CyborgDB provides a seamless integration with LangChain, enabling developers to leverage encrypted vector search for large language models (LLMs) and RAG pipelines. This integration is implemented via the CyborgVectorStore class, which conforms to the VectorStore interface from langchain-core.

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Installation (Python Client SDK)

To use the LangChain integration with the CyborgDB Python Client SDK, you need to install the cyborgdb package with the langchain extra: 
pip install cyborgdb[langchain]
This will install the necessary dependencies for the LangChain integration.

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Installation (Embedded Library)

To install CyborgDB with LangChain support, you must install either cyborgdb-core or cyborgdb-lite with the langchain extra. You can do this using pip: 
# For cyborgdb-core
pip install cyborgdb-core[langchain]

# For cyborgdb-lite
pip install cyborgdb-lite[langchain]
To learn more about the differences between cyborgdb, cyborgdb-core, and cyborgdb-lite, refer to the Deployment Models Guide.

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Usage

To use CyborgDB with LangChain, you can import the CyborgVectorStore class from the cyborgdb_core.integrations.langchain or cyborgdb_lite.integrations.langchain module, depending on which version you are using. 
from cyborgdb.integrations.langchain import CyborgVectorStore
# or
from cyborgdb_core.integrations.langchain import CyborgVectorStore
# or
from cyborgdb_lite.integrations.langchain import CyborgVectorStore

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Key Classes

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CyborgVectorStore

Implements the LangChain-compatible vector store interface. Supports:
  • .add_texts() / .add_documents()
  • .similarity_search() / .similarity_search_with_score()
  • .max_marginal_relevance_search()
  • .get_document() and .delete()
  • Async variants (.aadd_texts(), .asimilarity_search(), etc.)
  • Reversible document ID tracking
  • Configurable distance metric (cosine, euclidean, squared_euclidean)

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DBConfig

Configuration object for defining the location of index data (memory, redis, postgres, etc.).

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Usage Example

from cyborgdb_core.integrations.langchain import CyborgVectorStore
from cyborgdb_core import DBConfig

store = CyborgVectorStore.from_texts(
    texts=["hello world", "goodbye world"],
    embedding="all-MiniLM-L6-v2",  # sentence-transformer name
    index_key=CyborgVectorStore.generate_key(),
    api_key="your-api-key",
    index_location=DBConfig("memory"),
    config_location=DBConfig("memory"),
    index_type="ivfflat",
    metric="cosine"
)

docs = store.similarity_search("hello")