DocArray Hnsw 向量存储¶

DocArrayHnswVectorStore 是由 DocArray 提供的轻量级文档索引实现，完全在本地运行，最适合中小规模数据集。它将向量存储在 hnswlib 中，其他所有数据则存储在 SQLite 数据库里。

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

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%pip install llama-index-vector-stores-docarray

%pip install llama-index-vector-stores-docarray

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!pip install llama-index

!pip install llama-index

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import os
import sys
import logging
import textwrap

import warnings

warnings.filterwarnings("ignore")

# stop h|uggingface warnings
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# Uncomment to see debug logs
# logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from llama_index.core import (
    GPTVectorStoreIndex,
    SimpleDirectoryReader,
    Document,
)
from llama_index.vector_stores.docarray import DocArrayHnswVectorStore
from IPython.display import Markdown, display

import os import sys import logging import textwrap import warnings warnings.filterwarnings("ignore") # stop h|uggingface warnings os.environ["TOKENIZERS_PARALLELISM"] = "false" # Uncomment to see debug logs # logging.basicConfig(stream=sys.stdout, level=logging.INFO) # logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) from llama_index.core import ( GPTVectorStoreIndex, SimpleDirectoryReader, Document, ) from llama_index.vector_stores.docarray import DocArrayHnswVectorStore from IPython.display import Markdown, display

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import os

os.environ["OPENAI_API_KEY"] = "<your openai key>"

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

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'

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# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print(
    "Document ID:",
    documents[0].doc_id,
    "Document Hash:",
    documents[0].doc_hash,
)

# load documents documents = SimpleDirectoryReader("./data/paul_graham/").load_data() print( "Document ID:", documents[0].doc_id, "Document Hash:", documents[0].doc_hash, )

Document ID: 07d9ca27-ded0-46fa-9165-7e621216fd47 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e

初始化与索引¶

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from llama_index.core import StorageContext


vector_store = DocArrayHnswVectorStore(work_dir="hnsw_index")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = GPTVectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

from llama_index.core import StorageContext vector_store = DocArrayHnswVectorStore(work_dir="hnsw_index") storage_context = StorageContext.from_defaults(vector_store=vector_store) index = GPTVectorStoreIndex.from_documents( documents, storage_context=storage_context )

查询¶

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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?")
print(textwrap.fill(str(response), 100))

# 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?") print(textwrap.fill(str(response), 100))

Token indices sequence length is longer than the specified maximum sequence length for this model (1830 > 1024). Running this sequence through the model will result in indexing errors

 Growing up, the author wrote short stories, programmed on an IBM 1401, and nagged his father to buy
him a TRS-80 microcomputer. He wrote simple games, a program to predict how high his model rockets
would fly, and a word processor. He also studied philosophy in college, but switched to AI after
becoming bored with it. He then took art classes at Harvard and applied to art schools, eventually
attending RISD.

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response = query_engine.query("What was a hard moment for the author?")
print(textwrap.fill(str(response), 100))

response = query_engine.query("What was a hard moment for the author?") print(textwrap.fill(str(response), 100))

 A hard moment for the author was when he realized that the AI programs of the time were a hoax and
that there was an unbridgeable gap between what they could do and actually understanding natural
language.

使用过滤器进行查询¶

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from llama_index.core.schema import TextNode

nodes = [
    TextNode(
        text="The Shawshank Redemption",
        metadata={
            "author": "Stephen King",
            "theme": "Friendship",
        },
    ),
    TextNode(
        text="The Godfather",
        metadata={
            "director": "Francis Ford Coppola",
            "theme": "Mafia",
        },
    ),
    TextNode(
        text="Inception",
        metadata={
            "director": "Christopher Nolan",
        },
    ),
]

from llama_index.core.schema import TextNode nodes = [ TextNode( text="The Shawshank Redemption", metadata={ "author": "Stephen King", "theme": "Friendship", }, ), TextNode( text="The Godfather", metadata={ "director": "Francis Ford Coppola", "theme": "Mafia", }, ), TextNode( text="Inception", metadata={ "director": "Christopher Nolan", }, ), ]

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from llama_index.core import StorageContext


vector_store = DocArrayHnswVectorStore(work_dir="hnsw_filters")
storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = GPTVectorStoreIndex(nodes, storage_context=storage_context)

from llama_index.core import StorageContext vector_store = DocArrayHnswVectorStore(work_dir="hnsw_filters") storage_context = StorageContext.from_defaults(vector_store=vector_store) index = GPTVectorStoreIndex(nodes, storage_context=storage_context)

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from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters


filters = MetadataFilters(
    filters=[ExactMatchFilter(key="theme", value="Mafia")]
)

retriever = index.as_retriever(filters=filters)
retriever.retrieve("What is inception about?")

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters filters = MetadataFilters( filters=[ExactMatchFilter(key="theme", value="Mafia")] ) retriever = index.as_retriever(filters=filters) retriever.retrieve("What is inception about?")

Out[ ]:

[NodeWithScore(node=Node(text='director: Francis Ford Coppola\ntheme: Mafia\n\nThe Godfather', doc_id='d96456bf-ef6e-4c1b-bdb8-e90a37d881f3', embedding=None, doc_hash='b770e43e6a94854a22dc01421d3d9ef6a94931c2b8dbbadf4fdb6eb6fbe41010', extra_info=None, node_info=None, relationships={<DocumentRelationship.SOURCE: '1'>: 'None'}), score=0.4634347)]

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# remove created indices
import os, shutil

hnsw_dirs = ["hnsw_filters", "hnsw_index"]
for dir in hnsw_dirs:
    if os.path.exists(dir):
        shutil.rmtree(dir)

# remove created indices import os, shutil hnsw_dirs = ["hnsw_filters", "hnsw_index"] for dir in hnsw_dirs: if os.path.exists(dir): shutil.rmtree(dir)