Using FastEmbed with Qdrant for Vector Search

Install Qdrant Client

pip install qdrant-client

Install FastEmbed

Installing FastEmbed will let you quickly turn data to vectors, so that Qdrant can search over them.

pip install fastembed

Initialize the client

Qdrant Client has a simple in-memory mode that lets you try semantic search locally.

from qdrant_client import QdrantClient

client = QdrantClient(":memory:")  # Qdrant is running from RAM.

Add data

Now you can add two sample documents, their associated metadata, and a point id for each.

docs = ["Qdrant has a LangChain integration for chatbots.", "Qdrant has a LlamaIndex integration for agents."]
metadata = [
    {"source": "langchain-docs"},
    {"source": "llamaindex-docs"},
]
ids = [42, 2]

Load data to a collection

Create a test collection and upsert your two documents to it.

client.add(
    collection_name="test_collection",
    documents=docs,
    metadata=metadata,
    ids=ids
)

Run vector search

Here, you will ask a dummy question that will allow you to retrieve a semantically relevant result.

search_result = client.query(
    collection_name="test_collection",
    query_text="Which integration is best for agents?"
)
print(search_result)

The semantic search engine will retrieve the most similar result in order of relevance. In this case, the second statement about LlamaIndex is more relevant.

[QueryResponse(id=2, embedding=None, sparse_embedding=None, 
metadata={'document': 'Qdrant has a LlamaIndex integration for agents',
'source': 'llamaindex-docs'}, document='Qdrant has a LlamaIndex integration for agents.', 
score=0.8749180370667156), 
QueryResponse(id=42, embedding=None, sparse_embedding=None, 
metadata={'document': 'Qdrant has a LangChain integration for chatbots.', 
'source': 'langchain-docs'}, document='Qdrant has a LangChain integration for chatbots.', 
score=0.8351846822959111)]

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