CyborgDB - LangChain Integration

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.

Installation

Embedded
Python SDK
JS/TS

Install cyborgdb-core with the langchain extra:

pip install cyborgdb-core[langchain]

Install the cyborgdb package with the langchain extra:

pip install cyborgdb[langchain]

Install the cyborgdb npm package:

npm install cyborgdb

To learn more about the differences between cyborgdb and cyborgdb-core, refer to the Deployment Models Guide.

Import

Embedded
Python SDK
JS/TS

from cyborgdb_core.integrations.langchain import CyborgVectorStore

from cyborgdb.integrations.langchain import CyborgVectorStore

import { CyborgVectorStore } from 'cyborgdb';

Key Features

Full VectorStore API compatibility (add_texts, similarity_search, etc.)
Async operations support
Configurable distance metrics (cosine, euclidean, squared_euclidean)
Document ID tracking and management
Max marginal relevance search
Factory methods (from_texts, from_documents) for quick setup

Key Classes

`CyborgVectorStore`

Implements the LangChain-compatible vector store interface. Supports:

.add_texts() / .add_documents()
.similarity_search() / .similarity_search_with_score()
.max_marginal_relevance_search()
.get() / .list_ids() / .delete()
.as_retriever() for use in LangChain chains
Async variants (.aadd_texts(), .asimilarity_search(), etc.)
Reversible document ID tracking
Configurable distance metric (cosine, euclidean, squared_euclidean)

`DBConfig` (Embedded only)

Configuration object for defining the location of index data (memory, redis, postgres, rocksdb, threadsafememory). Used only with the Embedded library (cyborgdb-core).

Quick Start

Embedded
Python SDK
JS/TS

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

store = CyborgVectorStore.from_texts(
    texts=["hello world", "goodbye world"],
    embedding=OpenAIEmbeddings(),
    index_key=CyborgVectorStore.generate_key(save=True),
    api_key="your-api-key",
    index_location=DBConfig("memory"),
    config_location=DBConfig("memory"),
    index_type="ivfsq",
    metric="cosine"
)

docs = store.similarity_search("hello")

from cyborgdb.integrations.langchain import CyborgVectorStore

store = CyborgVectorStore.from_texts(
    texts=["hello world", "goodbye world"],
    embedding="all-MiniLM-L6-v2",
    index_key=CyborgVectorStore.generate_key(save=True),
    api_key="your-api-key",
    base_url="http://localhost:8000",
    index_type="ivfflat",
    metric="cosine"
)

docs = store.similarity_search("hello")

import { CyborgVectorStore } from 'cyborgdb';
import { OpenAIEmbeddings } from '@langchain/openai';

const store = await CyborgVectorStore.fromTexts(
    ["hello world", "goodbye world"],
    [{ source: "greeting" }, { source: "farewell" }],
    new OpenAIEmbeddings(),
    {
        baseUrl: "http://localhost:8000",
        apiKey: "your-api-key",
        indexName: "my_documents",
        indexKey: CyborgVectorStore.generateKey(),
        indexType: "ivfflat",
        metric: "cosine"
    }
);

const docs = await store.similaritySearch("hello");

fromExistingIndex() is available as a JS/TS-only convenience factory method for connecting to an already-created index without re-adding documents.

Introduction

CyborgVectorStore

Types

CyborgDB - LangChain Integration

Installation

Import

Key Features

Key Classes

`CyborgVectorStore`

`DBConfig` (Embedded only)

Quick Start

Introduction

CyborgVectorStore

Types

​Installation

​Import

​Key Features

​Key Classes

​CyborgVectorStore

​DBConfig (Embedded only)

​Quick Start

Installation

Import

Key Features

Key Classes

`CyborgVectorStore`

`DBConfig` (Embedded only)

Quick Start