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feat: add multi-provider LLM support with LiteLLM

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- Replace openai with litellm for unified LLM interface
- Support 100+ providers: OpenAI, Anthropic, Gemini, Azure, Ollama, etc.
- Add custom API base URL support (LLM_API_BASE)
- Add .env file support with python-dotenv
- Add .env.example configuration template

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
empty
2026-01-01 13:49:28 +08:00
parent 64ed46215f
commit 7e3872cdd8
5 changed files with 173 additions and 34 deletions
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5
.gitignore vendored
View File

@@ -24,6 +24,11 @@ ENV/
*.sqlite
*.sqlite3
# Environment variables
.env
.env.local
.env.*.local
# Logs
*.log

51
backend/.env.example Normal file
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@@ -0,0 +1,51 @@
# LLM Configuration for The Island Backend
# Copy this file to .env and fill in your values
# =============================================================================
# Option 1: OpenAI (default)
# =============================================================================
# OPENAI_API_KEY=sk-xxx
# LLM_MODEL=gpt-3.5-turbo
# =============================================================================
# Option 2: Anthropic Claude
# =============================================================================
# ANTHROPIC_API_KEY=sk-ant-xxx
# LLM_MODEL=claude-3-haiku-20240307
# =============================================================================
# Option 3: Google Gemini
# =============================================================================
# GEMINI_API_KEY=xxx
# LLM_MODEL=gemini/gemini-pro
# =============================================================================
# Option 4: Azure OpenAI
# =============================================================================
# AZURE_API_KEY=xxx
# AZURE_API_BASE=https://your-resource.openai.azure.com
# LLM_MODEL=azure/your-deployment-name
# =============================================================================
# Option 5: OpenRouter (access multiple models)
# =============================================================================
# OPENROUTER_API_KEY=sk-or-xxx
# LLM_MODEL=openrouter/anthropic/claude-3-haiku
# =============================================================================
# Option 6: Local Ollama
# =============================================================================
# OLLAMA_API_BASE=http://localhost:11434
# LLM_MODEL=ollama/llama2
# =============================================================================
# Option 7: Custom/Self-hosted (OpenAI-compatible endpoint)
# =============================================================================
# LLM_API_BASE=http://localhost:8000/v1
# LLM_API_KEY=your-key
# LLM_MODEL=qwen2.5
# =============================================================================
# Force mock mode (no API calls, uses predefined responses)
# =============================================================================
# LLM_MOCK_MODE=true

