diff --git a/.cursor/rules/python-developer.mdc b/.cursor/rules/python-developer.mdc
new file mode 100644
index 0000000..6a0d566
--- /dev/null
+++ b/.cursor/rules/python-developer.mdc
@@ -0,0 +1,29 @@
+---
+description: 
+globs: 
+alwaysApply: true
+---
+You are an AI assistant specialized in Python development. Your approach emphasizes:
+
+1. Clear project structure with separate directories for source code, tests, docs, and config.
+2. Modular design with distinct files for models, services, controllers, and utilities.
+3. Configuration management using environment variables.
+4. Robust error handling and logging, including context capture.
+5. Comprehensive testing with pytest. All tests should be fully annotated and should contain docstrings.
+6. Detailed documentation using docstrings and README files.
+7. Dependency management via uv.
+8. Code style consistency using pylint and black.
+9. CI/CD implementation with GitHub Actions.
+10. AI-friendly coding practices:
+   - Descriptive variable and function names
+   - Type hints using the built-in types if possible
+   - Detailed comments for complex logic
+   - Rich error context for debugging
+
+Follow the following rules:
+   - For any python file, be sure to ALWAYS add typing annotations to each function or class. 
+   - Be sure to include return types when necessary.
+   - Add descriptive google docstrings to all python functions and classes as well. Please use pep257 convention. Update existing docstrings if need be.
+   - Make sure you keep any comments that exist in a file.
+
+You provide code snippets and explanations tailored to these principles, optimizing for clarity and AI-assisted development.
\ No newline at end of file
diff --git a/.cursor/rules/python-type-hints.mdc b/.cursor/rules/python-type-hints.mdc
new file mode 100644
index 0000000..54d0cd9
--- /dev/null
+++ b/.cursor/rules/python-type-hints.mdc
@@ -0,0 +1,81 @@
+---
+description: 
+globs: 
+alwaysApply: true
+---
+# Python Type Hints Rule
+
+This rule enforces the use of built-in types for type hints in Python code when applicable. This follows modern Python practices (Python 3.9+) and makes the code more intuitive and maintainable.
+
+## Rules
+
+1. Use built-in types for type hints instead of their typing module counterparts when possible:
+   - Use `list[T]` instead of `List[T]`
+   - Use `dict[K, V]` instead of `Dict[K, V]`
+   - Use `set[T]` instead of `Set[T]`
+   - Use `tuple[T, ...]` instead of `Tuple[T, ...]`
+   - Use `frozenset[T]` instead of `FrozenSet[T]`
+
+2. Only use typing module types when:
+   - You need to support Python versions older than 3.9
+   - You're using special types like `Union`, `Optional`, `Any`, etc.
+   - You're using generic types that don't have built-in equivalents
+
+## Examples
+
+✅ Good:
+```python
+def process_data(data: list[str]) -> dict[str, int]:
+    return {"count": len(data)}
+
+def get_items() -> set[int]:
+    return {1, 2, 3}
+
+def create_pairs() -> tuple[str, int]:
+    return ("key", 42)
+```
+
+❌ Bad:
+```python
+from typing import List, Dict, Set, Tuple
+
+def process_data(data: List[str]) -> Dict[str, int]:
+    return {"count": len(data)}
+
+def get_items() -> Set[int]:
+    return {1, 2, 3}
+
+def create_pairs() -> Tuple[str, int]:
+    return ("key", 42)
+```
+
+## Exceptions
+
+1. Keep using typing module types for:
+   - `Any`
+   - `Union`
+   - `Optional`
+   - `Literal`
+   - `TypeVar`
+   - `Generic`
+   - `Protocol`
+   - Other special typing constructs
+
+2. When backward compatibility is required (Python < 3.9), use the typing module types.
+
+## Benefits
+
+1. More intuitive code that uses actual types rather than special typing module versions
+2. Reduced imports from typing module
+3. Better IDE support and type checking
+4. Follows modern Python best practices
+5. Makes the code more maintainable and easier to understand
+
+## Implementation
+
+When reviewing or modifying code:
+1. Check for typing module imports
+2. Replace typing module types with built-in types where applicable
+3. Keep special typing module types when needed
+4. Update docstrings to reflect the use of built-in types
+5. Ensure type hints are consistent throughout the codebase 
\ No newline at end of file
diff --git a/.cursor/rules/uv-dependency-manager.mdc b/.cursor/rules/uv-dependency-manager.mdc
new file mode 100644
index 0000000..2920ba3
--- /dev/null
+++ b/.cursor/rules/uv-dependency-manager.mdc
@@ -0,0 +1,71 @@
+---
+description: 
+globs: 
+alwaysApply: true
+---
+# UV Dependency Manager Rule
+
+This rule enforces the use of `uv` as the primary dependency management tool for Python projects.
+
+## Requirements
+
+1. All Python dependencies must be managed using `uv`:
+   - Use `uv.lock` for lockfile
+   - Use `pyproject.toml` for project metadata and dependencies
+   - Use `requirements.txt` only for development dependencies or when explicitly required
+
+2. Project Structure:
+   - Must have a `pyproject.toml` file
+   - Must have a `uv.lock` file
+   - Should not use `poetry.lock` or `Pipfile.lock`
+
+3. Commands:
+   - Use `uv pip install` instead of `pip install`
+   - Use `uv pip freeze` instead of `pip freeze`
+   - Use `uv pip compile` for generating requirements files
+
+4. Virtual Environment:
+   - Use `uv venv` for creating virtual environments
+   - Use `uv pip install -r requirements.txt` for installing dependencies
+
+## Examples
+
+✅ Correct:
+```bash
+# Creating a virtual environment
+uv venv
+
+# Installing dependencies
+uv pip install -r requirements.txt
+
+# Updating dependencies
+uv pip install --upgrade -r requirements.txt
+```
+
+❌ Incorrect:
+```bash
+# Using pip directly
+pip install -r requirements.txt
+
+# Using poetry
+poetry install
+
+# Using pipenv
+pipenv install
+```
+
+## Benefits
+
+1. Faster dependency resolution and installation
+2. Better reproducibility with lockfile
+3. Modern Python packaging standards
+4. Improved security with dependency scanning
+5. Better integration with modern Python tooling
+
+## Implementation
+
+When implementing this rule:
+1. Ensure all dependency management commands use `uv`
+2. Keep `uv.lock` and `pyproject.toml` in version control
+3. Document the use of `uv` in project README
+4. Set up CI/CD to use `uv` for dependency installation 
\ No newline at end of file
diff --git a/.cursorignore b/.cursorignore
new file mode 100644
index 0000000..d4837ee
--- /dev/null
+++ b/.cursorignore
@@ -0,0 +1,15 @@
+# Ignore all .env files
+.env*
+
+# Ignore all config files
+config*
+
+# Ignore all settings files
+settings*
+
+# Ignore all input files
+input*
+
+# Ignore all output files
+output*
+
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
new file mode 100644
index 0000000..bb1ebea
--- /dev/null
+++ b/.github/workflows/lint.yml
@@ -0,0 +1,46 @@
+name: Lint and Test
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.13"
+
+      - name: Install uv
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          echo "$HOME/.cargo/bin" >> $GITHUB_PATH
+
+      - name: Install dependencies
+        run: |
+          make install
+
+      - name: Run pylint
+        run: |
+          make check-lint
+
+      - name: Run black
+        run: |
+          make check-format
+
+      - name: Run mypy
+        run: |
+          make check-types
+
+      - name: Run tests with coverage
+        run: |
+          make test
diff --git a/.gitignore b/.gitignore
index 5ccb9ee..e0e107b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -12,9 +12,6 @@ wheels/
 # Ignore macOS files
 .DS_Store
 
-# Ignore cursor rules
-*.cursor
-
 # Ignore mypy cache
 .mypy_cache/
 
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..4096c5a
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,33 @@
+#!/usr/bin/make -f
+
+.PHONY: help check test
+
+help: ## Show this help text
+	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}'
+
+install:
+	@echo "Installing dependencies..."
+	@uv sync --all-groups
+
+check: check-lint check-format check-types
+	@echo "All checks passed"
+
+check-lint:
+	@echo "Checking lint..."
+	@uv run python -m pylint src/ tests/
+
+check-format:
+	@echo "Checking format..."
+	@uv run python -m black src/ tests/
+
+format:
+	@echo "Formatting..."
+	@uv run python -m black src/ tests/
+
+check-types:
+	@echo "Checking types..."
+	@uv run python -m mypy src/ tests/ --ignore-missing-imports  --explicit-package-bases
+
+test:
+	@echo "Running tests..."
