mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-14 19:09:03 +00:00
c2a3021bb0
Signed-off-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Dan O'Donovan <dan.odonovan@gmail.com> Co-authored-by: Tom Daniel Grande <tomdgrande@gmail.com> Co-authored-by: Grande <Tom.Daniel.Grande@statsbygg.no> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: ccurme <chester.curme@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Tomaz Bratanic <bratanic.tomaz@gmail.com> Co-authored-by: ZhangShenao <15201440436@163.com> Co-authored-by: Friso H. Kingma <fhkingma@gmail.com> Co-authored-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Nuno Campos <nuno@langchain.dev> Co-authored-by: Morgante Pell <morgantep@google.com>
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.retrievers import BaseRetriever
|
|
from pydantic import Field
|
|
|
|
|
|
class LlamaIndexRetriever(BaseRetriever):
|
|
"""`LlamaIndex` retriever.
|
|
|
|
It is used for the question-answering with sources over
|
|
an LlamaIndex data structure."""
|
|
|
|
index: Any = None
|
|
"""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 = None
|
|
"""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
|