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76 lines
2.2 KiB
Markdown
76 lines
2.2 KiB
Markdown
# ⚡️ What is FastEmbed?
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FastEmbed is a lightweight, fast, Python library built for embedding generation. We [support popular text models](https://qdrant.github.io/fastembed/examples/Supported_Models/). Please [open a Github issue](https://github.com/qdrant/fastembed/issues/new) if you want us to add a new model.
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1. Light & Fast
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- Quantized model weights
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- ONNX Runtime for inference
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2. Accuracy/Recall
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- Better than OpenAI Ada-002
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- Default is Flag Embedding, which has shown good results on the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard
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- List of [supported models](https://qdrant.github.io/fastembed/examples/Supported_Models/) - including multilingual models
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Here is an example for [Retrieval Embedding Generation](https://qdrant.github.io/fastembed/examples/Retrieval%20with%20FastEmbed/) and how to use [FastEmbed with Qdrant](https://qdrant.github.io/fastembed/examples/Usage_With_Qdrant/).
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## 🚀 Installation
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To install the FastEmbed library, pip works:
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```bash
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pip install fastembed
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```
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## 📖 Usage
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```python
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from fastembed import TextEmbedding
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documents: list[str] = [
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"passage: Hello, World!",
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"query: Hello, World!",
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"passage: This is an example passage.",
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"fastembed is supported by and maintained by Qdrant."
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]
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embedding_model = TextEmbedding()
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embeddings: list[np.ndarray] = embedding_model.embed(documents)
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```
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## Usage with Qdrant
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Installation with Qdrant Client in Python:
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```bash
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pip install qdrant-client[fastembed]
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```
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Might have to use ```pip install 'qdrant-client[fastembed]'``` on zsh.
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```python
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from qdrant_client import QdrantClient
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# Initialize the client
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client = QdrantClient(":memory:") # Using an in-process Qdrant
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# Prepare your documents, metadata, and IDs
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docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"]
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metadata = [
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{"source": "Langchain-docs"},
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{"source": "Llama-index-docs"},
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]
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ids = [42, 2]
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client.add(
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collection_name="demo_collection",
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documents=docs,
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metadata=metadata,
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ids=ids
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)
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search_result = client.query(
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collection_name="demo_collection",
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query_text="This is a query document"
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)
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print(search_result)
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```
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