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George
5388a2cb76
* new: replace .add and .query with local inference in fastembed semantic search * do not hardcode model dim, format example to avoid scrollbars * avoid scroll bars * joint installation of qdrant client and fastembed
107 lines
2.8 KiB
Markdown
107 lines
2.8 KiB
Markdown
---
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title: "FastEmbed & Qdrant"
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weight: 3
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---
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# Using FastEmbed with Qdrant for Vector Search
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## Install Qdrant Client and FastEmbed
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```python
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pip install "qdrant-client[fastembed]>=1.14.2"
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```
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## Initialize the client
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Qdrant Client has a simple in-memory mode that lets you try semantic search locally.
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```python
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from qdrant_client import QdrantClient, models
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client = QdrantClient(":memory:") # Qdrant is running from RAM.
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```
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## Add data
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Now you can add two sample documents, their associated metadata, and a point `id` for each.
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```python
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docs = [
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"Qdrant has a LangChain integration for chatbots.",
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"Qdrant has a LlamaIndex integration for agents.",
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]
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metadata = [
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{"source": "langchain-docs"},
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{"source": "llamaindex-docs"},
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]
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ids = [42, 2]
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```
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## Create a collection
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Qdrant stores vectors and associated metadata in collections.
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Collection requires vector parameters to be set during creation.
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In this tutorial, we'll be using `BAAI/bge-small-en` to compute embeddings.
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```python
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model_name = "BAAI/bge-small-en"
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client.create_collection(
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collection_name="test_collection",
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vectors_config=models.VectorParams(
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size=client.get_embedding_size(model_name),
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distance=models.Distance.COSINE
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), # size and distance are model dependent
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)
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```
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## Upsert documents to the collection
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Qdrant client can do inference implicitly within its methods via FastEmbed integration.
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It requires wrapping your data in models, like `models.Document` (or `models.Image` if you're working with images)
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```python
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metadata_with_docs = [
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{"document": doc, "source": meta["source"]} for doc, meta in zip(docs, metadata)
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]
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client.upload_collection(
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collection_name="test_collection",
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vectors=[models.Document(text=doc, model=model_name) for doc in docs],
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payload=metadata_with_docs,
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ids=ids,
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)
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```
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## Run vector search
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Here, you will ask a dummy question that will allow you to retrieve a semantically relevant result.
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```python
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search_result = client.query_points(
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collection_name="test_collection",
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query=models.Document(
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text="Which integration is best for agents?",
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model=model_name
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)
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).points
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print(search_result)
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```
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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.
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```python
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[
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ScoredPoint(
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id=2,
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score=0.87491801319731,
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payload={
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"document": "Qdrant has a LlamaIndex integration for agents.",
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"source": "llamaindex-docs",
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},
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...
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),
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ScoredPoint(
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id=42,
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score=0.8351846627714035,
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payload={
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"document": "Qdrant has a LangChain integration for chatbots.",
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"source": "langchain-docs",
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},
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...
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),
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]
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``` |