Skip to main content

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)

Metadata

Release files for llama-index-embeddings-siliconflow 0.5.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for llama-index-embeddings-siliconflow 0.5.0
File Size Uploaded Provenance Actions
llama_index_embeddings_siliconflow-0.5.0.tar.gz 5.7 kB Without provenance Details for llama_index_embeddings_siliconflow-0.5.0.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for llama-index-embeddings-siliconflow 0.5.0
File Interpreter ABI Platform Actions
llama_index_embeddings_siliconflow-0.5.0-py3-none-any.whl Python 3 none any Details for llama_index_embeddings_siliconflow-0.5.0-py3-none-any.whl

Total release size: 11.2 kB

Release files / llama_index_embeddings_siliconflow-0.5.0.tar.gz

Download URL llama_index_embeddings_siliconflow-0.5.0.tar.gz
Size 5.7 kB
Tags Source
SHA-256 checksum
How to use checksums
52807eed47a3285c4c784c50f6387647f593a006d04ad50c61261f68d1503832
BLAKE2b-256 checksum
How to use checksums
838470a5066e0ad7755d3cfce317a9283b31963bbb9cc1dd7506fb7db82bfd73
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via uv/0.12.7 {"installer":{"name":"uv","version":"0.12.7","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

Release files / llama_index_embeddings_siliconflow-0.5.0-py3-none-any.whl

Download URL llama_index_embeddings_siliconflow-0.5.0-py3-none-any.whl
Size 5.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
b1eef43256fdc6c37bb6fcf3cffc81b6e3bc548a229cdb7263dc4f45384c480f
BLAKE2b-256 checksum
How to use checksums
aa14fa2559495be63c6bb67c9a02f8ec2017bcacca2f70f9398de8ec545f3e02
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via uv/0.12.7 {"installer":{"name":"uv","version":"0.12.7","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

Security for llama-index-embeddings-siliconflow 0.5.0

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page