eq-chatbot-core 1.7.5

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 across cloud and local providers, security primitives, an MCP client, a RAG pipeline, and an optional HTTP/SSE sidecar — usable from any language.

Originally extracted from an Odoo 18 chatbot integration; works standalone without any Odoo dependency.

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
• Security — Fernet encryption, prompt-injection detection, file-upload validation, token-bucket rate limiting
• RAG Pipeline — chunking, embeddings, Qdrant-backed retrieval, context-window management
• MCP Client — HTTP/SSE and stdio transports, hardened against DNS rebinding and SSRF
• CLI Tool — provider testing, model discovery, programmatic JSON I/O chat
• HTTP/SSE Server Mode (v1.7.0) — run as a local sidecar (eq-chatbot serve) for cross-language integrations (Avalonia/.NET, Electron, native mobile)

Installation

# Basic installation
uv pip install eq-chatbot-core
# (or: pip install eq-chatbot-core)

# With optional extras
uv pip install eq-chatbot-core[pdf]       # PDF→image conversion (vision)
uv pip install eq-chatbot-core[security]  # MIME-type file validation
uv pip install eq-chatbot-core[azure]     # Azure AI Foundry
uv pip install eq-chatbot-core[vertex]    # Google Vertex AI
uv pip install eq-chatbot-core[server]    # HTTP/SSE sidecar (FastAPI + uvicorn)
uv pip install eq-chatbot-core[local]     # Local sentence-transformers embeddings

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

Quick Start

from eq_chatbot_core.providers import get_provider

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

response = provider.chat_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    model="gpt-4o",
)
print(response.content)

For more — streaming, other providers, ADC for Vertex, error handling — see docs/providers.md.

Documentation

Topic	Docs
Multi-provider integration	docs/providers.md
CLI commands	docs/cli.md
HTTP/SSE server mode	docs/server-mode.md
Security (encryption, injection, files, rate limit)	docs/security.md
MCP client (HTTP/SSE + stdio)	docs/mcp.md
RAG pipeline (chunking, embedding, retrieval)	docs/rag.md
Testing (markers, integration setup, cost-aware patterns)	docs/testing.md

Realtime Providers

ElevenLabs (Recommended GDPR Provider)

ElevenLabs Conversational AI ("elevenlabs") is the recommended provider for EU/GDPR deployments.

from eq_chatbot_core.realtime import get_realtime_provider

provider = get_realtime_provider(
    "elevenlabs",
    api_key="xi-...",
    agent_id="YOUR_AGENT_ID",
)

OpenAI Realtime and Gemini Live remain supported providers. ElevenLabs is recommended for EU-regulated deployments because it offers an enterprise-grade EU data residency path.

Full EU Compliance Checklist

Four conditions must ALL be met for complete data residency compliance:

1. Enterprise plan — EU data residency is available on the Enterprise plan only. Standard and Creator plans route data through US infrastructure.

2. Zero Retention Mode — Enable Zero Retention Mode in the ElevenLabs Enterprise dashboard and confirm it via the Zero Retention API. Covers TTS, STT, and Conversational AI sessions. Voice cloning models are excluded (see caveat below).

3. EU-hosted Custom LLM backend — ElevenLabs Agents orchestrate an LLM under the hood. For full EU residency, configure a Custom LLM endpoint hosted in the EU (e.g. Azure OpenAI EU region, or a self-hosted model in an EU data centre). Configure this in the ElevenLabs dashboard, not in the adapter.

4. EU data-residency endpoint — Pass the EU base URL as base_url:

   from eq_chatbot_core.realtime import get_realtime_provider
   
   provider = get_realtime_provider(
       "elevenlabs",
       api_key="YOUR_EU_API_KEY",   # EU key — different from global key
       agent_id="YOUR_AGENT_ID",
       base_url="wss://api.eu.residency.elevenlabs.io",
   )
   

   Important: The EU API key is a separate key provisioned by ElevenLabs Enterprise support. Your global xi-api-key will return 403 Forbidden on the EU endpoint.