144
backend/app/llm.py
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@@ -1,6 +1,22 @@
"""
LLM Service - Agent Brain Module.
Provides AI-powered responses for agents using OpenAI's API.
Provides AI-powered responses for agents using LiteLLM (supports multiple providers).
Supported providers (via environment variables):
- OpenAI: OPENAI_API_KEY → model="gpt-3.5-turbo" or "gpt-4"
- Anthropic: ANTHROPIC_API_KEY → model="claude-3-haiku-20240307" or "claude-3-sonnet-20240229"
- Google: GEMINI_API_KEY → model="gemini/gemini-pro"
- Azure OpenAI: AZURE_API_KEY + AZURE_API_BASE → model="azure/<deployment-name>"
- OpenRouter: OPENROUTER_API_KEY → model="openrouter/<model>"
- Ollama (local): OLLAMA_API_BASE → model="ollama/llama2"
- Custom/Self-hosted: LLM_API_KEY + LLM_API_BASE → any OpenAI-compatible endpoint
- And 100+ more providers...
Configuration:
- LLM_MODEL: Model to use (default: gpt-3.5-turbo)
- LLM_API_BASE: Custom API base URL (for self-hosted or proxy services)
- LLM_API_KEY: Generic API key (used with LLM_API_BASE)
- LLM_MOCK_MODE: Set to "true" to force mock mode
"""
import logging
@@ -41,42 +57,90 @@ MOCK_REACTIONS = {
],
}
# Default model configuration
DEFAULT_MODEL = "gpt-3.5-turbo"
class LLMService:
"""
Service for generating AI-powered agent reactions.
Falls back to mock responses if API key is not configured.
Service for generating AI-powered agent reactions using LiteLLM.
Supports multiple LLM providers through a unified interface.
Falls back to mock responses if no API key is configured.
"""
def __init__(self) -> None:
"""Initialize the LLM service with OpenAI client or mock mode."""
self._api_key = os.environ.get("OPENAI_API_KEY")
self._client = None
self._mock_mode = False
"""Initialize the LLM service with LiteLLM or mock mode."""
self._model = os.environ.get("LLM_MODEL", DEFAULT_MODEL)
self._api_base = os.environ.get("LLM_API_BASE") # Custom base URL
self._api_key = os.environ.get("LLM_API_KEY") # Generic API key
self._mock_mode = os.environ.get("LLM_MOCK_MODE", "").lower() == "true"
self._acompletion = None
if not self._api_key:
if self._mock_mode:
logger.info("LLMService running in MOCK mode (forced by LLM_MOCK_MODE)")
return
# Check for any supported API key (order matters for provider detection)
api_keys = {
"OPENAI_API_KEY": "OpenAI",
"ANTHROPIC_API_KEY": "Anthropic",
"GEMINI_API_KEY": "Google Gemini",
"AZURE_API_KEY": "Azure OpenAI",
"AZURE_API_BASE": "Azure OpenAI",
"OPENROUTER_API_KEY": "OpenRouter",
"COHERE_API_KEY": "Cohere",
"HUGGINGFACE_API_KEY": "HuggingFace",
"OLLAMA_API_BASE": "Ollama (local)",
"LLM_API_KEY": "Custom (with LLM_API_BASE)",
"LLM_API_BASE": "Custom endpoint",
}
found_provider = None
for key, provider in api_keys.items():
if os.environ.get(key):
found_provider = provider
break
if not found_provider:
logger.warning(
"OPENAI_API_KEY not found in environment. "
"LLMService running in MOCK mode - using predefined responses."
"No LLM API key found in environment. "
"LLMService running in MOCK mode - using predefined responses. "
f"Supported keys: {', '.join(api_keys.keys())}"
)
self._mock_mode = True
else:
try:
from openai import AsyncOpenAI
self._client = AsyncOpenAI(api_key=self._api_key)
logger.info("LLMService initialized with OpenAI API")
except ImportError:
logger.error("openai package not installed. Running in MOCK mode.")
self._mock_mode = True
except Exception as e:
logger.error(f"Failed to initialize OpenAI client: {e}. Running in MOCK mode.")
self._mock_mode = True
return
try:
from litellm import acompletion
self._acompletion = acompletion
# Log configuration details
config_info = f"provider: {found_provider}, model: {self._model}"
if self._api_base:
config_info += f", api_base: {self._api_base}"
logger.info(f"LLMService initialized with LiteLLM ({config_info})")
except ImportError:
logger.error("litellm package not installed. Running in MOCK mode.")
self._mock_mode = True
except Exception as e:
logger.error(f"Failed to initialize LiteLLM: {e}. Running in MOCK mode.")
self._mock_mode = True
@property
def is_mock_mode(self) -> bool:
"""Check if service is running in mock mode."""
return self._mock_mode
@property
def model(self) -> str:
"""Get the current model name."""
return self._model
@property
def api_base(self) -> str | None:
"""Get the custom API base URL if configured."""
return self._api_base
def _get_mock_response(self, event_type: str = "feed") -> str:
"""Get a random mock response for testing without API."""
responses = MOCK_REACTIONS.get(event_type, MOCK_REACTIONS["feed"])
@@ -112,15 +176,22 @@ class LLMService:
f"Respond in first person, as if speaking out loud."
)
response = await self._client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
# Build kwargs for acompletion
kwargs = {
"model": self._model,
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": event_description}
],
max_tokens=50,
temperature=0.8,
)
"max_tokens": 50,
"temperature": 0.8,
}
if self._api_base:
kwargs["api_base"] = self._api_base
if self._api_key:
kwargs["api_key"] = self._api_key
response = await self._acompletion(**kwargs)
return response.choices[0].message.content.strip()
@@ -165,15 +236,22 @@ class LLMService:
f"Speak naturally, as if talking to yourself or nearby survivors."
)
response = await self._client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
# Build kwargs for acompletion
kwargs = {
"model": self._model,
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": "What are you thinking right now?"}
],
max_tokens=40,
temperature=0.9,
)
"max_tokens": 40,
"temperature": 0.9,
}
if self._api_base:
kwargs["api_base"] = self._api_base
if self._api_key:
kwargs["api_key"] = self._api_key
response = await self._acompletion(**kwargs)
return response.choices[0].message.content.strip()

4
backend/app/main.py
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@@ -3,6 +3,10 @@ FastAPI entry point for the interactive live-stream game backend.
Configures the application, WebSocket routes, and lifecycle events.
"""
# Load .env file before any other imports
from dotenv import load_dotenv
load_dotenv()
import logging
from contextlib import asynccontextmanager
from pathlib import Path

3
backend/requirements.txt
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@@ -4,4 +4,5 @@ websockets>=12.0
pydantic>=2.5.0
sqlalchemy>=2.0.0
aiosqlite>=0.19.0
openai>=1.0.0
litellm>=1.40.0
python-dotenv>=1.0.0