Skip to main content

llama-index embeddings siliconflow integration

Project description

LlamaIndex Embeddings Integration: SiliconFlow

1. Product Introduction

SiliconCloud provides cost-effective GenAI services based on an excellent open-source foundation model. introduction: https://docs.siliconflow.cn/introduction

2. Product features

  • As a one-stop cloud service platform that integrates top large models, SiliconCloud is committed to providing developers with faster, cheaper, more comprehensive, and smoother model APIs.

    • SiliconCloud has been listed on Qwen2.5-72B, DeepSeek-V2.5, Qwen2, InternLM2.5-20B-Chat, BCE, BGE, SenseVoice-Small, Llama-3.1, FLUX.1, DeepSeek-Coder-V2, SD3 Medium, GLM-4-9B-Chat, A variety of open-source large language models, image generation models, code generation models, vector and reordering models, and multimodal large models, including InstantID.

    • Among them, Qwen 2.5 (7B), Llama 3.1 (8B) and other large model APIs are free to use, so that developers and product managers do not need to worry about the computing power costs caused by the R&D stage and large-scale promotion, and realize "token freedom".

  • Provide out-of-the-box large model inference acceleration services to bring a more efficient user experience to your GenAI applications.

3. Installation

pip install llama-index-embeddings-siliconflow

4. Usage

import asyncio
import os
from llama_index.embeddings.siliconflow import SiliconFlowEmbedding

embedding = SiliconFlowEmbedding(
    model="BAAI/bge-m3",
    api_key=os.getenv("SILICONFLOW_API_KEY"),
)

response = embedding.get_query_embedding("...")
print(response)

response = asyncio.run(embedding.aget_query_embedding("..."))
print(response)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

llama_index_embeddings_siliconflow-0.1.1.tar.gz (3.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

File details

Details for the file llama_index_embeddings_siliconflow-0.1.1.tar.gz.

File metadata

File hashes

Hashes for llama_index_embeddings_siliconflow-0.1.1.tar.gz
Algorithm Hash digest
SHA256 f07c4bb2deae5bfc5de7ae6b5ed131d71f9f19fc4066bb43578292a0e6ecccaa
MD5 7975f7eb80cb1f57113604fb3289d14e
BLAKE2b-256 398f913236b94178de4a56518e6cc05a3b3d979388d27e32a24ce485c59e3db5

See more details on using hashes here.

File details

Details for the file llama_index_embeddings_siliconflow-0.1.1-py3-none-any.whl.

File metadata

File hashes

Hashes for llama_index_embeddings_siliconflow-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 b36a319d06967e81c93f549037b2bc8350d00a88b64f56e8e5d4acec29cc9f06
MD5 631885b181639e015fab3c60802c0eb3
BLAKE2b-256 a19d515a0e8738f60ff14242b48a3b89bd85c195d67d3557e759595359570700

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page