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XuhuiRen 29fe569d34 Enable GraphRAG with Neo4J (#682 )

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* Update test_retrievers_neo4j_langchain.sh

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* add docker

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* Update retrievers-compose-cd.yaml

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* Update config.py

* Update test_retrievers_neo4j_langchain.sh

* Update test_retrievers_neo4j_langchain.sh

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Signed-off-by: XuhuiRen <xuhui.ren@intel.com>
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2024-09-15 18:12:29 +08:00

milvus/langchain

Refine READMEs after reorg (#666 )

2024-09-11 17:23:40 +08:00

multimodal/redis/langchain

Update example name into MultimodalQnA and update image names (#686 )

2024-09-13 17:07:38 +08:00

neo4j/langchain

Enable GraphRAG with Neo4J (#682 )

2024-09-15 18:12:29 +08:00

pathway/langchain

replace langchain/langchain:latest with python:3.11-slim (#669 )

2024-09-12 13:24:50 +08:00

pgvector/langchain

update torch cpu installation (#681 )

2024-09-12 22:01:38 +08:00

pinecone/langchain

replace langchain/langchain:latest with python:3.11-slim (#669 )

2024-09-12 13:24:50 +08:00

qdrant/haystack

Fix security issue (#673 )

2024-09-12 00:01:58 +08:00

redis

replace langchain/langchain:latest with python:3.11-slim (#669 )

2024-09-12 13:24:50 +08:00

vdms/langchain

replace langchain/langchain:latest with python:3.11-slim (#669 )

2024-09-12 13:24:50 +08:00

README.md

Refine READMEs after reorg (#666 )

2024-09-11 17:23:40 +08:00

README.md

Retriever Microservice

This retriever microservice is a highly efficient search service designed for handling and retrieving embedding vectors. It operates by receiving an embedding vector as input and conducting a similarity search against vectors stored in a VectorDB database. Users must specify the VectorDB's URL and the index name, and the service searches within that index to find documents with the highest similarity to the input vector.

The service primarily utilizes similarity measures in vector space to rapidly retrieve contentually similar documents. The vector-based retrieval approach is particularly suited for handling large datasets, offering fast and accurate search results that significantly enhance the efficiency and quality of information retrieval.

Overall, this microservice provides robust backend support for applications requiring efficient similarity searches, playing a vital role in scenarios such as recommendation systems, information retrieval, or any other context where precise measurement of document similarity is crucial.