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Merge PR #463: multi-provider LLM support via Prompture 

# Conflicts:
#	.env.example
#	backend/app/utils/llm_client.py
This commit is contained in:
Lucas Ercolano 2026-04-10 16:58:04 -03:00
parent dd671cb15b 25909ccdca
commit 842bfd4335
5 changed files with 297 additions and 71 deletions
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51
.env.example
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@ -1,19 +1,44 @@
# LLM API配置（支持 OpenAI SDK 格式的任意 LLM API）
# 推荐使用阿里百炼平台qwen-plus模型：https://bailian.console.aliyun.com/
# 注意消耗较大，可先进行小于40轮的模拟尝试
# ===== LLM API Configuration =====
# Default: any OpenAI-compatible API
# With Prompture installed (pip install prompture): 12+ providers supported
#
# ── OpenAI-compatible (default, no Prompture needed) ──
LLM_API_KEY=your_api_key_here
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
LLM_MODEL_NAME=qwen-plus
#
# ── With Prompture: use "provider/model" format ──
# LM Studio (free, local):
# LLM_MODEL_NAME=lmstudio/local-model
# LLM_BASE_URL=http://localhost:1234/v1
# LLM_API_KEY=lm-studio
#
# Ollama (free, local):
# LLM_MODEL_NAME=ollama/llama3.1:8b
#
# Kimi / Moonshot:
# LLM_MODEL_NAME=moonshot/moonshot-v1-8k
# LLM_API_KEY=your_moonshot_key
#
# Claude:
# LLM_MODEL_NAME=claude/claude-sonnet-4-20250514
# LLM_API_KEY=sk-ant-...
#
# Groq (fast, free tier):
# LLM_MODEL_NAME=groq/llama-3.1-70b-versatile
# LLM_API_KEY=gsk_...
#
# See all providers: https://github.com/jhd3197/prompture#providers
# ===== ZEP记忆图谱配置 =====
# 每月免费额度即可支撑简单使用：https://app.getzep.com/
# ===== ZEP Memory Graph =====
# Free monthly quota: https://app.getzep.com/
ZEP_API_KEY=your_zep_api_key_here
# ===== 加速 LLM 配置（可选）=====
# 注意如果不使用加速配置，env文件中就不要出现下面的配置项
LLM_BOOST_API_KEY=your_api_key_here
LLM_BOOST_BASE_URL=your_base_url_here
LLM_BOOST_MODEL_NAME=your_model_name_here
# ===== 前端API超时配置（可选）=====
# 本地大模型响应较慢时可以增加此值（毫秒）
# VITE_API_TIMEOUT=600000 # 10分钟
# ===== Boost LLM (optional) =====
# LLM_BOOST_API_KEY=your_api_key_here
# LLM_BOOST_BASE_URL=your_base_url_here
# LLM_BOOST_MODEL_NAME=your_model_name_here
# ===== Frontend API timeout (optional) =====
# Increase this value for slow local LLMs (milliseconds)
# VITE_API_TIMEOUT=600000 # 10 minutes

23
README.md
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@ -127,6 +127,29 @@ LLM_MODEL_NAME=qwen-plus
ZEP_API_KEY=your_zep_api_key
```
#### Multi-Provider Support (Optional)
Install [Prompture](https://github.com/jhd3197/prompture) to unlock 12+ LLM providers beyond OpenAI-compatible APIs:
```bash
pip install prompture
```
Then use `"provider/model"` format in your `.env`:
| Provider | `LLM_MODEL_NAME` | Cost |
|---|---|---|
| LM Studio | `lmstudio/local-model` | Free (local) |
| Ollama | `ollama/llama3.1:8b` | Free (local) |
| OpenAI | `openai/gpt-4o` | Paid |
| Claude | `claude/claude-sonnet-4-20250514` | Paid |
| Kimi / Moonshot | `moonshot/moonshot-v1-8k` | Paid |
| Groq | `groq/llama-3.1-70b-versatile` | Free tier |
| Google | `google/gemini-1.5-pro` | Paid |
| OpenRouter | `openrouter/anthropic/claude-2` | Paid |
> Without Prompture, the original OpenAI SDK backend works as before — no changes needed.
#### 2. Install Dependencies
```bash

