AI 从金鱼脑到过目不忘杀手级开源神器 https://github.com/run-llama/llama_index… 这就是 LlamaIndex，Python 生态最成熟的 RAG 框架，49k+ stars 爆款开源项目！ AI 助手最大的痛点就是记忆：今天问的事明天忘，上周聊的策略这周得重新说一遍。你需要一个真正有记忆的 AI，而不是金鱼。LlamaIndex 一句话搞定：把你的文档、对话、笔记、PDF、代码……全部向量化存进数据库，用语义搜索精准召回，再也不靠死板关键词匹配！ // 核心能力直接拉满： • 向量化存储，支持 PDF、Word、Markdown、Notion、网页等几乎所有格式 • 语义检索，懂你在问“上次那个策略”到底是哪个 • 跨会话永久记忆，浏览器重启、电脑重装都不丢 • 支持 Chroma、Qdrant、Weaviate、Pinecone 等几十种向量数据库，本地跑也丝滑 实际用起来就是降维打击：扔进去几百页文档，索引完后随便问问题，三秒内从海量历史里精准挖出答案，上下文连贯到让你怀疑它是不是偷看了你所有聊天记录。 完全开源、Python 原生、社区生态最完善，开发者、AI Agent 玩家、知识工作者、文档狂魔的终极记忆外挂！再也不用手动翻历史、复制粘贴、重复解释了。 从金鱼脑到过目不忘，就差这一个框架。 用了就回不去了

run-llama/llama_index

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

🗂️ LlamaIndex 🦙

LlamaIndex OSS (by LlamaIndex) is an open-source framework to build agentic applications. Parse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. You can use LlamaParse with this framework or on its own; see LlamaParse below for signup and product links.

📚 Documentation:

• LlamaParse
• LlamaIndex OSS
• LlamaAgents

Building with LlamaIndex typically involves working with LlamaIndex core and a chosen set of integrations (or plugins). There are two ways to start building with LlamaIndex in Python:

1. Starter: llama-index. A starter Python package that includes core LlamaIndex as well as a selection of integrations.

2. Customized: llama-index-core. Install core LlamaIndex and add your chosen LlamaIndex integration packages on LlamaHub that are required for your application. There are over 300 LlamaIndex integration packages that work seamlessly with core, allowing you to build with your preferred LLM, embedding, and vector store providers.

The LlamaIndex Python library is namespaced such that import statements which include core imply that the core package is being used. In contrast, those statements without core imply that an integration package is being used.

# typical pattern
from llama_index.core.xxx import ClassABC  # core submodule xxx
from llama_index.xxx.yyy import (
    SubclassABC,
)  # integration yyy for submodule xxx

# concrete example
from llama_index.core.llms import LLM
from llama_index.llms.openai import OpenAI

LlamaParse (document agent platform)

LlamaParse is its own platform—focused on document agents and agentic OCR. It includes Parse (parsing), LlamaAgents (deployed document agents), Extract (structured extraction), and Index (ingest and RAG). You can use it with the LlamaIndex framework or standalone.

• Sign up for LlamaParse — Create an account and get your API key.
• Parse — Agentic OCR and document parsing (130+ formats). Docs
• Extract — Structured data extraction from documents. Docs
• Index — Ingest, index, and RAG pipelines. Docs
• Split — Split large documents into subcategories. Docs
• Agents — Build end-to-end document agents with Workflows and Agent Builder. Docs

Important Links

Documentation

X (formerly Twitter)

LinkedIn

Reddit

Discord

🚀 Overview

NOTE: This README is not updated as frequently as the documentation. Please check out the documentation above for the latest updates!

Context

• LLMs are a phenomenal piece of technology for knowledge generation and reasoning. They are pre-trained on large amounts of publicly available data.
• How do we best augment LLMs with our own private data?

We need a comprehensive toolkit to help perform this data augmentation for LLMs.

Proposed Solution

That’s where LlamaIndex comes in. LlamaIndex is a “data framework” to help you build LLM apps. It provides the following tools:

• Offers data connectors to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.).
• Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.
• Provides an advanced retrieval/query interface over your data: Feed in any LLM input prompt, get back retrieved context and knowledge-augmented output.
• Allows easy integrations with your outer application framework (e.g. with LangChain, Flask, Docker, ChatGPT, or anything else).

LlamaIndex provides tools for both beginner users and advanced users. Our high-level API allows beginner users to use LlamaIndex to ingest and query their data in 5 lines of code. Our lower-level APIs allow advanced users to customize and extend any module (data connectors, indices, retrievers, query engines, reranking modules), to fit their needs.

💡 Contributing

Interested in contributing? Contributions to LlamaIndex core as well as contributing integrations that build on the core are both accepted and highly encouraged! See our Contribution Guide for more details.

New integrations should meaningfully integrate with existing LlamaIndex framework components. At the discretion of LlamaIndex maintainers, some integrations may be declined.

📄 Documentation

Full documentation can be found here

Please check it out for the most up-to-date tutorials, how-to guides, references, and other resources!

💻 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-ollama
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. LLMs hosted through Ollama:

from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.ollama import Ollama
from transformers import AutoTokenizer

# set the LLM
Settings.llm = Ollama(
    model="llama-3.1:latest",
    request_timeout=360.0,
)

# set tokenizer to match LLM
Settings.tokenizer = AutoTokenizer.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct"
)

# 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)

A note on Verification of Build Assets

By default, llama-index-core includes a _static folder that contains the nltk and tiktoken cache that is included with the package installation. This ensures that you can easily run llama-index in environments with restrictive disk access permissions at runtime.

To verify that these files are safe and valid, we use the github attest-build-provenance action. This action will verify that the files in the _static folder are the same as the files in the llama-index-core/llama_index/core/_static folder.

To verify this, you can run the following script (pointing to your installed package):

#!/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

📖 Citation

Reference to cite if you use LlamaIndex in a paper:

@software{Liu_LlamaIndex_2022,
author = {Liu, Jerry},
doi = {10.5281/zenodo.1234},
month = {11},
title = {{LlamaIndex}},
url = {https://github.com/jerryjliu/llama_index},
year = {2022}
}