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https://github.com/StanGirard/quivr.git
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feat(megaparse): add sdk (#3462)
What it does : Adds the MegaParse API call for parsing using the SDK
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@ -22,15 +22,12 @@ dependencies = [
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"transformers[sentencepiece]>=4.44.2",
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"faiss-cpu>=1.8.0.post1",
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"rapidfuzz>=3.10.1",
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"megaparse-sdk>=0.1.2",
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"markupsafe>=2.1.5",
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]
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readme = "README.md"
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requires-python = ">= 3.11"
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[project.optional-dependencies]
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all = [
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"unstructured[epub,docx,odt,doc,pptx,ppt,xlsx,md]>=0.15.5",
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"docx2txt>=0.8",
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]
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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@ -4,12 +4,22 @@ import yaml
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from pydantic import BaseModel
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class PdfParser(str, Enum):
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LLAMA_PARSE = "llama_parse"
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class ParserType(str, Enum):
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"""Parser type enumeration."""
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UNSTRUCTURED = "unstructured"
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LLAMA_PARSER = "llama_parser"
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MEGAPARSE_VISION = "megaparse_vision"
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class StrategyEnum(str, Enum):
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"""Method to use for the conversion"""
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FAST = "fast"
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AUTO = "auto"
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HI_RES = "hi_res"
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class MegaparseBaseConfig(BaseModel):
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@classmethod
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def from_yaml(cls, file_path: str):
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@ -22,6 +32,8 @@ class MegaparseBaseConfig(BaseModel):
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class MegaparseConfig(MegaparseBaseConfig):
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strategy: str = "fast"
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llama_parse_api_key: str | None = None
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pdf_parser: PdfParser = PdfParser.UNSTRUCTURED
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method: ParserType = ParserType.UNSTRUCTURED
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strategy: StrategyEnum = StrategyEnum.AUTO
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check_table: bool = False
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parsing_instruction: str | None = None
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model_name: str = "gpt-4o"
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@ -1,9 +1,10 @@
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import logging
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import os
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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 import MegaParse
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from megaparse_sdk import MegaParseSDK
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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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@ -29,7 +30,24 @@ class MegaparseProcessor(ProcessorBase):
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"""
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supported_extensions = [FileExtension.pdf]
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supported_extensions = [
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FileExtension.pdf,
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FileExtension.docx,
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FileExtension.doc,
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FileExtension.pptx,
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FileExtension.xls,
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FileExtension.xlsx,
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FileExtension.csv,
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FileExtension.epub,
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FileExtension.bib,
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FileExtension.odt,
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FileExtension.html,
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FileExtension.py,
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FileExtension.markdown,
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FileExtension.md,
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FileExtension.mdx,
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FileExtension.ipynb,
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]
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def __init__(
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self,
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@ -56,9 +74,24 @@ class MegaparseProcessor(ProcessorBase):
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}
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async def process_file_inner(self, file: QuivrFile) -> list[Document]:
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mega_parse = MegaParse(file_path=file.path, config=self.megaparse_config) # type: ignore
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document: Document = await mega_parse.aload()
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if len(document.page_content) > self.splitter_config.chunk_size:
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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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@ -3,7 +3,7 @@ import logging
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import types
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from dataclasses import dataclass, field
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from heapq import heappop, heappush
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from typing import Type, TypeAlias
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from typing import List, Type, TypeAlias
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from quivr_core.files.file import FileExtension
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@ -49,37 +49,41 @@ base_processors: ProcMapping = {
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def _append_proc_mapping(
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mapping: ProcMapping,
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file_ext: FileExtension | str,
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file_exts: List[FileExtension] | List[str],
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cls_mod: str,
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errtxt: str,
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priority: int | None,
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):
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if file_ext in mapping:
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try:
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prev_proc = heappop(mapping[file_ext])
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proc_entry = ProcEntry(
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priority=priority if priority is not None else prev_proc.priority - 1,
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cls_mod=cls_mod,
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err=errtxt,
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)
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# Push the previous processor back
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heappush(mapping[file_ext], prev_proc)
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heappush(mapping[file_ext], proc_entry)
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except IndexError:
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for file_ext in file_exts:
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if file_ext in mapping:
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try:
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prev_proc = heappop(mapping[file_ext])
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proc_entry = ProcEntry(
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priority=priority
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if priority is not None
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else prev_proc.priority - 1,
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cls_mod=cls_mod,
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err=errtxt,
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)
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# Push the previous processor back
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heappush(mapping[file_ext], prev_proc)
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heappush(mapping[file_ext], proc_entry)
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except IndexError:
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proc_entry = ProcEntry(
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priority=priority if priority is not None else _LOWEST_PRIORITY,
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cls_mod=cls_mod,
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err=errtxt,
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)
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heappush(mapping[file_ext], proc_entry)
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else:
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proc_entry = ProcEntry(
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priority=priority if priority is not None else _LOWEST_PRIORITY,
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cls_mod=cls_mod,
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err=errtxt,
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)
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heappush(mapping[file_ext], proc_entry)
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else:
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proc_entry = ProcEntry(
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priority=priority if priority is not None else _LOWEST_PRIORITY,
