eq-chatbot-core 1.3.0

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

eq-chatbot-core

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

Language / Sprache: DE | EN

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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, Azure AI, Google Vertex AI, 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 range 0-2, Claude max=1.0, Gemini 0-2, 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]

# With Azure AI support
pip install eq-chatbot-core[azure]

# With Google Vertex AI support
pip install eq-chatbot-core[vertex]

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

Quick Start

from eq_chatbot_core.providers import get_provider

# Cloud providers
provider = get_provider("openai", api_key="sk-...")
provider = get_provider("anthropic", api_key="sk-ant-...")
provider = get_provider("azure", api_key="...", base_url="https://your-resource.services.ai.azure.com/")
provider = get_provider("vertex", project="my-gcp-project", location="europe-west1")
provider = get_provider("langdock", api_key="ld-...", region="eu")
provider = get_provider("openrouter", api_key="sk-or-...")
provider = get_provider("mammouth", api_key="mm-...")

# Local providers (no API key needed)
provider = get_provider("lm_studio")   # localhost:1234
provider = get_provider("ollama")      # localhost:11434

# 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}")

Google Vertex AI Usage

Google Vertex AI uses Application Default Credentials (ADC) instead of API keys.

# Authenticate locally
gcloud auth application-default login
gcloud config set project YOUR-PROJECT-ID

# Or use service account
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account-key.json"

from eq_chatbot_core.providers import get_provider

provider = get_provider("vertex", project="my-project", location="europe-west1")

response = provider.chat_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    model="gemini-2.5-flash",
)
print(response.content)

# Streaming
for chunk in provider.stream_completion(
    messages=[{"role": "user", "content": "Tell me a story"}],
    model="gemini-2.5-pro",
):
    print(chunk.content, end="", flush=True)

Available EU regions for GDPR compliance: europe-west1 (Belgium), europe-west3 (Frankfurt), europe-west4 (Netherlands).

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

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
Azure AI	GPT-4o, GPT-4.1, o1, o3, o4, Claude, Mistral, Llama, Phi, DeepSeek	Depends on model	Yes	Yes
Vertex AI	Gemini 2.0, Gemini 2.5 Flash/Pro	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

---

Deutsch

Ueberblick

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

Urspruenglich fuer die Odoo 18 Chatbot-Integration entwickelt, funktioniert aber standalone ohne Odoo-Abhaengigkeiten.

Hauptfunktionen

• Multi-Provider-Unterstuetzung: OpenAI, Anthropic, Azure AI, Google Vertex AI, LangDock, OpenRouter, Mammouth AI, Local (LM Studio/Ollama)
• Einheitliche API: Gleiche Schnittstelle unabhaengig vom Provider
• Temperature-Sicherheit: Automatisches modellspezifisches Temperature-Clamping (GPT-4.1 Bereich 0-2, Claude max=1.0, Gemini 0-2, Reasoning-Modelle werden uebersprungen)
• Sicherheit:
  • Fernet-Verschluesselung fuer 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 fuer Model Context Protocol
• CLI-Tool: Kommandozeilen-Interface fuer Provider-Tests

Installation

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

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

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

# Mit Azure AI Unterstuetzung
pip install eq-chatbot-core[azure]

# Mit Google Vertex AI Unterstuetzung
pip install eq-chatbot-core[vertex]

# Alle optionalen Abhaengigkeiten
pip install eq-chatbot-core[pdf,security,azure,vertex,dev]

Google Vertex AI Verwendung

Google Vertex AI verwendet Application Default Credentials (ADC) anstelle von API-Keys.

# Lokal authentifizieren
gcloud auth application-default login
gcloud config set project DEIN-PROJEKT-ID

# Oder Service Account verwenden
export GOOGLE_APPLICATION_CREDENTIALS="/pfad/zum/service-account-key.json"

from eq_chatbot_core.providers import get_provider

provider = get_provider("vertex", project="mein-projekt", location="europe-west1")

response = provider.chat_completion(
    messages=[{"role": "user", "content": "Hallo!"}],
    model="gemini-2.5-flash",
)
print(response.content)

# Streaming
for chunk in provider.stream_completion(
    messages=[{"role": "user", "content": "Erzaehle mir eine Geschichte"}],
    model="gemini-2.5-pro",
):
    print(chunk.content, end="", flush=True)

Verfuegbare EU-Regionen fuer DSGVO-Konformitaet: europe-west1 (Belgien), europe-west3 (Frankfurt), europe-west4 (Niederlande).

CLI-Verwendung

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

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

# Nur Vision-faehige 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": "Erzaehle mir eine Geschichte"}],
    model="gpt-4o"
):
    print(chunk.content, end="", flush=True)

Unterstuetzte Provider

Provider	Modelle	Vision	Streaming	Temp. Clamping
OpenAI	GPT-4, GPT-4o, GPT-4.1, GPT-5, o1, o3, o4	Ja	Ja	Ja
Anthropic	Claude 3, Claude 3.5, Claude 4	Ja	Ja	Ja
Azure AI	GPT-4o, GPT-4.1, o1, o3, o4, Claude, Mistral, Llama, Phi, DeepSeek	Modellabhaengig	Ja	Ja
Vertex AI	Gemini 2.0, Gemini 2.5 Flash/Pro	Ja	Ja	Ja
LangDock	Alle via Gateway	Ja	Ja	Ja
OpenRouter	400+ Modelle via Gateway	Ja	Ja	Ja
Mammouth AI	30+ Modelle via Unified API	Ja	Ja	Ja
Local (LM Studio/Ollama)	Lokale Modelle	Nein	Ja	Nein

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Technical Information

Field	Value
Package Name	eq-chatbot-core
Version	1.3.0
Author	Equitania Software GmbH
Contact	[e-mail]
License	MIT
Python	>=3.10
Homepage	https://www.ownerp.com
Repository	https://github.com/equitania/eq-chatbot-core

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.