AI-powered CLI for analyzing hardware engineering documents

These details have not been verified by PyPI

Project links

Project description

Multifactor ADK Backend

An AI-powered engineering document processing pipeline that intelligently analyzes hardware engineering documents including schematics, datasheets, BOMs, and netlists. Built with Google's Gemini models, the system extracts structured data, generates documentation, and enables semantic search across processed documents.

🚀 Features

CLI-Based Pipeline: Process entire directories of engineering documents with a single command
Intelligent Document Analysis: Automated classification, text extraction, and schema mapping
BOM Generation: Extract components from schematics and generate CSV Bill of Materials
Datasheet Enrichment: Automatically download component datasheets from BOMs
Cheat Sheet Generation: AI-generated documentation for MCU datasheets, errata, debug setup, and functional blocks
RAG-Powered Queries: Query processed documents using ChromaDB-backed Retrieval-Augmented Generation
Web UI: Interactive interface for document processing and agent interaction (optional)
File Type Support: PDF, EDIF, PADS, KiCad netlists, CSV BOMs, and more

🚀 Quick Start

New to mfcli? Check out our Quick Start Guide for step-by-step instructions to get up and running in minutes!

The Quick Start Guide covers:

✅ Installing Cline for VS Code
✅ Installing mfcli with pipx (recommended)
✅ Setting up API keys
✅ Creating your first project
✅ Processing your hardware documents
✅ Querying your knowledge base with AI
✅ Currently unsupported features and workarounds

👉 Go to Quick Start Guide →

🏗️ Architecture

The system uses a streamlined architecture with a single controller agent that orchestrates a sequential processing pipeline:

Controller Agent
├── Tools:
│   ├── run (Sequential pipeline execution)
│   └── query_knowledgebase (RAG-based document queries)
│
└── Pipeline Stages:
    ├── Pre-processing
    │   ├── File classification & validation
    │   ├── Text extraction
    │   ├── Document sub-type detection (uses LLM when needed)
    │   ├── Schema mapping (for structured documents)
    │   ├── Data parsing
    │   └── Data enrichment
    │
    └── Analysis & Generation
        ├── Netlist-to-BOM mapping
        └── File generation:
            ├── BOM CSV files
            └── JSON cheat sheets (MCU, errata, debug setup, functional blocks)

The pipeline processes files sequentially, making LLM calls only when necessary for tasks like document sub-classification and schema mapping, rather than using a hierarchy of sub-agents.

📦 Prerequisites

Python 3.12+
Required API Keys:
- Google API Key (for Gemini models)
- OpenAI API Key (for embeddings)
- LlamaParse Cloud API Key (for document parsing)
- DigiKey API credentials (client ID & secret, for datasheet downloads)
- AWS credentials (for S3 storage, optional)
Database: SQLite (automatically managed)

🔧 Installation

Quick Install (Recommended)

The easiest way to install mfcli is using our automated installation script with pipx, which provides isolated dependency management while making the CLI globally available.

Windows (PowerShell):

iwr -useb https://raw.githubusercontent.com/MultifactorAI/multifactor-adk-backend/main/install.ps1 | iex

Linux/macOS:

curl -fsSL https://raw.githubusercontent.com/MultifactorAI/multifactor-adk-backend/main/install.sh | bash

The script will:

✅ Check Python 3.12 installation
✅ Install pipx if needed
✅ Install mfcli with isolated dependencies
✅ Set up configuration directory
✅ Make mfcli and mfcli-mcp commands globally available

Manual Installation

If you prefer manual installation or the script doesn't work:

Using pipx (Recommended)

# Install pipx if not already installed
python -m pip install --user pipx
python -m pipx ensurepath

# Install mfcli from GitHub
pipx install git+https://github.com/MultifactorAI/multifactor-adk-backend.git

# Or install from PyPI (once published)
pipx install mfcli

Why pipx?

