echomine 1.1.0

Library-first tool for parsing AI conversation exports with search, filtering, and markdown export

Echomine

Library-first tool for parsing AI conversation exports with search, filtering, and markdown export

Overview

Echomine is a Python library and CLI tool for parsing, searching, and exporting AI conversation exports. Initially designed for ChatGPT exports, it uses a multi-provider adapter pattern to support future AI platforms (Claude, Gemini, etc.).

Key Features

• Memory Efficient: Stream-based parsing handles 1GB+ files with constant memory usage
• Advanced Search: BM25 relevance ranking with exact phrase matching, boolean logic, role filtering, and keyword exclusion
• Message Snippets: Automatic preview generation for search results with match context
• Type Safe: Strict typing with Pydantic v2 and mypy --strict compliance
• Library First: All CLI capabilities available as importable Python library
• Multi-Provider Ready: Adapter pattern supports multiple AI export formats

Design Principles

1. Library-First Architecture: CLI built on top of library, not vice versa
2. Strict Type Safety: mypy --strict, no Any types in public API
3. Memory Efficiency: Stream-based parsing, never load entire file into memory
4. Test-Driven Development: All features test-first validated
5. YAGNI: Simple solutions, no speculative features

See Constitution for complete design principles.

Installation

From Source

# Clone repository
git clone https://github.com/echomine/echomine.git
cd echomine

# Install with development dependencies
pip install -e ".[dev]"

# Install pre-commit hooks (optional)
pre-commit install

From PyPI (when published)

pip install echomine

Quick Start

Library API (Primary Interface)

from echomine import OpenAIAdapter, SearchQuery
from pathlib import Path

# Initialize adapter (stateless, reusable)
adapter = OpenAIAdapter()
export_file = Path("conversations.json")

# 1. List all conversations (discovery)
for conversation in adapter.stream_conversations(export_file):
    print(f"[{conversation.created_at.date()}] {conversation.title}")
    print(f"  Messages: {len(conversation.messages)}")

# 2. Search with keywords (BM25 ranking)
query = SearchQuery(keywords=["algorithm", "design"], limit=10)
for result in adapter.search(export_file, query):
    print(f"{result.conversation.title} (score: {result.score:.2f})")
    print(f"  Preview: {result.snippet}")  # v1.1.0: automatic snippets

# 3. Advanced search with filters (v1.1.0+)
from datetime import date
query = SearchQuery(
    keywords=["refactor"],
    phrases=["algo-insights"],  # Exact phrase matching
    match_mode="all",  # Require ALL keywords (AND logic)
    exclude_keywords=["test"],  # Filter out unwanted results
    role_filter="user",  # Search only user messages
    from_date=date(2024, 1, 1),
    to_date=date(2024, 3, 31),
    limit=5
)
for result in adapter.search(export_file, query):
    print(f"[{result.score:.2f}] {result.conversation.title}")
    print(f"  Snippet: {result.snippet}")

# 4. Get specific conversation by ID
conversation = adapter.get_conversation_by_id(export_file, "conv-abc123")
if conversation:
    print(f"Found: {conversation.title}")

CLI Usage (Built on Library)

# List all conversations
echomine list export.json

# Search by keywords
echomine search export.json --keywords "algorithm,design" --limit 10

# Search by exact phrase (v1.1.0+)
echomine search export.json --phrase "algo-insights"

# Boolean match mode: require ALL keywords (v1.1.0+)
echomine search export.json -k "python" -k "async" --match-mode all

# Exclude unwanted results (v1.1.0+)
echomine search export.json -k "python" --exclude "django" --exclude "flask"

# Role filtering: search only user/assistant messages (v1.1.0+)
echomine search export.json -k "refactor" --role user

# Combine all filters (v1.1.0+)
echomine search export.json --phrase "api" -k "python" --exclude "test" --role user --match-mode all

# Search by title (fast, metadata-only)
echomine search export.json --title "Project"

# Filter by date range
echomine search export.json --from-date "2024-01-01" --to-date "2024-03-31"

# Get conversation by ID
echomine get conversation export.json conv-abc123

# Get message by ID (with conversation hint for performance)
echomine get message export.json msg-def456 -c conv-abc123

# Export conversation to markdown (default)
echomine export export.json conv-abc123 --output algo.md

# Export as JSON
echomine export export.json conv-abc123 --format json --output algo.json

# JSON to stdout for piping
echomine export export.json conv-abc123 -f json | jq '.messages | length'

# JSON output for search results
echomine search export.json --keywords "python" --json | jq '.results[].title'

# Version info
echomine --version

Search Filter Logic: Content matching (phrases OR keywords) happens first, then post-filtering (--exclude, --role, --title, dates) is applied. See CLI Usage for details.

See Quickstart Guide for detailed examples.

Development

Prerequisites

• Python 3.12 or higher
• Git

Setup Development Environment

# Clone repository
git clone https://github.com/echomine/echomine.git
cd echomine

# Install with development dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=echomine --cov-report=html

# Run specific test categories
pytest -m unit           # Unit tests only
pytest -m integration    # Integration tests only
pytest -m contract       # Contract tests only
pytest -m performance    # Performance benchmarks

Code Quality

# Type checking (strict mode)
mypy src/

# Linting and formatting
ruff check .
ruff format .

# Run pre-commit hooks manually
pre-commit run --all-files

Project Structure

echomine/
├── src/echomine/           # Library source code
│   ├── models/             # Pydantic data models
│   ├── adapters/           # Provider adapters (OpenAI, etc.)
│   ├── parsers/            # Streaming JSON parsers
│   ├── search/             # Search and ranking logic
│   ├── exporters/          # Export formatters (markdown, JSON)
│   └── cli/                # CLI commands
├── tests/                  # Test suite
│   ├── unit/               # Unit tests
│   ├── integration/        # Integration tests
│   ├── contract/           # Protocol contract tests
│   └── performance/        # Performance benchmarks
└── specs/                  # Design documents
    └── 001-ai-chat-parser/ # Feature specification

Documentation

Full Documentation - Comprehensive guides, API reference, and examples

Quick Links

• Getting Started
• Library Usage
• CLI Reference
• API Reference

Spec Documents

• Feature Specification
• Implementation Plan
• CLI Interface Contract
• Data Model

Performance

Echomine is designed for memory efficiency and speed:

• Memory: O(1) memory usage regardless of file size (streaming-based)
• Search: <30 seconds for 1.6GB files (10K conversations, 50K messages)
• Listing: <5 seconds for 10K conversations

See Performance Requirements for benchmarks.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for:

• Development setup and prerequisites
• TDD workflow (RED-GREEN-REFACTOR cycle mandatory)
• Testing guidelines (pytest, mypy --strict, ruff)
• Code quality standards and conventions
• Commit message format (conventional commits)
• Pull request process

License

AGPL-3.0 License - See LICENSE file for details

Acknowledgments

Built with:

• Pydantic - Data validation and type safety
• ijson - Streaming JSON parser
• Typer - CLI framework
• Rich - Terminal formatting
• structlog - Structured logging