---
title: "Use Qdrant with LangChain"
description: "Use a TIR Vector Database as the vector store for LangChain, to embed documents, store them, and run similarity and diverse searches."
content_type: "how-to"
product: "vector-database"
platform: "TIR"
url: "/tir/vector-database/how-to/use-qdrant-with-langchain/"
topic: ["integrations"]
---

# Use Qdrant with LangChain

LangChain builds applications on top of language models. Use your Qdrant database as its vector store
to embed documents and retrieve the ones closest to a question.

## Before you begin

You need a **Running** database with its **Endpoint URL** and **API Key**; see
[Connect to a vector database](/tir/vector-database/how-to/connect-to-a-vector-database/). You need
Python 3.8 or later and a text file of documents.

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

## Embed documents and search

`<your-endpoint-url>` is the **Endpoint URL**, and `<your-api-key>` is your API key. `<your-file>` is your text file, and `<your-query>` is the question to search for.

```python
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Qdrant

# Load and chunk documents
loader = TextLoader("<your-file>")
documents = loader.load()
docs = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0).split_documents(documents)

# Embed and insert into Qdrant
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
qdrant = Qdrant.from_documents(
    docs, embeddings,
    host="<your-endpoint-url>", port=6333,
    api_key="<your-api-key>",
    collection_name="my_documents",
)

# Similarity search
found_docs = qdrant.similarity_search_with_score("<your-query>")
document, score = found_docs[0]

# MMR search (diverse results)
found_docs = qdrant.max_marginal_relevance_search("<your-query>", k=2, fetch_k=10)
```

The first search returns the closest document and its score. The MMR search returns two results chosen
to differ from each other.

## What to do next

For more, see the [Qdrant LangChain documentation](https://qdrant.tech/documentation/frameworks/langchain/).
To create the embeddings with the TIR GenAI API, see
[Generate vector embeddings](/tir/genai-api/how-to/generate-vector-embeddings/). If a connection
fails, see [Troubleshooting Vector Database](/tir/vector-database/troubleshooting/vector-database/).