✅ Isolated dependencies - no conflicts with other Python packages
✅ Global CLI access - available in any terminal
✅ No virtual environment activation needed
✅ MCP server compatible - works with external tools like Cline
✅ Easy updates: pipx upgrade mfcli

Using pip (For Development)

# Clone the repository
git clone https://github.com/MultifactorAI/multifactor-adk-backend.git
cd multifactor-adk-backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

# Install in development mode
pip install -e .

Note: If you plan to use the MCP server with Cline/Claude Code, install with pipx instead to ensure global availability.

Verify Installation

After installation, verify everything is working:

# Check mfcli is installed
mfcli --help

# Run system health check
mfcli doctor

🗑️ Uninstalling

If you need to uninstall mfcli, we provide automated scripts and tools to ensure a clean removal.

Important: Stop MCP Server First

Before uninstalling, you must stop the MCP server if it's running. The MCP server (mfcli-mcp) runs as a background process when used with Cline or Claude Code. If it's still running, pipx cannot delete the virtual environment due to file locks.

Quick Pre-Uninstall Check

Run this command to check for running processes and get uninstall guidance:

mfcli pre-uninstall

This will:

✅ Check for running mfcli-mcp processes
⚠️ Warn you if processes are still running
📋 Provide step-by-step uninstall instructions

Automated Uninstall (Recommended)

Windows:

.\uninstall.ps1

Linux/macOS:

chmod +x uninstall.sh
./uninstall.sh

The scripts will:

Check for running processes
Offer to stop them automatically
Uninstall mfcli via pipx
Fallback to manual cleanup if needed
Preserve your configuration data in ~/Multifactor

Manual Uninstall

Close your IDE (VS Code, Cline, etc.) to stop the MCP server
Wait 5-10 seconds for processes to fully terminate
Run pipx uninstall:

pipx uninstall mfcli

Troubleshooting Permission Errors

If you get "Access is denied" or "Permission denied" errors:

The MCP server is still running - Close your IDE completely
Wait 10 seconds for background processes to terminate
Check Task Manager/Activity Monitor for remaining python processes
Use the uninstall script which can force-stop processes
Restart your computer if processes won't stop

Complete Documentation

For detailed uninstall instructions, troubleshooting, and data removal:

📖 See UNINSTALL.md for complete uninstallation guide

Note: Uninstalling mfcli does NOT delete your configuration and data files in ~/Multifactor. This preserves your API keys and project data for future reinstallation.

⚙️ Configuration

Interactive Configuration Wizard (Recommended)

The easiest way to configure mfcli is using the interactive wizard:

mfcli configure

This will guide you through setting up all required API keys with:

🔗 Direct links to get each API key
✅ Automatic validation of API keys
📝 Smart defaults for vectorization settings
💾 Automatic saving to the correct location

Manual Configuration

Alternatively, create a .env file at:

Windows: C:\Users\<username>\Multifactor\.env
macOS/Linux: ~/Multifactor/.env

# API Keys (Required)
google_api_key=your_google_api_key
openai_api_key=your_openai_api_key
llama_cloud_api_key=your_llamaparse_api_key
digikey_client_id=your_digikey_client_id
digikey_client_secret=your_digikey_client_secret

# Vector Database Configuration
chunk_size=1000
chunk_overlap=200
embedding_model=text-embedding-3-small
embedding_dimensions=1536

Check Configuration

To verify your configuration at any time:

# Check configuration status
mfcli configure --check

# Run comprehensive system check
mfcli doctor

Required API Keys & How to Get Them

Google API Key: Google AI Studio
OpenAI API Key: OpenAI Platform
LlamaParse API Key: LlamaIndex Cloud
DigiKey API: DigiKey Developer Portal
AWS Credentials: AWS IAM Console (optional)

Tip: The mfcli configure wizard provides these links interactively and validates your keys!

