如果你在 Colab 中打开这个 Notebook，可能需要先安装 LlamaIndex 🦙。

%pip install llama-index-embeddings-huggingface
%pip install llama-index-vector-stores-awadb

!pip install llama-index

创建 Awadb 索引

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

加载文档，构建 VectorStoreIndex

from llama_index.core import (
    SimpleDirectoryReader,
    VectorStoreIndex,
    StorageContext,
)
from IPython.display import Markdown, display
import openai

openai.api_key = ""

INFO:numexpr.utils:Note: NumExpr detected 12 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
Note: NumExpr detected 12 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
INFO:numexpr.utils:NumExpr defaulting to 8 threads.
NumExpr defaulting to 8 threads.

下载数据

!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'

加载数据

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

from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.vector_stores.awadb import AwaDBVectorStore

embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")

vector_store = AwaDBVectorStore()
storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, embed_model=embed_model
)

查询索引

# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")

display(Markdown(f"<b>{response}</b>"))

成长过程中，作者写短篇小说，在 IBM 1401 上尝试编程，缠着父亲买一台 TRS-80 电脑，编写简单游戏、一个用于预测模型火箭飞行高度的程序以及一个文字处理器。他还在大学学习哲学，后来转向 AI，并致力于构建 Web 基础设施。他写文章并在网上发表，每周四晚上为朋友们举办晚餐聚会，画画，并在剑桥买了一栋楼。

# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query(
    "What did the author do after his time at Y Combinator?"
)

display(Markdown(f"<b>{response}</b>"))

在 Y Combinator 工作之后，作者写文章、研究 Lisp 并画画。他还去俄勒冈州看望了母亲，并帮助她离开了疗养院。