langchain-agensgraph 0.1.0

AgensGraph integration for LangChain graph and vector stores

🦜️🔗 LangChain AgensGraph

This plugin provides support for GraphStore and VectorStore integration in langchain, for persisting graphs directly in AgensGraph.

See the associated guide below:

• Agens Graph Store

📦 Installation

pip install -U langchain-agensgraph

💻 Usage

AgensGraph

The AgensGraph class provides functionality to interact with agensgraph as a graphstore in langchain.

from langchain_agensgraph.graphs.agensgraph import AgensGraph

conf = {
    "dbname": "",
    "user": "",
    "password": "",
    "host": "",
    "port": ,
}

graph = AgensGraph(graph_name="", conf=conf)
graph.query("MATCH (n) RETURN n LIMIT 1;")

AgensgraphVector

The AgensgraphVector class provides functionality for managing an AgensGraph vector store. It enables you to create new vector indexes, add vectors to existing indexes, and perform queries using vector indexes.

from langchain.docstore.document import Document
from langchain_openai import OpenAIEmbeddings

from langchain_agensgraph.vectorstores.agensgraph_vector import AgensgraphVector

# Create a vector store from some documents and embeddings
docs = [
    Document(
        page_content=(
            "LangChain is a framework to build "
            "with LLMs by chaining interoperable components."
        ),
    )
]
embeddings = OpenAIEmbeddings(
    model="text-embedding-3-large",
    api_key="sk-...",  # Replace with your OpenAI API key
)
db = AgensgraphVector.from_documents(
    docs,
    embeddings,
    url="postgresql://username:password@host:port/dbname"
)
# Query the vector store for similar documents
docs_with_score = db.similarity_search_with_score("What is LangChain?", k=1)