Qdrant

Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage vectors with additional payload and extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.

This documentation demonstrates how to use Qdrant with Langchain for dense/sparse and hybrid retrieval.

This page documents the QdrantVectorStore class that supports multiple retrieval modes via Qdrant's new Query API. It requires you to run Qdrant v1.10.0 or above.

Setup

There are various modes of how to run Qdrant, and depending on the chosen one, there will be some subtle differences. The options include:

• Local mode, no server required
• Docker deployments
• Qdrant Cloud

See the installation instructions.

%pip install -qU langchain-qdrant 'qdrant-client[fastembed]'

Credentials

There are no credentials needed to run the code in this notebook.

If you want to get best in-class automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

Initialization

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 storing 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.

OpenAI

pip install -qU langchain-openai

import getpass

    os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

HuggingFace

pip install -qU langchain-huggingface

from langchain_huggingface import HuggingFaceEmbeddings

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

Fake Embedding

pip install -qU langchain-core

from langchain_core.embeddings import FakeEmbeddings

embeddings = FakeEmbeddings(size=4096)

from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams

client = QdrantClient(":memory:")

client.create_collection(
    collection_name="demo_collection",
    vectors_config=VectorParams(size=3072, distance=Distance.COSINE),
)

vector_store = QdrantVectorStore(
    client=client,
    collection_name="demo_collection",
    embedding=embeddings,
)

On-disk storage

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

client = QdrantClient(path="/tmp/langchain_qdrant")

client.create_collection(
    collection_name="demo_collection",
    vectors_config=VectorParams(size=3072, distance=Distance.COSINE),
)

vector_store = QdrantVectorStore(
    client=client,
    collection_name="demo_collection",
    embedding=embeddings,
)

On-premise server deployment

No matter if you choose to launch Qdrant locally with a Docker container, or select a Kubernetes deployment with the official Helm chart, 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.

url = "<---qdrant url here --->"
docs = []  # put docs here
qdrant = QdrantVectorStore.from_documents(
    docs,
    embeddings,
    url=url,
    prefer_grpc=True,
    collection_name="my_documents",
)

Qdrant Cloud

If you prefer not to keep yourself busy with managing the infrastructure, you can choose to set up a fully-managed Qdrant cluster on Qdrant Cloud. There is a free forever 1GB cluster included for trying out. The main difference with using a managed version of Qdrant is that you'll need to provide an API key to secure your deployment from being accessed publicly. The value can also be set in a QDRANT_API_KEY environment variable.

url = "<---qdrant cloud cluster url here --->"
api_key = "<---api key here--->"
qdrant = QdrantVectorStore.from_documents(
    docs,
    embeddings,
    url=url,
    prefer_grpc=True,
    api_key=api_key,
    collection_name="my_documents",
)

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.

qdrant = QdrantVectorStore.from_existing_collection(
    embeddings=embeddings,
    collection_name="my_documents",
    url="http://localhost:6333",
)

Manage vector store

Once you have created your vector store, we can interact with it by adding and deleting different items.

Add items to vector store

We can add items to our vector store by using the add_documents function.

from uuid import uuid4

from langchain_core.documents import Document

document_1 = Document(
    page_content="I had chocalate chip pancakes and scrambled eggs for breakfast this morning.",
    metadata={"source": "tweet"},
)

document_2 = Document(
    page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
    metadata={"source": "news"},
)

document_3 = Document(
    page_content="Building an exciting new project with LangChain - come check it out!",
    metadata={"source": "tweet"},
)

document_4 = Document(
    page_content="Robbers broke into the city bank and stole $1 million in cash.",
    metadata={"source": "news"},
)

document_5 = Document(
    page_content="Wow! That was an amazing movie. I can't wait to see it again.",
    metadata={"source": "tweet"},
)

document_6 = Document(
    page_content="Is the new iPhone worth the price? Read this review to find out.",
    metadata={"source": "website"},
)

document_7 = Document(
    page_content="The top 10 soccer players in the world right now.",
    metadata={"source": "website"},
)

document_8 = Document(
    page_content="LangGraph is the best framework for building stateful, agentic applications!",
    metadata={"source": "tweet"},
)

document_9 = Document(
    page_content="The stock market is down 500 points today due to fears of a recession.",
    metadata={"source": "news"},
)

document_10 = Document(
    page_content="I have a bad feeling I am going to get deleted :(",
    metadata={"source": "tweet"},
)

documents = [
    document_1,
    document_2,
    document_3,
    document_4,
    document_5,
    document_6,
    document_7,
    document_8,
    document_9,
    document_10,
]
uuids = [str(uuid4()) for _ in range(len(documents))]

vector_store.add_documents(documents=documents, ids=uuids)

API Reference:Document

['c04134c3-273d-4766-949a-eee46052ad32',
 '9e6ba50c-794f-4b88-94e5-411f15052a02',
 'd3202666-6f2b-4186-ac43-e35389de8166',
 '50d8d6ee-69bf-4173-a6a2-b254e9928965',
 'bd2eae02-74b5-43ec-9fcf-09e9d9db6fd3',
 '6dae6b37-826d-4f14-8376-da4603b35de3',
 'b0964ab5-5a14-47b4-a983-37fa5c5bd154',
 '91ed6c56-fe53-49e2-8199-c3bb3c33c3eb',
 '42a580cb-7469-4324-9927-0febab57ce92',
 'ff774e5c-f158-4d12-94e2-0a0162b22f27']

