DocArray 内存向量存储¶

DocArrayInMemoryVectorStore 是由 Docarray 提供的文档索引工具，它将文档存储在内存中。对于小型数据集而言，这是一个理想的起点，特别是当您不希望启动数据库服务器时。

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 huggingface 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 DocArrayInMemoryVectorStore
from IPython.display import Markdown, display

import os import sys import logging import textwrap import warnings warnings.filterwarnings("ignore") # stop huggingface 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 DocArrayInMemoryVectorStore 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: 1c21062a-50a3-4133-a0b1-75f837a953e5 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e

初始化与索引¶

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from llama_index.core import StorageContext


vector_store = DocArrayInMemoryVectorStore()
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 = DocArrayInMemoryVectorStore() 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. He had invested a lot of time and energy into learning about AI and was disappointed to
find out that it was not going to get him the results he had hoped for.

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

index = GPTVectorStoreIndex(nodes, storage_context=storage_context)

from llama_index.core import StorageContext vector_store = DocArrayInMemoryVectorStore() 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='41c99963-b200-4ce6-a9c4-d06ffeabdbc5', embedding=None, doc_hash='b770e43e6a94854a22dc01421d3d9ef6a94931c2b8dbbadf4fdb6eb6fbe41010', extra_info=None, node_info=None, relationships={<DocumentRelationship.SOURCE: '1'>: 'None'}), score=0.7681788983417586)]