MongoDB 演示

本指南向您展示如何直接使用我们基于MongoDB的DocumentStore抽象层。通过将节点存入文档存储库，您可以在同一底层文档存储上定义多个索引，而无需在不同索引间重复存储数据。

如果您在 Colab 上打开这个笔记本，您可能需要安装 LlamaIndex 🦙。

%pip install llama-index-storage-docstore-mongodb
%pip install llama-index-storage-index-store-mongodb
%pip install llama-index-llms-openai

!pip install llama-index

import nest_asyncio

nest_asyncio.apply()

import logging
import sys
import os

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

from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex
from llama_index.core import SummaryIndex
from llama_index.core import ComposableGraph
from llama_index.llms.openai import OpenAI
from llama_index.core.response.notebook_utils import display_response
from llama_index.core import Settings

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'

加载文档

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

解析为节点

from llama_index.core.node_parser import SentenceSplitter

nodes = SentenceSplitter().get_nodes_from_documents(documents)

添加到文档库

MONGO_URI = os.environ["MONGO_URI"]

from llama_index.storage.docstore.mongodb import MongoDocumentStore
from llama_index.storage.index_store.mongodb import MongoIndexStore

storage_context = StorageContext.from_defaults(
    docstore=MongoDocumentStore.from_uri(uri=MONGO_URI),
    index_store=MongoIndexStore.from_uri(uri=MONGO_URI),
)

storage_context.docstore.add_documents(nodes)

定义多个索引

每个索引使用相同的基础节点。

summary_index = SummaryIndex(nodes, storage_context=storage_context)

vector_index = VectorStoreIndex(nodes, storage_context=storage_context)

keyword_table_index = SimpleKeywordTableIndex(
    nodes, storage_context=storage_context
)

# NOTE: the docstore still has the same nodes
len(storage_context.docstore.docs)

测试保存与加载功能

# NOTE: docstore and index_store is persisted in MongoDB by default
# NOTE: here only need to persist simple vector store to disk
storage_context.persist()

# note down index IDs
list_id = summary_index.index_id
vector_id = vector_index.index_id
keyword_id = keyword_table_index.index_id

from llama_index.core import load_index_from_storage

# re-create storage context
storage_context = StorageContext.from_defaults(
    docstore=MongoDocumentStore.from_uri(uri=MONGO_URI),
    index_store=MongoIndexStore.from_uri(uri=MONGO_URI),
)

# load indices
summary_index = load_index_from_storage(
    storage_context=storage_context, index_id=list_id
)
vector_index = load_index_from_storage(
    storage_context=storage_context, index_id=vector_id
)
keyword_table_index = load_index_from_storage(
    storage_context=storage_context, index_id=keyword_id
)

尝试一些查询

chatgpt = OpenAI(temperature=0, model="gpt-3.5-turbo")

Settings.llm = chatgpt
Settings.chunk_size = 1024

query_engine = summary_index.as_query_engine()
list_response = query_engine.query("What is a summary of this document?")

display_response(list_response)

query_engine = vector_index.as_query_engine()
vector_response = query_engine.query("What did the author do growing up?")

display_response(vector_response)

query_engine = keyword_table_index.as_query_engine()
keyword_response = query_engine.query(
    "What did the author do after his time at YC?"
)

display_response(keyword_response)