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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
class LlamaIndexRetriever(BaseRetriever):
"""`LlamaIndex` retriever.
It is used for the question-answering with sources over
an LlamaIndex data structure."""
index: Any
"""LlamaIndex index to query."""
query_kwargs: Dict = Field(default_factory=dict)
"""Keyword arguments to pass to the query method."""
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""Get documents relevant for a query."""
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
class LlamaIndexGraphRetriever(BaseRetriever):
"""`LlamaIndex` graph data structure retriever.
It is used for question-answering with sources over an LlamaIndex
graph data structure."""
graph: Any
"""LlamaIndex graph to query."""
query_configs: List[Dict] = Field(default_factory=list)
"""List of query configs to pass to the query method."""
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""Get documents relevant for a query."""
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
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