+	@uv run pytest src/ tests/ --cov=src --cov-report=term-missing
\ No newline at end of file
diff --git a/README.md b/README.md
index 0bd7508..def2f59 100644
--- a/README.md
+++ b/README.md
@@ -36,7 +36,7 @@ source .venv/bin/activate  # On Unix/macOS
 3. Install dependencies:
 
 ```bash
-uv pip install .
+uv pip install -e ".[dev]"
 ```
 
 ## Configuration
@@ -133,6 +133,42 @@ In order to evaluate only the OCR part of the output file, we can remove all mar
 uv run src/evaluate.py -f <filename> -m
 ```
 
+## Running Tests
+
+### Unit Tests
+
+To run all unit tests:
+```bash
+uv run pytest tests/ -v
+```
+
+To run tests with coverage:
+```bash
+uv run pytest tests/ -v --cov=src --cov-report=term-missing
+```
+
+### Integration Tests
+
+Integration tests require external services to be configured. To run them:
+```bash
+uv run pytest tests/ -v -m integration
+```
+
+### Test Categories
+
+- `test_base.py`: Tests for the base parser functionality
+- `test_run.py`: Tests for the main script functionality
+- `test_integration.py`: Integration tests requiring external services
+
+## Test Configuration
+
+The test suite is configured in `pyproject.toml` with the following settings:
+
+- Test paths: `tests/`
+- Test file pattern: `test_*.py`
+- Coverage reporting: Enabled
+- Integration test marker: `@pytest.mark.integration`
+
 ## Development
 
 ### Code Style
diff --git a/pyproject.toml b/pyproject.toml
index b67ada4..eea4ccb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,10 +23,17 @@ dependencies = [
     "pymupdf>=1.25.4",
     "tenacity>=9.0.0",
     "rouge>=1.0.1",
+    "huggingface-hub>=0.30.2",
 ]
 
 [dependency-groups]
-dev = ["black>=25.1.0", "mypy>=1.15.0", "pylint>=3.3.6"]
+dev = [
+    "black>=25.1.0",
+    "mypy>=1.15.0",
+    "pylint>=3.3.6",
+    "pytest>=8.3.5",
+    "pytest-cov>=6.1.1",
+]
 eval = [
     "python-levenshtein>=0.27.1",
     "pandas>=2.2.3",
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000..274ea8e
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,28 @@
+[pytest]
+# Set log level
+log_cli = true
+log_cli_level = INFO
+log_cli_format = %(asctime)s [%(levelname)8s] %(message)s (%(filename)s:%(lineno)s)
+log_cli_date_format = %Y-%m-%d %H:%M:%S
+
+# Add src directory to Python path
+pythonpath = ./src
+
+# Define test markers
+markers =
+    unit: Unit tests that do not require external services
+    integration: Integration tests that require external services
+
+# Test collection settings
+testpaths = tests
+python_files = test_*.py
+python_classes = Test*
+python_functions = test_*
+
+# Test execution settings
+addopts = -v --tb=short
+
+# Filter warnings
+filterwarnings =
+    ignore::DeprecationWarning
+    ignore::UserWarning 
\ No newline at end of file
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/evaluate.py b/src/evaluate.py
index 850ba10..4c86843 100644
--- a/src/evaluate.py
+++ b/src/evaluate.py
@@ -9,6 +9,7 @@
 import os
 import re
 import sys
+from typing import Any
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -18,7 +19,7 @@
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 
-from utils.logging import setup_logging
+from utils.logging import setup_logging  # pylint: disable=no-name-in-module
 
 # Set up logging
 setup_logging()
@@ -258,7 +259,7 @@ def create_word_overlap_heatmap(
 
     # Create heatmap
     plt.figure(figsize=(12, 12))  # Increased figure size for better readability
-    ax = sns.heatmap(
+    ax = sns.heatmap(  # pylint: disable=unused-variable
         matrix,
         xticklabels=labels,
         yticklabels=labels,
@@ -351,7 +352,7 @@ def evaluate_files(
     # Calculate similarities
     cosine_sim = calculate_similarity(clean_text1, clean_text2)
     overlap_ratio, words1, words2 = calculate_word_overlap(clean_text1, clean_text2)
-    levenshtein_dist, levenshtein_ratio = calculate_levenshtein_metrics(clean_text1, clean_text2)
+    _, levenshtein_ratio = calculate_levenshtein_metrics(clean_text1, clean_text2)
     rouge_scores = calculate_rouge_scores(clean_text1, clean_text2)
 
     # logger.info results
@@ -384,6 +385,7 @@ def evaluate_files(
     return cosine_sim, overlap_ratio, levenshtein_ratio, rouge_scores
 
 
+# pylint: disable=too-many-locals
 def evaluate_all_files(filestem: str, parsers: list[str], remove_markdown: bool = True) -> None:
     """Evaluate similarity between all pairs of markdown files.
 
@@ -393,79 +395,84 @@ def evaluate_all_files(filestem: str, parsers: list[str], remove_markdown: bool
         remove_markdown: Whether to remove markdown formatting from the files.
             Default is True.
     """
-    # Find all files with the given filestem and parsers
     files = find_files(filestem, parsers)
+    suffix = " with Markdown" if not remove_markdown else ""
 
-    # Calculate overlap ratios for all pairs of files
-    overlap_ratios, levenshtein_ratios, cosine_ratios, rouge_ratios = [], [], [], []
+    # Calculate all metrics in a single pass
+    metrics_data: list[dict[str, Any]] = []
     for file1, file2 in itertools.combinations_with_replacement(files, 2):
         cosine_sim, overlap_ratio, levenshtein_ratio, rouge_scores = evaluate_files(
             file1, file2, preprocess=remove_markdown
         )
-        fname1 = os.path.basename(file1)
-        fname2 = os.path.basename(file2)
-        overlap_ratios.append({"file1": fname1, "file2": fname2, "value": overlap_ratio})
-        levenshtein_ratios.append({"file1": fname1, "file2": fname2, "value": levenshtein_ratio})
-        cosine_ratios.append({"file1": fname1, "file2": fname2, "value": cosine_sim})
-        rouge_ratios.append({"file1": fname1, "file2": fname2, "value": rouge_scores})
-
-    # Create and save heatmap
-    suffix = " with Markdown" if not remove_markdown else ""
-    metrics = [
+        metrics_data.append(
+            {
+                "files": (os.path.basename(file1), os.path.basename(file2)),
+                "word_overlap": overlap_ratio,
+                "levenshtein": levenshtein_ratio,
+                "cosine": cosine_sim,
+                "rouge": rouge_scores,
+            }
+        )
+
+    # Define metric configurations
+    metric_configs: list[dict[str, str]] = [
         {
-            "ratios": overlap_ratios,
             "name": "word_overlap",
             "title": f"Word Overlap Ratios Heatmap{suffix}",
             "label": "Overlap Ratio",
         },
         {
-            "ratios": levenshtein_ratios,
             "name": "levenshtein",
             "title": f"Levenshtein Ratios Heatmap{suffix}",
             "label": "Levenshtein Ratio",
         },
-        {
-            "ratios": cosine_ratios,
-            "name": "cosine",
-            "title": f"Cosine Ratios Heatmap{suffix}",
-            "label": "Cosine Ratio",
-        },
+        {"name": "cosine", "title": f"Cosine Ratios Heatmap{suffix}", "label": "Cosine Ratio"},
     ]
+
     # Add ROUGE metrics
     for rouge_type in ["rouge-1", "rouge-2", "rouge-l"]:
-        rouge_metric = {
-            "ratios": [
-                {
-                    "file1": r["file1"],
-                    "file2": r["file2"],
-                    "value": r["value"][rouge_type],
-                }
-                for r in rouge_ratios
-            ],
-            "name": rouge_type,
-            "title": f"{rouge_type.upper()} Scores Heatmap{suffix}",
-            "label": f"{rouge_type.upper()} Score",
-        }
-        metrics.append(rouge_metric)
+        metric_configs.append(
+            {
+                "name": rouge_type,
+                "title": f"{rouge_type.upper()} Scores Heatmap{suffix}",
+                "label": f"{rouge_type.upper()} Score",
+            }
+        )
 
-    # Save the metrics to a csv file
+    # Save metrics to CSV
     with open(f"output/{filestem}_metrics.csv", "w", encoding="utf-8") as f:
         writer = csv.writer(f)
         writer.writerow(["Metric", "File1", "File2", "Value"])
-        for metric in metrics:
-            for ratio in metric["ratios"]:
-                writer.writerow([metric["name"], ratio["file1"], ratio["file2"], ratio["value"]])
-
-    suffix = "_w_markdown" if not remove_markdown else ""
-    # Extract parser names from filenames (e.g., "output/test_anthropic.md" -> "anthropic")
+        for metric in metric_configs:
+            for data in metrics_data:
+                value = (
+                    data["rouge"][metric["name"]]
+                    if "rouge" in metric["name"]
+                    else data[metric["name"]]
+                )
+                writer.writerow([metric["name"], data["files"][0], data["files"][1], value])
+
+    # Create heatmaps
     labels = [
-        "_".join(file.replace(".md", "").split("_")[-2 if not "textract" in file else -1 :])
+        "_".join(file.replace(".md", "").split("_")[-2 if "textract" not in file else -1 :])
         for file in files
     ]
-    for metric in metrics:
+    for metric in metric_configs:
+        ratios = [
+            {
+                "file1": data["files"][0],
+                "file2": data["files"][1],
+                "value": (
+                    data["rouge"][metric["name"]]
+                    if "rouge" in metric["name"]
+                    else data[metric["name"]]
+                ),
+            }
+            for data in metrics_data
+        ]
         create_word_overlap_heatmap(
             files,
-            metric["ratios"],
+            ratios,
             labels,
             output_file=f"output/{metric['name']}_heatmap{suffix}.png",
             title=metric["title"],
diff --git a/src/parsers/__init__.py b/src/parsers/__init__.py
index 92f66dc..5185ed5 100644
--- a/src/parsers/__init__.py
+++ b/src/parsers/__init__.py
@@ -2,6 +2,7 @@
 
 from .anthropic import AnthropicParser
 from .googleai import GoogleAIParser
+from .huggingface import HuggingFaceParser
 from .mistral import MistralParser
 from .ollama import OllamaParser
 from .openai import OpenAIParser
@@ -11,6 +12,7 @@
 __all__ = [
     "AnthropicParser",
     "GoogleAIParser",
+    "HuggingFaceParser",
     "OllamaParser",
     "OpenAIParser",
     "MistralParser",
diff --git a/src/parsers/anthropic.py b/src/parsers/anthropic.py
index de23da5..0f4c7f5 100644
--- a/src/parsers/anthropic.py
+++ b/src/parsers/anthropic.py
@@ -53,12 +53,12 @@ def __init__(
         self.client = anthropic.Anthropic(api_key=api_key)
         self.logger.debug("Anthropic parser initialization complete")
 
-    def convert_pdf_to_markdown(self, pdf_path: str) -> str:
+    def convert_pdf_to_markdown(self, pdf_path: str, *, split_pages: bool = True) -> str:
         """Convert a PDF file to markdown using Anthropic.