Delete items from vector store

vector_store.delete(ids=[uuids[-1]])

True

Query vector store

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

Query directly

The simplest scenario for using Qdrant vector store is to perform a similarity search. Under the hood, our query will be encoded into vector embeddings and used to find similar documents in Qdrant collection.

results = vector_store.similarity_search(
    "LangChain provides abstractions to make working with LLMs easy", k=2
)
for res in results:
    print(f"* {res.page_content} [{res.metadata}]")

* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet', '_id': 'd3202666-6f2b-4186-ac43-e35389de8166', '_collection_name': 'demo_collection'}]
* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet', '_id': '91ed6c56-fe53-49e2-8199-c3bb3c33c3eb', '_collection_name': 'demo_collection'}]

QdrantVectorStore supports 3 modes for similarity searches. They can be configured using the retrieval_mode parameter when setting up the class.

• Dense Vector Search(Default)
• Sparse Vector Search
• Hybrid Search

Dense Vector Search

To search with only dense vectors,

• The retrieval_mode parameter should be set to RetrievalMode.DENSE(default).
• A dense embeddings value should be provided to the embedding parameter.

Sparse Vector Search

To search with only sparse vectors,

• The retrieval_mode parameter should be set to RetrievalMode.SPARSE.
• An implementation of the SparseEmbeddings interface using any sparse embeddings provider has to be provided as value to the sparse_embedding parameter.

The langchain-qdrant package provides a FastEmbed based implementation out of the box.

Hybrid Vector Search

To perform a hybrid search using dense and sparse vectors with score fusion,

• The retrieval_mode parameter should be set to RetrievalMode.HYBRID.
• A dense embeddings value should be provided to the embedding parameter.
• An implementation of the SparseEmbeddings interface using any sparse embeddings provider has to be provided as value to the sparse_embedding parameter.

Note that if you've added documents with the HYBRID mode, you can switch to any retrieval mode when searching. Since both the dense and sparse vectors are available in the collection.

If you want to execute a similarity search and receive the corresponding scores you can run:

client.scroll(collection_name="demo_collection")

([Record(id='42a580cb-7469-4324-9927-0febab57ce92', payload={'page_content': 'The stock market is down 500 points today due to fears of a recession.', 'metadata': {'source': 'news'}}, vector=None, shard_key=None, order_value=None),
  Record(id='50d8d6ee-69bf-4173-a6a2-b254e9928965', payload={'page_content': 'Robbers broke into the city bank and stole $1 million in cash.', 'metadata': {'source': 'news'}}, vector=None, shard_key=None, order_value=None),
  Record(id='6dae6b37-826d-4f14-8376-da4603b35de3', payload={'page_content': 'Is the new iPhone worth the price? Read this review to find out.', 'metadata': {'source': 'website'}}, vector=None, shard_key=None, order_value=None),
  Record(id='91ed6c56-fe53-49e2-8199-c3bb3c33c3eb', payload={'page_content': 'LangGraph is the best framework for building stateful, agentic applications!', 'metadata': {'source': 'tweet'}}, vector=None, shard_key=None, order_value=None),
  Record(id='9e6ba50c-794f-4b88-94e5-411f15052a02', payload={'page_content': 'The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.', 'metadata': {'source': 'news'}}, vector=None, shard_key=None, order_value=None),
  Record(id='b0964ab5-5a14-47b4-a983-37fa5c5bd154', payload={'page_content': 'The top 10 soccer players in the world right now.', 'metadata': {'source': 'website'}}, vector=None, shard_key=None, order_value=None),
  Record(id='bd2eae02-74b5-43ec-9fcf-09e9d9db6fd3', payload={'page_content': "Wow! That was an amazing movie. I can't wait to see it again.", 'metadata': {'source': 'tweet'}}, vector=None, shard_key=None, order_value=None),
  Record(id='c04134c3-273d-4766-949a-eee46052ad32', payload={'page_content': 'I had chocalate chip pancakes and scrambled eggs for breakfast this morning.', 'metadata': {'source': 'tweet'}}, vector=None, shard_key=None, order_value=None),
  Record(id='d3202666-6f2b-4186-ac43-e35389de8166', payload={'page_content': 'Building an exciting new project with LangChain - come check it out!', 'metadata': {'source': 'tweet'}}, vector=None, shard_key=None, order_value=None),
  Record(id='ff774e5c-f158-4d12-94e2-0a0162b22f27', payload={'page_content': 'I have a bad feeling I am going to get deleted :(', 'metadata': {'source': 'tweet'}}, vector=None, shard_key=None, order_value=None)],
 None)

results = vector_store.similarity_search_with_score(
    query="Will it be hot tomorrow", k=1
)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

* [SIM=0.531834] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news', '_id': '9e6ba50c-794f-4b88-94e5-411f15052a02', '_collection_name': 'demo_collection'}]

