From 9ff8e07f61f89e4af9e35d619179ac7b3b66bec4 Mon Sep 17 00:00:00 2001 From: tqtensor Date: Tue, 6 May 2025 15:21:27 +0000 Subject: [PATCH 1/9] feat: add LiteLLM model to supported models --- src/vision_parse/constants.py | 1 + src/vision_parse/llm.py | 8 +++----- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/src/vision_parse/constants.py b/src/vision_parse/constants.py index 9ddee2e..1819833 100644 --- a/src/vision_parse/constants.py +++ b/src/vision_parse/constants.py @@ -8,6 +8,7 @@ "deepseek-r1:32b": "ollama", "gpt-4o": "openai", "gpt-4o-mini": "openai", + "litellm/*": "openai", "gemini-1.5-flash": "gemini", "gemini-2.0-flash-exp": "gemini", "gemini-1.5-pro": "gemini", diff --git a/src/vision_parse/llm.py b/src/vision_parse/llm.py index 8e3db7a..9446e98 100644 --- a/src/vision_parse/llm.py +++ b/src/vision_parse/llm.py @@ -292,11 +292,9 @@ def _init_llm(self) -> None: def _get_provider_name(self, model_name: str) -> str: """Get the provider name for a given model name.""" try: - if model_name.startswith("gpt"): - model_name = "gpt-4o" - elif model_name.startswith("gemini"): - model_name = "gemini-1.5-flash" - return SUPPORTED_MODELS[model_name] + return SUPPORTED_MODELS[ + "litellm/*" if "litellm" in model_name else model_name + ] except KeyError: supported_models = ", ".join( f"'{model}' from {provider}" From bbb7c3495d3caa32dbbe7f341eb5a3577f9536c3 Mon Sep 17 00:00:00 2001 From: tqtensor Date: Tue, 6 May 2025 15:34:17 +0000 Subject: [PATCH 2/9] fix: remote litellm prefix from model name --- src/vision_parse/llm.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/vision_parse/llm.py b/src/vision_parse/llm.py index 9446e98..d297da7 100644 --- a/src/vision_parse/llm.py +++ b/src/vision_parse/llm.py @@ -459,6 +459,9 @@ async def _openai( self, base64_encoded: str, prompt: str, structured: bool = False ) -> Any: """Process base64-encoded image through OpenAI vision models.""" + if "litellm" in self.model_name: + self.model_name = self.model_name.replace("litellm/", "") + try: messages = [ { From d8279d0c7855eabbff8ca7e5dbe3cd4ba1b519b0 Mon Sep 17 00:00:00 2001 From: tqtensor Date: Thu, 8 May 2025 08:41:57 +0000 Subject: [PATCH 3/9] refactor: adopt LiteLLM --- pyproject.toml | 5 +- src/vision_parse/constants.py | 28 +- src/vision_parse/llm.py | 532 ++++------------- uv.lock | 1047 ++++++++++++++++++++++++++++++++- 4 files changed, 1157 insertions(+), 455 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 799a5d1..85dab71 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,12 +21,13 @@ dependencies = [ "jinja2>=3.0.0", "nest-asyncio>=1.6.0", "numpy>=2.0.0", - "ollama>=0.4.4", "opencv-python>=4.10.0.84", "pydantic>=2.0.0", "pymupdf>=1.22.0", "tenacity>=9.0.0", - "tqdm>=4.65.0" + "tqdm>=4.65.0", + "litellm>=1.0.0", + "instructor>=0.4.0" ] description = "Parse PDF documents into markdown formatted content using Vision LLMs" dynamic = ["version"] diff --git a/src/vision_parse/constants.py b/src/vision_parse/constants.py index 1819833..0a20bae 100644 --- a/src/vision_parse/constants.py +++ b/src/vision_parse/constants.py @@ -1,16 +1,16 @@ -from typing import Dict +from typing import Dict, List -SUPPORTED_MODELS: Dict[str, str] = { - "llama3.2-vision:11b": "ollama", - "llama3.2-vision:70b": "ollama", - "llava:13b": "ollama", - "llava:34b": "ollama", - "deepseek-r1:32b": "ollama", - "gpt-4o": "openai", - "gpt-4o-mini": "openai", - "litellm/*": "openai", - "gemini-1.5-flash": "gemini", - "gemini-2.0-flash-exp": "gemini", - "gemini-1.5-pro": "gemini", - "deepseek-chat": "deepseek", +SUPPORTED_PROVIDERS: Dict[str, str] = { + "openai": "OpenAI", + "azure": "Azure OpenAI", + "gemini": "Google AI Studio", + "deepseek": "DeepSeek", +} + +# Common model prefixes for provider detection +PROVIDER_PREFIXES: Dict[str, List[str]] = { + "openai": ["gpt-", "ft:gpt-"], + "azure": ["gpt-", "ft:gpt-"], + "gemini": ["gemini-"], + "deepseek": ["deepseek-"], } diff --git a/src/vision_parse/llm.py b/src/vision_parse/llm.py index d297da7..757f0b8 100644 --- a/src/vision_parse/llm.py +++ b/src/vision_parse/llm.py @@ -1,15 +1,15 @@ import logging -import os import re from typing import Any, Dict, Literal, Union import fitz +import instructor from jinja2 import Template +from litellm import acompletion, completion from pydantic import BaseModel from tenacity import retry, stop_after_attempt, wait_exponential -from tqdm import tqdm -from .constants import SUPPORTED_MODELS +from .constants import PROVIDER_PREFIXES, SUPPORTED_PROVIDERS from .utils import ImageData logger = logging.getLogger(__name__) @@ -26,8 +26,8 @@ class ImageDescription(BaseModel): confidence_score_text: float -class UnsupportedModelError(BaseException): - """Custom exception for unsupported model names""" +class UnsupportedProviderError(BaseException): + """Custom exception for unsupported provider names""" pass @@ -58,20 +58,16 @@ def __init__( api_key: Union[str, None], temperature: float, top_p: float, - ollama_config: Union[Dict, None], openai_config: Union[Dict, None], gemini_config: Union[Dict, None], image_mode: Literal["url", "base64", None], custom_prompt: Union[str, None], detailed_extraction: bool, enable_concurrency: bool, - device: Literal["cuda", "mps", None], - num_workers: int, **kwargs: Any, ): self.model_name = model_name self.api_key = api_key - self.ollama_config = ollama_config or {} self.openai_config = openai_config or {} self.gemini_config = gemini_config or {} self.temperature = temperature @@ -81,239 +77,125 @@ def __init__( self.detailed_extraction = detailed_extraction self.kwargs = kwargs self.enable_concurrency = enable_concurrency - self.device = device - self.num_workers = num_workers self.provider = self._get_provider_name(model_name) self._init_llm() def _init_llm(self) -> None: - """Initialize the LLM client.""" - if self.provider == "ollama": - import ollama - - try: - ollama.show(self.model_name) - except ollama.ResponseError as e: - if e.status_code == 404: - current_digest, bars = "", {} - for progress in ollama.pull(self.model_name, stream=True): - digest = progress.get("digest", "") - if digest != current_digest and current_digest in bars: - bars[current_digest].close() - - if not digest: - logger.info(progress.get("status")) - continue - - if digest not in bars and (total := progress.get("total")): - bars[digest] = tqdm( - total=total, - desc=f"pulling {digest[7:19]}", - unit="B", - unit_scale=True, - ) - - if completed := progress.get("completed"): - bars[digest].update(completed - bars[digest].n) - - current_digest = digest - except Exception as e: - raise LLMError( - f"Unable to download {self.model_name} from Ollama: {str(e)}" - ) - - try: - os.environ["OLLAMA_KEEP_ALIVE"] = str( - self.ollama_config.get("OLLAMA_KEEP_ALIVE", -1) - ) - if self.enable_concurrency: - self.aclient = ollama.AsyncClient( - host=self.ollama_config.get( - "OLLAMA_HOST", "http://localhost:11434" - ), - timeout=self.ollama_config.get("OLLAMA_REQUEST_TIMEOUT", 240.0), - ) - if self.device == "cuda": - os.environ["OLLAMA_NUM_GPU"] = str( - self.ollama_config.get( - "OLLAMA_NUM_GPU", self.num_workers // 2 - ) - ) - os.environ["OLLAMA_NUM_PARALLEL"] = str( - self.ollama_config.get( - "OLLAMA_NUM_PARALLEL", self.num_workers * 8 - ) - ) - os.environ["OLLAMA_GPU_LAYERS"] = str( - self.ollama_config.get("OLLAMA_GPU_LAYERS", "all") - ) - elif self.device == "mps": - os.environ["OLLAMA_NUM_GPU"] = str( - self.ollama_config.get("OLLAMA_NUM_GPU", 1) - ) - os.environ["OLLAMA_NUM_THREAD"] = str( - self.ollama_config.get( - "OLLAMA_NUM_THREAD", self.num_workers - ) - ) - os.environ["OLLAMA_NUM_PARALLEL"] = str( - self.ollama_config.get( - "OLLAMA_NUM_PARALLEL", self.num_workers * 8 - ) - ) - else: - os.environ["OLLAMA_NUM_THREAD"] = str( - self.ollama_config.get( - "OLLAMA_NUM_THREAD", self.num_workers - ) - ) - os.environ["OLLAMA_NUM_PARALLEL"] = str( - self.ollama_config.get( - "OLLAMA_NUM_PARALLEL", self.num_workers * 10 - ) - ) - else: - self.client = ollama.Client( - host=self.ollama_config.get( - "OLLAMA_HOST", "http://localhost:11434" - ), - timeout=self.ollama_config.get("OLLAMA_REQUEST_TIMEOUT", 240.0), - ) - except Exception as e: - raise LLMError(f"Unable to initialize Ollama client: {str(e)}") - - elif self.provider == "openai" or self.provider == "deepseek": - # support azure openai - if self.provider == "openai" and self.openai_config.get( - "AZURE_OPENAI_API_KEY" - ): - try: - import openai - from openai import AsyncAzureOpenAI, AzureOpenAI - except ImportError: - raise ImportError( - "OpenAI is not installed. Please install it using pip install 'vision-parse[openai]'." - ) + """Initialize the LLM client using litellm.""" + try: + # Initialize instructor client + self.client = instructor.patch( + completion if not self.enable_concurrency else acompletion, + mode=instructor.Mode.JSON, + ) + except Exception as e: + raise LLMError(f"Unable to initialize LLM client: {str(e)}") - try: - azure_subscription_key = self.openai_config.get( - "AZURE_OPENAI_API_KEY" - ) - azure_endpoint_url = self.openai_config.get("AZURE_ENDPOINT_URL") + def _get_provider_name(self, model_name: str) -> str: + """Get the provider name for a given model name based on its prefix.""" + for provider, prefixes in PROVIDER_PREFIXES.items(): + if any(model_name.startswith(prefix) for prefix in prefixes): + return provider - if not azure_endpoint_url or not azure_subscription_key: - raise LLMError( - "Set `AZURE_ENDPOINT_URL` and `AZURE_OPENAI_API_KEY` environment variables in `openai_config` parameter" - ) + supported_providers = ", ".join( + f"{name} ({provider})" for provider, name in SUPPORTED_PROVIDERS.items() + ) + raise UnsupportedProviderError( + f"Model '{model_name}' is not from a supported provider. " + f"Supported providers are: {supported_providers}" + ) - if self.openai_config.get("AZURE_DEPLOYMENT_NAME"): - self.model_name = self.openai_config.get( + def _get_model_params(self, structured: bool = False) -> Dict[str, Any]: + """Get model parameters based on provider and configuration.""" + params = { + "model": self.model_name, + "temperature": 0.0 if structured else self.temperature, + "top_p": 0.4 if structured else self.top_p, + **self.kwargs, + } + + if self.provider in ["openai", "azure"]: + if self.openai_config.get("AZURE_OPENAI_API_KEY"): + params.update( + { + "api_key": self.openai_config["AZURE_OPENAI_API_KEY"], + "api_base": self.openai_config["AZURE_ENDPOINT_URL"], + "api_version": self.openai_config.get( + "AZURE_OPENAI_API_VERSION", "2024-08-01-preview" + ), + "deployment_id": self.openai_config.get( "AZURE_DEPLOYMENT_NAME" - ) - - api_version = self.openai_config.get( - "AZURE_OPENAI_API_VERSION", "2024-08-01-preview" - ) - - # Initialize Azure OpenAI client with key-based authentication - if self.enable_concurrency: - self.aclient = AsyncAzureOpenAI( - azure_endpoint=azure_endpoint_url, - api_key=azure_subscription_key, - api_version=api_version, - ) - else: - self.client = AzureOpenAI( - azure_endpoint=azure_endpoint_url, - api_key=azure_subscription_key, - api_version=api_version, - ) - - except openai.OpenAIError as e: - raise LLMError( - f"Unable to initialize Azure OpenAI client: {str(e)}" - ) - + ), + } + ) else: - try: - import openai - except ImportError: - raise ImportError( - "OpenAI is not installed. Please install it using pip install 'vision-parse[openai]'." - ) - try: - if self.enable_concurrency: - self.aclient = openai.AsyncOpenAI( - api_key=self.api_key, - base_url=( - self.openai_config.get("OPENAI_BASE_URL", None) - if self.provider == "openai" - else "https://api.deepseek.com" - ), - max_retries=self.openai_config.get("OPENAI_MAX_RETRIES", 3), - timeout=self.openai_config.get("OPENAI_TIMEOUT", 240.0), - default_headers=self.openai_config.get( - "OPENAI_DEFAULT_HEADERS", None - ), - ) - else: - self.client = openai.OpenAI( - api_key=self.api_key, - base_url=( - self.openai_config.get("OPENAI_BASE_URL", None) - if self.provider == "openai" - else "https://api.deepseek.com" - ), - max_retries=self.openai_config.get("OPENAI_MAX_RETRIES", 3), - timeout=self.openai_config.get("OPENAI_TIMEOUT", 240.0), - default_headers=self.openai_config.get( - "OPENAI_DEFAULT_HEADERS", None - ), - ) - except openai.OpenAIError as e: - raise LLMError(f"Unable to initialize OpenAI client: {str(e)}") - - elif self.provider == "gemini": - try: - import google.generativeai as genai - except ImportError: - raise ImportError( - "Gemini is not installed. Please install it using pip install 'vision-parse[gemini]'." + params.update( + { + "api_key": self.api_key, + "base_url": self.openai_config.get("OPENAI_BASE_URL"), + "max_retries": self.openai_config.get("OPENAI_MAX_RETRIES", 3), + "timeout": self.openai_config.get("OPENAI_TIMEOUT", 240.0), + } ) + elif self.provider == "gemini": + params.update( + { + "api_key": self.api_key, + **self.gemini_config, + } + ) - try: - genai.configure(api_key=self.api_key) - self.client = genai.GenerativeModel(model_name=self.model_name) - self.generation_config = genai.GenerationConfig - except Exception as e: - raise LLMError(f"Unable to initialize Gemini client: {str(e)}") + return params - def _get_provider_name(self, model_name: str) -> str: - """Get the provider name for a given model name.""" + @retry( + reraise=True, + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + ) + async def _get_response( + self, base64_encoded: str, prompt: str, structured: bool = False + ) -> Any: + """Get response from LLM using litellm and instructor.""" try: - return SUPPORTED_MODELS[ - "litellm/*" if "litellm" in model_name else model_name + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": prompt}, + { + "type": "image_url", + "image_url": { + "url": f"data:image/png;base64,{base64_encoded}" + }, + }, + ], + } ] - except KeyError: - supported_models = ", ".join( - f"'{model}' from {provider}" - for model, provider in SUPPORTED_MODELS.items() - ) - raise UnsupportedModelError( - f"Model '{model_name}' is not supported. " - f"Supported models are: {supported_models}" - ) - async def _get_response( - self, base64_encoded: str, prompt: str, structured: bool = False - ): - if self.provider == "ollama": - return await self._ollama(base64_encoded, prompt, structured) - elif self.provider == "openai" or self.provider == "deepseek": - return await self._openai(base64_encoded, prompt, structured) - elif self.provider == "gemini": - return await self._gemini(base64_encoded, prompt, structured) + params = self._get_model_params(structured) + + if structured: + response = await self.client.chat.completions.create( + messages=messages, + response_model=ImageDescription, + **params, + ) + return response.model_dump_json() + else: + response = await self.client.chat.completions.create( + messages=messages, + **params, + ) + return re.sub( + r"```(?:markdown)?\n(.*?)\n```", + r"\1", + response.choices[0].message.content, + flags=re.DOTALL, + ) + + except Exception as e: + raise LLMError(f"LLM processing failed: {str(e)}") async def generate_markdown( self, base64_encoded: str, pix: fitz.Pixmap, page_number: int @@ -334,8 +216,7 @@ async def generate_markdown( return "" if ( - self.provider == "ollama" - and float(json_response.confidence_score_text) > 0.6 + float(json_response.confidence_score_text) > 0.6 and json_response.tables_detected.strip() == "No" and json_response.latex_equations_detected.strip() == "No" and ( @@ -393,198 +274,3 @@ async def generate_markdown( ) return markdown_content - - @retry( - reraise=True, - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=1, min=4, max=10), - ) - async def _ollama( - self, base64_encoded: str, prompt: str, structured: bool = False - ) -> Any: - """Process base64-encoded image through Ollama vision models.""" - try: - if self.enable_concurrency: - response = await self.aclient.chat( - model=self.model_name, - format=ImageDescription.model_json_schema() if structured else None, - messages=[ - { - "role": "user", - "content": prompt, - "images": [base64_encoded], - } - ], - options={ - "temperature": 0.0 if structured else self.temperature, - "top_p": 0.4 if structured else self.top_p, - **self.kwargs, - }, - keep_alive=-1, - ) - else: - response = self.client.chat( - model=self.model_name, - format=ImageDescription.model_json_schema() if structured else None, - messages=[ - { - "role": "user", - "content": prompt, - "images": [base64_encoded], - } - ], - options={ - "temperature": 0.0 if structured else self.temperature, - "top_p": 0.4 if structured else self.top_p, - **self.kwargs, - }, - keep_alive=-1, - ) - - return re.sub( - r"```(?:markdown)?\n(.*?)