可组合对象¶

在本笔记本中，我们将展示如何将多个对象组合成单个顶层索引。

该方法通过设置带有obj字段的IndexNode对象来实现，该字段可以指向：

• 查询引擎
• 检索器
• 查询管道
• 另一个节点！

object = IndexNode(index_id="my_object", obj=query_engine, text="some text about this object")

数据准备¶

In [ ]:

%pip install llama-index-storage-docstore-mongodb
%pip install llama-index-vector-stores-qdrant
%pip install llama-index-storage-docstore-firestore
%pip install llama-index-retrievers-bm25
%pip install llama-index-storage-docstore-redis
%pip install llama-index-storage-docstore-dynamodb
%pip install llama-index-readers-file pymupdf

%pip install llama-index-storage-docstore-mongodb %pip install llama-index-vector-stores-qdrant %pip install llama-index-storage-docstore-firestore %pip install llama-index-retrievers-bm25 %pip install llama-index-storage-docstore-redis %pip install llama-index-storage-docstore-dynamodb %pip install llama-index-readers-file pymupdf

In [ ]:

!wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "./llama2.pdf"
!wget --user-agent "Mozilla" "https://arxiv.org/pdf/1706.03762.pdf" -O "./attention.pdf"

!wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "./llama2.pdf" !wget --user-agent "Mozilla" "https://arxiv.org/pdf/1706.03762.pdf" -O "./attention.pdf"

In [ ]:

from llama_index.core import download_loader

from llama_index.readers.file import PyMuPDFReader

llama2_docs = PyMuPDFReader().load_data(
    file_path="./llama2.pdf", metadata=True
)
attention_docs = PyMuPDFReader().load_data(
    file_path="./attention.pdf", metadata=True
)

from llama_index.core import download_loader from llama_index.readers.file import PyMuPDFReader llama2_docs = PyMuPDFReader().load_data( file_path="./llama2.pdf", metadata=True ) attention_docs = PyMuPDFReader().load_data( file_path="./attention.pdf", metadata=True )

检索器配置¶

In [ ]:

import os

os.environ["OPENAI_API_KEY"] = "sk-..."

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

In [ ]:

from llama_index.core.node_parser import TokenTextSplitter

nodes = TokenTextSplitter(
    chunk_size=1024, chunk_overlap=128
).get_nodes_from_documents(llama2_docs + attention_docs)

from llama_index.core.node_parser import TokenTextSplitter nodes = TokenTextSplitter( chunk_size=1024, chunk_overlap=128 ).get_nodes_from_documents(llama2_docs + attention_docs)

In [ ]:

from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.storage.docstore.redis import RedisDocumentStore
from llama_index.storage.docstore.mongodb import MongoDocumentStore
from llama_index.storage.docstore.firestore import FirestoreDocumentStore
from llama_index.storage.docstore.dynamodb import DynamoDBDocumentStore

docstore = SimpleDocumentStore()
docstore.add_documents(nodes)

from llama_index.core.storage.docstore import SimpleDocumentStore from llama_index.storage.docstore.redis import RedisDocumentStore from llama_index.storage.docstore.mongodb import MongoDocumentStore from llama_index.storage.docstore.firestore import FirestoreDocumentStore from llama_index.storage.docstore.dynamodb import DynamoDBDocumentStore docstore = SimpleDocumentStore() docstore.add_documents(nodes)

In [ ]:

from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.retrievers.bm25 import BM25Retriever
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient

client = QdrantClient(path="./qdrant_data")
vector_store = QdrantVectorStore("composable", client=client)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex(nodes=nodes)
vector_retriever = index.as_retriever(similarity_top_k=2)
bm25_retriever = BM25Retriever.from_defaults(
    docstore=docstore, similarity_top_k=2
)

from llama_index.core import VectorStoreIndex, StorageContext from llama_index.retrievers.bm25 import BM25Retriever from llama_index.vector_stores.qdrant import QdrantVectorStore from qdrant_client import QdrantClient client = QdrantClient(path="./qdrant_data") vector_store = QdrantVectorStore("composable", client=client) storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex(nodes=nodes) vector_retriever = index.as_retriever(similarity_top_k=2) bm25_retriever = BM25Retriever.from_defaults( docstore=docstore, similarity_top_k=2 )

组合对象¶

在此我们构建 IndexNodes。需注意，文本内容将被顶级索引用于节点检索。

对于向量索引，文本会被嵌入处理；而对于关键词索引，文本则直接用于关键词匹配。

本例中使用的是 SummaryIndex，该索引在技术上并不需要依赖文本进行检索，因为它始终会检索全部节点。

In [ ]:

from llama_index.core.schema import IndexNode

vector_obj = IndexNode(
    index_id="vector", obj=vector_retriever, text="Vector Retriever"
)
bm25_obj = IndexNode(
    index_id="bm25", obj=bm25_retriever, text="BM25 Retriever"
)

from llama_index.core.schema import IndexNode vector_obj = IndexNode( index_id="vector", obj=vector_retriever, text="Vector Retriever" ) bm25_obj = IndexNode( index_id="bm25", obj=bm25_retriever, text="BM25 Retriever" )

