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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.

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 langchain-qdrant langchain-openai langchain

We will use OpenAIEmbeddings for demonstration.

from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_qdrant import QdrantVectorStore
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("some-file.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

Connecting to Qdrant from LangChain​

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.

qdrant = QdrantVectorStore.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 persist between runs.

qdrant = QdrantVectorStore.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, 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 --->"
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",
)

Recreating the collection​

The collection is reused if it already exists. Setting force_recreate to True allows to remove the old collection and start from scratch.

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

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.

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

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.
from langchain_qdrant import RetrievalMode

qdrant = QdrantVectorStore.from_documents(
    docs,
    embedding=embeddings,
    location=":memory:",
    collection_name="my_documents",
    retrieval_mode=RetrievalMode.DENSE,
)

query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.similarity_search(query)

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.

To use it, install the FastEmbed package.

%pip install fastembed
from langchain_qdrant import FastEmbedSparse, RetrievalMode

sparse_embeddings = FastEmbedSparse(model_name="Qdrant/BM25")

qdrant = QdrantVectorStore.from_documents(
    docs,
    sparse_embedding=sparse_embeddings,
    location=":memory:",
    collection_name="my_documents",
    retrieval_mode=RetrievalMode.SPARSE,
)

query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.similarity_search(query)

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.

from langchain_qdrant import FastEmbedSparse, RetrievalMode

sparse_embeddings = FastEmbedSparse(model_name="Qdrant/BM25")

qdrant = QdrantVectorStore.from_documents(
    docs,
    embedding=embeddings,
    sparse_embedding=sparse_embeddings,
    location=":memory:",
    collection_name="my_documents",
    retrieval_mode=RetrievalMode.HYBRID,
)

query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.similarity_search(query)

Similarity search with score​

Sometimes we might want to perform the search, but also obtain a relevancy score to know how good is a particular result. The returned distance score is cosine distance. Therefore, a lower score is better.

query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.similarity_search_with_score(query)
document, score = found_docs[0]
print(document.page_content)
print(f"\nScore: {score}")

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

query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.similarity_search_with_score(query, filter=models.Filter(...))

Maximum marginal relevance search (MMR)​

If you'd like to look up some similar documents, but you'd also like to receive diverse results, MMR is the method you should consider. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Note that MMR search is only available if you've added documents with DENSE or HYBRID modes. Since it requires dense vectors.

query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.max_marginal_relevance_search(query, k=2, fetch_k=10)
for i, doc in enumerate(found_docs):
    print(f"{i + 1}.", doc.page_content, "\n")

Qdrant as a Retriever​

Qdrant, as all the other vector stores, is a LangChain Retriever. 

retriever = qdrant.as_retriever()

It might be also specified to use MMR as a search strategy, instead of similarity.

retriever = qdrant.as_retriever(search_type="mmr")
query = "What did the president say about Ketanji Brown Jackson"
retriever.invoke(query)[0]

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.

QdrantVectorStore.from_documents(
    docs,
    embedding=embeddings,
    sparse_embedding=sparse_embeddings,
    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",
)

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