Using FastEmbed with Qdrant for Vector Search
Install Qdrant Client and FastEmbed
pip install "qdrant-client[fastembed]>=1.14.2"
Initialize the client
Qdrant Client has a simple in-memory mode that lets you try semantic search locally.
from qdrant_client import QdrantClient, models
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]
Create a collection
Qdrant stores vectors and associated metadata in collections.
Collection requires vector parameters to be set during creation.
In this tutorial, we’ll be using BAAI/bge-small-en to compute embeddings.
model_name = "BAAI/bge-small-en"
client.create_collection(
collection_name="test_collection",
vectors_config=models.VectorParams(
size=client.get_embedding_size(model_name),
distance=models.Distance.COSINE
), # size and distance are model dependent
)
Upsert documents to the collection
Qdrant client can do inference implicitly within its methods via FastEmbed integration.
It requires wrapping your data in models, like models.Document (or models.Image if you’re working with images)
metadata_with_docs = [
{"document": doc, "source": meta["source"]} for doc, meta in zip(docs, metadata)
]
client.upload_collection(
collection_name="test_collection",
vectors=[models.Document(text=doc, model=model_name) for doc in docs],
payload=metadata_with_docs,
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_points(
collection_name="test_collection",
query=models.Document(
text="Which integration is best for agents?",
model=model_name
)
).points
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.
[
ScoredPoint(
id=2,
score=0.87491801319731,
payload={
"document": "Qdrant has a LlamaIndex integration for agents.",
"source": "llamaindex-docs",
},
...
),
ScoredPoint(
id=42,
score=0.8351846627714035,
payload={
"document": "Qdrant has a LangChain integration for chatbots.",
"source": "langchain-docs",
},
...
),
]