🚀 Usage

Command-Line Interface

Getting Started with a Project

To analyze hardware design files, follow these steps:

1. Navigate to your hardware design files directory:

cd C:\Projects\hardware\board_v1

2. Initialize the project:

mfcli init

You'll be prompted to enter a project name (3-45 characters, alphanumeric with underscores/hyphens allowed). If your directory is a git repository, the repository name will be suggested as the default - simply press Enter to accept it or type a different name. This creates a multifactor/ folder at the git root (or current directory) containing:

design/ - Place your hardware design files here for processing
config.json - Project configuration with your project name
file_docket.json - File tracking and metadata
generated_files/, hw_cheat_sheets/, data_sheets/, etc. - Output folders

3. Place your files in the design folder:

# Copy or move your hardware design files to the design folder
# Example for git repo at C:\git\my-project\:
copy *.pdf C:\git\my-project\multifactor\design\
copy *.csv C:\git\my-project\multifactor\design\

4. Run the pipeline:

mfcli run

This will:

Process all supported files in the multifactor/design/ directory
Skip files that have already been processed (matching MD5 checksum)
Prompt for confirmation if a file has been modified (different MD5)
Generate BOM CSV files (if schematics are found)
Download datasheets for BOM components
Generate cheat sheets for MCU datasheets, errata, and schematics
Store vector embeddings for RAG queries

File Change Detection

The pipeline tracks files using MD5 checksums stored in multifactor/file_docket.json. When you run the pipeline:

New files: Automatically processed
Unchanged files: Skipped (MD5 matches previous run)

Modified files: You'll be prompted:

======================================================================
File has been modified: schematic.pdf
Path: C:\Projects\hardware\board_v1\schematic.pdf
Old MD5: abc123...
New MD5: def456...
======================================================================
Do you want to delete the old file data and process the new version? (yes/no):

Answer yes to remove old data from the knowledge base and reprocess
Answer no to skip the file and keep the old version

This ensures efficient processing by only analyzing new or changed files, while maintaining data consistency in the knowledge base.

CLI Commands Reference

The mfcli tool provides the following commands:

mfcli init - Initialize a new project in the current directory
mfcli run - Run the analysis pipeline on the current directory
mfcli web [--port PORT] - Start the web UI (default port: 9999)
mfcli add FILE [--purpose PURPOSE] - Add a file to ChromaDB knowledge base
mfcli ls - List all files that have been vectorized into the knowledge base
mfcli rm FILENAME [--yes] - Remove files from the knowledge base by filename
mfcli configure [--check] - Interactive setup wizard to configure API keys and settings
mfcli log-level LEVEL - Set the logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
mfcli setup-mcp - Auto-configure MCP server for Cline and Claude Code
mfcli doctor - Run system health checks and diagnose issues
mfcli clean [--accept] [--all] - Clean all mfcli app data
mfcli pre-uninstall - Check for running processes before uninstallation

Configure Logging Level

Set the logging verbosity level for mfcli operations:

# Set to DEBUG for detailed troubleshooting
mfcli log-level DEBUG

# Set to INFO for normal operation (default)
mfcli log-level INFO

# Set to WARNING to only see warnings and errors
mfcli log-level WARNING

# Set to ERROR to only see errors
mfcli log-level ERROR

# Set to CRITICAL for only critical errors
mfcli log-level CRITICAL

The log level setting is stored in your configuration file (~/Multifactor/.env) and will take effect on the next mfcli command. This is particularly useful when:

DEBUG: Troubleshooting issues or understanding detailed pipeline operations
INFO: Normal day-to-day operation with informative messages
WARNING: Production use where you only want to see potential issues
ERROR: When you only care about actual errors

Example:

# Enable detailed logging for troubleshooting
mfcli log-level DEBUG

# Run the pipeline with verbose output
mfcli run

# Return to normal logging
mfcli log-level INFO

Start Web UI

Launch the interactive web interface:

mfcli web

With custom port:

mfcli web --port 8080

The web UI will be available at http://localhost:9999/dev-ui/ (or your specified port).