In [ ]:

from llama_index.core import SummaryIndex

summary_index = SummaryIndex(objects=[vector_obj, bm25_obj])

from llama_index.core import SummaryIndex summary_index = SummaryIndex(objects=[vector_obj, bm25_obj])

查询¶

当我们执行查询时，系统会检索所有对象并利用它们生成节点，最终得出答案结果。

通过结合使用 tree_summarize 和 aquery() 方法，可以实现并发执行并获得更快的响应速度。

In [ ]:

query_engine = summary_index.as_query_engine(
    response_mode="tree_summarize", verbose=True
)

query_engine = summary_index.as_query_engine( response_mode="tree_summarize", verbose=True )

In [ ]:

response = await query_engine.aquery(
    "How does attention work in transformers?"
)

response = await query_engine.aquery( "How does attention work in transformers?" )

Retrieval entering vector: VectorIndexRetriever
Retrieval entering bm25: BM25Retriever

In [ ]:

print(str(response))

print(str(response))

Attention in transformers works by mapping a query and a set of key-value pairs to an output. The output is computed as a weighted sum of the values, where the weights are determined by the similarity between the query and the keys. In the transformer model, attention is used in three different ways: 

1. Encoder-decoder attention: The queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence.

2. Self-attention in the encoder: In a self-attention layer, all of the keys, values, and queries come from the same place, which is the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.

3. Self-attention in the decoder: Similar to the encoder, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. However, leftward information flow in the decoder is prevented to preserve the auto-regressive property.

Overall, attention in transformers allows the model to jointly attend to information from different representation subspaces at different positions, improving the model's ability to capture dependencies and relationships between different parts of the input sequence.

In [ ]:

response = await query_engine.aquery(
    "What is the architecture of Llama2 based on?"
)

response = await query_engine.aquery( "What is the architecture of Llama2 based on?" )

Retrieval entering vector: VectorIndexRetriever
Retrieval entering bm25: BM25Retriever

In [ ]:

print(str(response))

print(str(response))

The architecture of Llama 2 is based on the transformer model.

In [ ]:

response = await query_engine.aquery(
    "What was used before attention in transformers?"
)

response = await query_engine.aquery( "What was used before attention in transformers?" )

Retrieval entering vector: VectorIndexRetriever
Retrieval entering bm25: BM25Retriever

In [ ]:

print(str(response))

print(str(response))

Recurrent neural networks, such as long short-term memory (LSTM) and gated recurrent neural networks, were commonly used before attention in transformers. These models were widely used in sequence modeling and transduction problems, including language modeling and machine translation.

关于保存与加载的注意事项¶

由于对象在技术上不可序列化，因此在保存和加载时，这些对象也需要在加载时重新提供。

以下是一个演示如何保存/加载此配置的示例。

保存¶

In [ ]:

# qdrant is already saved automatically!
# we only need to save the docstore here

# save our docstore nodes for bm25
docstore.persist("./docstore.json")

# qdrant is already saved automatically! # we only need to save the docstore here # save our docstore nodes for bm25 docstore.persist("./docstore.json")

加载¶

In [ ]:

from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient

docstore = SimpleDocumentStore.from_persist_path("./docstore.json")

client = QdrantClient(path="./qdrant_data")
vector_store = QdrantVectorStore("composable", client=client)

from llama_index.core.storage.docstore import SimpleDocumentStore from llama_index.vector_stores.qdrant import QdrantVectorStore from qdrant_client import QdrantClient docstore = SimpleDocumentStore.from_persist_path("./docstore.json") client = QdrantClient(path="./qdrant_data") vector_store = QdrantVectorStore("composable", client=client)

In [ ]:

index = VectorStoreIndex.from_vector_store(vector_store)
vector_retriever = index.as_retriever(similarity_top_k=2)
bm25_retriever = BM25Retriever.from_defaults(
    docstore=docstore, similarity_top_k=2
)

index = VectorStoreIndex.from_vector_store(vector_store) vector_retriever = index.as_retriever(similarity_top_k=2) bm25_retriever = BM25Retriever.from_defaults( docstore=docstore, similarity_top_k=2 )

In [ ]:

from llama_index.core.schema import IndexNode

vector_obj = IndexNode(
    index_id="vector", obj=vector_retriever, text="Vector Retriever"
)
bm25_obj = IndexNode(
    index_id="bm25", obj=bm25_retriever, text="BM25 Retriever"
)

from llama_index.core.schema import IndexNode vector_obj = IndexNode( index_id="vector", obj=vector_retriever, text="Vector Retriever" ) bm25_obj = IndexNode( index_id="bm25", obj=bm25_retriever, text="BM25 Retriever" )

In [ ]:

# if we had added regular nodes to the summary index, we could save/load that as well
# summary_index.persist("./summary_index.json")
# summary_index = load_index_from_storage(storage_context, objects=objects)

from llama_index.core import SummaryIndex

summary_index = SummaryIndex(objects=[vector_obj, bm25_obj])

# if we had added regular nodes to the summary index, we could save/load that as well # summary_index.persist("./summary_index.json") # summary_index = load_index_from_storage(storage_context, objects=objects) from llama_index.core import SummaryIndex summary_index = SummaryIndex(objects=[vector_obj, bm25_obj])