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-0.3.1.tar.gz (5.7 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-0.3.1.tar.gz.

File metadata

File hashes

Hashes for llama_index_embeddings_siliconflow-0.3.1.tar.gz
Algorithm Hash digest
SHA256 0fdf9c5bdd044d93e7769be0e6d2bf673b82e8bd4f1dd3fa6158a18bed185c0c
MD5 4207de0f7f2fafea5d974fb6cc653327
BLAKE2b-256 eeda38117eb800c3b9a2d5a79af7f2f2408a64dedb4b140f8f83a1c288705690

See more details on using hashes here.

File details

Details for the file llama_index_embeddings_siliconflow-0.3.1-py3-none-any.whl.

File metadata

File hashes

Hashes for llama_index_embeddings_siliconflow-0.3.1-py3-none-any.whl
Algorithm Hash digest
SHA256 030925eda755b1fa86335fa8e352f7f7b67ec37a7edf1739fdf001413ee7718b
MD5 2c5c4a05974a5ec7ad130bac5d49ce52
BLAKE2b-256 6c35688bbcc446f1e04689b30dff0b5d5f1218602c070ab19130b99ed0f6d31b

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