Web UI Usage

The web interface allows you to:

Upload and process individual files
Run the pipeline on directories
Query processed documents using natural language
View processing status and results

Example queries in web UI:

"What components are in the processed schematic?"
"Tell me about the voltage ratings in the last datasheet"
"What are the errata for this MCU?"

🔌 MCP Server

This package includes a Model Context Protocol (MCP) server that exposes tools for AI assistants and development environments like Cline/Claude to interact with your engineering documentation knowledge base.

What is MCP?

The Model Context Protocol (MCP) is a standard that allows AI assistants to securely access external tools and data sources. The mfcli MCP server provides AI-powered access to your processed engineering documents through the local ChromaDB vector database.

Available Tools

The MCP server exposes the following tool:

`query_local_rag`

Query the local hardware knowledge base of processed engineering documents using natural language.

Parameters:

query (required): Your search query (e.g., "MSPM0L130x", "power management", "IEC 61000-4-2")
project_name (optional): The name of the project to query. If not provided, uses the last known project name from previous queries.
n_results (optional): Number of results to return (1-20, default: 8)

Returns:

Document chunks matching your query
Metadata (file names, document types)
Similarity scores (lower distance = more relevant)
ChromaDB database path
Project name that was used for the query

Note: The function automatically remembers the last project name used, so you only need to specify project_name for the first query or when switching between projects.

Example queries:

"MSPM0L130x specifications"
"What are the voltage requirements?"
"MCU pin configurations"
"Component datasheets for capacitors"

Configuration for Cline/Claude

To use the MCP server with Cline (or other MCP-compatible clients), add the following configuration to your MCP settings file:

Configuration Location:

VS Code (Cline): %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json
Cline standalone: ~/.cline/mcp_settings.json

Configuration:

{
  "mcpServers": {
    "mfcli-mcp": {
      "disabled": false,
      "timeout": 60,
      "type": "stdio",
      "command": "python",
      "args": ["-m", "mfcli.mcp.server"]
    }
  }
}

Setup Instructions

Install mfcli system-wide (see Installation section above):
```
pip install .
```
Process your engineering documents to populate the knowledge base:
```
cd /path/to/hardware/files
mfcli init
mfcli run
```
Add the MCP configuration to your Cline/Claude settings file (see Configuration above)
Restart Cline/Claude to load the MCP server
Use the tool in your AI assistant:
- Ask questions like: "Query the local RAG for MSPM0L130x in project test"
- The assistant will use the query_local_rag tool to search your documents

Troubleshooting MCP Server

Error: Module 'mfcli' not found

Solution: Ensure mfcli is installed system-wide (not just in a virtual environment)
```
deactivate  # Exit any virtual environment
pip install .
```

Error: ChromaDB directory not found

Solution: Run the pipeline at least once to create the vector database:
```
mfcli init
mfcli run
```

Error: MCP server timeout

Solution: Increase the timeout value in your MCP settings (default: 60 seconds)

Server not connecting:

Verify the MCP server configuration in your settings file
Check that Python is in your system PATH
Restart your IDE/editor after updating MCP settings

MCP Server Architecture

The MCP server is implemented in mfcli/mcp/ with the following structure:

mfcli/mcp/
├── server.py              # MCP server entry point
├── mcp_instance.py        # MCP server instance and tool definitions
└── tools/
    └── query_knowledgebase.py  # RAG query implementation

The server connects to your local ChromaDB instance (located in your system's application data directory) and provides semantic search capabilities over all processed engineering documents.