\n```", - r"\1", - response["message"]["content"], - flags=re.DOTALL, - ) - except Exception as e: - raise LLMError(f"Ollama Model processing failed: {str(e)}") - - @retry( - reraise=True, - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=1, min=4, max=10), - ) - async def _openai( - self, base64_encoded: str, prompt: str, structured: bool = False - ) -> Any: - """Process base64-encoded image through OpenAI vision models.""" - if "litellm" in self.model_name: - self.model_name = self.model_name.replace("litellm/", "") - - try: - messages = [ - { - "role": "user", - "content": [ - {"type": "text", "text": prompt}, - { - "type": "image_url", - "image_url": { - "url": f"data:image/png;base64,{base64_encoded}" - }, - }, - ], - } - ] - - if self.enable_concurrency: - if structured: - if os.getenv("AZURE_OPENAI_API_KEY") or self.provider == "deepseek": - response = await self.aclient.chat.completions.create( - model=self.model_name, - messages=messages, - temperature=0.0, - top_p=0.4, - response_format={"type": "json_object"}, - stream=False, - **self.kwargs, - ) - else: - response = await self.aclient.beta.chat.completions.parse( - model=self.model_name, - response_format=ImageDescription, - messages=messages, - temperature=0.0, - top_p=0.4, - **self.kwargs, - ) - return response.choices[0].message.content - - response = await self.aclient.chat.completions.create( - model=self.model_name, - messages=messages, - temperature=self.temperature, - top_p=self.top_p, - stream=False, - **self.kwargs, - ) - else: - if structured: - if os.getenv("AZURE_OPENAI_API_KEY") or self.provider == "deepseek": - response = self.client.chat.completions.create( - model=self.model_name, - messages=messages, - temperature=0.0, - top_p=0.4, - response_format={"type": "json_object"}, - stream=False, - **self.kwargs, - ) - else: - response = self.client.beta.chat.completions.parse( - model=self.model_name, - response_format=ImageDescription, - messages=messages, - temperature=0.0, - top_p=0.4, - **self.kwargs, - ) - return response.choices[0].message.content - - response = self.client.chat.completions.create( - model=self.model_name, - messages=messages, - temperature=self.temperature, - top_p=self.top_p, - stream=False, - **self.kwargs, - ) - - return re.sub( - r"```(?:markdown)?\n(.*?)\n```", - r"\1", - response.choices[0].message.content, - flags=re.DOTALL, - ) - except Exception as e: - raise LLMError(f"OpenAI Model processing failed: {str(e)}") - - @retry( - reraise=True, - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=1, min=4, max=10), - ) - async def _gemini( - self, base64_encoded: str, prompt: str, structured: bool = False - ) -> Any: - """Process base64-encoded image through Gemini vision models.""" - try: - if self.enable_concurrency: - response = await self.client.generate_content_async( - [{"mime_type": "image/png", "data": base64_encoded}, prompt], - generation_config=self.generation_config( - response_mime_type="application/json" if structured else None, - response_schema=ImageDescription if structured else None, - temperature=0.0 if structured else self.temperature, - top_p=0.4 if structured else self.top_p, - **self.kwargs, - ), - ) - else: - response = self.client.generate_content( - [{"mime_type": "image/png", "data": base64_encoded}, prompt], - generation_config=self.generation_config( - response_mime_type="application/json" if structured else None, - response_schema=ImageDescription if structured else None, - temperature=0.0 if structured else self.temperature, - top_p=0.4 if structured else self.top_p, - **self.kwargs, - ), - ) - - return re.sub( - r"```(?:markdown)?\n(.*?)\n```", r"\1", response.text, flags=re.DOTALL - ) - except Exception as e: - raise LLMError(f"Gemini Model processing failed: {str(e)}") diff --git a/uv.lock b/uv.lock index fc6e30f..97b8052 100644 --- a/uv.lock +++ b/uv.lock @@ -19,6 +19,125 @@ resolution-markers = [ "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", ] +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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-format: format-nb - black . - test: pytest -v --capture=no diff --git a/src/vision_parse/llm.py b/src/vision_parse/llm.py index 757f0b8..0f35de1 100644 --- a/src/vision_parse/llm.py +++ b/src/vision_parse/llm.py @@ -16,7 +16,16 @@ class ImageDescription(BaseModel): - """Model Schema for image description.""" + """Defines the schema for image description and analysis results. + + Attributes: + text_detected (Literal["Yes", "No"]): Indicates whether text is present in the image. + tables_detected (Literal["Yes", "No"]): Indicates whether tables are present in the image. + images_detected (Literal["Yes", "No"]): Indicates whether embedded images are present. + latex_equations_detected (Literal["Yes", "No"]): Indicates whether LaTeX equations are present. + extracted_text (str): Contains the raw text extracted from the image. + confidence_score_text (float): Provides the confidence score for text extraction accuracy. + """ text_detected: Literal["Yes", "No"] tables_detected: Literal["Yes", "No"] @@ -27,13 +36,21 @@ class ImageDescription(BaseModel): class UnsupportedProviderError(BaseException): - """Custom exception for unsupported provider names""" + """Raises an error when the specified LLM provider is not supported. + + This exception is raised when attempting to use a model from an unsupported + LLM provider or when the model name does not match any known provider prefix. + """ pass class LLMError(BaseException): - """Custom exception for Vision LLM errors""" + """Raises an error when LLM processing encounters a failure. + + This exception is raised when there are issues during LLM initialization, + API calls, or response processing. + """ pass @@ -82,7 +99,14 @@ def __init__( self._init_llm() def _init_llm(self) -> None: - """Initialize the LLM client using litellm.""" + """Initializes the LLM client with litellm integration. + + This method sets up the instructor client with appropriate completion mode + based on concurrency settings. + + Raises: + LLMError: If client initialization fails. + """ try: # Initialize instructor client self.client = instructor.patch( @@ -93,7 +117,17 @@ def _init_llm(self) -> None: raise LLMError(f"Unable to initialize LLM client: {str(e)}") def _get_provider_name(self, model_name: str) -> str: - """Get the provider name for a given model name based on its prefix.""" + """Determines the provider name based on the model name prefix. + + Args: + model_name (str): The name of the model to check. + + Returns: + str: The provider name (e.g., 'openai', 'gemini'). + + Raises: + UnsupportedProviderError: If the model name doesn't match any known provider. + """ for provider, prefixes in PROVIDER_PREFIXES.items(): if any(model_name.startswith(prefix) for prefix in prefixes): return provider @@ -107,7 +141,15 @@ def _get_provider_name(self, model_name: str) -> str: ) def _get_model_params(self, structured: bool = False) -> Dict[str, Any]: - """Get model parameters based on provider and configuration.""" + """Constructs model parameters based on provider and configuration. + + Args: + structured (bool, optional): Whether to use structured output parameters. + Defaults to False. + + Returns: + Dict[str, Any]: Dictionary containing model parameters for API calls. + """ params = { "model": self.model_name, "temperature": 0.0 if structured else self.temperature, @@ -156,7 +198,20 @@ def _get_model_params(self, structured: bool = False) -> Dict[str, Any]: async def _get_response( self, base64_encoded: str, prompt: str, structured: bool = False ) -> Any: - """Get response from LLM using litellm and instructor.""" + """Retrieves response from LLM using litellm and instructor. + + Args: + base64_encoded (str): Base64 encoded image data. + prompt (str): The prompt to send to the LLM. + structured (bool, optional): Whether to use structured output. + Defaults to False. + + Returns: + Any: The LLM response, either as structured JSON or raw text. + + Raises: + LLMError: If LLM processing fails. + """ try: messages = [ { @@ -200,7 +255,19 @@ async def _get_response( async def generate_markdown( self, base64_encoded: str, pix: fitz.Pixmap, page_number: int ) -> Any: - """Generate markdown formatted text from a base64-encoded image using appropriate model provider.""" + """Generates markdown formatted text from an image using the configured LLM provider. + + This method handles both detailed and simple extraction modes, processes + embedded images if configured, and formats the output as markdown. + + Args: + base64_encoded (str): Base64 encoded image data. + pix (fitz.Pixmap): The image pixmap for additional processing. + page_number (int): The page number being processed. + + Returns: + Any: The generated markdown text, including any embedded images if configured. + """ extracted_images = [] if self.detailed_extraction: try: diff --git a/src/vision_parse/parser.py b/src/vision_parse/parser.py index f72d301..69462e6 100644 --- a/src/vision_parse/parser.py +++ b/src/vision_parse/parser.py @@ -5,7 +5,7 @@ from pathlib import Path from typing import Any, Dict, List, Literal, Optional, Union -import fitz # PyMuPDF library for PDF processing +import fitz import nest_asyncio from pydantic import BaseModel from tqdm import tqdm @@ -18,28 +18,42 @@ class PDFPageConfig(BaseModel): - """Configuration settings for PDF page conversion.""" + """Configures settings for PDF page conversion. - dpi: int = 400 # Resolution for PDF to image conversion - color_space: str = "RGB" # Color mode for image output - include_annotations: bool = True # Include PDF annotations in conversion - preserve_transparency: bool = False # Control alpha channel in output + Attributes: + dpi (int): Resolution for PDF to image conversion. Defaults to 400. + color_space (str): Color mode for image output. Defaults to "RGB". + include_annotations (bool): Includes PDF annotations in conversion. Defaults to True. + preserve_transparency (bool): Preserves alpha channel in output. Defaults to False. + """ + + dpi: int = 400 + color_space: str = "RGB" + include_annotations: bool = True + preserve_transparency: bool = False class UnsupportedFileError(BaseException): - """Custom exception for handling unsupported file errors.""" + """Exception raised when an unsupported file type is provided. + + This exception is raised when attempting to process a file that is not a PDF. + """ pass class VisionParserError(BaseException): - """Custom exception for handling Markdown Parser errors.""" + """Exception raised when there is an error in the PDF to markdown conversion process. + + This exception is raised when there are issues during the conversion of PDF pages + to markdown format, such as conversion failures or processing errors. + """ pass class VisionParser: - """Convert PDF pages to base64-encoded images and then extract text from the images in markdown format.""" + """Converts PDF pages to base64-encoded images and extracts text from the images in markdown format.""" def __init__( self, @@ -58,7 +72,7 @@ def __init__( enable_concurrency: bool = False, **kwargs: Any, ): - """Initialize parser with PDFPageConfig and LLM configuration.""" + """Initializes parser with PDFPageConfig and LLM configuration.""" self.page_config = page_config or PDFPageConfig() self.device, self.num_workers = get_device_config() self.enable_concurrency = enable_concurrency @@ -94,7 +108,7 @@ def __init__( ) def _calculate_matrix(self, page: fitz.Page) -> fitz.Matrix: - """Calculate transformation matrix for page conversion.""" + """Calculates transformation matrix for page conversion.""" # Calculate zoom factor based on target DPI zoom = self.page_config.dpi / 72 matrix = fitz.Matrix(zoom * 2, zoom * 2) @@ -106,7 +120,7 @@ def _calculate_matrix(self, page: fitz.Page) -> fitz.Matrix: return matrix async def _convert_page(self, page: fitz.Page, page_number: int) -> str: - """Convert a single PDF page into base64-encoded PNG and extract markdown formatted text.""" + """Converts a single PDF page into base64-encoded PNG and extracts markdown formatted text.""" try: matrix = self._calculate_matrix(page) @@ -132,7 +146,7 @@ async def _convert_page(self, page: fitz.Page, page_number: int) -> str: pix = None async def _convert_pages_batch(self, pages: List[fitz.Page], start_idx: int): - """Process a batch of PDF pages concurrently.""" + """Processes a batch of PDF pages concurrently.""" try: tasks = [] for i, page in enumerate(pages): @@ -142,7 +156,7 @@ async def _convert_pages_batch(self, pages: List[fitz.Page], start_idx: int): await asyncio.sleep(0.5) def convert_pdf(self, pdf_path: Union[str, Path]) -> List[str]: - """Convert all pages in the given PDF file to markdown text.""" + """Converts all pages in the given PDF file to markdown text.""" pdf_path = Path(pdf_path) converted_pages = [] diff --git a/src/vision_parse/utils.py b/src/vision_parse/utils.py index bb296f0..dc3f233 100644 --- a/src/vision_parse/utils.py +++ b/src/vision_parse/utils.py @@ -1,5 +1,8 @@ import base64 import logging +import os +import platform +import subprocess from dataclasses import dataclass from threading import Lock from typing import ClassVar, List, Literal, Tuple @@ -12,20 +15,42 @@ class ImageExtractionError(BaseException): - """Custom exception for handling Image Extraction errors.""" + """Raises an error when image extraction or processing fails. + + This exception is raised when there are issues during image detection, + processing, or validation steps. + """ pass @dataclass class ImageData: - image_url: str # URL path for extracted images - base64_encoded: str | None # Base64 string if image_mode is base64, None otherwise - _lock: ClassVar[Lock] = Lock() # Lock for thread safety + """Represents extracted image data with its associated metadata. + + Attributes: + image_url (str): URL path for extracted images. + base64_encoded (str | None): Base64 string if image_mode is base64, None otherwise. + _lock (ClassVar[Lock]): Lock for thread safety during image processing. + """ + + image_url: str + base64_encoded: str | None + _lock: ClassVar[Lock] = Lock() @staticmethod def _prepare_image_for_detection(image: np.ndarray) -> np.ndarray: - """Process image to highlight potential image regions.""" + """Processes image to highlight potential image regions. + + Args: + image (np.ndarray): Input image in BGR format. + + Returns: + np.ndarray: Processed binary image highlighting potential image regions. + + Raises: + ImageExtractionError: If image processing fails. + """ try: grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) smooth = cv2.GaussianBlur(grayscale, (5, 5), 0) @@ -45,7 +70,19 @@ def _prepare_image_for_detection(image: np.ndarray) -> np.ndarray: def _check_region_validity( region: np.ndarray, contour: np.ndarray, region_dims: tuple ) -> bool: - """Determine if region contains a valid image based on statistical properties.""" + """Determines if region contains a valid image based on statistical properties. + + Args: + region (np.ndarray): Image region to validate. + contour (np.ndarray): Contour of the region. + region_dims (tuple): Dimensions (width, height) of the region. + + Returns: + bool: True if region contains a valid image, False otherwise. + + Raises: + ImageExtractionError: If image validation fails. + """ try: width, height = region_dims region_area = cv2.contourArea(contour) / (width * height) @@ -72,7 +109,24 @@ def extract_images( page_number: int, min_dimensions: tuple = (100, 100), ) -> List["ImageData"]: - """Extract images with their coordinates and hash versions based on image_mode.""" + """Extracts images from a PDF page with specified processing mode. + + This method processes the page image to detect and extract embedded images, + validates them, and saves them according to the specified image mode. + + Args: + pix (fitz.Pixmap): PDF page pixmap containing the image data. + image_mode (Literal["url", "base64", None]): Mode for image extraction. + page_number (int): Page number for image naming. + min_dimensions (tuple, optional): Minimum dimensions for valid images. + Defaults to (100, 100). + + Returns: + List[ImageData]: List of extracted images with their metadata. + + Raises: + ImageExtractionError: If image extraction or processing fails. + """ with cls._lock: try: min_width, min_height = min_dimensions @@ -145,11 +199,15 @@ def extract_images( def get_device_config() -> Tuple[Literal["cuda", "mps", "cpu"], int]: - """Get optimal number of worker processes based on device.""" - import os - import platform - import subprocess + """Determines optimal device configuration for processing. + + This function checks available hardware (GPU, MPS, CPU) and returns + the appropriate device type and number of worker processes. + Returns: + Tuple[Literal["cuda", "mps", "cpu"], int]: Device type and optimal number + of worker processes. + """ try: nvidia_smi = subprocess.run( ["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"], @@ -164,4 +222,5 @@ def get_device_config() -> Tuple[Literal["cuda", "mps", "cpu"], int]: if platform.system() == "Darwin" and platform.processor() == "arm": return "mps", 4 + return "cpu", max(2, (os.cpu_count() // 2)) From 42e504d7a4619e579d661fd06846c513fc59ca6c Mon Sep 17 00:00:00 2001 From: tqtensor Date: Thu, 8 May 2025 14:24:54 +0000 Subject: [PATCH 5/9] chore: store the first version of running code --- src/vision_parse/__init__.py | 16 +++++------ src/vision_parse/constants.py | 4 +-- src/vision_parse/llm.py | 51 ++++++++++++++++++++++------------- 3 files changed, 43 insertions(+), 28 deletions(-) diff --git a/src/vision_parse/__init__.py b/src/vision_parse/__init__.py index 9f59a73..c5ff09c 100644 --- a/src/vision_parse/__init__.py +++ b/src/vision_parse/__init__.py @@ -1,7 +1,7 @@ from importlib.metadata import PackageNotFoundError, version -from .constants import SUPPORTED_MODELS -from .llm import LLMError, UnsupportedModelError +from .constants import SUPPORTED_PROVIDERS +from .llm import LLMError, UnsupportedProviderError from .parser import PDFPageConfig, UnsupportedFileError, VisionParser, VisionParserError from .utils import ImageExtractionError @@ -12,13 +12,13 @@ __version__ = "0.0.0.dev0" __all__ = [ - "VisionParser", - "PDFPageConfig", "ImageExtractionError", - "VisionParserError", - "UnsupportedFileError", - "UnsupportedModelError", "LLMError", - "SUPPORTED_MODELS", + "PDFPageConfig", + "SUPPORTED_PROVIDERS", + "UnsupportedFileError", + "UnsupportedProviderError", + "VisionParser", + "VisionParserError", "__version__", ] diff --git a/src/vision_parse/constants.py b/src/vision_parse/constants.py index 0a20bae..1c0ad48 100644 --- a/src/vision_parse/constants.py +++ b/src/vision_parse/constants.py @@ -9,8 +9,8 @@ # Common model prefixes for provider detection PROVIDER_PREFIXES: Dict[str, List[str]] = { - "openai": ["gpt-", "ft:gpt-"], - "azure": ["gpt-", "ft:gpt-"], + "openai": ["gpt-"], + "azure": ["gpt-"], "gemini": ["gemini-"], "deepseek": ["deepseek-"], } diff --git a/src/vision_parse/llm.py b/src/vision_parse/llm.py index 0f35de1..d02a59b 100644 --- a/src/vision_parse/llm.py +++ b/src/vision_parse/llm.py @@ -1,6 +1,7 @@ import logging import re -from typing import Any, Dict, Literal, Union +from importlib.resources import files +from typing import Any, Dict, Literal import fitz import instructor @@ -56,10 +57,7 @@ class LLMError(BaseException): class LLM: - # Load prompts at class level try: - from importlib.resources import files - _image_analysis_prompt = Template( files("vision_parse").joinpath("image_analysis.j2").read_text() ) @@ -72,13 +70,13 @@ class LLM: def __init__( self, model_name: str, - api_key: Union[str, None], + api_key: str | None, temperature: float, top_p: float, - openai_config: Union[Dict, None], - gemini_config: Union[Dict, None], + openai_config: Dict | None, + gemini_config: Dict | None, image_mode: Literal["url", "base64", None], - custom_prompt: Union[str, None], + custom_prompt: str | None, detailed_extraction: bool, enable_concurrency: bool, **kwargs: Any, @@ -99,7 +97,7 @@ def __init__( self._init_llm() def _init_llm(self) -> None: - """Initializes the LLM client with litellm integration. + """Initializes the LLM client with LiteLLM integration. This method sets up the instructor client with appropriate completion mode based on concurrency settings. @@ -108,11 +106,9 @@ def _init_llm(self) -> None: LLMError: If client initialization fails. """ try: - # Initialize instructor client - self.client = instructor.patch( - completion if not self.enable_concurrency else acompletion, - mode=instructor.Mode.JSON, - ) + # Initialize instructor client with litellm + completion_func = acompletion if self.enable_concurrency else completion + self.client = instructor.from_litellm(completion_func) except Exception as e: raise LLMError(f"Unable to initialize LLM client: {str(e)}") @@ -128,6 +124,7 @@ def _get_provider_name(self, model_name: str) -> str: Raises: UnsupportedProviderError: If the model name doesn't match any known provider. """ + for provider, prefixes in PROVIDER_PREFIXES.items(): if any(model_name.startswith(prefix) for prefix in prefixes): return provider @@ -150,14 +147,23 @@ def _get_model_params(self, structured: bool = False) -> Dict[str, Any]: Returns: Dict[str, Any]: Dictionary containing model parameters for API calls. """ + # Base parameters that are common across providers params = { "model": self.model_name, "temperature": 0.0 if structured else self.temperature, "top_p": 0.4 if structured else self.top_p, - **self.kwargs, } + # Filter kwargs based on provider if self.provider in ["openai", "azure"]: + # Only include OpenAI-compatible parameters + openai_params = { + k: v + for k, v in self.kwargs.items() + if k not in ["device", "num_workers", "ollama_config"] + } + params.update(openai_params) + if self.openai_config.get("AZURE_OPENAI_API_KEY"): params.update( { @@ -181,6 +187,13 @@ def _get_model_params(self, structured: bool = False) -> Dict[str, Any]: } ) elif self.provider == "gemini": + # Only include Gemini-compatible parameters + gemini_params = { + k: v + for k, v in self.kwargs.items() + if k not in ["device", "num_workers", "ollama_config"] + } + params.update(gemini_params) params.update( { "api_key": self.api_key, @@ -198,7 +211,7 @@ def _get_model_params(self, structured: bool = False) -> Dict[str, Any]: async def _get_response( self, base64_encoded: str, prompt: str, structured: bool = False ) -> Any: - """Retrieves response from LLM using litellm and instructor. + """Retrieves response from LLM using LiteLLM and instructor. Args: base64_encoded (str): Base64 encoded image data. @@ -238,17 +251,18 @@ async def _get_response( ) return response.model_dump_json() else: + # For non-structured responses, use str as the response model response = await self.client.chat.completions.create( messages=messages, + response_model=str, **params, ) return re.sub( r"```(?:markdown)?\n(.*?)\n```", r"\1", - response.choices[0].message.content, + response, flags=re.DOTALL, ) - except Exception as e: raise LLMError(f"LLM processing failed: {str(e)}") @@ -268,6 +282,7 @@ async def generate_markdown( Returns: Any: The generated markdown text, including any embedded images if configured. """ + extracted_images = [] if self.detailed_extraction: try: From 52cd13715a7c7ba818e4f93166ed35897e041a94 Mon Sep 17 00:00:00 2001 From: tqtensor Date: Thu, 8 May 2025 16:13:45 +0000 Subject: [PATCH 6/9] refactor: update tests --- .gitignore | 1 - CITATION.cff | 8 - CONTRIBUTING.md | 78 ------- Dockerfile | 43 ---- Makefile | 4 + benchmarks/benchmark_results.md | 22 -- benchmarks/ground_truth.md | 67 ------ benchmarks/quantum_computing.pdf | Bin 16303 -> 0 bytes benchmarks/requirements.txt | 4 - benchmarks/scoring.py | 150 ------------ docker-compose.yml | 32 --- docs/docker_setup.md | 102 -------- docs/examples/deepseek_demo.ipynb | 68 ------ docs/examples/gemini_demo.ipynb | 137 ----------- docs/examples/gradio_app.py | 125 ---------- docs/examples/ollama_demo.ipynb | 115 --------- docs/examples/openai_demo.ipynb | 125 ---------- docs/examples/streamlit_app.py | 231 ------------------ docs/faq.md | 31 +-- docs/model_config.md | 41 ---- docs/ollama_setup.md | 27 --- pyproject.toml | 1 + src/vision_parse/exceptions.py | 37 +++ src/vision_parse/llm.py | 32 +-- src/vision_parse/parser.py | 109 +++++---- tests/conftest.py | 15 +- tests/test_llm.py | 377 +++--------------------------- tests/test_parser.py | 150 +++++------- uv.lock | 2 + 29 files changed, 230 insertions(+), 1904 deletions(-) delete mode 100644 CITATION.cff delete mode 100644 CONTRIBUTING.md delete mode 100644 Dockerfile delete mode 100644 benchmarks/benchmark_results.md delete mode 100644 benchmarks/ground_truth.md delete mode 100644 benchmarks/quantum_computing.pdf delete mode 100644 benchmarks/requirements.txt delete mode 100644 benchmarks/scoring.py delete mode 100644 docker-compose.yml delete mode 100644 docs/docker_setup.md delete mode 100644 docs/examples/deepseek_demo.ipynb delete mode 100644 docs/examples/gemini_demo.ipynb delete mode 100644 docs/examples/gradio_app.py delete mode 100644 docs/examples/ollama_demo.ipynb delete mode 100644 docs/examples/openai_demo.ipynb delete mode 100644 docs/examples/streamlit_app.py delete mode 100644 docs/model_config.md delete mode 100644 docs/ollama_setup.md create mode 100644 src/vision_parse/exceptions.py diff --git a/.gitignore b/.gitignore index 4a21278..0cac101 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,5 @@ # Environment variables .env -.env.* .DS_Store # Python-generated files diff --git a/CITATION.cff b/CITATION.cff deleted file mode 100644 index 582bf9d..0000000 --- a/CITATION.cff +++ /dev/null @@ -1,8 +0,0 @@ -cff-version: 1.2.0 -message: "If you use this software, please cite it as below." -authors: -- family-names: "Brahma" - given-names: "Arun" -title: "Vision-Parse: Parse PDFs into markdown using Vision LLMs" -date-released: 2024-12-31 -url: "https://github.com/iamarunbrahma/vision-parse" diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index abf4c55..0000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,78 +0,0 @@ -# Contributing to Vision Parse - -Thank you for your interest in contributing to Vision Parse! This document provides guidelines and instructions for contributing to the project. - -## Code of Conduct - -By participating in this project, you agree to abide by our Code of Conduct: - -- Be respectful and inclusive -- Accept constructive criticism gracefully -- Focus on what is best for the community - -## Getting Started - -1. **Fork the Repository** - - Visit the [Vision Parse repository](https://github.com/iamarunbrahma/vision-parse) - - Click the "Fork" button in the top-right corner - - Clone your forked repository: - ```bash - git clone https://github.com/YOUR_USERNAME/vision-parse.git - cd vision-parse - ``` - -2. **Set Up Development Environment** - - Install dependencies using uv (recommended): - - For Mac/Linux: - ```bash - curl -LsSf https://astral.sh/uv/install.sh | sh - ``` - - For Windows: - ```bash - powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" - ``` - - Install project dependencies: - ```bash - uv sync --all-extras && source .venv/bin/activate - ``` - -## Development Workflow - -1. **Create a New Branch** - ```bash - # If you are fixing a bug - git checkout -b bugfix/your-bug-name - - # If you are adding a new feature - git checkout -b feature/your-feature-name - ``` - -2. **Make Your Changes** - - Write your code and add tests if applicable - - Follow PEP 8 guidelines and use type hints where possible - - Update documentation as needed - -3. **Quality Checks** - Before submitting your changes, run: - ```bash - make lint # Run code linting - make format # Format code - make test # Run test suite - ``` - -## Pull Request Process - -1. **Prepare Your Changes** - ```bash - git add . - git commit -m "Description of your changes" - git push origin feature/your-feature-name - ``` - -2. **Create Pull Request** - - Go to your fork on GitHub and click "New Pull Request" - - Select your feature branch - - Fill in the PR template by describing your changes, and referencing related issue. diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 1f08d16..0000000 --- a/Dockerfile +++ /dev/null @@ -1,43 +0,0 @@ -FROM python:3.13-slim - -# Accept build argument -ARG MODEL_NAME - -# Install system dependencies -RUN apt-get update && apt-get install -y \ - build-essential \ - git \ - curl \ - libgl1-mesa-glx \ - libglib2.0-0 \ - && rm -rf /var/lib/apt/lists/* - -# Install Ollama only for Ollama-based models -RUN case "$MODEL_NAME" in \ - "llama3.2-vision:11b"|"llama3.2-vision:70b"|"llava:13b"|"llava:34b") \ - curl -fsSL https://ollama.com/install.sh | sh \ - ;; \ - esac - -# Set working directory -WORKDIR /app - -# Copy project files -COPY . . - -# Install vision-parse with all optional dependencies -RUN pip install --no-cache-dir 'vision-parse[all]' - -# Create a startup script -RUN echo '#!/bin/bash\n\ -case "$MODEL_NAME" in\n\ - "llama3.2-vision:11b"|"llama3.2-vision:70b"|"llava:13b"|"llava:34b")\n\ - ollama serve > /var/log/ollama.log 2>&1 &\n\ - sleep 10\n\ - ollama pull $MODEL_NAME\n\ - ;;\n\ -esac\n\ -exec "$@"' > /start.sh && chmod +x /start.sh - -ENTRYPOINT ["/start.sh"] -CMD ["tail", "-f", "/dev/null"] diff --git a/Makefile b/Makefile index 3091963..e9d7ebc 100644 --- a/Makefile +++ b/Makefile @@ -1,6 +1,7 @@ .PHONY: lint format test build release tag format-nb test: + uv pip install -e . --link-mode=copy pytest -v --capture=no build: @@ -15,3 +16,6 @@ tag: release: build tag @echo "Release workflow will be triggered by the tag push" @echo "Distribution files are available in ./dist directory" + +toml-sort: + uv run toml-sort -i pyproject.toml --no-sort-tables --sort-table-keys diff --git a/benchmarks/benchmark_results.md b/benchmarks/benchmark_results.md deleted file mode 100644 index e931f58..0000000 --- a/benchmarks/benchmark_results.md +++ /dev/null @@ -1,22 +0,0 @@ -# Benchmark Results - -Generated on: 2025-01-03 16:15:01 - -## Summary - -| Parser | Accuracy | -|--------|----------| -| Vision Parse | 0.88 | -| MarkItDown | 0.52 | - -## Detailed Results - -### Vision Parse - -- Number of runs: 3 -- Individual accuracy scores: 0.90, 0.90, 0.84 - -### MarkItDown - -- Number of runs: 3 -- Individual accuracy scores: 0.52, 0.52, 0.52 diff --git a/benchmarks/ground_truth.md b/benchmarks/ground_truth.md deleted file mode 100644 index 3928d91..0000000 --- a/benchmarks/ground_truth.md +++ /dev/null @@ -1,67 +0,0 @@ -# Quantum Computing: The Next Frontier ---- - -Quantum Computing is a revolutionary paradigm that leverages the principles of quantum mechanics to process information. Unlike classical computing, which relies on bits, quantum computing uses **quantum bits (qubits)** to achieve unprecedented computational power. - - -## Key Concepts in Quantum Computing ---- - -### 1. *Quantum Mechanics Basics* -Quantum computing is built on the principles of: -- **Superposition**: A qubit can represent both 0 and 1 simultaneously. -- **Entanglement**: Qubits can become interconnected, influencing each other regardless of distance. -- **Quantum Interference**: Amplifies correct solutions and suppresses incorrect ones. - -### 2. *Classical vs Quantum Computing* - -| **Feature** | **Classical Computing** | **Quantum Computing** | -|-----------------------|---------------------------|---------------------------| -| Unit of Data | Bit (0 or 1) | Qubit (superposition) | -| Speed | Sequential | Parallel | -| Problem-Solving | Limited by complexity | Solves complex problems | -| Applications | Everyday tasks | Optimization, cryptography| - - -## Applications of Quantum Computing ---- - -1. **Cryptography** - - Breaking traditional encryption systems (e.g., RSA). - - Building unbreakable quantum encryption. - -2. **Healthcare** - - Drug discovery through quantum simulations. - - Optimizing protein folding patterns. - -3. **Artificial Intelligence (AI)** - - Enhancing machine learning algorithms. - - Faster data processing and pattern recognition. - - -### Advantages and Challenges - -**Advantages** -1. Exponential computational power. -2. Solves problems intractable for classical computers. - -**Challenges** -1. **Hardware stability**: Qubits are fragile and error-prone. -2. **Scalability**: Building large quantum systems is expensive. -3. **Software development**: Limited programming frameworks. - - -### Key Players in Quantum Computing ---- - -Some organizations leading the quantum revolution include: -- [IBM Quantum](https://www.ibm.com/quantum-computing) -- [Google Quantum AI](https://quantumai.google) -- [Microsoft Azure Quantum](https://azure.microsoft.com/en-us/services/quantum/) -- [D-Wave](https://www.dwavesys.com) - - -## Conclusion ---- - -Quantum computing is still in its infancy but holds immense promise to redefine technology. 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-import nltk -from Levenshtein import distance -from markitdown import MarkItDown -from nltk.translate.bleu_score import sentence_bleu - -from vision_parse import VisionParser - -# Download required NLTK data -nltk.download("punkt") -nltk.download("punkt_tab") - - -def calculate_bleu_score(reference_text: str, candidate_text: str) -> float: - """Calculate BLEU score between reference and candidate text.""" - reference_tokens = nltk.word_tokenize(reference_text.lower()) - candidate_tokens = nltk.word_tokenize(candidate_text.lower()) - return sentence_bleu([reference_tokens], candidate_tokens) - - -def calculate_levenshtein_similarity(reference_text: str, candidate_text: str) -> float: - """Calculate normalized Levenshtein similarity between two texts.""" - max_len = max(len(reference_text), len(candidate_text)) - if max_len == 0: - return 1.0 - return 1 - (distance(reference_text, candidate_text) / max_len) - - -def benchmark_parser( - parser_name: str, - parser_func, - pdf_path: Path, - ground_truth: str, - num_runs: int = 3, -) -> dict: - """Benchmark a specific parser's performance.""" - accuracy_scores = [] - - for i in range(num_runs): - result = parser_func(pdf_path) - - # Extract text content from parser result - if hasattr(result, "text_content"): - parsed_text = result.text_content - elif isinstance(result, list): - parsed_text = "\n".join(result) - else: - parsed_text = str(result) - - # Calculate performance metrics - bleu_score = calculate_bleu_score(ground_truth, parsed_text) - levenshtein_score = calculate_levenshtein_similarity(ground_truth, parsed_text) - accuracy = (bleu_score + levenshtein_score) / 2 - - accuracy_scores.append(accuracy) - - # Calculate average metrics - avg_accuracy = statistics.mean(accuracy_scores) - - return { - "parser": parser_name, - "avg_accuracy": avg_accuracy, - "num_runs": num_runs, - "individual_runs": { - "accuracy_scores": accuracy_scores, - }, - } - - -def save_benchmark_results(results: list, output_file: Path): - """Save benchmark results to a markdown file.""" - with open(output_file, "w") as f: - f.write("# Benchmark Results\n\n") - f.write(f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n") - - # Write summary table - f.write("## Summary\n\n") - f.write("| Parser | Accuracy |\n") - f.write("|--------|----------|\n") - for result in results: - f.write(f"| {result['parser']} | {result['avg_accuracy']:.2f} |\n") - - # Write detailed results - f.write("\n## Detailed Results\n\n") - for result in results: - f.write(f"### {result['parser']}\n\n") - f.write(f"- Number of runs: {result['num_runs']}\n") - accuracy_str = ", ".join( - [f"{x:.2f}" for x in result["individual_runs"]["accuracy_scores"]] - ) - f.write(f"- Individual accuracy scores: {accuracy_str}\n\n") - - -def main(): - # Configure input/output paths - pdf_path = Path("quantum_computing.pdf") - benchmark_results_path = Path("benchmark_results.md") - - with open(Path("ground_truth.md"), "r") as f: - ground_truth = f.read() - - # Initialize parsers - vision_parser = VisionParser( - model_name="gpt-4o", - api_key=os.getenv("OPENAI_API_KEY"), - temperature=0.7, - top_p=0.5, - image_mode=None, - detailed_extraction=False, - enable_concurrency=True, - ) - markitdown_parser = MarkItDown() - - # Define parser functions - def vision_parse_func(path): - return vision_parser.convert_pdf(path) - - def markitdown_func(path): - return markitdown_parser.convert(str(path)) - - # Run benchmarks - results = [] - results.append( - benchmark_parser("Vision Parse", vision_parse_func, pdf_path, ground_truth) - ) - results.append( - benchmark_parser("MarkItDown", markitdown_func, pdf_path, ground_truth) - ) - - # Save results to file - save_benchmark_results(results, benchmark_results_path) - - # Print results to console - print("\nBenchmark Results:") - print("-" * 80) - print(f"{'Parser':<15} {'Accuracy':<15}") - print("-" * 80) - - for result in results: - print(f"{result['parser']:<15} " f"{result['avg_accuracy']:<15.2f} ") - - print(f"\nDetailed results saved to: {benchmark_results_path}") - - -if __name__ == "__main__": - main() diff --git a/docker-compose.yml b/docker-compose.yml deleted file mode 100644 index 72028d6..0000000 --- a/docker-compose.yml +++ /dev/null @@ -1,32 +0,0 @@ -name: vision-parse - -services: - vision-parse: - build: - context: . - dockerfile: Dockerfile - args: - - MODEL_NAME=${MODEL_NAME:?MODEL_NAME is required} - environment: - - MODEL_NAME=${MODEL_NAME:?MODEL_NAME is required} - - OPENAI_API_KEY=${OPENAI_API_KEY:-} # Optional: For OpenAI models - - GEMINI_API_KEY=${GEMINI_API_KEY:-} # Optional: For Gemini models - volumes: - - .:/app - working_dir: /app - tty: true - stdin_open: true - ports: - - '11434:11434' # Expose Ollama port - deploy: - resources: - limits: - memory: 16G # Set memory limit to 16GB - reservations: - memory: 8G # Guarantee at least 8GB - # Uncomment below lines if you have NVIDIA GPU available - # devices: - # - driver: nvidia - # count: all - # capabilities: [gpu] - command: tail -f /dev/null # Keep container running diff --git a/docs/docker_setup.md b/docs/docker_setup.md deleted file mode 100644 index fe24417..0000000 --- a/docs/docker_setup.md +++ /dev/null @@ -1,102 +0,0 @@ -# Docker Setup Guide for Vision Parse - -This guide explains setting up Vision Parse using Docker on macOS and Linux systems. - -## Prerequisites - -- Docker and Docker Compose installed on your system -- Nvidia GPU (optional, but recommended for better performance) - -## Installation Steps - -**macOS** -- Download and install [Docker Desktop](https://hub.docker.com/editions/community/docker-ce-desktop-mac) -- Docker Compose is included in Docker Desktop - -**Linux** -```bash -# Install Docker Engine -curl -fsSL https://get.docker.com -o get-docker.sh -sudo sh get-docker.sh - -# Install Docker Compose -sudo apt-get install docker-compose - -# For GPU Support (Optional) -curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg - -curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \ - sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \ - sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list - -sudo apt-get update -sudo apt-get install -y nvidia-container-toolkit -``` - -## Environment Setup - -Export the required environment variables in your terminal: -```bash -# Required: Choose one of the following models -export MODEL_NAME=llama3.2-vision:11b # select the model name from the list of supported models - -# Optional: API keys (required only for specific models) -export OPENAI_API_KEY=your_openai_api_key -export GEMINI_API_KEY=your_gemini_api_key -``` - -## Running Docker Container - -1. If you have Nvidia GPU, uncomment the following lines in docker-compose.yml: - ```yaml - deploy: - resources: - reservations: - devices: - - driver: nvidia - count: 1 - capabilities: [gpu] - ``` - -2. Build and start the container: - ```bash - # Build the image - docker compose build - - # Start the container in detached mode - docker compose up -d - ``` -3. Verify the container is running: - ```bash - docker ps - ``` - -## Running Vision Parse - -To run the Vision Parse application: - -```bash -# Execute the python script inside the container -docker compose exec vision-parse python docs/examples/gradio_app.py -``` - -## Troubleshooting - -1. If you're using Ollama-based models and encounter connection issues, check if port 11434 is already in use: -```bash -sudo lsof -i :11434 -``` - -2. Check container logs for errors: -```bash -docker compose logs vision-parse -``` - -## Managing the Container - -```bash -# Stop the container and preserve data -docker compose stop - -# Stop and remove containers, networks -docker compose down diff --git a/docs/examples/deepseek_demo.ipynb b/docs/examples/deepseek_demo.ipynb deleted file mode 100644 index 69b89c2..0000000 --- a/docs/examples/deepseek_demo.ipynb +++ /dev/null @@ -1,68 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using Vision Parse with DeepSeek" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook demonstrates how to use Vision Parse with DeepSeek." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install 'vision-parse[openai]' -Uqq # install the vision-parse package with openai" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from vision_parse import VisionParser\n", - "import os\n", - "\n", - "# Initialize parser\n", - "parser = VisionParser(\n", - " model_name=\"deepseek-chat\",\n", - " api_key=os.getenv(\"DEEPSEEK_API_KEY\"),\n", - " temperature=0.9,\n", - " top_p=0.4,\n", - " image_mode=None,\n", - " detailed_extraction=True,\n", - " enable_concurrency=True,\n", - ")\n", - "\n", - "pdf_path = \"../tests/Texas-Holdem-Rules.pdf\"\n", - "markdown_pages = parser.convert_pdf(pdf_path)\n", - "\n", - "# Print the markdown pages\n", - "for i, page_content in enumerate(markdown_pages):\n", - " print(f\"\\n--- Page {i+1} ---\\n{page_content}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "personal", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/examples/gemini_demo.ipynb b/docs/examples/gemini_demo.ipynb deleted file mode 100644 index 4fee681..0000000 --- a/docs/examples/gemini_demo.ipynb +++ /dev/null @@ -1,137 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using Vision Parse with Google Gemini" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook demonstrates how to use Vision Parse with Google Gemini." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install 'vision-parse[gemini]' -Uqq # install the vision-parse package with gemini" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/arunbrahma/anaconda3/envs/mp/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "Converting pages in PDF file into markdown format: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:15<00:00, 15.82s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Page 1 ---\n", - "# TEXAS A&M\n", - "# AGRILIFE\n", - "# EXTENSION\n", - "\n", - "March 3, 2020\n", - "\n", - "TO: JR Sprague, TCAAA D1 Chairman\n", - "\n", - "FROM: Andy Holloway, CEA ANR Hemphill County\n", - "\n", - "Regarding the three chairmanships appointed to me none have any action\n", - "needed at this time except for the rules to be submitted to be posted on the\n", - "TCAAA website for the Texas Holdem Tournament. Please note the following:\n", - "\n", - "# Texas Hold'em Basic Poker Rules\n", - "\n", - "Texas Hold'em is played with a standard deck of 52 cards. The object of the game is to make the best\n", - "possible five-card poker hand using any combination of the two card's in a player's hand (known as\n", - "the \"hole cards\") and the five community cards that the dealer places in the middle of the table, which\n", - "can be used by all players. The standard hand rankings are used, as follows (from best to worst):\n", - "\n", - "* Straight Flush: Any five cards of the same suit and consecutive rank; the best of these hands,\n", - "AKQJT of a single suit, is known as a Royal Flush. For these and regular straights, aces may be\n", - "played as high or low cards.\n", - "* Four of a Kind: Any four cards of the same rank.\n", - "* Full House: Any three cards from a single rank combined with a pair from a different rank (i.e.,\n", - "TT55).\n", - "* Flush: Any five cards of the same suit.\n", - "* Straight: Any five cards of consecutive ranks (i.e., 34567).\n", - "* Three of a Kind: Any three cards of the same rank.\n", - "* Two Pair: Any two pairs of cards from the same ranks (ie, 5599A).\n", - "* One Pair: Any two cards of the same rank.\n", - "* High Card: Hands that do not fit any of the above categories are ranked based on the highest card\n", - "in their hand (aces are high), then by the second highest card, and so on.\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from vision_parse import VisionParser\n", - "import os\n", - "\n", - "# Initialize parser\n", - "parser = VisionParser(\n", - " model_name=\"gemini-1.5-flash\",\n", - " api_key=os.getenv(\"GEMINI_API_KEY\"),\n", - " temperature=0.9,\n", - " top_p=0.4,\n", - " max_output_tokens=2048,\n", - " image_mode=\"url\",\n", - " detailed_extraction=True,\n", - ")\n", - "\n", - "pdf_path = \"../tests/Texas-Holdem-Rules.pdf\"\n", - "markdown_pages = parser.convert_pdf(pdf_path)\n", - "\n", - "# Print the markdown pages\n", - "for i, page_content in enumerate(markdown_pages):\n", - " print(f\"\\n--- Page {i+1} ---\\n{page_content}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mp", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/examples/gradio_app.py b/docs/examples/gradio_app.py deleted file mode 100644 index 46b6757..0000000 --- a/docs/examples/gradio_app.py +++ /dev/null @@ -1,125 +0,0 @@ -import os - -import gradio as gr -from dotenv import load_dotenv - -from vision_parse import VisionParser - -# Load environment variables -load_dotenv() - -# Constants -MAX_FILE_SIZE_MB: int = 5 -MAX_FILE_SIZE_BYTES: int = MAX_FILE_SIZE_MB * 1024 * 1024 # 5MB limit - - -def process_pdf(pdf_file) -> str: - """Process the uploaded PDF file using Vision Parse.""" - if pdf_file is None: - return "Please upload a PDF file." - - # Check file size - file_size = os.path.getsize(pdf_file.name) - if file_size > MAX_FILE_SIZE_BYTES: - return f"File size exceeds the limit of {MAX_FILE_SIZE_MB}MB" - - try: - # Process the PDF using Vision Parse - vision_parser = VisionParser( - model_name="gpt-4o-mini", - api_key=os.getenv("OPENAI_API_KEY"), - temperature=0.7, - top_p=0.4, - image_mode=None, - detailed_extraction=False, - ) - result = vision_parser.convert_pdf(pdf_file.name) - - # Format results - if isinstance(result, list): - return "\n\n".join( - content.replace("Page " + str(i + 1), "").strip() - for i, content in enumerate(result) - ) - return result - - except Exception as e: - return f"Error processing PDF: {str(e)}" - - -def create_demo() -> gr.Interface: - """Create and configure the Gradio interface.""" - - # Define the interface - demo = gr.Interface( - fn=process_pdf, - inputs=gr.File( - label="Upload PDF (max 5MB)", - file_types=[".pdf"], - height=200, - ), - outputs=gr.Textbox( - label="Extracted Text", - lines=15, - show_copy_button=True, - ), - title="Vision Parse - Demo", - description="Upload a PDF file to extract detailed information using Vision Parse.", - theme=gr.themes.Soft( - primary_hue=gr.themes.colors.indigo, - secondary_hue=gr.themes.colors.slate, - neutral_hue=gr.themes.colors.slate, - font=["Source Sans Pro", "ui-sans-serif", "system-ui", "sans-serif"], - ), - css=""" - .gradio-container { - max-width: 1200px !important; - margin: 0 auto; - padding: 20px; - } - #component-0 { - max-width: 100%; - border: 2px dashed #7c3aed; - border-radius: 8px; - background-color: #1e293b; - transition: border-color 0.3s ease; - } - #component-0:hover { - border-color: #4f46e5; - } - .upload-box { - height: 200px !important; - } - .gr-box { - border-radius: 12px !important; - background-color: #1e293b !important; - } - .gr-button { - border-radius: 8px !important; - } - .gr-button.primary { - background: linear-gradient(to right, #4f46e5, #7c3aed) !important; - } - .gr-form { - flex: 1; - gap: 20px; - } - .gr-padded { - padding: 20px !important; - } - .gr-text-input, .gr-text-output { - background-color: #1e293b !important; - border: 1px solid #4b5563 !important; - border-radius: 8px !important; - } - .dark { - color-scheme: dark; - } - """, - ) - return demo - - -if __name__ == "__main__": - demo = create_demo() - demo.launch(share=False, server_port=7860) diff --git a/docs/examples/ollama_demo.ipynb b/docs/examples/ollama_demo.ipynb deleted file mode 100644 index 1fcddf7..0000000 --- a/docs/examples/ollama_demo.ipynb +++ /dev/null @@ -1,115 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using Vision Parse with Ollama" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook demonstrates how to use Vision Parse with Ollama." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install vision-parse -Uqq # install the vision-parse package with ollama" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Converting pages in PDF file into markdown format: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:48<00:00, 48.24s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Page 1 ---\n", - "**Extracted Text**\n", - "\n", - "* **TO:** JR Sprague, TCAAA D1 Chairman\n", - " * FROM: Andy Holloway, CEA ANR Hemphill County\n", - " * Regarding the three chairmanships appointed to me none have any action needed at this time except for the rules to be submitted to be posted on the TCAAA website for the Texas Holdem Tournament. Please note the following:\n", - "* **Texas Hold'em Basic Poker Rules**\n", - " * Straight Flush: Any five cards of the same suit and consecutive rank; the best of these hands, AKQJT of a single suit, is known as a Royal Flush. For these and regular straights, Aces may be played as high or low cards.\n", - " * Four of a Kind: Any four cards of the same rank.\n", - " * Full House: Any three cards from a single rank combined with a pair from a different rank (i.e., TTT55).\n", - " * Flush: Any five cards of the same suit.\n", - " * Straight: Any five cards of consecutive ranks (i.e., 34567).\n", - " * Three of a Kind: Any three cards of the same rank.\n", - " * Two Pair: Any two pairs of cards from the same ranks (i.e., 5599A).\n", - " * One Pair: Any two cards of the same rank.\n", - " * High Card: Hands that do not fit any of the above categories are ranked based on the highest card in their hand (aces are high), then by the second highest card, and so on.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from vision_parse import VisionParser\n", - "\n", - "parser = VisionParser(\n", - " model_name=\"llama3.2-vision:11b\",\n", - " temperature=0.7,\n", - " top_p=0.4,\n", - " num_ctx=4096,\n", - " num_predict=4096,\n", - " ollama_config={\n", - " \"OLLAMA_NUM_PARALLEL\": \"10\",\n", - " },\n", - " image_mode=\"base64\",\n", - " detailed_extraction=True,\n", - " enable_concurrency=True,\n", - ")\n", - "\n", - "pdf_path = \"../tests/Texas-Holdem-Rules.pdf\"\n", - "markdown_pages = parser.convert_pdf(pdf_path)\n", - "\n", - "for i, page_content in enumerate(markdown_pages):\n", - " print(f\"\\n--- Page {i+1} ---\\n{page_content}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mp", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/examples/openai_demo.ipynb b/docs/examples/openai_demo.ipynb deleted file mode 100644 index edeb13b..0000000 --- a/docs/examples/openai_demo.ipynb +++ /dev/null @@ -1,125 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using Vision Parse with OpenAI" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook demonstrates how to use Vision Parse with OpenAI." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install 'vision-parse[openai]' -Uqq # install the vision-parse package with openai" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Converting pages in PDF file into markdown format: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:25<00:00, 25.21s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Page 1 ---\n", - "# TEXAS A&M \n", - "AGRILIFE \n", - "EXTENSION\n", - "\n", - "March 3, 2020\n", - "\n", - "TO: JR Sprague, TCAAA D1 Chairman \n", - "FROM: Andy Holloway, CEA ANR Hemphill County\n", - "\n", - "Regarding the three chairmanships appointed to me none have any action needed at this time except for the rules to be submitted to be posted on the TCAAA website for the Texas Holdem Tournament. Please note the following:\n", - "\n", - "## Texas Hold'em Basic Poker Rules\n", - "\n", - "Texas Hold'em is played with a standard deck of 52 cards. The object of the game is to make the best possible five-card poker hand using any combination of the two card's in a player's hand (known as the \"hole cards\") and the five community cards that the dealer places in the middle of the table, which can be used by all players. The standard hand rankings are used, as follows (from best to worst):\n", - "\n", - "- **Straight Flush**: Any five cards of the same suit and consecutive rank; the best of these hands, AKQJT of a single suit, is known as a Royal Flush. For these and regular straights, aces may be played as high or low cards.\n", - "- **Four of a Kind**: Any four cards of the same rank.\n", - "- **Full House**: Any three cards from a single rank combined with a pair from a different rank (i.e., TTT55).\n", - "- **Flush**: Any five cards of the same suit.\n", - "- **Straight**: Any five cards of consecutive ranks (i.e., 34567).\n", - "- **Three of a Kind**: Any three cards of the same rank.\n", - "- **Two Pair**: Any two pairs of cards from the same ranks (ie, 5599A).\n", - "- **One Pair**: Any two cards of the same rank.\n", - "- **High Card**: Hands that do not fit any of the above categories are ranked based on the highest card in their hand (aces are high), then by the second highest card, and so on.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from vision_parse import VisionParser\n", - "import os\n", - "\n", - "# Initialize parser\n", - "parser = VisionParser(\n", - " model_name=\"gpt-4o\",\n", - " api_key=os.getenv(\"OPENAI_API_KEY\"),\n", - " temperature=0.9,\n", - " top_p=0.4,\n", - " max_tokens=4096,\n", - " frequency_penalty=0.3,\n", - " image_mode=None,\n", - " detailed_extraction=True,\n", - " enable_concurrency=True,\n", - ")\n", - "\n", - "pdf_path = \"../tests/Texas-Holdem-Rules.pdf\"\n", - "markdown_pages = parser.convert_pdf(pdf_path)\n", - "\n", - "# Print the markdown pages\n", - "for i, page_content in enumerate(markdown_pages):\n", - " print(f\"\\n--- Page {i+1} ---\\n{page_content}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mp", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/examples/streamlit_app.py b/docs/examples/streamlit_app.py deleted file mode 100644 index 74e69db..0000000 --- a/docs/examples/streamlit_app.py +++ /dev/null @@ -1,231 +0,0 @@ -import gc -import os -from pathlib import Path -from typing import List, Optional, Union - -import pyperclip -import streamlit as st -from dotenv import load_dotenv - -from vision_parse import VisionParser - -# Load environment variables -load_dotenv() - -# Constants -MAX_FILE_SIZE_MB: int = 5 -MAX_FILE_SIZE_BYTES: int = MAX_FILE_SIZE_MB * 1024 * 1024 # 5MB limit - - -def init_page_config() -> None: - """Initialize Streamlit page configuration.""" - st.set_page_config(page_title="Vision Parse Demo", page_icon="🦩", layout="wide") - - -def apply_custom_styles() -> None: - """Apply custom CSS styles to the Streamlit app.""" - st.markdown( - """ - - """, - unsafe_allow_html=True, - ) - - -def init_session_state() -> None: - """Initialize Streamlit session state variables.""" - if "results" not in st.session_state: - st.session_state.results = None - if "is_processing" not in st.session_state: - st.session_state.is_processing = False - if "uploaded_file" not in st.session_state: - st.session_state.uploaded_file = None - if "file_processed" not in st.session_state: - st.session_state.file_processed = False - - -def process_pdf(pdf_file) -> Optional[Union[str, List[str]]]: - """Process the uploaded PDF file using Vision Parse.""" - # Check file size - file_size = len(pdf_file.getvalue()) - if file_size > MAX_FILE_SIZE_BYTES: - st.error(f"File size exceeds the limit of {MAX_FILE_SIZE_MB}MB") - return None - - temp_path = Path(f"temp_{os.getpid()}.pdf") - try: - # Save uploaded file temporarily - temp_path.write_bytes(pdf_file.getvalue()) - - # Process the PDF using Vision Parse - vision_parser = VisionParser( - model_name="gpt-4o-mini", - api_key=os.getenv("OPENAI_API_KEY"), - temperature=0.7, - top_p=0.4, - image_mode=None, - detailed_extraction=False, - ) - result = vision_parser.convert_pdf(temp_path) - return result - except Exception as e: - st.error(f"Error processing PDF: {str(e)}") - return None - finally: - # Clean up temporary file - if temp_path.exists(): - temp_path.unlink() - - -def clear_output() -> None: - """Clear all session state and temporary files.""" - # Reset session state - st.session_state.results = None - st.session_state.file_processed = False - st.session_state.uploaded_file = None - - # Remove file uploader from session state - if "file_uploader" in st.session_state: - del st.session_state["file_uploader"] - - # Clean up temporary files - for temp_file in Path(".").glob("temp_*.pdf"): - if temp_file.exists(): - temp_file.unlink() - - gc.collect() - - -def copy_to_clipboard() -> None: - """Copy content to clipboard and show confirmation toast.""" - if st.session_state.results: - pyperclip.copy(st.session_state.results) - st.toast("Copied to clipboard!", icon="✂️") - - -def format_results(results: Union[str, List[str]]) -> str: - """Format the results by combining pages and removing page numbers.""" - if isinstance(results, list): - return "\n\n".join( - content.replace("Page " + str(i + 1), "").strip() - for i, content in enumerate(results) - ) - return results - - -def main() -> None: - """Main application function.""" - init_page_config() - apply_custom_styles() - init_session_state() - - # Title and description - st.title("Vision Parse - Demo") - st.markdown("Upload a PDF file to extract detailed information using Vision Parse.") - - # File uploader - st.session_state.uploaded_file = st.file_uploader( - "Choose a PDF file (max 5MB limit)", - type="pdf", - disabled=st.session_state.is_processing, - key="file_uploader", - ) - - # Process uploaded file - if ( - st.session_state.uploaded_file is not None - and not st.session_state.is_processing - and not st.session_state.file_processed - ): - st.session_state.is_processing = True - with st.spinner("Processing PDF..."): - try: - st.toast( - "Please wait while the current file is being processed...", - icon="🔄", - ) - result = process_pdf(st.session_state.uploaded_file) - if result: - st.session_state.results = result - st.session_state.file_processed = True - except Exception as e: - st.error(f"Error processing PDF: {str(e)}") - finally: - st.session_state.is_processing = False - - # Display results - if st.session_state.results: - st.markdown("### Markdown Output") - - # Format results - if isinstance(st.session_state.results, list): - st.session_state.results = format_results(st.session_state.results) - - # Create button columns - col1, col2 = st.columns([6, 1]) - - # Display results and buttons - st.markdown(st.session_state.results) - with col1: - st.button( - "Copy to Clipboard", - on_click=copy_to_clipboard, - type="secondary", - key="copy_btn", - ) - with col2: - st.button("Clear", on_click=clear_output, type="primary", key="clear_btn") - - -if __name__ == "__main__": - main() diff --git a/docs/faq.md b/docs/faq.md index e6b1543..3eefebd 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -1,31 +1,16 @@ # Frequently Asked Questions (FAQ) -1. **Does it work for scanned PDF documents?** +1. **Does Vision Parse work with scanned PDF documents?** Yes, Vision Parse is specifically designed to handle scanned PDF documents effectively. It uses advanced Vision LLMs to extract text, tables, images, and LaTeX equations from both regular and scanned PDF documents with high precision. -2. **I am facing latency issues while running llama3.2-vision locally. How can I improve the performance of locally hosted vision models?** - - This is a known limitation with locally hosted Ollama models. Here are some solutions: - - - **Use API-based Models**: For better performance, consider using API-based models like OpenAI, DeepSeek, or Gemini, which are significantly faster and more accurate. - - **Enable Concurrency**: Set `enable_concurrency` to `True` so that multiple pages are processed in parallel, thereby reducing latency. You can also increase the value of `OLLAMA_NUM_PARALLEL` to maximize the number of pages that can be processed in parallel. - - **Disable Detailed Extraction**: Disable the `detailed_extraction` parameter for simpler PDF documents, which can improve latency. - -3. **The llama3.2-vision:11b model was hallucinating and unable to extract content accurately from the PDF document. How can I improve the extraction accuracy of locally hosted vision models?** - - To improve extraction accuracy with the llama3.2-vision:11b model: - - - **Adjust Model Parameters**: Lower the `temperature` and `top_p` for more deterministic outputs and to reduce hallucinations. - - **Define Custom Prompts**: By defining custom prompts according to your document structure, you can guide the model to extract content more accurately. - - **Enable Detailed Extraction**: Enabling `detailed_extraction` will help the Vision LLM detect the presence of images, LaTeX equations, structured, and semi-structured tables, and then extract them with high accuracy. - - **Consider Using Alternative Models**: Try API-based models like gpt-4o or gemini-1.5-pro for better accuracy and performance. Avoid using smaller models that are prone to hallucination. - -4. **What are the recommended values for model parameters such as temperature, top_p, etc., to improve extraction accuracy?** +2. **What are the recommended values for model parameters to improve extraction accuracy?** Here are the recommended values for model parameters to improve extraction accuracy: - - Set `temperature` to 0.7 and `top_p` to 0.5. - - For Ollama models, increase `num_ctx` to 16384 and `num_predict` to 8092 (depending on the model size) and set `repeat_penalty` to 1.3. - - For OpenAI models, increase `max_tokens` to 8192 (depending on the model size) and set `frequency_penalty` to 0.3. + - Set `temperature` to 0.7 and `top_p` to 0.5 + + - For OpenAI models: + - Increase `max_tokens` to 8192 (depending on the model size) + - Set `frequency_penalty` to 0.3 - Note: The recommended values are generic and may need to be adjusted based on your document structure and the model's capabilities. + Note: These recommended values are generic and may need to be adjusted based on your document structure and the model's capabilities. diff --git a/docs/model_config.md b/docs/model_config.md deleted file mode 100644 index 2c1fd4d..0000000 --- a/docs/model_config.md +++ /dev/null @@ -1,41 +0,0 @@ -## Custom Configuration for Vision LLM providers: - -### Ollama Client: - -Pass the following configuration settings to `ollama_config` parameter while initializing the VisionParser class. - -| **Custom Configuration** | **Description** | -|:---------:|:-----------:| -| OLLAMA_NUM_PARALLEL | Number of parallel requests to Ollama server | -| OLLAMA_REQUEST_TIMEOUT | Timeout for requests to the Ollama server (by default, it's set to 240.0) | -| OLLAMA_NUM_GPU | Number of GPUs to use if available | -| OLLAMA_NUM_THREAD | Number of CPU threads to use | -| OLLAMA_KEEP_ALIVE | Keep-alive timeout for the Ollama server (by default, it's set to -1) | -| OLLAMA_HOST | Host URL for the Ollama server (by default, it's set to http://localhost:11434 ) | -| OLLAMA_GPU_LAYERS | Number of layers to use if GPU is available | - -For model-specific parameters (like temperature, top_p, etc.), please refer to the [Ollama Model Parameters documentation](https://github.com/ollama/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values). You can pass model-specific parameters as additional kwargs to the `VisionParser` class. - -### OpenAI and DeepSeek Clients: - -Pass the following configuration settings to `openai_config` parameter while initializing the VisionParser class. - -| **Custom Configuration** | **Description** | -|:---------:|:-----------:| -| OPENAI_BASE_URL | Base URL for the OpenAI server (by default, it's set to None) | -| OPENAI_MAX_RETRIES | Maximum number of retries for OpenAI requests (by default, it's set to 3) | -| OPENAI_TIMEOUT | Timeout for OpenAI requests (by default, it's set to 240.0) | -| OPENAI_DEFAULT_HEADERS | Default headers for OpenAI requests (by default, it's set to None) | - -For model-specific parameters (like temperature, max_tokens, etc.), please refer to the [OpenAI Chat Completion API documentation](https://platform.openai.com/docs/api-reference/chat/create). You can pass model-specific parameters as additional kwargs to the `VisionParser` class. - -### Azure OpenAI Client: - -Pass the following configuration settings to `openai_config` parameter while initializing the VisionParser class. - -| **Custom Configuration** | **Description** | -|:---------:|:-----------:| -| AZURE_OPENAI_API_KEY | Azure OpenAI API key | -| AZURE_ENDPOINT_URL | Azure OpenAI Endpoint URL | -| AZURE_DEPLOYMENT_NAME | Azure OpenAI Deployment Name | -| AZURE_OPENAI_API_VERSION | Azure OpenAI API Version (by default, it's set to "2024-08-01-preview") | diff --git a/docs/ollama_setup.md b/docs/ollama_setup.md deleted file mode 100644 index e3df5c3..0000000 --- a/docs/ollama_setup.md +++ /dev/null @@ -1,27 +0,0 @@ -## Setting up Ollama locally: - -1. **Install Ollama** based on your operating system: - - **Linux:** - ```bash - curl -fsSL https://ollama.com/install.sh | sh - ``` - - **MacOS:** - ```bash - brew install ollama - ``` - - **Windows:** - Download and install from [Ollama Website](https://ollama.com/download/OllamaSetup.exe) - -2. **Pull and start** the Ollama server: - ```bash - ollama pull llama3.2-vision:11b - ollama serve - ``` - -3. **Verify** server status: - ```bash - curl http://localhost:11434/api/version - ``` diff --git a/pyproject.toml b/pyproject.toml index 85dab71..7d94989 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -61,6 +61,7 @@ dev = [ "pre-commit>=4.2.0", "pytest>=8.3.4", "pytest-asyncio>=0.23.5", + "python-dotenv>=1.1.0", "ruff>=0.8.3", "toml-sort>=0.24.2", "twine>=6.0.1" diff --git a/src/vision_parse/exceptions.py b/src/vision_parse/exceptions.py new file mode 100644 index 0000000..bc0f010 --- /dev/null +++ b/src/vision_parse/exceptions.py @@ -0,0 +1,37 @@ +class UnsupportedProviderError(BaseException): + """Raises an error when the specified LLM provider is not supported. + + This exception is raised when attempting to use a model from an unsupported + LLM provider or when the model name does not match any known provider prefix. + """ + + pass + + +class LLMError(BaseException): + """Raises an error when LLM processing encounters a failure. + + This exception is raised when there are issues during LLM initialization, + API calls, or response processing. + """ + + pass + + +class UnsupportedFileError(BaseException): + """Raises an error when an unsupported file type is provided. + + This exception is raised when attempting to process a file that is not a PDF. + """ + + pass + + +class VisionParserError(BaseException): + """Raises an error when there is an error in the PDF to markdown conversion process. + + This exception is raised when there are issues during the conversion of PDF pages + to markdown format, such as conversion failures or processing errors. + """ + + pass diff --git a/src/vision_parse/llm.py b/src/vision_parse/llm.py index d02a59b..2fb370f 100644 --- a/src/vision_parse/llm.py +++ b/src/vision_parse/llm.py @@ -1,7 +1,7 @@ import logging import re from importlib.resources import files -from typing import Any, Dict, Literal +from typing import Any, Dict, Literal, Optional import fitz import instructor @@ -11,6 +11,7 @@ from tenacity import retry, stop_after_attempt, wait_exponential from .constants import PROVIDER_PREFIXES, SUPPORTED_PROVIDERS +from .exceptions import LLMError, UnsupportedProviderError from .utils import ImageData logger = logging.getLogger(__name__) @@ -36,26 +37,6 @@ class ImageDescription(BaseModel): confidence_score_text: float -class UnsupportedProviderError(BaseException): - """Raises an error when the specified LLM provider is not supported. - - This exception is raised when attempting to use a model from an unsupported - LLM provider or when the model name does not match any known provider prefix. - """ - - pass - - -class LLMError(BaseException): - """Raises an error when LLM processing encounters a failure. - - This exception is raised when there are issues during LLM initialization, - API calls, or response processing. - """ - - pass - - class LLM: try: _image_analysis_prompt = Template( @@ -70,13 +51,13 @@ class LLM: def __init__( self, model_name: str, - api_key: str | None, + api_key: Optional[str], temperature: float, top_p: float, - openai_config: Dict | None, - gemini_config: Dict | None, + openai_config: Optional[Dict], + gemini_config: Optional[Dict], image_mode: Literal["url", "base64", None], - custom_prompt: str | None, + custom_prompt: Optional[str], detailed_extraction: bool, enable_concurrency: bool, **kwargs: Any, @@ -105,6 +86,7 @@ def _init_llm(self) -> None: Raises: LLMError: If client initialization fails. """ + try: # Initialize instructor client with litellm completion_func = acompletion if self.enable_concurrency else completion diff --git a/src/vision_parse/parser.py b/src/vision_parse/parser.py index 69462e6..a3f162e 100644 --- a/src/vision_parse/parser.py +++ b/src/vision_parse/parser.py @@ -1,7 +1,6 @@ import asyncio import base64 import logging -import warnings from pathlib import Path from typing import Any, Dict, List, Literal, Optional, Union @@ -10,6 +9,7 @@ from pydantic import BaseModel from tqdm import tqdm +from .exceptions import UnsupportedFileError, VisionParserError from .llm import LLM from .utils import get_device_config @@ -33,32 +33,11 @@ class PDFPageConfig(BaseModel): preserve_transparency: bool = False -class UnsupportedFileError(BaseException): - """Exception raised when an unsupported file type is provided. - - This exception is raised when attempting to process a file that is not a PDF. - """ - - pass - - -class VisionParserError(BaseException): - """Exception raised when there is an error in the PDF to markdown conversion process. - - This exception is raised when there are issues during the conversion of PDF pages - to markdown format, such as conversion failures or processing errors. - """ - - pass - - class VisionParser: - """Converts PDF pages to base64-encoded images and extracts text from the images in markdown format.""" - def __init__( self, page_config: Optional[PDFPageConfig] = None, - model_name: str = "llama3.2-vision:11b", + model_name: str = "gpt-4o", api_key: Optional[str] = None, temperature: float = 0.7, top_p: float = 0.7, @@ -68,28 +47,31 @@ def __init__( image_mode: Literal["url", "base64", None] = None, custom_prompt: Optional[str] = None, detailed_extraction: bool = False, - extraction_complexity: bool = False, # Deprecated Parameter enable_concurrency: bool = False, **kwargs: Any, ): - """Initializes parser with PDFPageConfig and LLM configuration.""" + """Initializes the parser with PDF page configuration and LLM settings. + + Args: + page_config (Optional[PDFPageConfig]): Configuration for PDF page processing. + model_name (str): Name of the LLM model to use. Defaults to "gpt-4o". + api_key (Optional[str]): API key for the LLM provider. + temperature (float): Controls randomness in LLM output. Defaults to 0.7. + top_p (float): Controls diversity in LLM output. Defaults to 0.7. + ollama_config (Optional[Dict]): Configuration for Ollama provider. + openai_config (Optional[Dict]): Configuration for OpenAI provider. + gemini_config (Optional[Dict]): Configuration for Google AI Studio provider. + image_mode (Literal["url", "base64", None]): Mode for handling embedded images. + custom_prompt (Optional[str]): Custom prompt for LLM processing. + detailed_extraction (bool): Enables detailed text extraction. Defaults to False. + enable_concurrency (bool): Enables concurrent page processing. Defaults to False. + **kwargs: Additional keyword arguments for LLM configuration. + """ + self.page_config = page_config or PDFPageConfig() self.device, self.num_workers = get_device_config() self.enable_concurrency = enable_concurrency - if extraction_complexity: - if not detailed_extraction: - detailed_extraction = True - warnings.warn( - "`extraction_complexity` is deprecated, and was renamed to `detailed_extraction`.", - DeprecationWarning, - ) - - else: - raise ValueError( - "`extraction_complexity` is deprecated, and was renamed to `detailed_extraction`. Please use `detailed_extraction` instead." - ) - self.llm = LLM( model_name=model_name, api_key=api_key, @@ -108,7 +90,15 @@ def __init__( ) def _calculate_matrix(self, page: fitz.Page) -> fitz.Matrix: - """Calculates transformation matrix for page conversion.""" + """Calculates the transformation matrix for page conversion. + + Args: + page (fitz.Page): The PDF page to process. + + Returns: + fitz.Matrix: The calculated transformation matrix. + """ + # Calculate zoom factor based on target DPI zoom = self.page_config.dpi / 72 matrix = fitz.Matrix(zoom * 2, zoom * 2) @@ -120,7 +110,19 @@ def _calculate_matrix(self, page: fitz.Page) -> fitz.Matrix: return matrix async def _convert_page(self, page: fitz.Page, page_number: int) -> str: - """Converts a single PDF page into base64-encoded PNG and extracts markdown formatted text.""" + """Converts a single PDF page into markdown formatted text. + + Args: + page (fitz.Page): The PDF page to convert. + page_number (int): The page number being processed. + + Returns: + str: The markdown formatted text extracted from the page. + + Raises: + VisionParserError: If page conversion fails. + """ + try: matrix = self._calculate_matrix(page) @@ -146,7 +148,16 @@ async def _convert_page(self, page: fitz.Page, page_number: int) -> str: pix = None async def _convert_pages_batch(self, pages: List[fitz.Page], start_idx: int): - """Processes a batch of PDF pages concurrently.""" + """Processes a batch of PDF pages concurrently. + + Args: + pages (List[fitz.Page]): List of PDF pages to process. + start_idx (int): Starting index for page numbering. + + Returns: + List[str]: List of markdown formatted texts from the processed pages. + """ + try: tasks = [] for i, page in enumerate(pages): @@ -156,7 +167,20 @@ async def _convert_pages_batch(self, pages: List[fitz.Page], start_idx: int): await asyncio.sleep(0.5) def convert_pdf(self, pdf_path: Union[str, Path]) -> List[str]: - """Converts all pages in the given PDF file to markdown text.""" + """Converts all pages in a PDF file to markdown text. + + Args: + pdf_path (Union[str, Path]): Path to the PDF file to convert. + + Returns: + List[str]: List of markdown formatted texts for each page. + + Raises: + FileNotFoundError: If the PDF file does not exist. + UnsupportedFileError: If the file is not a PDF. + VisionParserError: If PDF conversion fails. + """ + pdf_path = Path(pdf_path) converted_pages = [] @@ -199,7 +223,6 @@ def convert_pdf(self, pdf_path: Union[str, Path]) -> List[str]: pbar.update(1) return converted_pages - except Exception as e: raise VisionParserError( f"Failed to convert PDF file into markdown content: {str(e)}" diff --git a/tests/conftest.py b/tests/conftest.py index afa55af..105e204 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,7 +1,9 @@ +import os from pathlib import Path -import fitz # PyMuPDF library for PDF handling +import fitz import pytest +from dotenv import load_dotenv from vision_parse.parser import PDFPageConfig, VisionParser @@ -16,10 +18,10 @@ def pdf_path(): @pytest.fixture def page_config(): return PDFPageConfig( - dpi=400, # High DPI for better image quality - color_space="RGB", # Standard color space for vision models - include_annotations=True, # Include PDF annotations in output - preserve_transparency=False, # Flatten transparency for consistent rendering + dpi=400, + color_space="RGB", + include_annotations=True, + preserve_transparency=False, ) @@ -27,7 +29,8 @@ def page_config(): @pytest.fixture def markdown_parser(page_config): return VisionParser( - model_name="llama3.2-vision:11b", + model_name="gpt-4o", + api_key=os.getenv("OPENAI_API_KEY"), temperature=0.7, top_p=0.7, page_config=page_config, diff --git a/tests/test_llm.py b/tests/test_llm.py index 3a3f233..625df03 100644 --- a/tests/test_llm.py +++ b/tests/test_llm.py @@ -1,10 +1,10 @@ import base64 import json -from unittest.mock import AsyncMock, MagicMock, patch +from unittest.mock import MagicMock, patch import pytest -from vision_parse.llm import LLM, LLMError, UnsupportedModelError +from vision_parse.llm import LLM, UnsupportedProviderError @pytest.fixture @@ -37,7 +37,7 @@ def mock_markdown_response(): @pytest.fixture def mock_pixmap(): mock = MagicMock() - mock.samples = b"\x00" * (200 * 200 * 3) # Create correct size buffer + mock.samples = b"\x00" * (200 * 200 * 3) mock.height = 200 mock.width = 200 mock.n = 3 @@ -47,13 +47,13 @@ def mock_pixmap(): def test_unsupported_model(): """Test error handling for unsupported models.""" - with pytest.raises(UnsupportedModelError) as exc_info: + + with pytest.raises(UnsupportedProviderError) as exc_info: LLM( model_name="unsupported-model", temperature=0.7, top_p=0.7, api_key=None, - ollama_config=None, openai_config=None, gemini_config=None, image_mode=None, @@ -63,161 +63,25 @@ def test_unsupported_model(): device=None, num_workers=1, ) - assert "is not supported" in str(exc_info.value) + assert "not from a supported provider" in str(exc_info.value) @pytest.mark.asyncio -@patch("ollama.AsyncClient") -async def test_ollama_generate_markdown( - mock_async_client, - sample_base64_image, - mock_pixmap, -): - """Test markdown generation using Ollama.""" - # Mock the Ollama async client - mock_client = AsyncMock() - mock_async_client.return_value = mock_client - - # Mock the chat responses - mock_chat = AsyncMock() - mock_chat.side_effect = [ - { - "message": { - "content": json.dumps( - { - "text_detected": "Yes", - "tables_detected": "No", - "images_detected": "No", - "latex_equations_detected": "No", - "extracted_text": "Test content", - "confidence_score_text": 0.9, - } - ) - } - } - ] - mock_client.chat = mock_chat - - llm = LLM( - model_name="llama3.2-vision:11b", - temperature=0.7, - top_p=0.7, - api_key=None, - ollama_config=None, - openai_config=None, - gemini_config=None, - image_mode=None, - custom_prompt=None, - detailed_extraction=True, - enable_concurrency=True, - device=None, - num_workers=1, - ) - result = await llm.generate_markdown(sample_base64_image, mock_pixmap, 0) - - assert isinstance(result, str) - assert "Test content" in result - assert mock_chat.call_count == 1 - - -@pytest.mark.asyncio -@patch("ollama.AsyncClient") -async def test_ollama_deepseek_r1_generate_markdown( - mock_async_client, - sample_base64_image, - mock_pixmap, -): - """Test markdown generation using Ollama.""" - # Mock the Ollama async client - mock_client = AsyncMock() - mock_async_client.return_value = mock_client - - # Mock the chat responses - mock_chat = AsyncMock() - mock_chat.side_effect = [ - { - "message": { - "content": json.dumps( - { - "text_detected": "Yes", - "tables_detected": "No", - "images_detected": "No", - "latex_equations_detected": "No", - "extracted_text": "Test content", - "confidence_score_text": 0.9, - } - ) - } - } - ] - mock_client.chat = mock_chat - - llm = LLM( - model_name="deepseek-r1:32b", - temperature=0.7, - top_p=0.7, - api_key=None, - ollama_config=None, - openai_config=None, - gemini_config=None, - image_mode=None, - custom_prompt=None, - detailed_extraction=True, - enable_concurrency=True, - device=None, - num_workers=1, - ) - assert "deepseek llm" in llm - result = await llm.generate_markdown(sample_base64_image, mock_pixmap, 0) - - assert isinstance(result, str) - assert "Test content" in result - assert mock_chat.call_count == 1 - - -@pytest.mark.asyncio -@patch("openai.AsyncOpenAI") +@patch("vision_parse.llm.LLM._get_response") async def test_openai_generate_markdown( - MockAsyncOpenAI, sample_base64_image, mock_pixmap + mock_get_response, sample_base64_image, mock_pixmap ): """Test markdown generation using OpenAI.""" - mock_client = AsyncMock() - MockAsyncOpenAI.return_value = mock_client - - # Mock structured analysis response - mock_parse = AsyncMock() - mock_parse.choices = [ - AsyncMock( - message=AsyncMock( - content=json.dumps( - { - "text_detected": "Yes", - "tables_detected": "No", - "images_detected": "No", - "latex_equations_detected": "No", - "extracted_text": "Test content", - "confidence_score_text": 0.9, - } - ) - ) - ) - ] - mock_client.beta.chat.completions.parse = AsyncMock(return_value=mock_parse) - # Mock markdown conversion response - mock_create = AsyncMock() - mock_create.choices = [ - AsyncMock(message=AsyncMock(content="# Test Header\n\nTest content")) - ] - mock_client.chat.completions.create = AsyncMock(return_value=mock_create) + # Mock the LLM's _get_response method + mock_get_response.return_value = "# Test Header\n\nTest content" llm = LLM( model_name="gpt-4o", api_key="test-key", temperature=0.7, top_p=0.7, - ollama_config=None, - openai_config=None, + openai_config={"OPENAI_API_KEY": "test-key"}, gemini_config=None, image_mode=None, custom_prompt=None, @@ -230,55 +94,24 @@ async def test_openai_generate_markdown( assert isinstance(result, str) assert "Test content" in result - assert mock_client.beta.chat.completions.parse.called - assert mock_client.chat.completions.create.called + assert mock_get_response.called @pytest.mark.asyncio -@patch("openai.AsyncAzureOpenAI") +@patch("vision_parse.llm.LLM._get_response") async def test_azure_openai_generate_markdown( - MockAsyncAzureOpenAI, sample_base64_image, mock_pixmap + mock_get_response, sample_base64_image, mock_pixmap ): """Test markdown generation using Azure OpenAI.""" - mock_client = AsyncMock() - MockAsyncAzureOpenAI.return_value = mock_client - - # Mock structured analysis response - mock_parse = AsyncMock() - mock_parse.choices = [ - AsyncMock( - message=AsyncMock( - content=json.dumps( - { - "text_detected": "Yes", - "tables_detected": "No", - "images_detected": "No", - "latex_equations_detected": "No", - "extracted_text": "Test content", - "confidence_score_text": 0.9, - } - ) - ) - ) - ] - - # Mock markdown conversion response - mock_create = AsyncMock() - mock_create.choices = [ - AsyncMock(message=AsyncMock(content="# Test Header\n\nTest content")) - ] - # Set up side effects to return mock_parse first, then mock_create - mock_client.chat.completions.create = AsyncMock( - side_effect=[mock_parse, mock_create] - ) + # Mock the LLM's _get_response method + mock_get_response.return_value = "# Test Header\n\nTest content" llm = LLM( model_name="gpt-4o", api_key=None, temperature=0.7, top_p=0.7, - ollama_config=None, openai_config={ "AZURE_ENDPOINT_URL": "https://test.openai.azure.com/", "AZURE_DEPLOYMENT_NAME": "gpt-4o", @@ -297,45 +130,26 @@ async def test_azure_openai_generate_markdown( assert isinstance(result, str) assert "Test content" in result - assert mock_client.chat.completions.create.called + assert mock_get_response.called @pytest.mark.asyncio -@patch("google.generativeai.GenerativeModel") +@patch("vision_parse.llm.LLM._get_response") async def test_gemini_generate_markdown( - MockGenerativeModel, sample_base64_image, mock_pixmap + mock_get_response, sample_base64_image, mock_pixmap ): """Test markdown generation using Gemini.""" - mock_client = AsyncMock() - MockGenerativeModel.return_value = mock_client - - # Mock responses for both structured analysis and markdown generation - mock_response1 = AsyncMock() - mock_response1.text = json.dumps( - { - "text_detected": "Yes", - "tables_detected": "No", - "images_detected": "No", - "latex_equations_detected": "No", - "extracted_text": "Test content", - "confidence_score_text": 0.9, - } - ) - mock_response2 = AsyncMock() - mock_response2.text = "# Test Header\n\nTest content" - mock_client.generate_content_async = AsyncMock( - side_effect=[mock_response1, mock_response2] - ) + # Mock the LLM's _get_response method + mock_get_response.return_value = "# Test Header\n\nTest content" llm = LLM( - model_name="gemini-1.5-pro", - api_key="test-key", + model_name="gemini-2.5-pro", + api_key=None, temperature=0.7, top_p=0.7, - ollama_config=None, openai_config=None, - gemini_config=None, + gemini_config={"GOOGLE_API_KEY": "test-key"}, image_mode=None, custom_prompt=None, detailed_extraction=True, @@ -347,53 +161,24 @@ async def test_gemini_generate_markdown( assert isinstance(result, str) assert "Test content" in result - assert mock_client.generate_content_async.call_count == 2 + assert mock_get_response.called @pytest.mark.asyncio -@patch("openai.AsyncOpenAI") +@patch("vision_parse.llm.LLM._get_response") async def test_deepseek_generate_markdown( - MockAsyncOpenAI, sample_base64_image, mock_pixmap + mock_get_response, sample_base64_image, mock_pixmap ): """Test markdown generation using Deepseek.""" - mock_client = AsyncMock() - MockAsyncOpenAI.return_value = mock_client - - # Mock structured analysis response - mock_parse = AsyncMock() - mock_parse.choices = [ - AsyncMock( - message=AsyncMock( - content=json.dumps( - { - "text_detected": "Yes", - "tables_detected": "No", - "images_detected": "No", - "latex_equations_detected": "No", - "extracted_text": "Test content", - "confidence_score_text": 0.9, - } - ) - ) - ) - ] - - # Mock markdown conversion response - mock_create = AsyncMock() - mock_create.choices = [ - AsyncMock(message=AsyncMock(content="# Test Header\n\nTest content")) - ] - # Set up side effects to return mock_parse first, then mock_create - mock_client.chat.completions.create = AsyncMock( - side_effect=[mock_parse, mock_create] - ) + + # Mock the LLM's _get_response method + mock_get_response.return_value = "# Test Header\n\nTest content" llm = LLM( - model_name="deepseek-chat", + model_name="deepseek-vision", api_key="test-key", temperature=0.7, top_p=0.7, - ollama_config=None, openai_config=None, gemini_config=None, image_mode=None, @@ -407,102 +192,4 @@ async def test_deepseek_generate_markdown( assert isinstance(result, str) assert "Test content" in result - assert mock_client.chat.completions.create.call_count == 2 - - -@pytest.mark.asyncio -@patch("ollama.AsyncClient") -async def test_ollama_base64_image_mode( - mock_async_client, - sample_base64_image, - mock_pixmap, -): - """Test markdown generation with base64 image mode.""" - # Mock the Ollama async client - mock_client = AsyncMock() - mock_async_client.return_value = mock_client - - # Mock the chat responses - mock_chat = AsyncMock() - mock_chat.side_effect = [ - { - "message": { - "content": json.dumps( - { - "text_detected": "Yes", - "tables_detected": "No", - "images_detected": "Yes", - "extracted_text": "Test content with image", - "confidence_score_text": 0.9, - } - ) - } - }, - { - "message": { - "content": "# Test Header\n\n![Image 1](data:image/png;base64,test_image)" - } - }, - ] - mock_client.chat = mock_chat - - # Mock the pixmap for image extraction - mock_pixmap.samples = b"\x00" * (200 * 200 * 3) # Create correct size buffer - mock_pixmap.height = 200 - mock_pixmap.width = 200 - mock_pixmap.n = 3 - - llm = LLM( - model_name="llama3.2-vision:11b", - temperature=0.7, - top_p=0.7, - api_key=None, - ollama_config=None, - openai_config=None, - gemini_config=None, - image_mode="base64", - custom_prompt=None, - detailed_extraction=True, - enable_concurrency=True, - device=None, - num_workers=1, - ) - result = await llm.generate_markdown(sample_base64_image, mock_pixmap, 0) - - assert isinstance(result, str) - assert "# Test Header" in result - assert "data:image/png;base64,test_image" in result - assert mock_chat.call_count == 2 - - -@pytest.mark.asyncio -@patch("ollama.AsyncClient") -async def test_ollama_llm_error(mock_async_client, sample_base64_image, mock_pixmap): - """Test LLMError handling for Ollama.""" - # Mock the Ollama async client - mock_client = AsyncMock() - mock_async_client.return_value = mock_client - - # Mock a failed Ollama API call - mock_client.chat.side_effect = Exception("Ollama processing failed") - - llm = LLM( - model_name="llama3.2-vision:11b", - temperature=0.7, - top_p=0.7, - api_key=None, - ollama_config=None, - openai_config=None, - gemini_config=None, - image_mode=None, - custom_prompt=None, - detailed_extraction=True, - enable_concurrency=True, - device=None, - num_workers=1, - ) - - with pytest.raises(LLMError) as exc_info: - await llm.generate_markdown(sample_base64_image, mock_pixmap, 0) - assert "Ollama Model processing failed" in str(exc_info.value) - assert mock_client.chat.call_count == 1 + assert mock_get_response.called diff --git a/tests/test_parser.py b/tests/test_parser.py index f69b29e..1215884 100644 --- a/tests/test_parser.py +++ b/tests/test_parser.py @@ -1,5 +1,4 @@ -import json -from unittest.mock import AsyncMock, MagicMock, patch +from unittest.mock import MagicMock, patch import pytest @@ -13,6 +12,7 @@ def test_convert_pdf_nonexistent_file(markdown_parser): """Test error handling for non-existent PDF files.""" + with pytest.raises(FileNotFoundError) as exc_info: markdown_parser.convert_pdf("non-existent.pdf") assert "PDF file not found" in str(exc_info.value) @@ -20,7 +20,7 @@ def test_convert_pdf_nonexistent_file(markdown_parser): def test_convert_pdf_invalid_file(markdown_parser, tmp_path): """Test error handling for invalid file types.""" - # Create a temporary text file + invalid_file = tmp_path / "test.txt" invalid_file.write_text("test content") @@ -31,6 +31,7 @@ def test_convert_pdf_invalid_file(markdown_parser, tmp_path): def test_calculate_matrix(markdown_parser, pdf_document): """Test the matrix calculation for PDF page transformation.""" + page = pdf_document[0] matrix = markdown_parser._calculate_matrix(page) expected_zoom = markdown_parser.page_config.dpi / 72 * 2 @@ -38,14 +39,33 @@ def test_calculate_matrix(markdown_parser, pdf_document): assert matrix.d == expected_zoom -def test_convert_pdf_integration(markdown_parser, pdf_path): - """Integration test for PDF to markdown conversion.""" - converted_pages = markdown_parser.convert_pdf(pdf_path) - assert isinstance(converted_pages, list) - assert len(converted_pages) > 0 - assert any( - isinstance(page, str) and len(page.strip()) > 0 for page in converted_pages - ) +@pytest.mark.asyncio +@patch("vision_parse.llm.LLM._get_response") +async def test_convert_pdf_integration(mock_get_response, markdown_parser, pdf_path): + """Test PDF conversion with mocked LLM.""" + + # Mock the LLM's _get_response method + mock_get_response.return_value = "# Test Header\n\nTest content" + + # Mock the PDF document + with patch("fitz.open") as mock_open: + mock_doc = MagicMock() + mock_page = MagicMock() + mock_pixmap = MagicMock() + mock_pixmap.samples = b"\x00" * (200 * 200 * 3) # Create correct size buffer + mock_pixmap.height = 200 + mock_pixmap.width = 200 + mock_pixmap.n = 3 + mock_pixmap.tobytes = MagicMock(return_value=b"test_image_data") + mock_page.get_pixmap.return_value = mock_pixmap + mock_doc.page_count = 1 + mock_doc.__getitem__.return_value = mock_page + mock_open.return_value.__enter__.return_value = mock_doc + + converted_pages = markdown_parser.convert_pdf(pdf_path) + assert len(converted_pages) == 1 + assert "Test content" in converted_pages[0] + assert mock_get_response.called def test_convert_pdf_vision_parser_error(markdown_parser, monkeypatch, pdf_path): @@ -62,41 +82,20 @@ def mock_convert_page(*args, **kwargs): @pytest.mark.asyncio -@patch("ollama.AsyncClient") +@patch("vision_parse.llm.LLM._get_response") @patch("tqdm.tqdm") -async def test_parser_with_base64_image_mode(mock_tqdm, mock_async_client, pdf_path): +async def test_parser_with_base64_image_mode(mock_tqdm, mock_get_response, pdf_path): """Test parser with base64 image mode configuration.""" - # Mock the Ollama async client - mock_client = AsyncMock() - mock_async_client.return_value = mock_client - - # Mock the chat responses - mock_chat = AsyncMock() - mock_chat.side_effect = [ - { - "message": { - "content": json.dumps( - { - "text_detected": "Yes", - "tables_detected": "No", - "images_detected": "Yes", - "extracted_text": "Test content with image", - "confidence_score_text": 0.9, - } - ) - } - }, - { - "message": { - "content": "# Test Header\n\n![Image 1](data:image/png;base64,test_image)" - } - }, - ] - mock_client.chat = mock_chat + + # Mock the LLM's _get_response method + mock_get_response.return_value = ( + "# Test Header\n\n![Image 1](data:image/png;base64,test_image)" + ) parser = VisionParser( page_config=PDFPageConfig(dpi=300), - model_name="llama3.2-vision:11b", + model_name="gpt-4o", + api_key="test-key", temperature=0.7, top_p=0.7, image_mode="base64", @@ -128,46 +127,24 @@ async def test_parser_with_base64_image_mode(mock_tqdm, mock_async_client, pdf_p assert len(converted_pages) > 0 # Check if any page contains base64 image data assert any("data:image/png;base64," in page for page in converted_pages if page) - assert mock_chat.call_count == 2 + assert mock_get_response.called @pytest.mark.asyncio -@patch("ollama.AsyncClient") +@patch("vision_parse.llm.LLM._get_response") @patch("tqdm.tqdm") async def test_parser_with_concurrent_processing( - mock_tqdm, mock_async_client, pdf_path + mock_tqdm, mock_get_response, pdf_path ): """Test parser with concurrent processing enabled.""" - # Mock the Ollama async client - mock_client = AsyncMock() - mock_async_client.return_value = mock_client - - # Mock the chat responses - mock_chat = AsyncMock() - mock_chat.side_effect = [ - { - "message": { - "content": json.dumps( - { - "text_detected": "Yes", - "tables_detected": "No", - "images_detected": "No", - "extracted_text": f"Test content for page {i}", - "confidence_score_text": 0.9, - } - ) - } - } - for i in range(2) - ] + [ - {"message": {"content": f"# Page {i}\n\nTest content for page {i}"}} - for i in range(2) - ] - mock_client.chat = mock_chat + + # Mock the LLM's _get_response method + mock_get_response.return_value = "# Test Header\n\nTest content" parser = VisionParser( page_config=PDFPageConfig(dpi=300), - model_name="llama3.2-vision:11b", + model_name="gpt-4o", + api_key="test-key", temperature=0.7, top_p=0.7, enable_concurrency=True, @@ -195,15 +172,13 @@ async def test_parser_with_concurrent_processing( converted_pages = parser.convert_pdf(pdf_path) assert isinstance(converted_pages, list) assert len(converted_pages) > 0 - assert mock_chat.call_count == 2 - assert all( - f"Test content for page {i}" in page - for i, page in enumerate(converted_pages) - ) + assert mock_get_response.called + assert all("Test content" in page for page in converted_pages) def test_parser_with_custom_page_config(): """Test parser initialization with custom page configuration.""" + custom_config = PDFPageConfig( dpi=600, color_space="GRAY", @@ -212,7 +187,8 @@ def test_parser_with_custom_page_config(): ) parser = VisionParser( page_config=custom_config, - model_name="llama3.2-vision:11b", + model_name="gpt-4o", + api_key="test-key", temperature=0.7, top_p=0.7, ) @@ -222,17 +198,19 @@ def test_parser_with_custom_page_config(): assert parser.page_config.preserve_transparency -def test_parser_with_ollama_config(): - """Test parser initialization with Ollama configuration.""" - ollama_config = { - "OLLAMA_HOST": "http://localhost:11434", - "OLLAMA_NUM_GPU": "1", - "OLLAMA_NUM_THREAD": "4", +def test_parser_with_openai_config(): + """Test parser initialization with OpenAI configuration.""" + + openai_config = { + "OPENAI_BASE_URL": "https://api.openai.com/v1", + "OPENAI_MAX_RETRIES": 3, + "OPENAI_TIMEOUT": 240.0, } parser = VisionParser( - model_name="llama3.2-vision:11b", + model_name="gpt-4o", + api_key="test-key", temperature=0.7, top_p=0.7, - ollama_config=ollama_config, + openai_config=openai_config, ) - assert parser.llm.ollama_config == ollama_config + assert parser.llm.openai_config == openai_config diff --git a/uv.lock b/uv.lock index 97b8052..710c9bf 100644 --- a/uv.lock +++ b/uv.lock @@ -2923,6 +2923,7 @@ dev = [ { name = "pre-commit" }, { name = "pytest" }, { name = "pytest-asyncio" }, + { name = "python-dotenv" }, { name = "ruff" }, { name = "toml-sort" }, { name = "twine" }, @@ -2955,6 +2956,7 @@ requires-dist = [ { name = "pymupdf", specifier = ">=1.22.0" }, { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.3.4" }, { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.23.5" }, + { name = "python-dotenv", marker = "extra == 'dev'", specifier = ">=1.1.0" }, { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.8.3" }, { name = "tenacity", specifier = ">=9.0.0" }, { name = "toml-sort", marker = "extra == 'dev'", specifier = ">=0.24.2" }, From 120a7a7c69f8ad4a82bd14ec961076c88e7709ef Mon Sep 17 00:00:00 2001 From: tqtensor Date: Thu, 8 May 2025 19:06:04 +0000 Subject: [PATCH 7/9] chore: move prompts into sub folder --- Makefile | 2 +- docs/faq.md | 16 ---------------- pyproject.toml | 4 ++-- src/vision_parse/llm.py | 4 ++-- src/vision_parse/{ => prompts}/image_analysis.j2 | 0 .../{ => prompts}/markdown_prompt.j2 | 0 6 files changed, 5 insertions(+), 21 deletions(-) delete mode 100644 docs/faq.md rename src/vision_parse/{ => prompts}/image_analysis.j2 (100%) rename src/vision_parse/{ => prompts}/markdown_prompt.j2 (100%) diff --git a/Makefile b/Makefile index e9d7ebc..a7a274c 100644 --- a/Makefile +++ b/Makefile @@ -2,7 +2,7 @@ test: uv pip install -e . --link-mode=copy - pytest -v --capture=no + pytest -v --capture=no -W ignore build: python -m build diff --git a/docs/faq.md b/docs/faq.md deleted file mode 100644 index 3eefebd..0000000 --- a/docs/faq.md +++ /dev/null @@ -1,16 +0,0 @@ -# Frequently Asked Questions (FAQ) - -1. **Does Vision Parse work with scanned PDF documents?** - - Yes, Vision Parse is specifically designed to handle scanned PDF documents effectively. It uses advanced Vision LLMs to extract text, tables, images, and LaTeX equations from both regular and scanned PDF documents with high precision. - -2. **What are the recommended values for model parameters to improve extraction accuracy?** - - Here are the recommended values for model parameters to improve extraction accuracy: - - Set `temperature` to 0.7 and `top_p` to 0.5 - - - For OpenAI models: - - Increase `max_tokens` to 8192 (depending on the model size) - - Set `frequency_penalty` to 0.3 - - Note: These recommended values are generic and may need to be adjusted based on your document structure and the model's capabilities. diff --git a/pyproject.toml b/pyproject.toml index 7d94989..d5d3d00 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,8 +77,8 @@ openai = [ packages = ["src/vision_parse"] [tool.hatch.build.targets.wheel.force-include] -"src/vision_parse/image_analysis.j2" = "vision_parse/image_analysis.j2" -"src/vision_parse/markdown_prompt.j2" = "vision_parse/markdown_prompt.j2" +"src/vision_parse/prompts/image_analysis.j2" = "vision_parse/prompts/image_analysis.j2" +"src/vision_parse/prompts/markdown_prompt.j2" = "vision_parse/prompts/markdown_prompt.j2" [tool.hatch.build.targets.sdist] include = [ diff --git a/src/vision_parse/llm.py b/src/vision_parse/llm.py index 2fb370f..b8c0b4c 100644 --- a/src/vision_parse/llm.py +++ b/src/vision_parse/llm.py @@ -40,10 +40,10 @@ class ImageDescription(BaseModel): class LLM: try: _image_analysis_prompt = Template( - files("vision_parse").joinpath("image_analysis.j2").read_text() + files("vision_parse").joinpath("prompts/image_analysis.j2").read_text() ) _md_prompt_template = Template( - files("vision_parse").joinpath("markdown_prompt.j2").read_text() + files("vision_parse").joinpath("prompts/markdown_prompt.j2").read_text() ) except Exception as e: raise FileNotFoundError(f"Failed to load prompt files: {str(e)}") diff --git a/src/vision_parse/image_analysis.j2 b/src/vision_parse/prompts/image_analysis.j2 similarity index 100% rename from src/vision_parse/image_analysis.j2 rename to src/vision_parse/prompts/image_analysis.j2 diff --git a/src/vision_parse/markdown_prompt.j2 b/src/vision_parse/prompts/markdown_prompt.j2 similarity index 100% rename from src/vision_parse/markdown_prompt.j2 rename to src/vision_parse/prompts/markdown_prompt.j2 From 3a1690b470e94c5d55913d46fc8830cd3fbe0894 Mon Sep 17 00:00:00 2001 From: tqtensor Date: Thu, 8 May 2025 19:24:45 +0000 Subject: [PATCH 8/9] refactor: revise README --- Makefile | 2 +- README.md | 199 +++++++++++++++++++----------------------------------- 2 files changed, 71 insertions(+), 130 deletions(-) diff --git a/Makefile b/Makefile index a7a274c..993cd27 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -.PHONY: lint format test build release tag format-nb +.PHONY: test build release tag toml-sort test: uv pip install -e . --link-mode=copy diff --git a/README.md b/README.md index 61d7b73..86f77cc 100644 --- a/README.md +++ b/README.md @@ -4,15 +4,13 @@ [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT) [![Author: Arun Brahma](https://img.shields.io/badge/Author-Arun%20Brahma-purple)](https://github.com/iamarunbrahma) -[![PyPI version](https://img.shields.io/pypi/v/vision-parse.svg)](https://pypi.org/project/vision-parse/) > Parse PDF documents into beautifully formatted markdown content using state-of-the-art Vision Language Models - all with just a few lines of code! [Getting Started](#-getting-started) • [Usage](#-usage) • -[Supported Models](#-supported-models) • -[Parameters](#-customization-parameters) • -[Benchmarks](#-benchmarks) +[Tested Models](#-tested-models) • +[Configuration](#-configuration-options) ## 🎯 Introduction @@ -21,48 +19,33 @@ Vision Parse harnesses the power of Vision Language Models to revolutionize docu - 📝 **Scanned Document Processing**: Intelligently identifies and extracts text, tables, and LaTeX equations from scanned documents into markdown-formatted content with high precision - 🎨 **Advanced Content Formatting**: Preserves LaTeX equations, hyperlinks, images, and document hierarchy for markdown-formatted content -- 🤖 **Multi-LLM Support**: Seamlessly integrates with multiple Vision LLM providers such as OpenAI, Gemini, and Llama for optimal accuracy and speed -- 📁 **Local Model Hosting**: Supports local model hosting with Ollama for secure, no-cost, private, and offline document processing +- 🤖 **Multi-LLM Support**: Seamlessly integrates with multiple Vision LLM providers such as OpenAI, Gemini, and DeepSeek for optimal accuracy and speed +## ⚠️ Important Notice + +> [!NOTE] +> This repository is a revised version of the original work by [Arun Brahma](https://github.com/iamarunbrahma/vision-parse). +> +> The key improvements in this fork include: +> - Integration with [LiteLLM](https://github.com/BerriAI/litellm) to support multiple LLM providers with a unified interface +> - Implementation of [instructor](https://github.com/567-labs/instructor) for structured outputs and improved response handling +> - Enhanced reliability and performance with multiple Vision LLM providers ## 🚀 Getting Started ### Prerequisites - 🐍 Python >= 3.9 -- 🖥️ Ollama (if you want to use local models) -- 🤖 API Key for OpenAI or Google Gemini (if you want to use OpenAI or Google Gemini) +- 🤖 API Key for OpenAI, Google Gemini, or DeepSeek ### Installation -**Install the core package using pip (Recommended):** - -```bash -pip install vision-parse -``` - -**Install the additional dependencies for OpenAI or Gemini:** - -```bash -# To install all the additional dependencies -pip install 'vision-parse[all]' -``` - **Install the package from source:** ```bash pip install 'git+https://github.com/tqtensor/vision-parse.git#egg=vision-parse[all]' ``` -### Setting up Ollama (Optional) -See [Ollama Setup Guide](docs/ollama_setup.md) on how to setup Ollama locally. - -> [!IMPORTANT] -> While Ollama provides free local model hosting, please note that vision models from Ollama can be significantly slower in processing documents and may not produce optimal results when handling complex PDF documents. For better accuracy and performance with complex layouts in PDF documents, consider using API-based models like OpenAI or Gemini. - -### Setting up Vision Parse with Docker (Optional) -Check out [Docker Setup Guide](docs/docker_setup.md) on how to setup Vision Parse with Docker. - ## 📚 Usage ### Basic Example Usage @@ -72,16 +55,17 @@ from vision_parse import VisionParser # Initialize parser parser = VisionParser( - model_name="llama3.2-vision:11b", # For local models, you don't need to provide the api key + model_name="gpt-4o", + api_key="your-openai-api-key", temperature=0.4, top_p=0.5, - image_mode="url", # Image mode can be "url", "base64" or None - detailed_extraction=False, # Set to True for more detailed extraction - enable_concurrency=False, # Set to True for parallel processing + image_mode="url", # image mode can be "url", "base64" or None + detailed_extraction=False, # set to True for more detailed extraction + enable_concurrency=False, # set to True for parallel processing ) # Convert PDF to markdown -pdf_path = "input_document.pdf" # local path to your pdf file +pdf_path = "input_document.pdf" # local path to your PDF file markdown_pages = parser.convert_pdf(pdf_path) # Process results @@ -89,38 +73,6 @@ for i, page_content in enumerate(markdown_pages): print(f"\n--- Page {i+1} ---\n{page_content}") ``` -### Customize Ollama configuration for better performance - -```python -from vision_parse import VisionParser - -custom_prompt = """ -Strictly preserve markdown formatting during text extraction from scanned document. -""" - -# Initialize parser with Ollama configuration -parser = VisionParser( - model_name="llama3.2-vision:11b", - temperature=0.7, - top_p=0.6, - num_ctx=4096, - image_mode="base64", - custom_prompt=custom_prompt, - detailed_extraction=True, - ollama_config={ - "OLLAMA_NUM_PARALLEL": 8, - "OLLAMA_REQUEST_TIMEOUT": 240, - }, - enable_concurrency=True, -) - -# Convert PDF to markdown -pdf_path = "input_document.pdf" # local path to your pdf file -markdown_pages = parser.convert_pdf(pdf_path) -``` -> [!TIP] -> Please refer to [FAQs](docs/faq.md) for more details on how to improve the performance of locally hosted vision models. - ### API Models Usage (OpenAI, Azure OpenAI, Gemini, DeepSeek) ```python @@ -130,11 +82,11 @@ from vision_parse import VisionParser # Initialize parser with OpenAI model parser = VisionParser( model_name="gpt-4o", - api_key="your-openai-api-key", # Get the OpenAI API key from https://platform.openai.com/api-keys + api_key="your-openai-api-key", # get the OpenAI API key from https://platform.openai.com/api-keys temperature=0.7, top_p=0.4, image_mode="url", - detailed_extraction=False, # Set to True for more detailed extraction + detailed_extraction=False, # set to True for more detailed extraction enable_concurrency=True, ) @@ -142,13 +94,13 @@ parser = VisionParser( parser = VisionParser( model_name="gpt-4o", image_mode="url", - detailed_extraction=False, # Set to True for more detailed extraction + detailed_extraction=False, # set to True for more detailed extraction enable_concurrency=True, openai_config={ - "AZURE_ENDPOINT_URL": "https://****.openai.azure.com/", # replace with your azure endpoint url - "AZURE_DEPLOYMENT_NAME": "*******", # replace with azure deployment name, if needed - "AZURE_OPENAI_API_KEY": "***********", # replace with your azure openai api key - "AZURE_OPENAI_API_VERSION": "2024-08-01-preview", # replace with latest azure openai api version + "AZURE_ENDPOINT_URL": "https://****.openai.azure.com/", # replace with your Azure endpoint URL + "AZURE_DEPLOYMENT_NAME": "*******", # replace with Azure deployment name, if needed + "AZURE_OPENAI_API_KEY": "***********", # replace with your Azure OpenAI API key + "AZURE_OPENAI_API_VERSION": "2024-08-01-preview", # replace with latest Azure OpenAI API version }, ) @@ -156,97 +108,86 @@ parser = VisionParser( # Initialize parser with Google Gemini model parser = VisionParser( model_name="gemini-1.5-flash", - api_key="your-gemini-api-key", # Get the Gemini API key from https://aistudio.google.com/app/apikey + api_key="your-gemini-api-key", # get the Gemini API key from Google AI Studio: https://aistudio.google.com/app/apikey temperature=0.7, top_p=0.4, image_mode="url", - detailed_extraction=False, # Set to True for more detailed extraction + detailed_extraction=False, # set to True for more detailed extraction enable_concurrency=True, ) # Initialize parser with DeepSeek model parser = VisionParser( model_name="deepseek-chat", - api_key="your-deepseek-api-key", # Get the DeepSeek API key from https://platform.deepseek.com/api_keys + api_key="your-deepseek-api-key", # get the DeepSeek API key from https://platform.deepseek.com/api_keys temperature=0.7, top_p=0.4, image_mode="url", - detailed_extraction=False, # Set to True for more detailed extraction + detailed_extraction=False, # set to True for more detailed extraction enable_concurrency=True, ) ``` -## ✅ Supported Models +## ✅ Tested Models -This package supports the following Vision LLM models: +The following Vision LLM models have been thoroughly tested with Vision Parse, but thanks to our [LiteLLM](https://github.com/BerriAI/litellm) integration, you can experiment with other vision-capable models as well: | **Model Name** | **Provider Name** | | :------------------: | :---------------: | | gpt-4o | OpenAI | | gpt-4o-mini | OpenAI | -| gemini-1.5-flash | Google | -| gemini-2.0-flash-exp | Google | -| gemini-1.5-pro | Google | -| llava:13b | Ollama | -| llava:34b | Ollama | -| llama3.2-vision:11b | Ollama | -| llama3.2-vision:70b | Ollama | -| deepseek-r1:32b | Ollama | +| gemini-1.5-flash | Google AI Studio | +| gemini-2.0-flash-exp | Google AI Studio | +| gemini-1.5-pro | Google AI Studio | | deepseek-chat | DeepSeek | -## 🔧 Customization Parameters - -Vision Parse offers several customization parameters to enhance document processing: - -| **Parameter** | **Description** | **Value Type** | -| :-----------------: | :---------------------------------------------------------------------------------------------------------------: | :------------: | -| model_name | Name of the Vision LLM model to use | str | -| custom_prompt | Define custom prompt for the model and it will be used as a suffix to the default prompt | str | -| ollama_config | Specify custom configuration for Ollama client initialization | dict | -| openai_config | Specify custom configuration for OpenAI, Azure OpenAI or DeepSeek client initialization | dict | -| gemini_config | Specify custom configuration for Gemini client initialization | dict | -| image_mode | Sets the image output format for the model i.e. if you want image url in markdown content or base64 encoded image | str | -| detailed_extraction | Enable advanced content extraction to extract complex information such as LaTeX equations, tables, images, etc. | bool | -| enable_concurrency | Enable parallel processing of multiple pages in a PDF document in a single request | bool | - > [!TIP] -> For more details on custom model configuration i.e. `openai_config`, `gemini_config`, and `ollama_config`; please refer to [Model Configuration](docs/model_config.md). - -## 📊 Benchmarks +> To use other vision-capable models, simply pass the appropriate model identifier as supported by LiteLLM. For a complete list of supported providers and models, check the [LiteLLM documentation](https://docs.litellm.ai/docs/providers). -I conducted benchmarking to evaluate Vision Parse's performance against MarkItDown and Nougat. The benchmarking was conducted using a curated dataset of 100 diverse machine learning papers from arXiv, and the Marker library was used to generate the ground truth markdown formatted data. +## 🔧 Configuration Options -Since there are no other ground truth data available for this task, I relied on the Marker library to generate the ground truth markdown formatted data. +### Core Parameters -### Results +- **model_name** `(str)`: Name of the Vision LLM model to use (e.g., "gpt-4o", "gemini-1.5-flash") +- **api_key** `(str)`: API key for the chosen provider +- **temperature** `(float)`: Controls randomness in the generation (0.0-1.0) +- **top_p** `(float)`: Controls diversity via nucleus sampling (0.0-1.0) -| Parser | Accuracy Score | -| :----------: | :------------: | -| Vision Parse | 92% | -| MarkItDown | 67% | -| Nougat | 79% | +### Content Processing Options -> [!NOTE] -> I used gpt-4o model for Vision Parse to extract markdown content from the pdf documents. I have used model parameter settings as in `scoring.py` script. The above results may vary depending on the model you choose for Vision Parse and the model parameter settings. +- **detailed_extraction** `(bool)`: When `True`, enables advanced extraction of complex elements (LaTeX, tables, etc.) +- **custom_prompt** `(str)`: Custom instructions to guide the model's extraction behavior +- **image_mode** `(str)`: How images are handled in the output ("url", "base64", or `None`) +- **enable_concurrency** `(bool)`: When `True`, processes multiple pages in parallel -### Run Your Own Benchmarks +### Provider-Specific Configuration -You can benchmark the performance of Vision Parse on your machine using your own dataset. Run `scoring.py` to generate a detailed comparison report in the output directory. - -1. Install packages from requirements.txt: -```bash -pip install --no-cache-dir -r benchmarks/requirements.txt -``` +#### OpenAI Configuration -2. Run the benchmark script: -```bash -# Change `pdf_path` to your pdf file path and `benchmark_results_path` to your desired output path -python benchmarks/scoring.py +```python +openai_config = { + # For standard OpenAI + "OPENAI_BASE_URL": "https://api.openai.com/v1", # optional + "OPENAI_MAX_RETRIES": 3, # optional + "OPENAI_TIMEOUT": 240.0, # optional + + # For Azure OpenAI + "AZURE_ENDPOINT_URL": "https://your-resource.openai.azure.com/", + "AZURE_DEPLOYMENT_NAME": "your-deployment-name", + "AZURE_OPENAI_API_KEY": "your-azure-api-key", + "AZURE_OPENAI_API_VERSION": "2024-08-01-preview", +} ``` -## 🤝 Contributing +#### Gemini Configuration (Google AI Studio) -Contributions to Vision Parse are welcome! Whether you're fixing bugs, adding new features, or creating example notebooks, your help is appreciated. Please check out [contributing guidelines](CONTRIBUTING.md) for instructions on setting up the development environment, code style requirements, and the pull request process. +```python +gemini_config = { + "GOOGLE_API_KEY": "your-google-api-key", # API key from Google AI Studio (not Vertex AI) + "GEMINI_MAX_RETRIES": 3, # optional + "GEMINI_TIMEOUT": 240.0, # optional +} +``` ## 📄 License From fc7c52ddefa1020189609b126033b4b768348e66 Mon Sep 17 00:00:00 2001 From: tqtensor Date: Thu, 8 May 2025 20:32:23 +0000 Subject: [PATCH 9/9] chore: retest models with actual API keys --- README.md | 18 +++++++++--------- src/vision_parse/constants.py | 8 ++++---- src/vision_parse/llm.py | 7 +++++++ 3 files changed, 20 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 86f77cc..3defd19 100644 --- a/README.md +++ b/README.md @@ -118,7 +118,7 @@ parser = VisionParser( # Initialize parser with DeepSeek model parser = VisionParser( - model_name="deepseek-chat", + model_name="deepseek/deepseek-chat", api_key="your-deepseek-api-key", # get the DeepSeek API key from https://platform.deepseek.com/api_keys temperature=0.7, top_p=0.4, @@ -132,14 +132,14 @@ parser = VisionParser( The following Vision LLM models have been thoroughly tested with Vision Parse, but thanks to our [LiteLLM](https://github.com/BerriAI/litellm) integration, you can experiment with other vision-capable models as well: -| **Model Name** | **Provider Name** | -| :------------------: | :---------------: | -| gpt-4o | OpenAI | -| gpt-4o-mini | OpenAI | -| gemini-1.5-flash | Google AI Studio | -| gemini-2.0-flash-exp | Google AI Studio | -| gemini-1.5-pro | Google AI Studio | -| deepseek-chat | DeepSeek | +| **Model Name** | **Provider Name** | +| :--------------: | :---------------: | +| gpt-4o | OpenAI | +| gpt-4o-mini | OpenAI | +| gpt-4.1 | OpenAI | +| gemini-1.5-pro | Google AI Studio | +| gemini-2.0-flash | Google AI Studio | +| deepseek-chat | DeepSeek | > [!TIP] > To use other vision-capable models, simply pass the appropriate model identifier as supported by LiteLLM. For a complete list of supported providers and models, check the [LiteLLM documentation](https://docs.litellm.ai/docs/providers). diff --git a/src/vision_parse/constants.py b/src/vision_parse/constants.py index 1c0ad48..13f55c0 100644 --- a/src/vision_parse/constants.py +++ b/src/vision_parse/constants.py @@ -9,8 +9,8 @@ # Common model prefixes for provider detection PROVIDER_PREFIXES: Dict[str, List[str]] = { - "openai": ["gpt-"], - "azure": ["gpt-"], - "gemini": ["gemini-"], - "deepseek": ["deepseek-"], + "openai": ["gpt"], + "azure": ["gpt"], + "gemini": ["gemini"], + "deepseek": ["deepseek"], } diff --git a/src/vision_parse/llm.py b/src/vision_parse/llm.py index b8c0b4c..21335e1 100644 --- a/src/vision_parse/llm.py +++ b/src/vision_parse/llm.py @@ -182,6 +182,13 @@ def _get_model_params(self, structured: bool = False) -> Dict[str, Any]: **self.gemini_config, } ) + elif self.provider == "deepseek": + # Handle DeepSeek parameters + params.update( + { + "api_key": self.api_key, + } + ) return params