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feat: multi-provider LLM support via Prompture 

Add optional Prompture integration for 12+ LLM providers (LM Studio,
Ollama, Claude, Groq, Kimi/Moonshot, etc.) as a drop-in backend.
Zero breaking changes — falls back to the existing OpenAI SDK client
when Prompture is not installed.

- Rewrite llm_client.py with dual-backend architecture
- Update .env.example with provider/model format examples
- Add multi-provider table to README Quick Start section
- Add prompture as optional dependency in requirements.txt

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Juan Denis 2026-04-04 01:18:04 -04:00
parent fa0f6519b1
commit 79edc61563
4 changed files with 225 additions and 67 deletions
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44
.env.example
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@ -1,16 +1,40 @@
# 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
# ===== 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

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

218
backend/app/utils/llm_client.py
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@ -1,103 +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
_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)
else:
content = self._chat_openai(messages, temperature, max_tokens, response_format)
# 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
"""
if _HAS_PROMPTURE:
response = self._chat_prompture(messages, temperature, max_tokens)
else:
response = self._chat_openai(
messages, temperature, max_tokens,
response_format={"type": "json_object"},
)
# 清理markdown代码块标记
cleaned = response.strip()
cleaned = re.sub(r'^```(?:json)?\s*\n?', '', cleaned, flags=re.IGNORECASE)
cleaned = re.sub(r'\n?```\s*$', '', cleaned)
cleaned = cleaned.strip()
try:
return json.loads(cleaned)
except json.JSONDecodeError:
raise ValueError(f"LLM返回的JSON格式无效: {cleaned}")
# ── Private: Prompture path ────────────────────────────────────
def _chat_prompture(
self,
messages: List[Dict[str, str]],
temperature: float,
max_tokens: int,
) -> str:
conv = self._make_conversation(temperature, max_tokens)
# Inject system prompt
system_parts = [m["content"] for m in messages if m["role"] == "system"]
if system_parts:
conv._messages.append({"role": "system", "content": "\n".join(system_parts)})
# Replay prior turns
non_system = [m for m in messages if m["role"] != "system"]
for msg in non_system[:-1]:
conv._messages.append({"role": msg["role"], "content": msg["content"]})
prompt = non_system[-1]["content"] if non_system else ""
return conv.ask(prompt)
# ── Private: OpenAI fallback path ──────────────────────────────
def _chat_openai(
self,
messages: List[Dict[str, str]],
temperature: float,
max_tokens: int,
response_format: Optional[Dict] = None,
) -> str:
kwargs = {
"model": self.model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens,
}
if response_format:
kwargs["response_format"] = response_format
response = self.client.chat.completions.create(**kwargs)
content = response.choices[0].message.content
# 部分模型（如MiniMax M2.5）会在content中包含<think>思考内容，需要移除
content = re.sub(r'<think>[\s\S]*?</think>', '', content).strip()
return content
def chat_json(
self,
messages: List[Dict[str, str]],
temperature: float = 0.3,
max_tokens: int = 4096
) -> Dict[str, Any]:
"""
发送聊天请求并返回JSON
Args:
messages: 消息列表
temperature: 温度参数
max_tokens: 最大token数
Returns:
解析后的JSON对象
"""
response = self.chat(
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
response_format={"type": "json_object"}
)
# 清理markdown代码块标记
cleaned_response = response.strip()
cleaned_response = re.sub(r'^```(?:json)?\s*\n?', '', cleaned_response, flags=re.IGNORECASE)
cleaned_response = re.sub(r'\n?```\s*$', '', 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