🔄 Pipeline

The pipeline processes engineering documents in two main phases:

Phase 1: Pre-processing

For each file in the input directory:

Classification & Validation (classifier.py)
- Determines file type (PDF, EDIF, CSV, etc.)
- Validates file integrity and MIME type
- Checks file size limits
Gemini File Upload (PDFs only)
- Uploads PDF files to Gemini's Files API for vision-based processing
Text Extraction (extractor.py)
- Extracts text content from documents
- Handles various formats (PDF, netlist formats, CSV)
Sub-type Classification (sub_classifier.py)
- Determines document sub-type (e.g., schematic, BOM, datasheet, MCU datasheet, errata)
- Uses LLM analysis when necessary
Schema Mapping (schema_mapper.py)
- Maps document structure to database schemas
- Skipped for schemaless files like schematics
Data Parsing (parser.py)
- Parses structured data from documents
- Stores in SQLite database
Data Enrichment (data_enricher.py)
- Enriches parsed data with additional information
- Downloads component datasheets for BOM entries

Phase 2: Analysis & Generation

After all files are pre-processed:

Netlist-to-BOM Mapping (bom_netlist_mapper.py)
- Maps netlist components to BOM entries
- Correlates design files with component lists
File Generation (generator.py)
- BOM CSV: Extracts components from schematics, generates CSV with reference, value, quantity, manufacturer, MPN, description
- Cheat Sheets: Generates JSON cheat sheets for:
  - MCU datasheets (register maps, peripherals, specs)
  - MCU errata (known issues, workarounds)
  - Debug setup (pin configurations, debugging instructions)
  - Functional blocks (system architecture, block diagrams)

Supported File Types

PDF: Schematics, datasheets, MCU documentation, errata sheets
EDIF: Electronic Design Interchange Format netlists
PADS: PADS ASCII netlist format
KiCad: Legacy netlist (.net), SPICE circuit (.cir), and schematic (.kicad_sch) formats
CSV: Bill of Materials files

📂 Output Directories

The pipeline creates directories in two locations:

1. User Application Data

Platform-specific storage for global application data:

Windows:

C:\Users\<username>\AppData\Local\Multifactor\
└── chromadb/              # Vector embeddings database

macOS:

/Users/<username>/Library/Application Support/Multifactor/
└── chromadb/              # Vector embeddings database

Linux:

~/.local/share/Multifactor/
└── chromadb/              # Vector embeddings database

Contents:

chromadb/ - Vector embeddings of processed documents for RAG queries

2. Project Output Directories

Created within a "multifactor" folder at the git repository root if you're in a git repo, or in the current directory if not:

<git_root_or_current_directory>/
└── multifactor/                 # Parent folder for all project files and outputs
    ├── config.json              # Project configuration (project name, etc.)
    ├── file_docket.json         # File tracking and processing metadata
    ├── design/                  # Place your hardware design files here for processing
    ├── generated_files/         # BOM CSV files generated from schematics
    ├── hw_cheat_sheets/         # JSON cheat sheets (MCU, errata, debug, functional blocks)
    ├── data_sheets/             # Downloaded component datasheets (from BOM processing)
    ├── pdf_parts/               # Extracted PDF parts for analysis
    ├── fw_tasks/                # (Reserved for future use)
    └── requirements/            # (Reserved for future use)

Git Repository Example:

If you're in a git repository at C:\git\my-hardware-project\ and run mfcli init from any subdirectory, all project folders will be created within a "multifactor" folder at the git root:

C:\git\my-hardware-project\multifactor\config.json - Project configuration
C:\git\my-hardware-project\multifactor\file_docket.json - File tracking
C:\git\my-hardware-project\multifactor\design\ - Place source files here
C:\git\my-hardware-project\multifactor\generated_files\ - Generated BOM files
C:\git\my-hardware-project\multifactor\hw_cheat_sheets\ - Cheat sheets
C:\git\my-hardware-project\multifactor\data_sheets\ - Downloaded datasheets
C:\git\my-hardware-project\multifactor\pdf_parts\ - PDF parts

Non-Git Example:

If you're NOT in a git repository and run mfcli init from C:\Projects\hardware\board_v1\, all folders will be created within a "multifactor" folder in that same directory:

