mirror of
https://github.com/hwchase17/langchain.git
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8516a03a02
This PR upgrades langchain-community to pydantic 2.
* Most of this PR was auto-generated using code mods with gritql
(https://github.com/eyurtsev/migrate-pydantic/tree/main)
* Subsequently, some code was fixed manually due to accommodate
differences between pydantic 1 and 2
Breaking Changes:
- Use TEXTEMBED_API_KEY and TEXTEMBEB_API_URL for env variables for text
embed integrations:
cbea780492
Other changes:
- Added pydantic_settings as a required dependency for community. This
may be removed if we have enough time to convert the dependency into an
optional one.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
87 lines
3.1 KiB
Python
87 lines
3.1 KiB
Python
from typing import Any, Dict, List, cast
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from langchain_core.callbacks import CallbackManagerForRetrieverRun
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from langchain_core.documents import Document
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from langchain_core.retrievers import BaseRetriever
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from pydantic import Field
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class LlamaIndexRetriever(BaseRetriever):
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"""`LlamaIndex` retriever.
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It is used for the question-answering with sources over
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an LlamaIndex data structure."""
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index: Any
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"""LlamaIndex index to query."""
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query_kwargs: Dict = Field(default_factory=dict)
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"""Keyword arguments to pass to the query method."""
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def _get_relevant_documents(
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self, query: str, *, run_manager: CallbackManagerForRetrieverRun
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) -> List[Document]:
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"""Get documents relevant for a query."""
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try:
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from llama_index.core.base.response.schema import Response
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from llama_index.core.indices.base import BaseGPTIndex
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except ImportError:
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raise ImportError(
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"You need to install `pip install llama-index` to use this retriever."
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)
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index = cast(BaseGPTIndex, self.index)
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response = index.query(query, **self.query_kwargs)
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response = cast(Response, response)
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# parse source nodes
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docs = []
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for source_node in response.source_nodes:
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metadata = source_node.metadata or {}
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docs.append(
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Document(page_content=source_node.get_content(), metadata=metadata)
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)
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return docs
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class LlamaIndexGraphRetriever(BaseRetriever):
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"""`LlamaIndex` graph data structure retriever.
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It is used for question-answering with sources over an LlamaIndex
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graph data structure."""
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graph: Any
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"""LlamaIndex graph to query."""
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query_configs: List[Dict] = Field(default_factory=list)
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"""List of query configs to pass to the query method."""
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def _get_relevant_documents(
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self, query: str, *, run_manager: CallbackManagerForRetrieverRun
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) -> List[Document]:
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"""Get documents relevant for a query."""
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try:
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from llama_index.core.base.response.schema import Response
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from llama_index.core.composability.base import (
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QUERY_CONFIG_TYPE,
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ComposableGraph,
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)
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except ImportError:
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raise ImportError(
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"You need to install `pip install llama-index` to use this retriever."
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)
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graph = cast(ComposableGraph, self.graph)
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# for now, inject response_mode="no_text" into query configs
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for query_config in self.query_configs:
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query_config["response_mode"] = "no_text"
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query_configs = cast(List[QUERY_CONFIG_TYPE], self.query_configs)
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response = graph.query(query, query_configs=query_configs)
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response = cast(Response, response)
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# parse source nodes
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docs = []
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for source_node in response.source_nodes:
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metadata = source_node.metadata or {}
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docs.append(
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Document(page_content=source_node.get_content(), metadata=metadata)
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)
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return docs
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