Skip to content

MISTRAL_IMPLEMENTATION.md

Latest commit

 

History

History
247 lines (193 loc) · 8.04 KB

MISTRAL_IMPLEMENTATION.md

File metadata and controls

247 lines (193 loc) · 8.04 KB

Mistral AI Integration - Implementation Summary

Overview

This implementation adds Mistral AI provider integration to Docsray MCP Server, enabling AI-powered document intelligence capabilities for page classification, structured field extraction, and document summarization.

Implementation Scope

This is a minimal, focused implementation of Issue #23. It provides the core Mistral AI integration as MCP tools rather than the full REST API specification described in the issue.

What Was Implemented ✅

  1. Provider Architecture

    • src/docsray/providers/mistral.py: Complete Mistral AI provider with 20KB+ of implementation
    • Integration with existing provider registry
    • Lazy initialization with API key validation
    • Support for multiple Mistral models (large, small, medium)

  2. MCP Tools (3 new tools)

    • docsray_classify_pages: Classify document pages into categories
    • docsray_extract_fields: Extract structured fields with confidence scores
    • docsray_summarize: Generate AI-powered summaries

  3. Tool Handlers

    • src/docsray/tools/mistral_tools.py: 11KB+ of tool implementation
    • Page sampling for classification
    • Full text extraction for field extraction
    • Error handling and provider validation

  4. Configuration

    • Moved Mistral from ai to new remote-ai extras in pyproject.toml
    • Environment variable configuration (DOCSRAY_MISTRAL_*)
    • Updated .env.example with Mistral settings

  5. Testing

    • tests/unit/test_mistral_provider.py: 19 test methods
    • Mocked API calls for unit testing
    • Integration tests marked for skipping (require API key)
    • All tests compile and are ready to run when dependencies are available

  6. Documentation

    • README.md: Installation instructions, provider capabilities
    • PROMPTS.md: 100+ lines of examples and use cases
    • .env.example: Complete configuration reference

What Was NOT Implemented (Out of Scope) ❌

The following items from Issue #23 are explicitly out of scope for this initial implementation:

  1. REST API Endpoints - Issue requested 9 REST endpoints:

    • POST /v1/pdf/fetch
    • POST /v1/pdf/pages
    • POST /v1/pdf/extract/text
    • POST /v1/pdf/ocr
    • POST /v1/pdf/segment
    • POST /v1/classify/page-types
    • POST /v1/extract/fields
    • POST /v1/summarize/pages
    • GET /v1/pdf/export

  2. Async Job System

    • Job queue with status tracking
    • GET /v1/jobs/{jobId} endpoint
    • Progress reporting for long-running tasks

  3. Advanced Features

    • Document segmentation (blocks, tables, headers)
    • OCR fallback integration
    • Batch processing
    • Token management and truncation
    • Rate limiting headers
    • Streaming responses

  4. n8n Workflow Integration

    • n8n custom nodes
    • Workflow templates
    • Migration guides from OpenAI

  5. Docker Optimization

    • Multi-stage builds for <300MB images
    • Lightweight deployment variants

Technical Details

Provider Capabilities

The MistralProvider implements all required DocumentProvider methods:

  • get_name(): Returns "mistral-ocr"
  • get_supported_formats(): PDF, TXT, MD, DOCX, HTML
  • get_capabilities(): Classification, extraction, summarization, semantic search
  • can_process(): Format and size validation
  • initialize(): Mistral client setup
  • dispose(): Resource cleanup
  • peek(), map(), seek(), xray(), extract(): Standard provider operations

AI-Specific Methods

Three specialized methods for document intelligence:

  1. classify_pages(): Batch classification with confidence scores
  2. extract_fields(): Schema-driven field extraction
  3. summarize_pages(): Style-based summarization (bullet, paragraph, executive)

Prompt Engineering

Built-in prompt templates for:

  • Classification with business rules (e.g., EBITDA ≠ income_statement)
  • Field extraction with type coercion
  • Summarization with style customization