 
         Args:
             pdf_path: The path to the PDF file to convert.
-
+            split_pages: Whether to split the PDF into pages. Default is True.
         Returns:
             The converted markdown text.
 
@@ -72,7 +72,7 @@ def convert_pdf_to_markdown(self, pdf_path: str) -> str:
         try:
             page_count = self.get_pdf_page_count(pdf_path)
 
-            if page_count > 1:
+            if page_count > 1 and split_pages:
                 pdf_pages = self.split_pdf_into_pages(pdf_path)
             else:
                 pdf_pages = [pdf_path]
diff --git a/src/parsers/base.py b/src/parsers/base.py
index 54c21bc..06abaec 100644
--- a/src/parsers/base.py
+++ b/src/parsers/base.py
@@ -1,18 +1,22 @@
 """Base parser class for PDF to Markdown conversion."""
 
 import base64
+import io
 import logging
 import tempfile
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Optional
+from typing import Generator
 
 import fitz  # PyMuPDF
+from pdf2image import convert_from_path
+from PIL import Image
 
 
 class BaseParser(ABC):
     """Base class for PDF to Markdown parsers."""
 
+    MAX_IMAGE_WIDTH = 1920
     SYSTEM_PROMPT = """
     You are a specialized PDF to Markdown converter. Your task is to convert PDF content to clean, properly formatted markdown text. Follow these strict rules:
 
@@ -96,7 +100,6 @@ def convert_pdf_to_markdown(self, pdf_path: str) -> str:
             FileNotFoundError: If the PDF file doesn't exist.
             IOError: If there's an error reading the PDF file.
         """
-        pass
 
     def validate_pdf_path(self, pdf_path: str) -> None:
         """Validate that the PDF file exists and is readable.
@@ -137,6 +140,52 @@ def read_pdf_as_base64(self, pdf_path: str) -> str:
             base64_encoded_data = base64.standard_b64encode(binary_data)
             return base64_encoded_data.decode("utf-8")
 
+    def resize_image(self, image: Image.Image) -> Image.Image:
+        """Resize image to maintain aspect ratio with max width.
+
+        Args:
+            image: PIL Image to resize.
+
+        Returns:
+            Resized PIL Image.
+        """
+        if image.width > self.MAX_IMAGE_WIDTH:
+            ratio = self.MAX_IMAGE_WIDTH / image.width
+            new_height = int(image.height * ratio)
+            return image.resize((self.MAX_IMAGE_WIDTH, new_height), Image.Resampling.LANCZOS)
+        return image
+
+    def read_pdf_as_base64_img(self, pdf_path: str) -> Generator[str, None, None]:
+        """Convert PDF pages to base64-encoded images.
+
+        Args:
+            pdf_path: Path to the PDF file.
+
+        Raises:
+            FileNotFoundError: If the PDF file doesn't exist.
+            IOError: If there's an error reading the PDF file.
+
+        Returns:
+            List[str]: List of base64-encoded image strings.
+        """
+        try:
+            # Convert PDF to images
+            images = convert_from_path(pdf_path)
+
+            # Convert each image to base64
+            for image in images:
+                # Resize image if needed
+                image = self.resize_image(image)
+
+                img_byte_io = io.BytesIO()
+                image.save(img_byte_io, format="PNG")
+                img_byte_arr = img_byte_io.getvalue()
+
+                yield base64.b64encode(img_byte_arr).decode("utf-8")
+        except Exception as e:
+            self.logger.error(f"Error converting PDF to images: {str(e)}", exc_info=True)
+            raise
+
     def save_markdown(self, markdown_text: str, output_path: str) -> None:
         """Save the markdown text to a file.
 
diff --git a/src/parsers/googleai.py b/src/parsers/googleai.py
index 5b148ba..867b7ba 100644
--- a/src/parsers/googleai.py
+++ b/src/parsers/googleai.py
@@ -89,10 +89,9 @@ def generate_response(self, prompt: str, pdf_file_id: File) -> str:
                 pdf_file_id,
             ],
         )
-
         return response.text  # type: ignore
 
-    def convert_pdf_to_markdown(self, pdf_path: str, split_pages: bool = False) -> str:
+    def convert_pdf_to_markdown(self, pdf_path: str, *, split_pages: bool = False) -> str:
         """Convert a PDF file to markdown using Gemini.
 
         Args:
diff --git a/src/parsers/huggingface.py b/src/parsers/huggingface.py
new file mode 100644
index 0000000..99cce97
--- /dev/null
+++ b/src/parsers/huggingface.py
@@ -0,0 +1,140 @@
+"""Hugging Face parser for PDF to Markdown conversion."""
+
+import os
+
+import tenacity
+from huggingface_hub import InferenceClient, InferenceTimeoutError
+
+from .base import BaseParser
+
+
+class HuggingFaceParser(BaseParser):
+    """A class to handle PDF to Markdown conversion using Hugging Face's inference endpoints."""
+
+    AVAILABLE_MODELS = [
+        "meta-llama/Llama-4-Scout-17B-16E-Instruct",
+        "meta-llama/Llama-4-Maverick-17B-128E-Instruct",
+        "Qwen/Qwen2.5-VL-7B-Instruct",
+    ]
+    DEFAULT_MODEL = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
+    PROVIDER_TO_ENV_VAR = {
+        "hf-inference": "HUGGINGFACE_API_KEY",
+    }
+
+    def __init__(
+        self,
+        model: str = DEFAULT_MODEL,
+        provider: str = "hf-inference",
+        temperature: float = 0.1,
+        top_p: float = 0.1,
+        top_k: int = 40,
+        max_tokens: int = 8192,
+    ) -> None:
+        """Initialize the Hugging Face parser.
+
+        Args:
+            model: The name of the Hugging Face model to use.
+            provider: The provider to use.
+            temperature: The temperature parameter for text generation.
+            top_p: The top_p parameter for text generation.
+            top_k: The top_k parameter for text generation.
+            max_tokens: The maximum number of tokens to generate.
+
+        Raises:
+            ValueError: If the HUGGINGFACE_API_KEY environment variable is not set.
+            ValueError: If the model is not supported.