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cls_mod=cls_mod,
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err=errtxt,
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)
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mapping[file_ext] = [proc_entry]
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mapping[file_ext] = [proc_entry]
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def defaults_to_proc_entries(
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@ -109,7 +113,7 @@ def defaults_to_proc_entries(
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ext_str = ext.value if isinstance(ext, FileExtension) else ext
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_append_proc_mapping(
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mapping=base_processors,
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file_ext=ext,
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file_exts=[ext],
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cls_mod=f"quivr_core.processor.implementations.default.{processor_name}",
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errtxt=f"can't import {processor_name}. Please install quivr-core[{ext_str}] to access {processor_name}",
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priority=None,
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@ -117,13 +121,30 @@ def defaults_to_proc_entries(
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# TODO(@aminediro): Megaparse should register itself
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# Append Megaparse
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# _append_proc_mapping(
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# mapping=base_processors,
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# file_ext=FileExtension.pdf,
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# cls_mod="quivr_core.processor.implementations.megaparse_processor.MegaparseProcessor",
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# errtxt=f"can't import MegaparseProcessor. Please install quivr-core[{ext_str}] to access MegaparseProcessor",
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# priority=None,
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# )
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_append_proc_mapping(
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mapping=base_processors,
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file_exts=[
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FileExtension.pdf,
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FileExtension.docx,
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FileExtension.doc,
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FileExtension.pptx,
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FileExtension.xls,
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FileExtension.xlsx,
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FileExtension.csv,
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FileExtension.epub,
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FileExtension.bib,
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FileExtension.odt,
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FileExtension.html,
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FileExtension.py,
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FileExtension.markdown,
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FileExtension.md,
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FileExtension.mdx,
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FileExtension.ipynb,
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],
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cls_mod="quivr_core.processor.implementations.megaparse_processor.MegaparseProcessor",
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errtxt=f"can't import MegaparseProcessor. Please install quivr-core[{ext_str}] to access MegaparseProcessor",
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priority=None,
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)
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return base_processors
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@ -181,7 +202,7 @@ def register_processor(
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if all(proc_cls != proc.cls_mod for proc in known_processors[file_ext]):
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_append_proc_mapping(
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known_processors,
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file_ext=file_ext,
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file_exts=[file_ext],
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cls_mod=proc_cls,
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errtxt=errtxt
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or f"{proc_cls} import failed for processor of {file_ext}",
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@ -1,51 +0,0 @@
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from pathlib import Path
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from uuid import uuid4
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import pytest
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from quivr_core.files.file import FileExtension, QuivrFile
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from quivr_core.processor.implementations.megaparse_processor import MegaparseProcessor
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from quivr_core.processor.registry import get_processor_class
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all_but_pdf = list(filter(lambda ext: ext != ".pdf", list(FileExtension)))
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unstructured = pytest.importorskip("unstructured")
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def test_get_default_processors_megaparse():
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cls = get_processor_class(FileExtension.pdf)
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assert cls == MegaparseProcessor
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@pytest.mark.asyncio
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async def test_megaparse_pdf_processor():
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p = Path("./tests/processor/pdf/sample.pdf")
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f = QuivrFile(
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id=uuid4(),
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brain_id=uuid4(),
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original_filename=p.stem,
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path=p,
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file_extension=FileExtension.pdf,
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file_sha1="123",
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)
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processor = MegaparseProcessor()
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result = await processor.process_file(f)
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assert len(result) > 0
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# FIXME: @chloedia once move to megaparse api
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# assert len(result[0].page_content) > 0
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@pytest.mark.parametrize("ext", all_but_pdf)
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@pytest.mark.asyncio
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async def test_megaparse_fail(ext):
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p = Path("./tests/processor/pdf/sample.pdf")
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f = QuivrFile(
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id=uuid4(),
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brain_id=uuid4(),
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original_filename=p.stem,
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path=p,
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file_extension=ext,
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file_sha1="123",
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)
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processor = MegaparseProcessor()
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with pytest.raises(ValueError):
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await processor.process_file(f)
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44
examples/simple_question_megaparse.py
Normal file
44
examples/simple_question_megaparse.py
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import os
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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from quivr_core import Brain
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from quivr_core.llm.llm_endpoint import LLMEndpoint
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from quivr_core.rag.entities.config import LLMEndpointConfig
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from rich.console import Console
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from rich.panel import Panel
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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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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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),
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)
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embedder = embeddings = OpenAIEmbeddings(
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model="text-embedding-3-large",
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)
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# Check brain info
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brain.print_info()
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console = Console()
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console.print(Panel.fit("Ask your brain !", style="bold magenta"))
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while True:
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# Get user input
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question = Prompt.ask("[bold cyan]Question[/bold cyan]")
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# Check if user wants to exit
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if question.lower() == "exit":
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console.print(Panel("Goodbye!", style="bold yellow"))
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break
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answer = brain.ask(question)
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# Print the answer with typing effect
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console.print(f"[bold green]Quivr Assistant[/bold green]: {answer.answer}")
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console.print("-" * console.width)
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brain.print_info()
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