# ⚡️ What is FastEmbed?
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.
The default text embedding (`TextEmbedding`) model is Flag Embedding, presented 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/qdrant/Retrieval_with_FastEmbed/) and how to use [FastEmbed with Qdrant](https://qdrant.github.io/fastembed/qdrant/Usage_With_Qdrant/).
## 📈 Why FastEmbed?
1. Light: FastEmbed is a lightweight library with few external dependencies. We don't require a GPU and don't download GBs of PyTorch dependencies, and instead use the ONNX Runtime. This makes it a great candidate for serverless runtimes like AWS Lambda.
2. Fast: FastEmbed is designed for speed. We use the ONNX Runtime, which is faster than PyTorch. We also use data-parallelism for encoding large datasets.
3. Accurate: FastEmbed is better than OpenAI Ada-002. We also [supported](https://qdrant.github.io/fastembed/examples/Supported_Models/) an ever expanding set of models, including a few multilingual models.
## 🚀 Installation
To install the FastEmbed library, pip works best. You can install it with or without GPU support:
```bash
pip install fastembed
```
### ⚡️ With GPU
```bash
pip install fastembed-gpu
```
## 📖 Quickstart
```python
from fastembed import TextEmbedding
from typing import List
# Example list of documents
documents: List[str] = [
"This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc.",
"fastembed is supported by and maintained by Qdrant.",
]
# This will trigger the model download and initialization
embedding_model = TextEmbedding()
print("The model BAAI/bge-small-en-v1.5 is ready to use.")
embeddings_generator = embedding_model.embed(documents) # reminder this is a generator
embeddings_list = list(embedding_model.embed(documents))
# you can also convert the generator to a list, and that to a numpy array
len(embeddings_list[0]) # Vector of 384 dimensions
```
### ⚡️ FastEmbed on a GPU
FastEmbed supports running on GPU devices. It requires installation of the `fastembed-gpu` package.
Make sure not to have the `fastembed` package installed, as it might interfere with the `fastembed-gpu` package.
```bash
pip install fastembed-gpu
```
```python
from fastembed import TextEmbedding
embedding_model = TextEmbedding(model_name="BAAI/bge-small-en-v1.5", providers=["CUDAExecutionProvider"])
print("The model BAAI/bge-small-en-v1.5 is ready to use on a GPU.")
```
## Usage with Qdrant
Installation with Qdrant Client in Python:
```bash
pip install qdrant-client[fastembed]
```
or
```bash
pip install qdrant-client[fastembed-gpu]
```
You might have to use quotes ```pip install 'qdrant-client[fastembed]'``` on zsh.
```python
from qdrant_client import QdrantClient
# Initialize the client
client = QdrantClient("localhost", port=6333) # For production
# client = QdrantClient(":memory:") # For small experiments
# Prepare your documents, metadata, and IDs
docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"]
metadata = [
{"source": "Langchain-docs"},
{"source": "Llama-index-docs"},
]
ids = [42, 2]
# If you want to change the model:
# client.set_model("sentence-transformers/all-MiniLM-L6-v2")
# List of supported models: https://qdrant.github.io/fastembed/examples/Supported_Models
# Use the new add() instead of upsert()
# This internally calls embed() of the configured embedding model
client.add(
collection_name="demo_collection",
documents=docs,
metadata=metadata,
ids=ids
)
search_result = client.query(
collection_name="demo_collection",
query_text="This is a query document"
)
print(search_result)
```
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"description": "# \u26a1\ufe0f What is FastEmbed?\n\nFastEmbed 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.\n\nThe default text embedding (`TextEmbedding`) model is Flag Embedding, presented 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/qdrant/Retrieval_with_FastEmbed/) and how to use [FastEmbed with Qdrant](https://qdrant.github.io/fastembed/qdrant/Usage_With_Qdrant/).\n\n## \ud83d\udcc8 Why FastEmbed?\n\n1. Light: FastEmbed is a lightweight library with few external dependencies. We don't require a GPU and don't download GBs of PyTorch dependencies, and instead use the ONNX Runtime. This makes it a great candidate for serverless runtimes like AWS Lambda. \n\n2. Fast: FastEmbed is designed for speed. We use the ONNX Runtime, which is faster than PyTorch. We also use data-parallelism for encoding large datasets.\n\n3. Accurate: FastEmbed is better than OpenAI Ada-002. We also [supported](https://qdrant.github.io/fastembed/examples/Supported_Models/) an ever expanding set of models, including a few multilingual models.\n\n## \ud83d\ude80 Installation\n\nTo install the FastEmbed library, pip works best. You can install it with or without GPU support:\n\n```bash\npip install fastembed\n```\n\n### \u26a1\ufe0f With GPU\n\n```bash\npip install fastembed-gpu\n```\n\n## \ud83d\udcd6 Quickstart\n\n```python\nfrom fastembed import TextEmbedding\nfrom typing import List\n\n# Example list of documents\ndocuments: List[str] = [\n \"This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc.\",\n \"fastembed is supported by and maintained by Qdrant.\",\n]\n\n# This will trigger the model download and initialization\nembedding_model = TextEmbedding()\nprint(\"The model BAAI/bge-small-en-v1.5 is ready to use.\")\n\nembeddings_generator = embedding_model.embed(documents) # reminder this is a generator\nembeddings_list = list(embedding_model.embed(documents))\n # you can also convert the generator to a list, and that to a numpy array\nlen(embeddings_list[0]) # Vector of 384 dimensions\n```\n\n### \u26a1\ufe0f FastEmbed on a GPU\n\nFastEmbed supports running on GPU devices. It requires installation of the `fastembed-gpu` package.\nMake sure not to have the `fastembed` package installed, as it might interfere with the `fastembed-gpu` package.\n\n```bash\npip install fastembed-gpu\n``` \n\n```python\nfrom fastembed import TextEmbedding\n\nembedding_model = TextEmbedding(model_name=\"BAAI/bge-small-en-v1.5\", providers=[\"CUDAExecutionProvider\"])\nprint(\"The model BAAI/bge-small-en-v1.5 is ready to use on a GPU.\")\n\n```\n\n## Usage with Qdrant\n\nInstallation with Qdrant Client in Python:\n\n```bash\npip install qdrant-client[fastembed]\n```\n\nor \n\n```bash\npip install qdrant-client[fastembed-gpu]\n```\n\nYou might have to use quotes ```pip install 'qdrant-client[fastembed]'``` on zsh.\n\n```python\nfrom qdrant_client import QdrantClient\n\n# Initialize the client\nclient = QdrantClient(\"localhost\", port=6333) # For production\n# client = QdrantClient(\":memory:\") # For small experiments\n\n# Prepare your documents, metadata, and IDs\ndocs = [\"Qdrant has Langchain integrations\", \"Qdrant also has Llama Index integrations\"]\nmetadata = [\n {\"source\": \"Langchain-docs\"},\n {\"source\": \"Llama-index-docs\"},\n]\nids = [42, 2]\n\n# If you want to change the model:\n# client.set_model(\"sentence-transformers/all-MiniLM-L6-v2\")\n# List of supported models: https://qdrant.github.io/fastembed/examples/Supported_Models\n\n# Use the new add() instead of upsert()\n# This internally calls embed() of the configured embedding model\nclient.add(\n collection_name=\"demo_collection\",\n documents=docs,\n metadata=metadata,\n ids=ids\n)\n\nsearch_result = client.query(\n collection_name=\"demo_collection\",\n query_text=\"This is a query document\"\n)\nprint(search_result)\n```\n",
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