C:\Projects\hardware\board_v1\multifactor\config.json - Project configuration
C:\Projects\hardware\board_v1\multifactor\file_docket.json - File tracking
C:\Projects\hardware\board_v1\multifactor\design\ - Place source files here
C:\Projects\hardware\board_v1\multifactor\generated_files\ - Generated BOM files
C:\Projects\hardware\board_v1\multifactor\hw_cheat_sheets\ - Cheat sheets
C:\Projects\hardware\board_v1\multifactor\data_sheets\ - Downloaded datasheets
C:\Projects\hardware\board_v1\multifactor\pdf_parts\ - PDF parts

Why This Structure?

This design ensures that all project-related files are organized within a single "multifactor" folder, making it easier to:

✅ Keep all generated files contained in one location
✅ Easy to add to .gitignore if you don't want to version-control outputs
✅ Clean separation between source files and generated outputs
✅ Avoid cluttering the repository root with multiple folders
✅ Simple to backup or delete all mfcli outputs at once

💾 Data Storage

SQLite Database

Located at sessions.db in the project root (or path specified in .env).

Stores:

Pipeline run metadata and status
File metadata and processing results
Parsed component data (BOMs, netlists, datasheets)
MCU information and errata
ADK session data and conversation history

ChromaDB Vector Store

Located in the user application data directory.

Stores:

Vector embeddings of processed documents
Enables semantic search and RAG queries
Uses OpenAI embeddings (text-embedding-3-small)

S3 Storage (Optional)

If AWS credentials are configured:

Uploaded files
Generated outputs
Long-term document storage

📁 Project Structure

multifactor-adk-backend/
├── app/
│   ├── agents/
│   │   ├── controller/              # Main controller agent
│   │   │   ├── agent.py            # Agent definition
│   │   │   ├── config.yaml         # Agent configuration
│   │   │   └── tools.py            # Agent tools
│   │   └── tools/
│   │       └── general.py          # Shared tools
│   ├── alembic/                    # Database migrations
│   ├── cli/
│   │   └── main.py                 # CLI entry point (mfcli)
│   ├── client/                     # External service clients
│   │   ├── chroma_db.py           # ChromaDB vector store
│   │   ├── gemini.py              # Gemini API client
│   │   ├── llama_parse.py         # LlamaParse client
│   │   └── vector_db.py           # Vector DB interface
│   ├── constants/                  # Enums and constants
│   ├── crud/                       # Database operations
│   ├── digikey/                    # DigiKey API integration
│   ├── models/                     # SQLAlchemy models
│   ├── pipeline/
│   │   ├── pipeline.py            # Main pipeline orchestration
│   │   ├── classifier.py          # File classification
│   │   ├── extractor.py           # Text extraction
│   │   ├── sub_classifier.py      # Document sub-typing
│   │   ├── schema_mapper.py       # Schema mapping
│   │   ├── parser.py              # Data parsing
│   │   ├── data_enricher.py       # Data enrichment
│   │   ├── analysis/
│   │   │   ├── bom_netlist_mapper.py
│   │   │   └── generators/        # Output generators
│   │   │       ├── generator.py   # Main generator
│   │   │       ├── bom/          # BOM generation
│   │   │       ├── debug_setup/  # Debug setup cheat sheets
│   │   │       ├── functional_blocks/  # Functional block diagrams
│   │   │       ├── mcu/          # MCU documentation
│   │   │       └── mcu_errata/   # Errata cheat sheets
│   │   ├── extractors/           # Format-specific extractors
│   │   └── parsers/
│   │       └── netlist/          # Netlist parsers (EDIF, KiCad, PADS)
│   ├── tests/                     # Unit tests
│   └── utils/                     # Utility functions
│       ├── config.py
│       ├── directory_manager.py   # Output directory management
│       ├── logger.py
│       └── ...
├── .env                           # Environment configuration (create this)
├── .gitignore
├── alembic.ini                    # Alembic configuration
├── pyproject.toml                 # Package configuration
├── requirements.txt               # Python dependencies
└── README.md                      # This file

🛠️ Development

Running the Pipeline

First, navigate to your hardware files directory and initialize:

cd /path/to/hardware/files
mfcli init
mfcli run

Starting the Web UI

mfcli web --port 9999

Development Status

⚠️ This project is currently in development. Features and APIs may change.

