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**Description:** Added support for FalkorDB Vector Store, including its implementation, unit tests, documentation, and an example notebook. The FalkorDB integration allows users to efficiently manage and query embeddings in a vector database, with relevance scoring and maximal marginal relevance search. The following components were implemented: - Core implementation for FalkorDBVector store. - Unit tests ensuring proper functionality and edge case coverage. - Example notebook demonstrating an end-to-end setup, search, and retrieval using FalkorDB. **Twitter handle:** @tariyekorogha --------- Co-authored-by: Erick Friis <erick@langchain.dev> |
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| .. | ||
| redis | ||
| __init__.py | ||
| test_aerospike.py | ||
| test_azure_search.py | ||
| test_databricks_vector_search.py | ||
| test_elasticsearch.py | ||
| test_faiss.py | ||
| test_falkordb_vector_utils.py | ||
| test_hanavector.py | ||
| test_imports.py | ||
| test_indexing_docs.py | ||
| test_inmemory.py | ||
| test_neo4j.py | ||
| test_pgvector.py | ||
| test_sklearn.py | ||
| test_tencentvectordb.py | ||
| test_utils.py | ||