Requirements: Last time I wrote an article about how to choose an embedding model, how to deploy it after selecting the embedding model?
Infinity
Infinity is a high-throughput, low-latency REST API for providing text embeddings, reordering models, clips, claps, and colpali. MIT Open Source Protocol.
Source:The hyperlink login is visible.
Documentation:The hyperlink login is visible.
Parameter:The hyperlink login is visible.
Why Infinity
- Deploy any model from HuggingFace: Deploy any embedding, reranking, clipping, and sentence converter model from HuggingFace
- Fast Inference Backend: Inference servers are built on PyTorch, optimum (ONNX/TensorRT), and CTranslate2 to take full advantage of your NVIDIA CUDA, AMD ROCM, CPU, AWS INF2, or APPLE MPS accelerators with FlashAttention. Infinity uses dynamic batching and tokenization dedicated to worker threads.
- Multi-mode and multi-model: Mix and match multiple models. Infinity is responsible for coordinating them.
- Tested implementations: unit and end-to-end testing. Correctly embedded through infinite embeddings. Let API users create infinite embeddings and beyond.
- Easy to use: Built on FastAPI. Infinity CLI v2 allows all parameters to be started via environment variables or parameters. OpenAPI aligns with OpenAI's API specifications.
Docker deploys Infinity
This article uses Docker to deploy the Infinity CPU version, system environment: Rocky Linux 9, CPU: N150, memory: 16G, Docker version: 27.4.1, and the embedding model:BAAI/bge-m3
Before deploying, install git and git-lfs with the following command:
Download the BAAI/bge-m3 model (For network reasons, it is recommended to download the model first), the command is as follows:
Deploy Infinity using Docker with the following command:
When the startup is complete, open it using a browserhttp://ip:8081/docsto see all the interfaces, as shown in the figure below:
See all models:
Call the embedding model to generate vectors, as shown in the following figure:
curl -X 'POST' \
'http://192.168.50.112:8081/embeddings' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "BAAI/bge-m3",
"input": [
"Screen name: Xiao Zhi Zha , main skills: .NET development, introduction: Architect forum webmaster"
]
}'
Response:
{
"object": "list",
"data": [
{
"object": "embedding",
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],
"model": "BAAI/bge-m3",
"usage": {
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"id": "infinity-e4e5bf9a-35d6-41cf-9f96-a0a9a1b45cde",
"created": 1742349740
}
(End) |
Posted on 2025-3-19 10:05:25
|
|
|
|
Landlord|
Posted on 2025-3-21 09:24:36
|
