AwaDB 向量存储¶

如果您正在 Colab 上打开此 Notebook，您可能需要安装 LlamaIndex 🦙。

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

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

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!pip install llama-index

!pip install llama-index

创建 AwaDB 索引¶

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import logging
import sys

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

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¶

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

openai.api_key = ""

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¶

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

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

加载数据¶

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

# 加载文档 documents = SimpleDirectoryReader("./data/paul_graham/").load_data()

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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
)

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¶

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# 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?")

# 将日志记录设置为 DEBUG 以获取更详细的输出 query_engine = index.as_query_engine() response = query_engine.query("What did the author do growing up?")

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

display(Markdown(f"{response}"))

作者在成长过程中写短篇故事，在 IBM 1401 上尝试编程，缠着父亲买了一台 TRS-80 电脑，写了简单的游戏、一个预测模型火箭飞行高度的程序以及一个文字处理器。他还在大学里学习哲学，后来转学人工智能，并致力于构建网络的底层基础设施。他写文章并在网上发表，每周四晚上为一群朋友举办晚宴，画画，并在剑桥买了一栋楼。

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# 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?"
)

# 将日志记录设置为 DEBUG 以获取更详细的输出 query_engine = index.as_query_engine() response = query_engine.query( "What did the author do after his time at Y Combinator?" )

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

display(Markdown(f"{response}"))

在 Y Combinator 之后，作者写文章，研究 Lisp，并画画。他还去俄勒冈探望了母亲，并帮助她离开了疗养院。