Documentation Index
Fetch the complete documentation index at: https://docs.cyborg.co/llms.txt
Use this file to discover all available pages before exploring further.
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: 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.