+        """
+        if model not in self.AVAILABLE_MODELS:
+            raise ValueError(
+                f"Unsupported model: {model}. Available models: {', '.join(self.AVAILABLE_MODELS)}"
+            )
+
+        super().__init__(model, temperature, top_p, top_k, max_tokens)
+
+        api_key = os.getenv(self.PROVIDER_TO_ENV_VAR[provider])
+        if not api_key:
+            raise ValueError(f"{self.PROVIDER_TO_ENV_VAR[provider]} environment variable not set")
+
+        self.api_key = api_key
+        self.client = InferenceClient(
+            provider=provider,  # type: ignore
+            api_key=api_key,
+        )
+        self.logger.debug("Hugging Face parser initialization complete")
+
+    @tenacity.retry(
+        stop=tenacity.stop_after_attempt(3),
+        wait=tenacity.wait_exponential(multiplier=1, min=4, max=15),
+        retry=tenacity.retry_if_exception_type(InferenceTimeoutError),
+    )
+    def generate_response(self, prompt: str, base64_img_str: str) -> str:
+        """Generate a response from the Hugging Face API.
+
+        Args:
+            prompt (str): The prompt to generate a response for.
+            base64_image (str): The base64 encoded image string.
+
+        Returns:
+            str: The generated response.
+
+        Raises:
+            Exception: If there's an error generating the response.
+        """
+        try:
+            completion = self.client.chat.completions.create(
+                model=self.model,
+                messages=[
+                    {"role": "system", "content": self.SYSTEM_PROMPT},
+                    {
+                        "role": "user",
+                        "content": prompt,
+                        "images": ["data:image/png;base64," + base64_img_str],
+                    },
+                ],
+                max_tokens=self.max_tokens,
+                temperature=self.temperature,
+                top_p=self.top_p,
+                stream=True,
+            )
+            output = ""
+            for chunk in completion:
+                output += chunk.choices[0].delta.content or ""
+            return output
+        except Exception as e:
+            self.logger.error(f"Error generating response: {str(e)}", exc_info=True)
+            raise
+
+    def convert_pdf_to_markdown(self, pdf_path: str, *, split_pages: bool = True) -> str:
+        """Convert a PDF file to markdown using Gemini.
+
+        Args:
+            pdf_path: The path to the PDF file to convert.
+            split_pages: Whether to split the PDF into pages. Default is False.
+
+        Returns:
+            The converted markdown text.
+
+        Raises:
+            FileNotFoundError: If the PDF file doesn't exist.
+            IOError: If there's an error reading the PDF file.
+        """
+        self.logger.debug(f"Starting PDF to Markdown conversion for file: {pdf_path}")
+
+        try:
+            # In order to work around the free tier output token limit, we split the PDF into pages
+            # and process each page separately.
+            markdown_text = ""
+            for base64_image in self.read_pdf_as_base64_img(pdf_path):
+                prompt = "Convert this image to markdown format for me please."
+
+                response_text = self.generate_response(prompt, base64_image)
+
+                markdown_text += response_text + "\n\n"
+
+            self.logger.debug("Successfully received response from Gemini API")
+
+            return markdown_text
+
+        except Exception as e:
+            self.logger.error(f"Error during PDF conversion: {str(e)}", exc_info=True)
+            raise
diff --git a/src/parsers/mistral.py b/src/parsers/mistral.py
index 06f0518..39eb866 100644
--- a/src/parsers/mistral.py
+++ b/src/parsers/mistral.py
@@ -51,11 +51,12 @@ def __init__(
         self.client = Mistral(api_key=api_key)
         self.logger.debug("Mistral parser initialization complete")
 
-    def convert_pdf_to_markdown(self, pdf_path: str) -> str:
+    def convert_pdf_to_markdown(self, pdf_path: str, *, split_pages: bool = True) -> str:
         """Convert a PDF file to markdown using Mistral.
 
         Args:
             pdf_path: The path to the PDF file to convert.
+            split_pages: Whether to split the PDF into pages. Default is True.
 
         Returns:
             The converted markdown text.
diff --git a/src/parsers/ollama.py b/src/parsers/ollama.py
index 60bb702..0604e54 100644
--- a/src/parsers/ollama.py
+++ b/src/parsers/ollama.py
@@ -55,61 +55,12 @@ def __init__(
         }
         self.logger.debug("Ollama parser initialization complete")
 
-    def resize_image(self, image: Image.Image) -> Image.Image:
-        """Resize image to maintain aspect ratio with max width.
-
-        Args:
-            image: PIL Image to resize.
-
-        Returns:
-            Resized PIL Image.
-        """
-        if image.width > self.MAX_IMAGE_WIDTH:
-            ratio = self.MAX_IMAGE_WIDTH / image.width
-            new_height = int(image.height * ratio)
-            return image.resize((self.MAX_IMAGE_WIDTH, new_height), Image.Resampling.LANCZOS)
-        return image
-
-    def read_pdf_as_base64_img(self, pdf_path: str) -> List[str]:
-        """Convert PDF pages to base64-encoded images.
-
-        Args:
-            pdf_path: Path to the PDF file.
-
-        Raises:
-            FileNotFoundError: If the PDF file doesn't exist.
-            IOError: If there's an error reading the PDF file.
-
-        Returns:
-            List[str]: List of base64-encoded image strings.
-        """
-        try:
-            # Convert PDF to images
-            images = convert_from_path(pdf_path)
-
-            # Convert each image to base64
-            base64_images = []
-            for image in images:
-                # Resize image if needed
-                image = self.resize_image(image)
-
-                import io
-
-                img_byte_arr = io.BytesIO()
-                image.save(img_byte_arr, format="PNG")
-                img_byte_arr = img_byte_arr.getvalue()
-                base64_images.append(base64.b64encode(img_byte_arr).decode("utf-8"))
-
-            return base64_images
-        except Exception as e:
-            self.logger.error(f"Error converting PDF to images: {str(e)}", exc_info=True)
-            raise
-
-    def convert_pdf_to_markdown(self, pdf_path: str) -> str:
+    def convert_pdf_to_markdown(self, pdf_path: str, *, split_pages: bool = True) -> str:
         """Convert a PDF file to markdown using Ollama.
 
         Args:
             pdf_path: The path to the PDF file to convert.
+            split_pages: Whether to split the PDF into pages. Default is True.
 
         Raises:
             FileNotFoundError: If the PDF file doesn't exist.
@@ -123,7 +74,7 @@ def convert_pdf_to_markdown(self, pdf_path: str) -> str:
         try:
             # Convert PDF to base64 images
             self.logger.debug(f"Converting PDF to images: {pdf_path}")
-            base64_images = self.read_pdf_as_base64_img(pdf_path)
+            base64_images = list(self.read_pdf_as_base64_img(pdf_path))
 
             prompt = "Convert the attached pdf to markdown format for me please."
 
diff --git a/src/parsers/openai.py b/src/parsers/openai.py
index 9e5c498..c3f9d12 100644
--- a/src/parsers/openai.py
+++ b/src/parsers/openai.py
@@ -49,11 +49,12 @@ def __init__(
         openai.api_key = api_key
         self.logger.debug("OpenAI parser initialization complete")
 
-    def convert_pdf_to_markdown(self, pdf_path: str) -> str:
+    def convert_pdf_to_markdown(self, pdf_path: str, *, split_pages: bool = True) -> str:
         """Convert a PDF file to markdown using OpenAI.
 
         Args:
             pdf_path: The path to the PDF file to convert.
+            split_pages: Whether to split the PDF into pages. Default is True.
 
         Returns:
             The converted markdown text.
@@ -69,7 +70,7 @@ def convert_pdf_to_markdown(self, pdf_path: str) -> str:
         try:
             page_count = self.get_pdf_page_count(pdf_path)
 
-            if page_count > 1:
+            if page_count > 1 and split_pages:
                 pdf_pages = self.split_pdf_into_pages(pdf_path)
             else:
                 pdf_pages = [pdf_path]
diff --git a/src/parsers/textract.py b/src/parsers/textract.py
index 992408c..2e7eb93 100644
--- a/src/parsers/textract.py
+++ b/src/parsers/textract.py
@@ -12,8 +12,8 @@
 class TextractParser(BaseParser):
     """A class to handle PDF to Markdown conversion using AWS Textract."""
 
-    AVAILABLE_MODELS = []
-    DEFAULT_MODEL = ""
+    AVAILABLE_MODELS: list[str] = []
+    DEFAULT_MODEL: str = ""
 
     def __init__(
         self,
@@ -46,12 +46,12 @@ def __init__(
         )
         self.logger.debug("Textract parser initialization complete")
 
-    def convert_pdf_to_markdown(self, pdf_path: str) -> str:
+    def convert_pdf_to_markdown(self, pdf_path: str, *, split_pages: bool = True) -> str:
         """Convert a PDF file to markdown using AWS Textract.
 
         Args:
             pdf_path: The path to the PDF file to convert.
-
+            split_pages: Whether to split the PDF into pages. Default is True.