219
backend/app/utils/llm_client.py
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@ -1,104 +1,209 @@
"""
LLM客户端封装
统一使用OpenAI格式调用
Supports two backends:
1. Prompture (optional) — 12+ providers: LM Studio, Ollama, Claude, Groq, Kimi, etc.
2. OpenAI SDK (default fallback) — any OpenAI-compatible API
Install Prompture for multi-provider support: pip install prompture
"""
import json
import re
from typing import Optional, Dict, Any, List
from openai import OpenAI
from ..config import Config
# Try to import Prompture; fall back to OpenAI SDK if not installed
try:
from prompture.agents import Conversation
from prompture.infra.provider_env import ProviderEnvironment
from prompture.extraction.tools import strip_think_tags, clean_json_text
_HAS_PROMPTURE = True
except ImportError:
_HAS_PROMPTURE = False
if not _HAS_PROMPTURE:
from openai import OpenAI
# Provider name → ProviderEnvironment field name
_KEY_MAP = {
"openai": "openai_api_key",
"claude": "claude_api_key",
"google": "google_api_key",
"groq": "groq_api_key",
"grok": "grok_api_key",
"openrouter": "openrouter_api_key",
"moonshot": "moonshot_api_key",
}
class LLMClient:
"""LLM客户端"""
"""LLM客户端
When Prompture is installed, ``model`` accepts the ``"provider/model"``
format for multi-provider support::
"lmstudio/local-model" → LM Studio (free, local)
"ollama/llama3.1:8b" → Ollama (free, local)
"openai/gpt-4o" → OpenAI
"claude/claude-sonnet-4-20250514" → Anthropic
"moonshot/moonshot-v1-8k" → Kimi / Moonshot
"groq/llama-3.1-70b" → Groq
Without Prompture, the original OpenAI SDK backend is used (any
OpenAI-compatible API via LLM_BASE_URL).
"""
def __init__(
self,
api_key: Optional[str] = None,
base_url: Optional[str] = None,
model: Optional[str] = None
model: Optional[str] = None,
):
self.api_key = api_key or Config.LLM_API_KEY
self.base_url = base_url or Config.LLM_BASE_URL
self.model = model or Config.LLM_MODEL_NAME
if _HAS_PROMPTURE:
self._init_prompture()
else:
self._init_openai()
# ── Prompture backend ──────────────────────────────────────────
def _init_prompture(self):
env_kwargs: Dict[str, Any] = {}
if self.api_key:
provider = self.model.split("/")[0] if "/" in self.model else "openai"
env_field = _KEY_MAP.get(provider)
if env_field:
env_kwargs[env_field] = self.api_key
self._env = ProviderEnvironment(**env_kwargs) if env_kwargs else None
self._driver_options: Dict[str, Any] = {}
if self.base_url:
self._driver_options["base_url"] = self.base_url
def _make_conversation(self, temperature: float, max_tokens: int) -> "Conversation":
opts: Dict[str, Any] = {
"temperature": temperature,
"max_tokens": max_tokens,
**self._driver_options,
}
return Conversation(self.model, options=opts, env=self._env)
# ── OpenAI fallback backend ────────────────────────────────────
def _init_openai(self):
if not self.api_key:
raise ValueError("LLM_API_KEY 未配置")
self.client = OpenAI(
api_key=self.api_key,
base_url=self.base_url
)
self.client = OpenAI(api_key=self.api_key, base_url=self.base_url)
# ── Public API ─────────────────────────────────────────────────
def chat(
self,
messages: List[Dict[str, str]],
temperature: float = 0.7,
max_tokens: int = 4096,
response_format: Optional[Dict] = None
response_format: Optional[Dict] = None,
) -> str:
"""
发送聊天请求
Args:
messages: 消息列表
temperature: 温度参数
max_tokens: 最大token数
response_format: 响应格式（如JSON模式）
Returns:
模型响应文本
"""
if _HAS_PROMPTURE:
content = self._chat_prompture(messages, temperature, max_tokens)
return strip_think_tags(content)
else:
content = self._chat_openai(messages, temperature, max_tokens, response_format)
# Fallback: strip think tags with regex when Prompture is not available
return re.sub(r'<think>[\s\S]*?</think>', '', content).strip()
def chat_json(
self,
messages: List[Dict[str, str]],
temperature: float = 0.3,
max_tokens: int = 4096,
) -> Dict[str, Any]:
"""
发送聊天请求并返回JSON
Args:
messages: 消息列表
temperature: 温度参数
max_tokens: 最大token数
Returns:
解析后的JSON对象
"""
if _HAS_PROMPTURE:
response = self._chat_prompture(messages, temperature, max_tokens)
# Prompture's clean_json_text strips think tags + markdown fences
cleaned = clean_json_text(response)
else:
response = self._chat_openai(
messages, temperature, max_tokens
)
# Fallback cleaning when Prompture is not available
cleaned = re.sub(r'<think>[\s\S]*?</think>', '', response).strip()
cleaned = re.sub(r'^```(?:json)?\s*\n?', '', cleaned, flags=re.IGNORECASE)
cleaned = re.sub(r'\n?```\s*$', '', cleaned)
cleaned = cleaned.strip()
try:
return json.loads(cleaned)
except json.JSONDecodeError:
raise ValueError(f"LLM返回的JSON格式无效: {cleaned}")
# ── Private: Prompture path ────────────────────────────────────
def _chat_prompture(
self,
messages: List[Dict[str, str]],
temperature: float,
max_tokens: int,
) -> str:
conv = self._make_conversation(temperature, max_tokens)
# Inject system prompt
system_parts = [m["content"] for m in messages if m["role"] == "system"]
if system_parts:
conv._messages.append({"role": "system", "content": "\n".join(system_parts)})
# Replay prior turns
non_system = [m for m in messages if m["role"] != "system"]
for msg in non_system[:-1]:
conv._messages.append({"role": msg["role"], "content": msg["content"]})
prompt = non_system[-1]["content"] if non_system else ""
return conv.ask(prompt)
# ── Private: OpenAI fallback path ──────────────────────────────
def _chat_openai(
self,
messages: List[Dict[str, str]],
temperature: float,
max_tokens: int,
response_format: Optional[Dict] = None,
) -> str:
kwargs = {
"model": self.model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens,
}
if response_format:
kwargs["response_format"] = response_format
response = self.client.chat.completions.create(**kwargs)
content = response.choices[0].message.content
# 部分模型（如MiniMax M2.5）会在content中包含<think>思考内容，需要移除
content = re.sub(r'<think>[\s\S]*?</think>', '', content).strip()
return content
def chat_json(
self,
messages: List[Dict[str, str]],
temperature: float = 0.3,
max_tokens: int = 4096
) -> Dict[str, Any]:
"""
发送聊天请求并返回JSON
Args:
messages: 消息列表
temperature: 温度参数
max_tokens: 最大token数
Returns:
解析后的JSON对象
"""
response = self.chat(
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
# 不設 response_format 以相容 LM Studio / Ollama 等本地模型
# 依賴 prompt 中的 JSON 指示 + 下方的 markdown 清理邏輯
)
# 清理markdown代码块标记
cleaned_response = response.strip()
cleaned_response = re.sub(r'^```(?:json)?\s*\n?', '', cleaned_response, flags=re.IGNORECASE)
cleaned_response = re.sub(r'\n?```\s*$', '', cleaned_response)
cleaned_response = cleaned_response.strip()
try:
return json.loads(cleaned_response)
except json.JSONDecodeError:
raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response}")
return response.choices[0].message.content

