---
title: LangChain
weight: 100
aliases:
  - ../integrations/langchain/
  - /documentation/overview/integrations/langchain/
---

# LangChain

LangChain is a library that makes developing Large Language Model-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 distributes their Qdrant integration in their community package. It might be installed with pip:

```bash
pip install langchain-community langchain-qdrant
```

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

```python
from langchain_qdrant import Qdrant
from langchain_community.embeddings.huggingface 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"
)
```

## Using an existing collection

To get an instance of `langchain_qdrant.Qdrant` without loading any new documents or texts, you can use the `Qdrant.from_existing_collection()` method.

```python
doc_store = Qdrant.from_existing_collection(
    embeddings=embeddings,
    collection_name="my_documents",
    url="<qdrant-url>",
    api_key="<qdrant-api-key>",
)
```

## Local mode

Python client allows you to run the same code in local mode without running the Qdrant server. That's great for testing things
out and debugging or if you plan to store just a small amount of vectors. The embeddings might be fully kept in memory or
persisted on disk.

### In-memory

For some testing scenarios and quick experiments, you may prefer to keep all the data in memory only, so it gets lost when the
client is destroyed - usually at the end of your script/notebook.

```python
qdrant = Qdrant.from_documents(
    docs,
    embeddings,
    location=":memory:",  # Local mode with in-memory storage only
    collection_name="my_documents",
)
```

### On-disk storage

Local mode, without using the Qdrant server, may also store your vectors on disk so they’re persisted between runs.

```python
qdrant = Qdrant.from_documents(
    docs,
    embeddings,
    path="/tmp/local_qdrant",
    collection_name="my_documents",
)
```

### On-premise server deployment

No matter if you choose to launch Qdrant locally with [a Docker container](/documentation/guides/installation/), or
select a Kubernetes deployment with [the official Helm chart](https://github.com/qdrant/qdrant-helm), the way you're
going to connect to such an instance will be identical. You'll need to provide a URL pointing to the service.

```python
url = "<---qdrant url here --->"
qdrant = Qdrant.from_documents(
    docs,
    embeddings,
    url,
    prefer_grpc=True,
    collection_name="my_documents",
)
```

## Next steps

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](/articles/langchain-integration/). Some more information
might also be found in the [LangChain documentation](https://python.langchain.com/docs/integrations/vectorstores/qdrant).

- [Source Code](https://github.com/langchain-ai/langchain/tree/master/libs%2Fpartners%2Fqdrant)