueryEngine para responder a perguntas sobre seus dados. Quando solicitado, o mecanismo de query executa a seguinte ação:
- Usa o MongoDB Vector Search como um recuperador para fazer query de documentos semanticamente semelhantes com base no prompt.
- Chama o LLM que você especificou ao configurar seu ambiente para gerar uma resposta sensível ao contexto com base nos documentos recuperados.
Solicita ao LLM um exemplo de query sobre as recomendações de segurança do Atlas.
Retorna a resposta do LLM e os documentos usados como contexto. A resposta gerada pode variar.

# 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:
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Próximos passos

Para explorar a biblioteca completa de FERRAMENTAS para aplicativos RAG do LlamaIndex, que inclui conectores de dados, índices e mecanismos de consulta, consulte LlamaHub.

Para estender o aplicação neste tutorial para ter conversas de vai e vem, consulte Mecanismo de bate-papo.

O MongoDB também fornece os seguintes recursos para desenvolvedores:

Repositório do GitHub para desenvolvedores do MongoDB

Dica

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Integração Python