Publishing to PyPI

This project is automatically published to PyPI when changes are merged into the dev branch. See PYPI_PUBLISHING.md for details on:

How automated publishing works
PyPI configuration setup (trusted publishing or API tokens)
Version management and semantic versioning
Testing and troubleshooting
Manual publishing (if needed)

Quick version update workflow:

Update version in pyproject.toml
Commit and push to a feature branch
Create and merge PR to dev
Automated workflow builds and publishes to PyPI

Database Migrations

This project uses Alembic for database schema management.

Create a New Migration

alembic revision -m "description of changes"

Apply Migrations

alembic upgrade head

Rollback Migration

alembic downgrade -1

Logging

Logs are configured in utils/logger.py. View logs for debugging:

from mfcli.utils.logger import get_logger
logger = get_logger(__name__)
logger.info("Message")
logger.error("Error message")

Running Tests

pytest app/tests/

🐛 Troubleshooting

Common Issues

1. Installation Issues

Error: ModuleNotFoundError: No module named 'google'

Solution: Reinstall package: pip install .

Error: Command 'mfcli' not found

Solution: Ensure virtual environment is activated and package is installed: pip install .

2. API Key Errors

Error: google.api_core.exceptions.Unauthenticated: 401 API key not valid

Solution: Verify google_api_key in .env file
Get key from: https://aistudio.google.com/app/apikey

Error: OpenAI API error

Solution: Check openai_api_key in .env
Ensure API key has billing enabled

3. ChromaDB Issues

Error: ChromaDB directory not found

Solution: ChromaDB will be created automatically on first run in AppData folder

Error: Embedding dimension mismatch

Solution: Delete ChromaDB directory and restart to rebuild with correct dimensions

4. Pipeline Processing Failures

Error: Could not find metadata file. Please initialize this repo with "mfcli init"

Solution: You need to run mfcli init in your hardware files directory before running mfcli run

Error: File not found

Solution: Make sure you're running mfcli run from within your hardware files directory (where you ran mfcli init)

Error: File extension is not supported

Solution: Check that your files are in supported formats (PDF, EDIF, CSV, .net, .cir, .asc)

Error: No components extracted from schematic

Solution:
- Ensure schematic PDF is clear and readable
- Check that component designators are visible
- Verify file is an actual schematic (not layout or other document type)

5. Database Issues

Error: Database connection failed

Solution: Check that SQLite database path in .env is valid
Run migrations: alembic upgrade head

Verify Configuration

Run this Python snippet to verify your configuration:

from mfcli.utils.config import get_config
config = get_config()
print(f"Google API Key set: {'Yes' if config.google_api_key else 'No'}")
print(f"OpenAI API Key set: {'Yes' if config.openai_api_key else 'No'}")
print(f"Database path: {config.sqlite_db_path}")

Debug Mode

For detailed debugging, check the console output when running mfcli pipeline or mfcli web. The application uses structured logging to help diagnose issues.

Getting Help

Issues: GitHub Issues
Documentation: Check this README and inline code documentation

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

This project is in active development. Contributions, issues, and feature requests are welcome!

