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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 points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.

This notebook shows how to use functionality related to the Qdrant vector database.

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
  • On-premise server deployment
  • Qdrant Cloud

See the installation instructions.

%pip install --upgrade --quiet  langchain-qdrant langchain-openai langchain langchain-community

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
OpenAI API Key: ········
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_qdrant import Qdrant
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../how_to/state_of_the_union.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 if you plan to store just a small amount of vectors. The embeddings might be fully kepy 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 = 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.

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, 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 = Qdrant.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 = Qdrant.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 = Qdrant.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 = Qdrant.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 with the embedding_function and used to find similar documents in Qdrant collection.

query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.similarity_search(query)
print(found_docs[0].page_content)
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. 

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

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}")
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. 

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

Score: 0.8153784913324512

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 as rest

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

Maximum marginal relevance search (MMR)​

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

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")
1. Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. 

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. 

2. We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. 

I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. 

They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. 

Officer Mora was 27 years old. 

Officer Rivera was 22. 

Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. 

I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. 

I’ve worked on these issues a long time. 

I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.

Qdrant as a Retriever​

Qdrant, as all the other vector stores, is a LangChain Retriever, by using cosine similarity. 

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]
Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})

Customizing Qdrant​

There are some 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. Langchain requires just a single embedding per document and, by default, uses a single vector. However, if you work with a collection created externally or want to have the named vector used, you can configure it by providing its name.

Qdrant.from_documents(
    docs,
    embeddings,
    location=":memory:",
    collection_name="my_documents_2",
    vector_name="custom_vector",
)

As a Langchain user, you won't see any difference whether you use named vectors or not. Qdrant integration will handle the conversion under the hood.

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.

Qdrant.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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