Source code for langchain_community.retrievers.llama_index
from typing import Any, Dict, List, cast
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import Field
from langchain_core.retrievers import BaseRetriever
[docs]class LlamaIndexRetriever(BaseRetriever):
"""`LlamaIndex` 检索器。
它用于在 LlamaIndex 数据结构上使用来源进行问答。"""
index: Any
"""LlamaIndex索引用于查询。"""
query_kwargs: Dict = Field(default_factory=dict)
"""要传递给查询方法的关键字参数。"""
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""获取与查询相关的文档。"""
try:
from llama_index.core.base.response.schema import Response
from llama_index.core.indices.base import BaseGPTIndex
except ImportError:
raise ImportError(
"You need to install `pip install llama-index` to use this retriever."
)
index = cast(BaseGPTIndex, self.index)
response = index.query(query, **self.query_kwargs)
response = cast(Response, response)
# parse source nodes
docs = []
for source_node in response.source_nodes:
metadata = source_node.metadata or {}
docs.append(
Document(page_content=source_node.get_content(), metadata=metadata)
)
return docs
[docs]class LlamaIndexGraphRetriever(BaseRetriever):
"""`LlamaIndex`图数据结构检索器。
它用于在LlamaIndex图数据结构上使用来源进行问答。"""
graph: Any
"""LlamaIndex图用于查询。"""
query_configs: List[Dict] = Field(default_factory=list)
"""传递给查询方法的查询配置列表。"""
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""获取与查询相关的文档。"""
try:
from llama_index.core.base.response.schema import Response
from llama_index.core.composability.base import (
QUERY_CONFIG_TYPE,
ComposableGraph,
)
except ImportError:
raise ImportError(
"You need to install `pip install llama-index` to use this retriever."
)
graph = cast(ComposableGraph, self.graph)
# for now, inject response_mode="no_text" into query configs
for query_config in self.query_configs:
query_config["response_mode"] = "no_text"
query_configs = cast(List[QUERY_CONFIG_TYPE], self.query_configs)
response = graph.query(query, query_configs=query_configs)
response = cast(Response, response)
# parse source nodes
docs = []
for source_node in response.source_nodes:
metadata = source_node.metadata or {}
docs.append(
Document(page_content=source_node.get_content(), metadata=metadata)
)
return docs