mirror of
https://github.com/hwchase17/langchain
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4384fa8e49
[Dria](https://dria.co/) is a hub of public RAG models for developers to both contribute and utilize a shared embedding lake. This PR adds a retriever that can retrieve documents from Dria.
88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
"""Wrapper around Dria Retriever."""
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from typing import Any, 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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from langchain_community.utilities import DriaAPIWrapper
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class DriaRetriever(BaseRetriever):
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"""`Dria` retriever using the DriaAPIWrapper."""
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api_wrapper: DriaAPIWrapper
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def __init__(self, api_key: str, contract_id: Optional[str] = None, **kwargs: Any):
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"""
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Initialize the DriaRetriever with a DriaAPIWrapper instance.
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Args:
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api_key: The API key for Dria.
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contract_id: The contract ID of the knowledge base to interact with.
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"""
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api_wrapper = DriaAPIWrapper(api_key=api_key, contract_id=contract_id)
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super().__init__(api_wrapper=api_wrapper, **kwargs)
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def create_knowledge_base(
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self,
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name: str,
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description: str,
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category: str = "Unspecified",
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embedding: str = "jina",
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) -> str:
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"""Create a new knowledge base in Dria.
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Args:
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name: The name of the knowledge base.
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description: The description of the knowledge base.
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category: The category of the knowledge base.
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embedding: The embedding model to use for the knowledge base.
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Returns:
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The ID of the created knowledge base.
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"""
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response = self.api_wrapper.create_knowledge_base(
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name, description, category, embedding
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)
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return response
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def add_texts(
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self,
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texts: List,
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) -> None:
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"""Add texts to the Dria knowledge base.
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Args:
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texts: An iterable of texts and metadatas to add to the knowledge base.
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Returns:
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List of IDs representing the added texts.
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"""
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data = [{"text": text["text"], "metadata": text["metadata"]} for text in texts]
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self.api_wrapper.insert_data(data)
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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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"""Retrieve relevant documents from Dria based on a query.
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Args:
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query: The query string to search for in the knowledge base.
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run_manager: Callback manager for the retriever run.
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Returns:
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A list of Documents containing the search results.
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"""
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results = self.api_wrapper.search(query)
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docs = [
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Document(
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page_content=result["metadata"],
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metadata={"id": result["id"], "score": result["score"]},
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)
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for result in results
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]
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return docs
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