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fix: megaparse sdk with nats (#3496)
* Adapt deps * Change megaparse processor inner file processing
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@ -23,7 +23,7 @@ dependencies = [
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"faiss-cpu>=1.8.0.post1",
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"rapidfuzz>=3.10.1",
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"markupsafe>=2.1.5",
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"megaparse[all]== 0.0.43",
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"megaparse-sdk==0.1.7"
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]
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readme = "README.md"
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requires-python = ">= 3.11"
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@ -3,8 +3,8 @@ import logging
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import tiktoken
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from langchain_core.documents import Document
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from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
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from megaparse.core.megaparse import MegaParse
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from megaparse.core.parser.unstructured_parser import UnstructuredParser
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from megaparse_sdk.client import MegaParseNATSClient
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from megaparse_sdk.config import ClientNATSConfig
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from quivr_core.config import MegaparseConfig
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from quivr_core.files.file import QuivrFile
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@ -75,9 +75,9 @@ class MegaparseProcessor(ProcessorBase):
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async def process_file_inner(self, file: QuivrFile) -> list[Document]:
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logger.info(f"Uploading file {file.path} to MegaParse")
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parser = UnstructuredParser(**self.megaparse_config.model_dump())
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megaparse = MegaParse(parser)
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response = await megaparse.aload(file.path)
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async with MegaParseNATSClient(ClientNATSConfig()) as client:
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response = await client.parse_file(file=file.path)
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logger.info(f"File : {response}")
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document = Document(
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page_content=response,
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@ -87,28 +87,3 @@ class MegaparseProcessor(ProcessorBase):
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for doc in docs:
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doc.metadata = {"chunk_size": len(self.enc.encode(doc.page_content))}
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return docs
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# async def process_file_inner(self, file: QuivrFile) -> list[Document]:
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# api_key = str(os.getenv("MEGAPARSE_API_KEY"))
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# megaparse = MegaParseSDK(api_key)
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# logger.info(f"Uploading file {file.path} to MegaParse")
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# data = {
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# "method": self.megaparse_config.method,
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# "strategy": self.megaparse_config.strategy,
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# "check_table": self.megaparse_config.check_table,
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# "parsing_instruction": self.megaparse_config.parsing_instruction,
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# "model_name": self.megaparse_config.model_name,
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# }
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# response = await megaparse.file.upload(
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# file_path=str(file.path),
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# **data,
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# )
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# document = Document(
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# page_content=response["result"],
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# )
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# if len(response) > self.splitter_config.chunk_size:
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# docs = self.text_splitter.split_documents([document])
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# for doc in docs:
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# doc.metadata = {"chunk_size": len(self.enc.encode(doc.page_content))}
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# return docs
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# return [document]
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@ -11,7 +11,7 @@ from rich.prompt import Prompt
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if __name__ == "__main__":
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brain = Brain.from_files(
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name="test_brain",
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file_paths=["./tests/processor/docx/demo.docx"],
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file_paths=["./tests/processor/pdf/sample.pdf"],
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llm=LLMEndpoint(
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llm_config=LLMEndpointConfig(model="gpt-4o"),
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llm=ChatOpenAI(model="gpt-4o", api_key=str(os.getenv("OPENAI_API_KEY"))),
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