mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
98380cff9b
- **Description:** Removing this line ```python response = index.query(query, response_mode="no_text", **self.query_kwargs) ``` to ```python response = index.query(query, **self.query_kwargs) ``` Since llama index query does not support response_mode anymore : ``` | TypeError: BaseQueryEngine.query() got an unexpected keyword argument 'response_mode'```` - **Twitter handle:** @maximeperrin_ --------- Co-authored-by: Maxime Perrin <mperrin@doing.fr>
87 lines
3.1 KiB
Python
87 lines
3.1 KiB
Python
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
|