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Documentation Index

Fetch the complete documentation index at: https://docs.praison.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

vLLM provides high-throughput embedding inference for self-hosted deployments.

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Quick Start

from praisonaiagents import embedding

result = embedding(
    input="Hello world",
    model="hosted_vllm/intfloat/e5-mistral-7b-instruct",
    api_base="http://localhost:8000"
)
print(f"Dimensions: {len(result.embeddings[0])}")

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CLI Usage

praisonai embed "Hello world" --model hosted_vllm/intfloat/e5-mistral-7b-instruct

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Setup

  1. Start vLLM server with embedding model:
python -m vllm.entrypoints.openai.api_server \
    --model intfloat/e5-mistral-7b-instruct \
    --task embed
  1. Set environment variable:
export HOSTED_VLLM_API_BASE="http://localhost:8000"