LlamaIndex 基础 RAG

使用 LlamaIndex 实现基础 RAG

🌐 查看英文原文 | 源码 Notebook

RAG Pipeline with LlamaIndex

In this notebook we will look into building Basic RAG Pipeline with LlamaIndex. The pipeline has following steps.

Setup LLM and Embedding Model.
Download Data.
Load Data.
Index Data.
Create Query Engine.
Querying.

Installation

!pip install llama-index
!pip install llama-index-llms-anthropic
!pip install llama-index-embeddings-huggingface

Setup API Keys

import os

os.environ["ANTHROPIC_API_KEY"] = "YOUR Claude API KEY"

Setup LLM and Embedding model

We will use anthropic latest released Claude 3 Opus models

from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.anthropic import Anthropic

llm = Anthropic(temperature=0.0, model="claude-opus-4-1")
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")

config.json:   0%|          | 0.00/777 [00:00<?, ?B/s]

model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]

tokenizer_config.json:   0%|          | 0.00/366 [00:00<?, ?B/s]

vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]

tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]

special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]

from llama_index.core import Settings

Settings.llm = llm
Settings.embed_model = embed_model
Settings.chunk_size = 512

Download Data

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

--2024-03-08 06:51:30--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 75042 (73K) [text/plain]
Saving to: ‘data/paul_graham/paul_graham_essay.txt’

data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.002s  

2024-03-08 06:51:30 (34.6 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]

from llama_index.core import (
    SimpleDirectoryReader,
    VectorStoreIndex,
)

Load Data

documents = SimpleDirectoryReader("./data/paul_graham").load_data()

Index Data

index = VectorStoreIndex.from_documents(
    documents,
)

Create Query Engine

query_engine = index.as_query_engine(similarity_top_k=3)

Test Query

response = query_engine.query("What did author do growing up?")

print(response)

Based on the information provided, the author worked on two main things outside of school before college: writing and programming.

For writing, he wrote short stories as a beginning writer, though he felt they were awful, with hardly any plot and just characters with strong feelings.

In terms of programming, in 9th grade he tried writing his first programs on an IBM 1401 computer that his school district used. He and his friend got permission to use it, programming in an early version of Fortran using punch cards. However, he had difficulty figuring out what to actually do with the computer at that stage given the limited inputs available.

Tip

中文读者提示：本章节的代码和输出为英文原文。如需理解具体实现细节，可参考上方中文导读和代码注释。如有疑问，欢迎在 GitHub Issues 讨论。