         Raises:
             FileNotFoundError: If the PDF file doesn't exist.
             IOError: If there's an error reading the PDF file.
@@ -65,7 +65,7 @@ def convert_pdf_to_markdown(self, pdf_path: str) -> str:
         try:
             page_count = self.get_pdf_page_count(pdf_path)
 
-            if page_count > 1:
+            if page_count > 1 and split_pages:
                 pdf_pages = self.split_pdf_into_pages(pdf_path)
             else:
                 pdf_pages = [pdf_path]
diff --git a/src/parsers/unstructuredio.py b/src/parsers/unstructuredio.py
index b6d09c2..1838408 100644
--- a/src/parsers/unstructuredio.py
+++ b/src/parsers/unstructuredio.py
@@ -62,11 +62,12 @@ def __init__(
         self.client = unstructured_client.UnstructuredClient(api_key_auth=api_key)
         self.logger.debug("UnstructuredIO parser initialization complete")
 
-    def convert_pdf_to_markdown(self, pdf_path: str) -> str:
+    def convert_pdf_to_markdown(self, pdf_path: str, *, split_pages: bool = True) -> str:
         """Convert a PDF file to markdown using UnstructuredIO.
 
         Args:
             pdf_path: The path to the PDF file to convert.
+            split_pages: Whether to split the PDF into pages. Default is True.
 
         Returns:
             The converted markdown text.
diff --git a/src/run.py b/src/run.py
index be890a1..0415338 100644
--- a/src/run.py
+++ b/src/run.py
@@ -7,14 +7,15 @@
 
 from dotenv import load_dotenv
 
-from parsers.anthropic import AnthropicParser
-from parsers.googleai import GoogleAIParser
-from parsers.mistral import MistralParser
-from parsers.ollama import OllamaParser
-from parsers.openai import OpenAIParser
-from parsers.textract import TextractParser
-from parsers.unstructuredio import UnstructuredIOParser
-from utils.logging import setup_logging
+from parsers.anthropic import AnthropicParser  # type: ignore
+from parsers.googleai import GoogleAIParser  # type: ignore
+from parsers.huggingface import HuggingFaceParser  # type: ignore
+from parsers.mistral import MistralParser  # type: ignore
+from parsers.ollama import OllamaParser  # type: ignore
+from parsers.openai import OpenAIParser  # type: ignore
+from parsers.textract import TextractParser  # type: ignore
+from parsers.unstructuredio import UnstructuredIOParser  # type: ignore
+from utils.logging import setup_logging  # type: ignore  # pylint: disable=no-name-in-module
 
 # Load environment variables
 load_dotenv()
@@ -32,6 +33,7 @@
     "openai": OpenAIParser,
     "textract": TextractParser,
     "unstructuredio": UnstructuredIOParser,
+    "huggingface": HuggingFaceParser,
 }
 
 
@@ -138,6 +140,11 @@ def setup_cli_args() -> argparse.Namespace:
         choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
         help="The logging level to use. Defaults to INFO.",
     )
+    parser.add_argument(
+        "--split-pages",
+        action="store_true",
+        help="Split the PDF into pages and process each page separately. Not used in all parsers.",
+    )
 
     args = parser.parse_args()
 
@@ -173,7 +180,9 @@ def main() -> None:
         )
 
         # Convert PDF to markdown
-        markdown_text = pdf_parser.convert_pdf_to_markdown(args.input_pdf_path)
+        markdown_text = pdf_parser.convert_pdf_to_markdown(
+            args.input_pdf_path, split_pages=args.split_pages
+        )
 
         # Save the markdown
         pdf_parser.save_markdown(markdown_text, str(output_path))
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..23c6603
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,72 @@
+"""Common test fixtures for the PDF to Markdown conversion tests."""
+
+import os
+import tempfile
+from pathlib import Path
+from typing import Generator
+
+import pytest
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+
+@pytest.fixture
+def temp_pdf_file() -> Generator[Path, None, None]:
+    """Create a temporary PDF file for testing.
+
+    Yields:
+        Path: Path to the temporary PDF file.
+    """
+    with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as temp_file:
+        # Create a minimal PDF file
+        temp_file.write(
+            b"%PDF-1.4\n1 0 obj\n<</Type /Catalog /Pages 2 0 R>>\nendobj\n"
+            b"2 0 obj\n<</Type /Pages /Kids [3 0 R] /Count 1>>\nendobj\n"
+            b"3 0 obj\n<</Type /Page /Parent 2 0 R /Resources <<>> "
+            b"/MediaBox [0 0 612 792]>>\nendobj\n"
+            b"xref\n0 4\n0000000000 65535 f\n0000000010 00000 n\n0000000056 00000 n\n"
+            b"0000000102 00000 n\ntrailer\n<</Size 4/Root 1 0 R>>\nstartxref\n149\n%%EOF"
+        )
+        temp_file.flush()
+        yield Path(temp_file.name)
+        os.unlink(temp_file.name)
+
+
+@pytest.fixture
+def temp_output_dir() -> Generator[Path, None, None]:
+    """Create a temporary output directory for testing.
+
+    Yields:
+        Path: Path to the temporary output directory.
+    """
+    with tempfile.TemporaryDirectory() as temp_dir:
+        yield Path(temp_dir)
+
+
+@pytest.fixture
+def sample_markdown() -> str:
+    """Return a sample markdown text for testing.
+
+    Returns:
+        str: Sample markdown text.
+    """
+    return """# Test Document
+
+## Section 1
+
+This is a test paragraph.
+
+### Subsection 1.1
+
+- Item 1
+- Item 2
+- Item 3
+
+## Section 2
+
+| Header 1 | Header 2 |
+|----------|----------|
+| Value 1  | Value 2  |
+"""
diff --git a/tests/test_base.py b/tests/test_base.py
new file mode 100644
index 0000000..529f0fe
--- /dev/null
+++ b/tests/test_base.py
@@ -0,0 +1,185 @@
+"""Tests for the base parser functionality."""
+
+import os
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+from PIL import Image
+
+from parsers.base import BaseParser
+
+
+class TestBaseParser(BaseParser):
+    """Test implementation of BaseParser for testing."""
+
+    AVAILABLE_MODELS = ["test-model"]
+    DEFAULT_MODEL = "test-model"
+
+    def convert_pdf_to_markdown(self, pdf_path: str) -> str:
+        """Test implementation of convert_pdf_to_markdown."""
+        return "Test markdown content"
+
+
+@pytest.mark.unit
+def test_validate_pdf_path(temp_pdf_file: Path) -> None:
+    """Test PDF path validation with valid PDF file.
+
+    Args:
+        temp_pdf_file: Temporary PDF file fixture.
+    """
+    parser = TestBaseParser("test-model")
+    parser.validate_pdf_path(str(temp_pdf_file))
+
+
+@pytest.mark.unit
+def test_validate_pdf_path_nonexistent() -> None:
+    """Test PDF path validation with nonexistent file."""
+    parser = TestBaseParser("test-model")
+    with pytest.raises(FileNotFoundError):
+        parser.validate_pdf_path("nonexistent.pdf")
+
+
+@pytest.mark.unit
+def test_validate_pdf_path_not_pdf(temp_output_dir: Path) -> None:
+    """Test PDF path validation with non-PDF file.
+
+    Args:
+        temp_output_dir: Temporary output directory fixture.
+    """
+    parser = TestBaseParser("test-model")
+    not_pdf = temp_output_dir / "not_pdf.txt"
+    not_pdf.write_text("Not a PDF")
+    with pytest.raises(ValueError):
+        parser.validate_pdf_path(str(not_pdf))
+
+
+@pytest.mark.unit
+def test_read_pdf_as_base64(temp_pdf_file: Path) -> None:
+    """Test reading PDF as base64.
+
+    Args:
+        temp_pdf_file: Temporary PDF file fixture.
+    """
+    parser = TestBaseParser("test-model")
+    base64_content = parser.read_pdf_as_base64(str(temp_pdf_file))
+    assert isinstance(base64_content, str)
+    assert base64_content.startswith("JVBERi0x")
+
+
+@pytest.mark.unit
+def test_save_markdown(temp_output_dir: Path, sample_markdown: str) -> None:
+    """Test saving markdown to file.
+
+    Args:
+        temp_output_dir: Temporary output directory fixture.
+        sample_markdown: Sample markdown text fixture.
+    """
+    parser = TestBaseParser("test-model")
+    output_path = temp_output_dir / "test.md"
+    parser.save_markdown(sample_markdown, str(output_path))
+    assert output_path.exists()
+    assert output_path.read_text() == sample_markdown
+
+
+@pytest.mark.unit
+def test_split_pdf_into_pages(temp_pdf_file: Path) -> None:
+    """Test splitting PDF into pages.
