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)]