fa4a01caed
chore(AZLyricsLoader.py): reorder imports to improve readability and maintain consistency chore(AirbyteJSONLoader.py): reorder imports to improve readability and maintain consistency chore(CoNLLULoader.py): add import for List from typing module to fix type hinting chore(CollegeConfidentialLoader.py): add import for List from typing module to fix type hinting chore(EverNoteLoader.py): reorder imports to improve readability and maintain consistency chore(FacebookChatLoader.py): add import for List from typing module to fix type hinting chore(GitbookLoader.py): reorder imports to improve readability and maintain consistency chore(HNLoader.py): add import for List from typing module to fix type hinting chore(IMSDbLoader.py): add import for List from typing module to fix type hinting chore(TextLoader.py): reorder imports to improve readability and maintain consistency chore(CohereEmbeddings.py): change default value of cohere_api_key parameter to an empty string chore(OpenAIEmbeddings.py): change default values of allowed_special, disallowed_special, chunk_size, embedding_ctx_length, max_retries, show_progress_bar, skip_empty, and tikToken_enable parameters to their respective types
42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
from typing import Optional
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from langchain.chains import RetrievalQAWithSourcesChain
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from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
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from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
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from langflow import CustomComponent
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from langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever
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class RetrievalQAWithSourcesChainComponent(CustomComponent):
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display_name = "RetrievalQAWithSourcesChain"
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description = "Question-answering with sources over an index."
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def build_config(self):
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return {
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"llm": {"display_name": "LLM"},
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"chain_type": {
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"display_name": "Chain Type",
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"options": ["stuff", "map_reduce", "map_rerank", "refine"],
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},
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"memory": {"display_name": "Memory"},
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"return_source_documents": {"display_name": "Return Source Documents"},
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}
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def build(
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self,
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retriever: BaseRetriever,
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llm: BaseLanguageModel,
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combine_documents_chain: BaseCombineDocumentsChain,
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chain_type: str,
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memory: Optional[BaseMemory] = None,
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return_source_documents: Optional[bool] = True,
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) -> BaseQAWithSourcesChain:
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return RetrievalQAWithSourcesChain.from_chain_type(
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llm=llm,
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chain_type=chain_type,
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combine_documents_chain=combine_documents_chain,
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memory=memory,
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return_source_documents=return_source_documents,
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retriever=retriever,
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)
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