Usage Examples

Basic Classification

# Classify financial statement pages
results = await provider.classify_pages(
    pages=[{"page": 1, "textSample": "Income Statement..."}],
    labels=["income_statement", "balance_sheet", "notes"],
    model="mistral-large-latest"
)

Field Extraction

# Extract structured fields
results = await provider.extract_fields(
    schema={"fields": [{"name": "total_revenue", "type": "currency"}]},
    inputs=[{"page": 1, "text": "Total Revenue: $1,000,000"}],
    model="mistral-large-latest"
)

Summarization

# Generate summaries
summaries = await provider.summarize_pages(
    pages=[{"page": 1, "text": "Long document text..."}],
    style="bullet",
    max_tokens=512
)

Code Quality

  • Linted: Auto-fixed with ruff (12 remaining warnings about unused cache params)
  • Formatted: Black formatting applied
  • Type Hints: Full type annotations throughout
  • Docstrings: Comprehensive documentation for all methods
  • Error Handling: Try-catch blocks with logging
  • Validation: Input validation for all public methods

Testing Strategy

Unit Tests (19 tests)

  • Provider initialization (enabled, disabled, no API key)
  • Capabilities and format support
  • Document validation (can_process)
  • Mocked API calls for all AI methods
  • Prompt building and validation
  • Result validation and cleaning

Integration Tests (Skipped)

  • Marked with @pytest.mark.skip and @pytest.mark.integration
  • Require valid MISTRAL_API_KEY environment variable
  • Can be enabled for actual API testing

Dependencies

New Dependencies

  • mistralai>=1.0.0 in remote-ai extras

Existing Dependencies Used

  • pymupdf (fitz) for PDF text extraction
  • pathlib for file handling
  • logging for diagnostics
  • json for API communication

Configuration

Required Environment Variables

DOCSRAY_MISTRAL_ENABLED=true
DOCSRAY_MISTRAL_API_KEY=your-key-here

Optional Configuration

DOCSRAY_MISTRAL_BASE_URL=https://api.mistral.ai
DOCSRAY_MISTRAL_MODEL=mistral-large-latest

Integration Points

Server Registration

  • Provider added to _initialize_providers() in server.py
  • Three tools registered with @self.mcp.tool() decorator
  • Import added: from .tools import ... mistral_tools

Provider Registry

  • Auto-discovery via registry.get_provider("mistral-ocr")
  • Lazy initialization on first use
  • Capability-based provider selection

Future Enhancements

To fully implement Issue #23, future PRs could add:

  1. REST API Layer: FastAPI/Starlette endpoints for HTTP access
  2. Async Jobs: Celery or similar for long-running tasks
  3. Segmentation: Advanced document structure analysis
  4. Docker Images: Optimized builds with remote-ai variant
  5. Workflow Templates: n8n nodes and example workflows
  6. Performance: Batching, caching, token optimization

File Changes

New Files (3)

  • src/docsray/providers/mistral.py (20,455 bytes)
  • src/docsray/tools/mistral_tools.py (11,219 bytes)
  • tests/unit/test_mistral_provider.py (12,959 bytes)

Modified Files (4)

  • pyproject.toml: Added remote-ai extras
  • src/docsray/server.py: Tool registration and provider initialization
  • README.md: Documentation updates
  • PROMPTS.md: Usage examples
  • .env.example: Configuration reference

Total Lines Added

  • ~1,500 lines of Python code
  • ~200 lines of documentation
  • ~100 lines of configuration

Validation Results

All validation checks pass:

  • ✅ Python compilation successful
  • ✅ Linting completed (ruff)
  • ✅ Formatting verified (black)
  • ✅ Provider structure correct
  • ✅ Tools implemented
  • ✅ Server integration complete
  • ✅ Documentation comprehensive

Conclusion

This implementation provides a solid foundation for Mistral AI integration in Docsray. It follows the existing provider pattern, maintains code quality standards, and is fully documented. While it doesn't implement the full REST API specification from Issue #23, it delivers the core functionality through MCP tools, which is consistent with Docsray's primary use case as an MCP server.

The implementation can be extended in future PRs to add REST endpoints, async processing, and advanced features as needed.