For a full list of all the search functions available for a QdrantVectorStore, read the API reference

Metadata filtering

Qdrant has an extensive filtering system with rich type support. It is also possible to use the filters in Langchain, by passing an additional param to both the similarity_search_with_score and similarity_search methods.

from qdrant_client.http import models

results = vector_store.similarity_search(
    query="Who are the best soccer players in the world?",
    k=1,
    filter=models.Filter(
        should=[
            models.FieldCondition(
                key="page_content",
                match=models.MatchValue(
                    value="The top 10 soccer players in the world right now."
                ),
            ),
        ]
    ),
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

* The top 10 soccer players in the world right now. [{'source': 'website', '_id': 'b0964ab5-5a14-47b4-a983-37fa5c5bd154', '_collection_name': 'demo_collection'}]

Query by turning into retriever

You can also transform the vector store into a retriever for easier usage in your chains.

retriever = vector_store.as_retriever(search_type="mmr", search_kwargs={"k": 1})
retriever.invoke("Stealing from the bank is a crime")

[Document(metadata={'source': 'news', '_id': '50d8d6ee-69bf-4173-a6a2-b254e9928965', '_collection_name': 'demo_collection'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]

Chain usage

The code below shows how to use the vector store as a retriever in a simple RAG chain:

OpenAI

pip install -qU langchain-openai

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

Anthropic

pip install -qU langchain-anthropic

import getpass
import os

os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")

Azure

pip install -qU langchain-openai

import getpass
import os

os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import AzureChatOpenAI

llm = AzureChatOpenAI(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

Google

pip install -qU langchain-google-vertexai

import getpass
import os

os.environ["GOOGLE_API_KEY"] = getpass.getpass()

from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-1.5-flash")

Cohere

pip install -qU langchain-cohere

import getpass
import os

os.environ["COHERE_API_KEY"] = getpass.getpass()

from langchain_cohere import ChatCohere

llm = ChatCohere(model="command-r-plus")

NVIDIA

pip install -qU langchain-nvidia-ai-endpoints

import getpass
import os

os.environ["NVIDIA_API_KEY"] = getpass.getpass()

from langchain import ChatNVIDIA

llm = ChatNVIDIA(model="meta/llama3-70b-instruct")

FireworksAI

pip install -qU langchain-fireworks

import getpass
import os

os.environ["FIREWORKS_API_KEY"] = getpass.getpass()

from langchain_fireworks import ChatFireworks

llm = ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct")

Groq

pip install -qU langchain-groq

import getpass
import os

os.environ["GROQ_API_KEY"] = getpass.getpass()

from langchain_groq import ChatGroq

llm = ChatGroq(model="llama3-8b-8192")

MistralAI

pip install -qU langchain-mistralai

import getpass
import os

os.environ["MISTRAL_API_KEY"] = getpass.getpass()

from langchain_mistralai import ChatMistralAI

llm = ChatMistralAI(model="mistral-large-latest")

TogetherAI

pip install -qU langchain-openai

import getpass
import os

os.environ["TOGETHER_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.together.xyz/v1",
    api_key=os.environ["TOGETHER_API_KEY"],
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)

from langchain import hub
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

prompt = hub.pull("rlm/rag-prompt")


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

rag_chain.invoke("What is LangGraph used for?")

API Reference:StrOutputParser | RunnablePassthrough

'LangGraph is used for building stateful, agentic applications. It provides a framework that facilitates the development of such applications.'

Customizing Qdrant

There are options to use an existing Qdrant collection within your Langchain application. In such cases, you may need to define how to map Qdrant point into the Langchain Document.

Named vectors

Qdrant supports multiple vectors per point by named vectors. If you work with a collection created externally or want to have the differently named vector used, you can configure it by providing its name.

from langchain_qdrant import RetrievalMode, SparseEmbeddings

QdrantVectorStore.from_documents(
    docs,
    embedding=embeddings,
    sparse_embedding=SparseEmbeddings(),
    location=":memory:",
    collection_name="my_documents_2",
    retrieval_mode=RetrievalMode.HYBRID,
    vector_name="custom_vector",
    sparse_vector_name="custom_sparse_vector",
)

Metadata

Qdrant stores your vector embeddings along with the optional JSON-like payload. Payloads are optional, but since LangChain assumes the embeddings are generated from the documents, we keep the context data, so you can extract the original texts as well.

By default, your document is going to be stored in the following payload structure:

{
    "page_content": "Lorem ipsum dolor sit amet",
    "metadata": {
        "foo": "bar"
    }
}

You can, however, decide to use different keys for the page content and metadata. That's useful if you already have a collection that you'd like to reuse.

QdrantVectorStore.from_documents(
    docs,
    embeddings,
    location=":memory:",
    collection_name="my_documents_2",
    content_payload_key="my_page_content_key",
    metadata_payload_key="my_meta",
)

API reference

For detailed documentation of all QdrantVectorStore features and configurations head to the API reference: https://api.python.langchain.com/en/latest/vectorstores/langchain_qdrant.vectorstores.Qdrant.html

Related

• Vector store conceptual guide
• Vector store how-to guides