Voice Cloning Caveat

Voice cloning models are not eligible for Zero Retention Mode — cloned voice model data persists in ElevenLabs infrastructure. If your use case requires voice cloning, assess whether that data qualifies as personal data under GDPR before deploying in an EU-regulated context.

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Deutsch

Überblick

eq-chatbot-core ist eine Python-Bibliothek zur Integration von Large Language Models (LLMs) in Anwendungen. Bietet eine einheitliche Schnittstelle über Cloud- und lokale Provider, Security-Primitives, einen MCP-Client, eine RAG-Pipeline und einen optionalen HTTP/SSE-Sidecar — aus jeder Sprache nutzbar.

Ursprünglich aus einer Odoo-18-Chatbot-Integration extrahiert; funktioniert standalone ohne Odoo-Abhängigkeit.

Hauptfunktionen

• Multi-Provider-Unterstützung — OpenAI, Anthropic, Azure AI, Google Vertex AI, LangDock, OpenRouter, Mammouth AI, Local (LM Studio/Ollama)
• Einheitliche API — gleiche Schnittstelle unabhängig vom Provider
• Temperature-Sicherheit — automatisches modellspezifisches Temperature-Clamping
• Sicherheit — Fernet-Verschlüsselung, Prompt-Injection-Erkennung, File-Upload-Validierung, Token-Bucket-Rate-Limiting
• RAG-Pipeline — Chunking, Embeddings, Qdrant-basiertes Retrieval, Context-Window-Management
• MCP-Client — HTTP/SSE und stdio Transports, gehärtet gegen DNS-Rebinding und SSRF
• CLI-Tool — Provider-Tests, Modell-Discovery, programmatische JSON-I/O-Chat-Calls
• HTTP/SSE-Server-Mode (v1.7.0) — lokaler Sidecar (eq-chatbot serve) für Cross-Language-Integrationen (Avalonia/.NET, Electron, native Mobile)

Installation

# Basis-Installation
uv pip install eq-chatbot-core
# (oder: pip install eq-chatbot-core)

# Mit optionalen Extras
uv pip install eq-chatbot-core[pdf]       # PDF→Bild-Konvertierung (Vision)
uv pip install eq-chatbot-core[security]  # MIME-Type-File-Validation
uv pip install eq-chatbot-core[azure]     # Azure AI Foundry
uv pip install eq-chatbot-core[vertex]    # Google Vertex AI
uv pip install eq-chatbot-core[server]    # HTTP/SSE-Sidecar (FastAPI + uvicorn)
uv pip install eq-chatbot-core[local]     # Lokale sentence-transformers-Embeddings

# Alle optionalen Abhängigkeiten
uv pip install eq-chatbot-core[pdf,security,azure,vertex,server,local,dev]

Quick Start

from eq_chatbot_core.providers import get_provider

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

response = provider.chat_completion(
    messages=[{"role": "user", "content": "Hallo!"}],
    model="gpt-4o",
)
print(response.content)

Für mehr — Streaming, andere Provider, ADC für Vertex, Error-Handling — siehe docs/providers.md.

Dokumentation

Thema	Docs
Multi-Provider-Integration	docs/providers.md
CLI-Befehle	docs/cli.md
HTTP/SSE-Server-Mode	docs/server-mode.md
Security (Verschlüsselung, Injection, Files, Rate-Limit)	docs/security.md
MCP-Client (HTTP/SSE + stdio)	docs/mcp.md
RAG-Pipeline (Chunking, Embedding, Retrieval)	docs/rag.md
Testing (Marker, Integration-Setup, Cost-Aware-Patterns)	docs/testing.md

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Technical Information

Field	Value
Package Name	eq-chatbot-core
Version	1.7.4
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

MIT — see LICENSE.