+
+    Args:
+        temp_pdf_file: Temporary PDF file fixture.
+    """
+    parser = TestBaseParser("test-model")
+    page_files = parser.split_pdf_into_pages(str(temp_pdf_file))
+    assert len(page_files) == 1
+    assert all(Path(f).exists() for f in page_files)
+    # Clean up
+    for f in page_files:
+        os.unlink(f)
+
+
+@pytest.mark.unit
+def test_get_pdf_page_count(temp_pdf_file: Path) -> None:
+    """Test getting PDF page count.
+
+    Args:
+        temp_pdf_file: Temporary PDF file fixture.
+    """
+    parser = TestBaseParser("test-model")
+    page_count = parser.get_pdf_page_count(str(temp_pdf_file))
+    assert page_count == 1
+
+
+@pytest.mark.unit
+@patch("parsers.base.fitz")
+def test_get_pdf_page_count_error(mock_fitz: MagicMock) -> None:
+    """Test error handling in get_pdf_page_count.
+
+    Args:
+        mock_fitz: Mocked fitz module.
+    """
+    mock_fitz.open.side_effect = Exception("Test error")
+    parser = TestBaseParser("test-model")
+    with pytest.raises(Exception):
+        parser.get_pdf_page_count("test.pdf")
+
+
+@pytest.mark.unit
+def test_resize_image() -> None:
+    """Test image resizing functionality.
+
+    Tests that:
+    1. Images wider than MAX_IMAGE_WIDTH are resized while maintaining aspect ratio
+    2. Images narrower than MAX_IMAGE_WIDTH are returned unchanged
+    """
+    parser = TestBaseParser("test-model")
+
+    # Create a test image wider than MAX_IMAGE_WIDTH
+    wide_image = Image.new("RGB", (parser.MAX_IMAGE_WIDTH + 100, 500))
+    resized_image = parser.resize_image(wide_image)
+    assert resized_image.width == parser.MAX_IMAGE_WIDTH
+    assert resized_image.height == int(
+        500 * (parser.MAX_IMAGE_WIDTH / (parser.MAX_IMAGE_WIDTH + 100))
+    )
+
+    # Create a test image narrower than MAX_IMAGE_WIDTH
+    narrow_image = Image.new("RGB", (parser.MAX_IMAGE_WIDTH - 100, 500))
+    unchanged_image = parser.resize_image(narrow_image)
+    assert unchanged_image.width == parser.MAX_IMAGE_WIDTH - 100
+    assert unchanged_image.height == 500
+
+
+@pytest.mark.unit
+@patch("parsers.base.convert_from_path")
+def test_read_pdf_as_base64_img(mock_convert: MagicMock) -> None:
+    """Test converting PDF pages to base64-encoded images.
+
+    Args:
+        mock_convert: Mocked convert_from_path function.
+    """
+    # Create test images
+    test_images = [Image.new("RGB", (100, 100)), Image.new("RGB", (100, 100))]
+    mock_convert.return_value = test_images
+
+    parser = TestBaseParser("test-model")
+    base64_images = list(parser.read_pdf_as_base64_img("test.pdf"))
+
+    # Verify results
+    assert len(base64_images) == 2
+    for img_str in base64_images:
+        assert isinstance(img_str, str)
+        assert img_str.startswith("iVBORw0KGgo")  # PNG base64 header
+
+
+@pytest.mark.unit
+@patch("parsers.base.convert_from_path")
+def test_read_pdf_as_base64_img_error(mock_convert: MagicMock) -> None:
+    """Test error handling in read_pdf_as_base64_img.
+
+    Args:
+        mock_convert: Mocked convert_from_path function.
+    """
+    mock_convert.side_effect = Exception("Test error")
+    parser = TestBaseParser("test-model")
+    with pytest.raises(Exception):
+        list(parser.read_pdf_as_base64_img("test.pdf"))
diff --git a/tests/test_evaluate.py b/tests/test_evaluate.py
new file mode 100644
index 0000000..fbaf803
--- /dev/null
+++ b/tests/test_evaluate.py
@@ -0,0 +1,284 @@
+"""Unit tests for the evaluate module."""
+
+import os
+import tempfile
+from pathlib import Path
+from unittest.mock import MagicMock, mock_open, patch
+
+import numpy as np
+import pytest
+
+# Fix the import path
+from evaluate import (
+    calculate_levenshtein_metrics,
+    calculate_rouge_scores,
+    calculate_similarity,
+    calculate_word_overlap,
+    create_word_overlap_heatmap,
+    evaluate_all_files,
+    evaluate_files,
+    find_files,
+    preprocess_markdown,
+)
+
+
+@pytest.mark.unit
+def test_preprocess_markdown() -> None:
+    """Test removing markdown formatting from text."""
+    markdown_text = """
+# Header 1
+## Header 2
+
+This is **bold** and *italic* text.
+
+- List item 1
+- List item 2
+
+1. Numbered item 1
+2. Numbered item 2
+
+> Blockquote
+
+```python
+def test():
+    pass
+```
+
+[Link](https://example.com)
+
+| Header 1 | Header 2 |
+|----------|----------|
+| Cell 1   | Cell 2   |
+| Cell 3   | Cell 4   |
+
+$$E = mc^2$$
+
+---
+    """
+
+    expected = (
+        "Header 1 Header 2 "
+        "This is bold and italic text "
+        "List item 1 List item 2 "
+        "Numbered item 1 Numbered item 2 "
+        "Blockquote "
+        "python def test pass "
+        "Link "
+        "Header 1 Header 2Cell 1 Cell 2 Cell 3 Cell 4"
+    )
+    result = preprocess_markdown(markdown_text)
+
+    # We normalize spaces to avoid test failures due to whitespace differences
+    expected = " ".join(expected.split())
+    result = " ".join(result.split())
+
+    assert result == expected
+
+
+@pytest.mark.unit
+def test_calculate_similarity() -> None:
+    """Test calculating similarity between two texts."""
+    text1 = "This is a test sentence for similarity comparison"
+    text2 = "This is a test phrase for similarity comparison"
+
+    similarity = calculate_similarity(text1, text2)
+    assert 0 <= similarity <= 1
+    assert round(similarity, 2) == 0.75  # Should be high for similar texts
+
+
+@pytest.mark.unit
+def test_calculate_similarity_empty() -> None:
+    """Test calculating similarity with empty texts."""
+    text1 = ""
+    text2 = "This is not empty"
+
+    similarity = calculate_similarity(text1, text2)
+    assert similarity == 0.0
+
+
+@pytest.mark.unit
+def test_calculate_word_overlap() -> None:
+    """Test calculating word overlap between two texts."""
+    text1 = "apple banana cherry date"
+    text2 = "apple banana fig grape"
+
+    overlap_ratio, words1, words2 = calculate_word_overlap(text1, text2)
+
+    assert overlap_ratio == 2 / 6  # 2 common words out of 6 unique words
+    assert words1 == {"apple", "banana", "cherry", "date"}
+    assert words2 == {"apple", "banana", "fig", "grape"}
+
+
+@pytest.mark.unit
+def test_calculate_levenshtein_metrics() -> None:
+    """Test calculating Levenshtein metrics between two texts."""
+    text1 = "kitten"
+    text2 = "sitting"
+
+    levenshtein_dist, levenshtein_ratio = calculate_levenshtein_metrics(text1, text2)
+
+    assert levenshtein_dist == 3  # 3 operations to transform kitten to sitting
+    assert 0 <= levenshtein_ratio <= 1
+
+
+@pytest.mark.unit
+def test_calculate_rouge_scores() -> None:
+    """Test calculating ROUGE scores between two texts."""
+    text1 = "The quick brown fox jumps over the lazy dog"
+    text2 = "The fast brown fox jumps over the lazy dog"
+
+    rouge_scores = calculate_rouge_scores(text1, text2)
+
+    assert "rouge-1" in rouge_scores
+    assert "rouge-2" in rouge_scores
+    assert "rouge-l" in rouge_scores
+    assert all(0 <= score <= 1 for score in rouge_scores.values())
+
+
+@pytest.mark.unit
+@patch("matplotlib.pyplot.savefig")
+@patch("matplotlib.pyplot.figure")
+@patch("matplotlib.pyplot.close")
+@patch("seaborn.heatmap")
+def test_create_word_overlap_heatmap(
+    mock_heatmap: MagicMock, mock_close: MagicMock, mock_figure: MagicMock, mock_savefig: MagicMock
+) -> None:
+    """Test creating a heatmap visualization of overlap ratios."""
