langchain/docs/modules/indexes/vectorstores/examples/qdrant.ipynb

Qdrant

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

In [1]:

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Qdrant
from langchain.document_loaders import TextLoader

In [2]:

loader = TextLoader('../../../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.

In [3]:

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.

In [4]:

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.

In [5]:

url = "<---qdrant url here --->"
qdrant = Qdrant.from_documents(
    docs, embeddings, 
    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.

In [6]:

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

Reusing the same collection

Both Qdrant.from_texts and Qdrant.from_documents methods are great to start using Qdrant with LangChain, but they are going to destroy the collection and create it from scratch! If you want to reuse the existing collection, you can always create an instance of Qdrant on your own and pass the QdrantClient instance with the connection details.

In [7]:

del qdrant

In [8]:

import qdrant_client

client = qdrant_client.QdrantClient(
    path="/tmp/local_qdrant", prefer_grpc=True
)
qdrant = Qdrant(
    client=client, collection_name="my_documents", 
    embedding_function=embeddings.embed_query
)

Similarity search

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.

In [9]:

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

In [10]:

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.

In [11]:

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

In [12]:

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

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.

In [13]:

query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.max_marginal_relevance_search(query, k=2, fetch_k=10)

In [14]:

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 preventionand 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.

In [15]:

retriever = qdrant.as_retriever()
retriever

Out [15]:

VectorStoreRetriever(vectorstore=<langchain.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='similarity', search_kwargs={})

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

In [16]:

retriever = qdrant.as_retriever(search_type="mmr")
retriever

Out [16]:

VectorStoreRetriever(vectorstore=<langchain.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='mmr', search_kwargs={})

In [17]:

query = "What did the president say about Ketanji Brown Jackson"
retriever.get_relevant_documents(query)[0]

Out [17]:

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

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. You can always change the

In [19]:

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

Out [19]:

<langchain.vectorstores.qdrant.Qdrant at 0x7fc4e2baa230>

In [ ]: