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* feat: Added nomic-embed-text-v1 support * chore: xenova/nomic-embed-text-v1 -> nomic-ai/nomic-embed-text-v1 --------- Co-authored-by: Nirant <NirantK@users.noreply.github.com>
91 lines
3.2 KiB
Markdown
91 lines
3.2 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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The default text embedding (`TextEmbedding`) model is Flag Embedding, the top model in the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard. It supports "query" and "passage" prefixes for the input text. Here is an example for [Retrieval Embedding Generation](https://qdrant.github.io/fastembed/examples/Retrieval_with_FastEmbed/) and how to use [FastEmbed with Qdrant](https://qdrant.github.io/fastembed/examples/Usage_With_Qdrant/).
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1. Light & Fast
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- Quantized model weights
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- ONNX Runtime, no PyTorch dependency
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- CPU-first design
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- Data-parallelism for encoding of large datasets
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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 is top of 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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## 🚀 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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## 📖 Quickstart
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```python
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from fastembed import TextEmbedding
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from typing import List
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import numpy as np
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documents: List[str] = [
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"passage: Hello, World!",
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"query: Hello, World!", # these are two different embedding
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"passage: This is an example passage.",
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"fastembed is supported by and maintained by Qdrant." # You can leave out the prefix but it's recommended
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]
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embedding_model = TextEmbedding(model_name="BAAI/bge-base-en")
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embeddings: List[np.ndarray] = list(embedding_model.embed(documents)) # Note the list() call - this is a generator
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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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You 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("localhost", port=6333) # For production
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# client = QdrantClient(":memory:") # For small experiments
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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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# If you want to change the model:
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# client.set_model("sentence-transformers/all-MiniLM-L6-v2")
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# List of supported models: https://qdrant.github.io/fastembed/examples/Supported_Models
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# Use the new add() instead of upsert()
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# This internally calls embed() of the configured embedding model
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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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#### Similar Work
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Ilyas M. wrote about using [FlagEmbeddings with Optimum](https://twitter.com/IlysMoutawwakil/status/1705215192425288017) over CUDA.
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