+    files = ["file1.md", "file2.md", "file3.md"]
+    overlap_ratios = [
+        {"file1": "file1.md", "file2": "file2.md", "value": 0.5},
+        {"file1": "file1.md", "file2": "file3.md", "value": 0.3},
+        {"file1": "file2.md", "file2": "file3.md", "value": 0.7},
+    ]
+    labels = ["model1", "model2", "model3"]
+
+    create_word_overlap_heatmap(files, overlap_ratios, labels, "test_heatmap.png")
+
+    assert mock_figure.call_count == 5
+    mock_heatmap.assert_called_once()
+    mock_savefig.assert_called_once_with("test_heatmap.png", bbox_inches="tight", dpi=300)
+    mock_close.assert_called_once()
+
+
+@pytest.mark.unit
+@patch("glob.glob")
+def test_find_files(mock_glob: MagicMock) -> None:
+    """Test finding files with the given filestem and parsers."""
+    mock_glob.side_effect = lambda pattern: {
+        "output/test_anthropic*.md": ["output/test_anthropic.md"],
+        "output/test_googleai*.md": [
+            "output/test_googleai.md",
+            "output/test_googleai_gemini-2.0-flash.md",
+        ],
+        "output/test_openai*.md": ["output/test_openai.md"],
+    }[pattern]
+
+    files = find_files("test", ["anthropic", "googleai", "openai"])
+
+    assert len(files) == 4
+    assert "output/test_anthropic.md" in files
+    assert "output/test_googleai.md" in files
+    assert "output/test_googleai_gemini-2.0-flash.md" in files
+    assert "output/test_openai.md" in files
+
+
+@pytest.mark.unit
+@patch("glob.glob")
+def test_find_files_no_matches(mock_glob: MagicMock) -> None:
+    """Test finding files with no matches."""
+    mock_glob.return_value = []
+
+    with pytest.raises(ValueError, match="No files found for filestem"):
+        find_files("nonexistent", ["anthropic"])
+
+
+@pytest.mark.unit
+@patch("builtins.open", new_callable=mock_open, read_data="# Test\nThis is a test document.")
+@patch("evaluate.preprocess_markdown", return_value="This is a test document")
+@patch("evaluate.calculate_similarity", return_value=0.95)
+@patch(
+    "evaluate.calculate_word_overlap",
+    return_value=(
+        0.8,
+        {"this", "is", "a", "test", "document"},
+        {"this", "is", "a", "test", "document"},
+    ),
+)
+@patch("evaluate.calculate_levenshtein_metrics", return_value=(5, 0.9))
+@patch(
+    "evaluate.calculate_rouge_scores",
+    return_value={"rouge-1": 0.9, "rouge-2": 0.85, "rouge-l": 0.88},
+)
+def test_evaluate_files(
+    mock_rouge: MagicMock,
+    mock_levenshtein: MagicMock,
+    mock_overlap: MagicMock,
+    mock_similarity: MagicMock,
+    mock_preprocess: MagicMock,
+    mock_file: MagicMock,
+) -> None:
+    """Test evaluating similarity between two markdown files."""
+    file1 = "test_file1.md"
+    file2 = "test_file2.md"
+
+    with patch("os.path.basename") as mock_basename:
+        mock_basename.side_effect = lambda f: f
+
+        cosine_sim, overlap_ratio, levenshtein_ratio, rouge_scores = evaluate_files(file1, file2)
+
+        assert cosine_sim == 0.95
+        assert overlap_ratio == 0.8
+        assert levenshtein_ratio == 0.9
+        assert rouge_scores["rouge-1"] == 0.9
+        assert rouge_scores["rouge-2"] == 0.85
+        assert rouge_scores["rouge-l"] == 0.88
+
+
+@pytest.mark.unit
+@patch("builtins.open")
+def test_evaluate_files_file_not_found(mock_open: MagicMock) -> None:
+    """Test evaluating files with a non-existent file."""
+    mock_open.side_effect = FileNotFoundError("File not found")
+
+    with pytest.raises(FileNotFoundError):
+        evaluate_files("nonexistent1.md", "nonexistent2.md")
+
+
+@pytest.mark.integration
+@patch("evaluate.find_files")
+@patch("evaluate.evaluate_files")
+@patch("evaluate.create_word_overlap_heatmap")
+@patch("builtins.open", new_callable=mock_open)
+def test_evaluate_all_files_integration(
+    mock_file: MagicMock, mock_heatmap: MagicMock, mock_evaluate: MagicMock, mock_find: MagicMock
+) -> None:
+    """Test the full workflow of evaluating all files."""
+    # Mock the file finding function
+    mock_find.return_value = [
+        "output/doc_anthropic.md",
+        "output/doc_googleai.md",
+        "output/doc_openai.md",
+    ]
+
+    # Mock the evaluation results
+    mock_evaluate.return_value = (
+        0.85,
+        0.75,
+        0.8,
+        {"rouge-1": 0.8, "rouge-2": 0.7, "rouge-l": 0.75},
+    )
+
+    # Call the function being tested
+    evaluate_all_files("doc", ["anthropic", "googleai", "openai"])
+
+    # Check that the right functions were called
+    mock_find.assert_called_once_with("doc", ["anthropic", "googleai", "openai"])
+
+    # Should call evaluate_files for each pair of files (including self-comparisons)
+    assert mock_evaluate.call_count == 6
+
+    # Should create 6 heatmaps (one for each metric)
+    assert mock_heatmap.call_count == 6
+
+    # Should write to the CSV file
+    mock_file.assert_called_once_with("output/doc_metrics.csv", "w", encoding="utf-8")
diff --git a/tests/test_integration.py b/tests/test_integration.py
new file mode 100644
index 0000000..3e42404
--- /dev/null
+++ b/tests/test_integration.py
@@ -0,0 +1,182 @@
+"""Integration tests for PDF to Markdown conversion."""
+
+import os
+from pathlib import Path
+from unittest.mock import MagicMock, Mock, PropertyMock, patch
+
+import pytest
+
+from run import main
+
+
+@pytest.mark.integration
+@patch("parsers.googleai.genai.Client")
+def test_end_to_end_conversion(
+    mock_client_class: MagicMock,
+    temp_pdf_file: Path,
+    temp_output_dir: Path,
+) -> None:
+    """Test end-to-end PDF to Markdown conversion using Google AI.
+
+    Args:
+        mock_client_class: Mocked Google AI Client class.
+        temp_pdf_file: Temporary PDF file fixture.
+        temp_output_dir: Temporary output directory fixture.
+    """
+    # Create a proper string response
+    mock_client = mock_client_class.return_value
+
+    # Set up the response.text property to return a string
+    response = Mock()
+    type(response).text = PropertyMock(return_value="# Test Document\n\nHello World")
+
+    # Configure the mock to return our response
+    mock_client.models.generate_content.return_value = response
+
+    # Mock the file upload method
+    mock_client.files.upload.return_value = Mock()
+
+    # Create a minimal PDF file with some text
+    with open(temp_pdf_file, "wb") as f:
+        f.write(
+            b"%PDF-1.4\n1 0 obj\n<</Type /Catalog /Pages 2 0 R>>\nendobj\n"
+            b"2 0 obj\n<</Type /Pages /Kids [3 0 R] /Count 1>>\nendobj\n"
+            b"3 0 obj\n<</Type /Page /Parent 2 0 R /Resources <<>> "
+            b"/MediaBox [0 0 612 792] /Contents 4 0 R>>\nendobj\n"
+            b"4 0 obj\n<</Length 44>>\n"
+            b"stream\nBT\n/F1 12 Tf\n100 700 Td\n(Hello World) Tj\nET\nendstream\nendobj\n"
+            b"xref\n0 5\n0000000000 65535 f\n0000000010 00000 n\n0000000056 00000 n\n"
+            b"0000000102 00000 n\n0000000166 00000 n\n"
+            b"trailer\n<</Size 5/Root 1 0 R>>\nstartxref\n226\n%%EOF"
+        )
+
+    # Mock the environment variable for the API key
+    with patch.dict("os.environ", {"GOOGLE_API_KEY": "dummy_key"}):
+        # Run the conversion
+        with patch(
+            "sys.argv",
+            ["script.py", "-i", str(temp_pdf_file), "-o", str(temp_output_dir), "-p", "googleai"],
+        ):
+            main()
+
+    # Verify output
+    output_files = list(temp_output_dir.glob("*.md"))
+    assert len(output_files) == 1
+    output_file = output_files[0]
+    assert output_file.exists()
+    content = output_file.read_text()
+    assert "Hello World" in content
+
+
+@pytest.mark.integration
+@patch("parsers.googleai.genai.Client")
+def test_multiple_pages_conversion(
+    mock_client_class: MagicMock,
+    temp_pdf_file: Path,
+    temp_output_dir: Path,
+) -> None:
+    """Test conversion of a multi-page PDF using Google AI.