7
backend/requirements.txt
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@ -10,9 +10,14 @@ flask>=3.0.0
flask-cors>=6.0.0
# ============= LLM 相关 =============
# OpenAI SDK（统一使用 OpenAI 格式调用 LLM）
# OpenAI SDK（默认 LLM 后端）
openai>=1.0.0
# Prompture（可选）— 多供应商 LLM 支持：LM Studio, Ollama, Claude, Groq, Kimi 等
# Install for multi-provider support: pip install prompture
# https://github.com/jhd3197/prompture
# prompture>=0.1.0
# ============= Zep Cloud =============
zep-cloud==3.13.0

68
backend/scripts/test_lmstudio.py Normal file
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@ -0,0 +1,68 @@
"""
Quick test: MiroFish LLMClient → LM Studio via Prompture
"""
import sys, os
# Add backend to path so we can import app modules
sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
# Override env vars for LM Studio before Config loads
os.environ["LLM_MODEL_NAME"] = "lmstudio/deepseek/deepseek-r1-0528-qwen3-8b"
os.environ["LLM_BASE_URL"] = "http://localhost:1234/v1"
os.environ["LLM_API_KEY"] = "lm-studio"
# Provide a dummy ZEP key so Config.validate() won't complain
os.environ.setdefault("ZEP_API_KEY", "dummy")
from app.utils.llm_client import LLMClient
def test_basic_chat():
print("=== Test 1: Basic chat ===")
client = LLMClient()
from app.utils.llm_client import _HAS_PROMPTURE
print(f" Backend: Prompture={_HAS_PROMPTURE}")
print(f" Model: {client.model}")
response = client.chat([
{"role": "system", "content": "You are a helpful assistant. Reply in one sentence."},
{"role": "user", "content": "What is social media simulation?"},
], temperature=0.5, max_tokens=256)
print(f" Response: {response[:300]}")
print()
def test_json_chat():
print("=== Test 2: JSON response ===")
client = LLMClient()
result = client.chat_json([
{"role": "system", "content": "You are a JSON-only assistant. Always respond with valid JSON."},
{"role": "user", "content": 'Return a JSON object with keys "platform" and "agents" (an integer). Example: {"platform":"twitter","agents":5}'},
], temperature=0.2, max_tokens=256)
print(f" Parsed JSON: {result}")
print(f" Type: {type(result)}")
print()
def test_multi_turn():
print("=== Test 3: Multi-turn conversation ===")
client = LLMClient()
r1 = client.chat([
{"role": "user", "content": "My name is MiroFish. Remember it."},
], max_tokens=128)
print(f" Turn 1: {r1[:200]}")
r2 = client.chat([
{"role": "user", "content": "My name is MiroFish. Remember it."},
{"role": "assistant", "content": r1},
{"role": "user", "content": "What is my name?"},
], max_tokens=128)
print(f" Turn 2: {r2[:200]}")
print()
if __name__ == "__main__":
print(f"Prompture installed: True")
print(f"LM Studio endpoint: http://localhost:1234/v1\n")
try:
test_basic_chat()
test_json_chat()
test_multi_turn()
print("All tests passed!")
except Exception as e:
print(f"ERROR: {e}")
import traceback; traceback.print_exc()