mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
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"""Wrapper around Embedchain Retriever."""
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from __future__ import annotations
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from typing import Any, Iterable, List, Optional
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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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class EmbedchainRetriever(BaseRetriever):
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"""`Embedchain` retriever."""
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client: Any
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"""Embedchain Pipeline."""
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@classmethod
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def create(cls, yaml_path: Optional[str] = None) -> EmbedchainRetriever:
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"""
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Create a EmbedchainRetriever from a YAML configuration file.
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Args:
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yaml_path: Path to the YAML configuration file. If not provided,
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a default configuration is used.
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Returns:
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An instance of EmbedchainRetriever.
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"""
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from embedchain import Pipeline
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# Create an Embedchain Pipeline instance
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if yaml_path:
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client = Pipeline.from_config(yaml_path=yaml_path)
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else:
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client = Pipeline()
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return cls(client=client)
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def add_texts(
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self,
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texts: Iterable[str],
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) -> List[str]:
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"""Run more texts through the embeddings and add to the retriever.
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Args:
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texts: Iterable of strings/URLs to add to the retriever.
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Returns:
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List of ids from adding the texts into the retriever.
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"""
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ids = []
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for text in texts:
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_id = self.client.add(text)
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ids.append(_id)
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return ids
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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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res = self.client.search(query)
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docs = []
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for r in res:
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docs.append(
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Document(
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page_content=r["context"],
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metadata={"source": r["source"], "document_id": r["document_id"]},
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)
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)
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return docs
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