landing_page/qdrant-landing/content/documentation/integrations/langchain.md

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LangChain	100

LangChain

LangChain is a library that makes developing Large Language Models based applications much easier. It unifies the interfaces to different libraries, including major embedding providers and Qdrant. Using LangChain, you can focus on the business value instead of writing the boilerplate.

Langchain comes with the Qdrant integration by default. It might be installed with pip:

pip install langchain

Qdrant acts as a vector index that may store the embeddings with the documents used to generate them. There are various ways how to use it, but calling Qdrant.from_texts is probably the most straightforward way how to get started:

from langchain.vectorstores import Qdrant
from langchain.embeddings import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-mpnet-base-v2"
)
doc_store = Qdrant.from_texts(
    texts, embeddings, url="<qdrant-url>", api_key="<qdrant-api-key>", collection_name="texts"
)

Calling Qdrant.from_documents or Qdrant.from_texts will always recreate the collection and remove all the existing points. That's fine for some experiments, but you'll prefer not to start from scratch every single time in a real-world scenario. If you prefer reusing an existing collection, you can create an instance of Qdrant on your own:

import qdrant_client

embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-mpnet-base-v2"
)

client = qdrant_client.QdrantClient(
    "<qdrant-url>",
    api_key="<qdrant-api-key>", # For Qdrant Cloud, None for local instance
)

doc_store = Qdrant(
    client=client, collection_name="texts", 
    embeddings=embeddings,
)

If you'd like to know more about running Qdrant in a LangChain-based application, please read our article Question Answering with LangChain and Qdrant without boilerplate. Some more information might also be found in the LangChain documentation.