run-llama/llama_index 源码下载_部署教程

项目概述

LlamaIndex is the leading framework for building LLM-powered agents over your data.

项目地址

https://github.com/run-llama/llama_index

项目页面预览

run-llama/llama_index preview

关键指标

Stars：46318
主要语言：Python
License：MIT License
最近更新：2026-01-13T20:02:22Z
默认分支：main

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源码压缩包下载：点击下载（本站镜像）
SHA256：70a3eb34954e1ab8f727f8ca72b7feed9fb4947fb83c36a98bf0be843922327e

安装部署要点（README 精选）

💻 Example Usage

# custom selection of integrations to work with core
pip install llama-index-core
pip install llama-index-llms-openai
pip install llama-index-llms-replicate
pip install llama-index-embeddings-huggingface

Examples are in the docs/examples folder. Indices are in the indices folder (see list of indices below).

To build a simple vector store index using OpenAI:

import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data()
index = VectorStoreIndex.from_documents(documents)

To build a simple vector store index using non-OpenAI LLMs, e.g. Llama 2 hosted on Replicate, where you can easily create a free trial API token:

import os

os.environ["REPLICATE_API_TOKEN"] = "YOUR_REPLICATE_API_TOKEN"

from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.replicate import Replicate
from transformers import AutoTokenizer

# set the LLM
llama2_7b_chat = "meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e"
Settings.llm = Replicate(
    model=llama2_7b_chat,
    temperature=0.01,
    additional_kwargs={"top_p": 1, "max_new_tokens": 300},
)

# set tokenizer to match LLM
Settings.tokenizer = AutoTokenizer.from_pretrained(
    "NousResearch/Llama-2-7b-chat-hf"
)

# set the embed model
Settings.embed_model = HuggingFaceEmbedding(
    model_name="BAAI/bge-small-en-v1.5"
)

documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data()
index = VectorStoreIndex.from_documents(
    documents,
)

To query:

query_engine = index.as_query_engine()
query_engine.query("YOUR_QUESTION")

By default, data is stored in-memory.
To persist to disk (under ./storage):

index.storage_context.persist()

To reload from disk:

from llama_index.core import StorageContext, load_index_from_storage

# rebuild storage context
storage_context = StorageContext.from_defaults(persist_dir="./storage")
# load index
index = load_index_from_storage(storage_context)

常用命令（从 README 提取）

# custom selection of integrations to work with core
pip install llama-index-core
pip install llama-index-llms-openai
pip install llama-index-llms-replicate
pip install llama-index-embeddings-huggingface

cd <desired-package-folder>
pip install poetry
poetry install --with dev

#!/bin/bash
STATIC_DIR="venv/lib/python3.13/site-packages/llama_index/core/_static"
REPO="run-llama/llama_index"

find "$STATIC_DIR" -type f | while read -r file; do
    echo "Verifying: $file"
    gh attestation verify "$file" -R "$REPO" || echo "Failed to verify: $file"
done