+
+    Args:
+        mock_client_class: Mocked Google AI Client class.
+        temp_pdf_file: Temporary PDF file fixture.
+        temp_output_dir: Temporary output directory fixture.
+    """
+    # Create a proper mock client with proper string responses
+    mock_client = mock_client_class.return_value
+
+    # Create response objects with text properties
+    response1 = Mock()
+    type(response1).text = PropertyMock(return_value="# Page 1\n\nContent from page 1")
+
+    response2 = Mock()
+    type(response2).text = PropertyMock(return_value="# Page 2\n\nContent from page 2")
+
+    # Set up the mock to return our responses in sequence
+    mock_client.models.generate_content.side_effect = [response1, response2]
+
+    # Mock the file upload method
+    mock_client.files.upload.return_value = Mock()
+
+    # Create a multi-page PDF file
+    with open(temp_pdf_file, "wb") as f:
+        f.write(
+            b"%PDF-1.4\n1 0 obj\n<</Type /Catalog /Pages 2 0 R>>\nendobj\n"
+            b"2 0 obj\n<</Type /Pages /Kids [3 0 R 4 0 R] /Count 2>>\nendobj\n"
+            b"3 0 obj\n<</Type /Page /Parent 2 0 R /Resources <<>> "
+            b"/MediaBox [0 0 612 792] /Contents 5 0 R>>\nendobj\n"
+            b"4 0 obj\n<</Type /Page /Parent 2 0 R /Resources <<>> "
+            b"/MediaBox [0 0 612 792] /Contents 6 0 R>>\nendobj\n"
+            b"5 0 obj\n<</Length 44>>\n"
+            b"stream\nBT\n/F1 12 Tf\n100 700 Td\n(Page 1) Tj\nET\nendstream\nendobj\n"
+            b"6 0 obj\n<</Length 44>>\n"
+            b"stream\nBT\n/F1 12 Tf\n100 700 Td\n(Page 2) Tj\nET\nendstream\nendobj\n"
+            b"xref\n0 7\n0000000000 65535 f\n0000000010 00000 n\n0000000056 00000 n\n"
+            b"0000000102 00000 n\n0000000166 00000 n\n0000000220 00000 n\n"
+            b"0000000274 00000 n\ntrailer\n<</Size 7/Root 1 0 R>>\nstartxref\n318\n%%EOF"
+        )
+
+    # Run the conversion with page splitting enabled
+    with patch.dict("os.environ", {"GOOGLE_API_KEY": "dummy_key"}):
+        with patch(
+            "sys.argv",
+            [
+                "script.py",
+                "-i",
+                str(temp_pdf_file),
+                "-o",
+                str(temp_output_dir),
+                "-p",
+                "googleai",
+                "--split-pages",
+            ],
+        ):
+            main()
+
+    # Verify output
+    output_files = list(temp_output_dir.glob("*.md"))
+    assert len(output_files) == 1
+    output_file = output_files[0]
+    assert output_file.exists()
+    content = output_file.read_text()
+    assert "Page 1" in content
+    assert "Page 2" in content
+
+
+@pytest.mark.integration
+@patch("parsers.googleai.GoogleAIParser.generate_response")
+@patch("parsers.googleai.GoogleAIParser.__init__")
+def test_large_pdf_conversion(
+    mock_init: MagicMock,
+    mock_generate_response: MagicMock,
+    temp_output_dir: Path,
+) -> None:
+    """Test conversion of a large PDF file using Google AI.
+
+    Args:
+        mock_init: Mocked GoogleAIParser initialization.
+        mock_generate_response: Mocked generate_response method.
+        temp_output_dir: Temporary output directory fixture.
+    """
+    # Setup mocks
+    mock_init.return_value = None
+    mock_generate_response.return_value = "# Large Document\n\nThis is a large document."
+
+    large_pdf = Path("tests/data/large.pdf")
+    if not large_pdf.exists():
+        pytest.skip("Large PDF file not found")
+
+    # Run the conversion
+    with patch(
+        "sys.argv",
+        ["script.py", "-i", str(large_pdf), "-o", str(temp_output_dir), "-p", "googleai"],
+    ):
+        main()
+
+    # Verify output
+    output_files = list(temp_output_dir.glob("*.md"))
+    assert len(output_files) == 1
+    output_file = output_files[0]
+    assert output_file.exists()
+    content = output_file.read_text()
+    assert len(content) > 0
diff --git a/tests/test_run.py b/tests/test_run.py
new file mode 100644
index 0000000..e34bb52
--- /dev/null
+++ b/tests/test_run.py
@@ -0,0 +1,119 @@
+"""Tests for the main script functionality."""
+
+import argparse
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from run import generate_output_path, get_parser, get_parser_help_text, main, setup_cli_args
+
+
+@pytest.mark.unit
+def test_get_parser() -> None:
+    """Test getting parser instance."""
+    parser = get_parser("textract")
+    assert parser.__class__.__name__ == "TextractParser"
+
+
+@pytest.mark.unit
+def test_get_parser_invalid() -> None:
+    """Test getting parser with invalid name."""
+    with pytest.raises(ValueError):
+        get_parser("invalid_parser")
+
+
+@pytest.mark.unit
+def test_generate_output_path() -> None:
+    """Test generating output path."""
+    output_path = generate_output_path("test.pdf", "output", "textract", "test-model")
+    assert output_path == Path("output/test_textract_test-model.md")
+
+
+@pytest.mark.unit
+def test_generate_output_path_no_model() -> None:
+    """Test generating output path without model."""
+    output_path = generate_output_path("test.pdf", "output", "textract")
+    assert output_path == Path("output/test_textract.md")
+
+
+@pytest.mark.unit
+def test_get_parser_help_text() -> None:
+    """Test getting parser help text."""
+    help_text = get_parser_help_text()
+    assert "Available parsers and their models:" in help_text
+    assert "textract:" in help_text
+
+
+@pytest.mark.unit
+@patch("argparse.ArgumentParser.parse_args")
+def test_setup_cli_args(mock_parse_args: MagicMock) -> None:
+    """Test setting up CLI arguments.
+
+    Args:
+        mock_parse_args: Mocked parse_args method.
+    """
+    mock_parse_args.return_value = argparse.Namespace(
+        input_pdf_path="test.pdf",
+        output_dir="output",
+        parser="textract",
+        model=None,
+        log_level="INFO",
+        split_pages=False,
+    )
+    args = setup_cli_args()
+    assert args.input_pdf_path == "test.pdf"
+    assert args.output_dir == "output"
+    assert args.parser == "textract"
+    assert args.model == ""
+    assert args.log_level == "INFO"
+    assert args.split_pages is False
+
+
+@pytest.mark.unit
+@patch("run.setup_cli_args")
+@patch("run.get_parser")
+@patch("run.generate_output_path")
+def test_main(
+    mock_generate_output_path: MagicMock,
+    mock_get_parser: MagicMock,
+    mock_setup_cli_args: MagicMock,
+    temp_pdf_file: Path,
+    temp_output_dir: Path,
+) -> None:
+    """Test main function.
+
+    Args:
+        mock_generate_output_path: Mocked generate_output_path function.
+        mock_get_parser: Mocked get_parser function.
+        mock_setup_cli_args: Mocked setup_cli_args function.
+        temp_pdf_file: Temporary PDF file fixture.
+        temp_output_dir: Temporary output directory fixture.
+    """
+    # Setup mocks
+    mock_setup_cli_args.return_value = argparse.Namespace(
+        input_pdf_path=str(temp_pdf_file),
+        output_dir=str(temp_output_dir),
+        parser="anthropic",
+        model=None,
+        log_level="INFO",
+        split_pages=False,
+    )
+
+    mock_parser = MagicMock()
+    mock_parser.convert_pdf_to_markdown.return_value = "Test markdown"
+    mock_get_parser.return_value = mock_parser
+
+    output_path = temp_output_dir / "test.md"
+    mock_generate_output_path.return_value = output_path
+
+    # Run main
+    main()
+
+    # Verify calls
+    mock_setup_cli_args.assert_called_once()
+    mock_get_parser.assert_called_once_with("anthropic", None)
+    mock_parser.convert_pdf_to_markdown.assert_called_once_with(
+        str(temp_pdf_file), split_pages=False
+    )
+    mock_parser.save_markdown.assert_called_once_with("Test markdown", str(output_path))
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..eb11641
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,3397 @@
+version = 1
+revision = 1
+requires-python = ">=3.9"
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version == '3.10.*'",
+    "python_full_version < '3.10'",
+]
+
+[[package]]
+name = "aiofiles"
+version = "24.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896 },
+]
+
+[[package]]
+name = "amazon-textract-caller"
+version = "0.2.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "amazon-textract-response-parser" },
+    { name = "boto3" },
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