Built with Google Gemini and Google Agent Development Kit (ADK)

Project details

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

0.2.18

Mar 23, 2026

0.2.17

Mar 21, 2026

0.2.16

Mar 21, 2026

0.2.15

Feb 23, 2026

0.2.14

Feb 17, 2026

0.2.13

Feb 17, 2026

0.2.12

Jan 30, 2026

This version

0.2.11

Jan 30, 2026

0.2.10

Jan 27, 2026

0.2.9

Jan 23, 2026

0.2.8

Jan 22, 2026

0.2.7

Jan 22, 2026

0.2.6

Jan 20, 2026

0.2.5

Jan 19, 2026

0.2.4

Jan 19, 2026

0.2.3

Jan 18, 2026

0.2.2

Jan 18, 2026

0.2.1

Jan 17, 2026

0.2.0

Jan 15, 2026

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mfcli-0.2.11.tar.gz (156.6 kB view details)

Uploaded Jan 30, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

mfcli-0.2.11-py3-none-any.whl (177.9 kB view details)

Uploaded Jan 30, 2026 Python 3

File details

Details for the file mfcli-0.2.11.tar.gz.

File metadata

Download URL: mfcli-0.2.11.tar.gz
Upload date: Jan 30, 2026
Size: 156.6 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for mfcli-0.2.11.tar.gz
Algorithm	Hash digest
SHA256	`9eb9f1855e7ea6d0c4579bc97077d43c4868605a826dc147b8e4a918f6a8832c`
MD5	`0ea7f7ef882867ba09fee9cbc0e551bb`
BLAKE2b-256	`ce480763529edbc995b62a6575ca26b765d2db3723fbc2a4efde82f1da28fa28`

See more details on using hashes here.

File details

Details for the file mfcli-0.2.11-py3-none-any.whl.

File metadata

Download URL: mfcli-0.2.11-py3-none-any.whl
Upload date: Jan 30, 2026
Size: 177.9 kB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for mfcli-0.2.11-py3-none-any.whl
Algorithm	Hash digest
SHA256	`0b2eb23bb55960ee38b5bf58ab359ba11d149142a75be79f6a0c050ecab9970a`
MD5	`5e303045cd63061f739dce073d31964a`
BLAKE2b-256	`e9fba969fd3fa0207a81fb20ab67310a7e9b7d6671abc31ae1a9950f893e8811`

See more details on using hashes here.

mfcli 0.2.11

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

Multifactor ADK Backend

🚀 Features

📋 Table of Contents

🚀 Quick Start

🏗️ Architecture

📦 Prerequisites

🔧 Installation

Quick Install (Recommended)

Manual Installation

Using pipx (Recommended)

Using pip (For Development)

Verify Installation

🗑️ Uninstalling

Important: Stop MCP Server First

Quick Pre-Uninstall Check

Automated Uninstall (Recommended)

Manual Uninstall

Troubleshooting Permission Errors

Complete Documentation

⚙️ Configuration

Interactive Configuration Wizard (Recommended)

Manual Configuration

Check Configuration

Required API Keys & How to Get Them

🚀 Usage

Command-Line Interface

Getting Started with a Project

File Change Detection

CLI Commands Reference

Configure Logging Level

Start Web UI

Web UI Usage

🔌 MCP Server

What is MCP?

Available Tools

query_local_rag

Configuration for Cline/Claude

Setup Instructions

Troubleshooting MCP Server

MCP Server Architecture

🔄 Pipeline

Phase 1: Pre-processing

Phase 2: Analysis & Generation

Supported File Types

📂 Output Directories

1. User Application Data

2. Project Output Directories

💾 Data Storage

SQLite Database

ChromaDB Vector Store

S3 Storage (Optional)

📁 Project Structure

🛠️ Development

Running the Pipeline

Starting the Web UI

Development Status

Publishing to PyPI

Database Migrations

Create a New Migration

Apply Migrations

Rollback Migration

Logging

Running Tests

🐛 Troubleshooting

Common Issues

1. Installation Issues

2. API Key Errors

3. ChromaDB Issues

4. Pipeline Processing Failures

5. Database Issues

Verify Configuration

`query_local_rag`