eq-chatbot-core 1.0.0

Core library for LLM chatbot integration with multi-provider support (OpenAI, Anthropic, LangDock, OpenRouter, Local)

eq-chatbot-core

Core library for LLM chatbot integration with multi-provider support.

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English

Overview

eq-chatbot-core is a Python library for integrating Large Language Models (LLMs) into your applications. It provides a unified interface for multiple LLM providers, security features, and RAG (Retrieval-Augmented Generation) capabilities.

Originally developed for Odoo 18 chatbot integration, but works standalone without any Odoo dependencies.

Key Features

• Multi-Provider Support: OpenAI, Anthropic, LangDock, OpenRouter, Mammouth AI, Local (LM Studio/Ollama)
• Unified API: Same interface regardless of provider
• Temperature Safety: Automatic model-specific temperature clamping (GPT-4.1 min=1.0, Claude max=1.0, reasoning models skip)
• Security:
  • Fernet encryption for API key storage
  • Prompt injection protection
  • File upload validation
• RAG Pipeline:
  • Text chunking with configurable strategies
  • Embedding generation
  • Vector retrieval integration
• MCP Client: HTTP/SSE and stdio transports for Model Context Protocol
• CLI Tool: Command-line interface for provider testing

Installation

# Basic installation
pip install eq-chatbot-core
# Or with UV (recommended)
uv pip install eq-chatbot-core

# With PDF support (for OpenAI/LangDock vision)
pip install eq-chatbot-core[pdf]

# With file validation
pip install eq-chatbot-core[security]

# All optional dependencies
pip install eq-chatbot-core[pdf,security,dev]

CLI Usage

# Test provider connection
eq-chatbot test-provider -p openai -k YOUR_API_KEY

# List available models
eq-chatbot list-models -p anthropic -k YOUR_API_KEY

# Show only vision-capable models
eq-chatbot list-models -p langdock -k YOUR_KEY --vision-only

# Output as JSON
eq-chatbot list-models -p openai -k YOUR_KEY --json

# Show package info
eq-chatbot info

Python Usage

from eq_chatbot_core.providers import get_provider

# Initialize provider
provider = get_provider("openai", api_key="sk-...")

# Simple chat completion
response = provider.chat_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    model="gpt-4o"
)
print(response.content)
print(f"Tokens used: {response.total_tokens}")

# Streaming
for chunk in provider.stream_completion(
    messages=[{"role": "user", "content": "Tell me a story"}],
    model="gpt-4o"
):
    print(chunk.content, end="", flush=True)

# List available models
models = provider.list_models()
for model in models:
    print(f"{model.id} - Vision: {model.supports_vision}")

Encryption Example

from eq_chatbot_core.security.encryption import FernetEncryption

# Encrypt API keys for safe storage
encryption = FernetEncryption()
key = encryption.generate_key()

encrypted = encryption.encrypt("sk-your-api-key", key)
decrypted = encryption.decrypt(encrypted, key)

Supported Providers

Provider	Models	Vision	Streaming	Temp. Clamping
OpenAI	GPT-4, GPT-4o, GPT-4.1, GPT-5, o1, o3, o4	Yes	Yes	Yes
Anthropic	Claude 3, Claude 3.5, Claude 4	Yes	Yes	Yes
LangDock	All via gateway	Yes	Yes	Yes
OpenRouter	400+ models via gateway	Yes	Yes	Yes
Mammouth AI	30+ models via unified API	Yes	Yes	Yes
Local (LM Studio/Ollama)	Local models	No	Yes	No

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Deutsch

Überblick

eq-chatbot-core ist eine Python-Bibliothek zur Integration von Large Language Models (LLMs) in Anwendungen. Sie bietet eine einheitliche Schnittstelle für mehrere LLM-Anbieter, Sicherheitsfunktionen und RAG-Fähigkeiten (Retrieval-Augmented Generation).

Ursprünglich für die Odoo 18 Chatbot-Integration entwickelt, funktioniert aber standalone ohne Odoo-Abhängigkeiten.

Hauptfunktionen

• Multi-Provider-Unterstützung: OpenAI, Anthropic, LangDock, OpenRouter, Mammouth AI, Local (LM Studio/Ollama)
• Einheitliche API: Gleiche Schnittstelle unabhängig vom Provider
• Temperature-Sicherheit: Automatisches modellspezifisches Temperature-Clamping (GPT-4.1 min=1.0, Claude max=1.0, Reasoning-Modelle werden übersprungen)
• Sicherheit:
  • Fernet-Verschlüsselung für API-Key-Speicherung
  • Schutz vor Prompt-Injection
  • Datei-Upload-Validierung
• RAG-Pipeline:
  • Text-Chunking mit konfigurierbaren Strategien
  • Embedding-Generierung
  • Vektor-Retrieval-Integration
• MCP-Client: HTTP/SSE und stdio Transports für Model Context Protocol
• CLI-Tool: Kommandozeilen-Interface für Provider-Tests

Installation

# Basis-Installation
pip install eq-chatbot-core
# Oder mit UV (empfohlen)
uv pip install eq-chatbot-core

# Mit PDF-Unterstützung (für OpenAI/LangDock Vision)
pip install eq-chatbot-core[pdf]

# Mit Datei-Validierung
pip install eq-chatbot-core[security]

# Alle optionalen Abhängigkeiten
pip install eq-chatbot-core[pdf,security,dev]

CLI-Verwendung

# Provider-Verbindung testen
eq-chatbot test-provider -p openai -k YOUR_API_KEY

# Verfügbare Modelle auflisten
eq-chatbot list-models -p anthropic -k YOUR_API_KEY

# Nur Vision-fähige Modelle anzeigen
eq-chatbot list-models -p langdock -k YOUR_KEY --vision-only

# Ausgabe als JSON
eq-chatbot list-models -p openai -k YOUR_KEY --json

# Paket-Informationen anzeigen
eq-chatbot info

Python-Verwendung

from eq_chatbot_core.providers import get_provider

# Provider initialisieren
provider = get_provider("openai", api_key="sk-...")

# Einfache Chat-Completion
response = provider.chat_completion(
    messages=[{"role": "user", "content": "Hallo!"}],
    model="gpt-4o"
)
print(response.content)
print(f"Tokens verwendet: {response.total_tokens}")

# Streaming
for chunk in provider.stream_completion(
    messages=[{"role": "user", "content": "Erzähle mir eine Geschichte"}],
    model="gpt-4o"
):
    print(chunk.content, end="", flush=True)

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Technical Information

Field	Value
Package Name	eq-chatbot-core
Version	1.0.0
Author	Equitania Software GmbH
License	MIT
Python	>=3.10
Homepage	https://www.ownerp.com

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

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