a280217254
- Create MiniMax LLM connector for CrewAI integration - Implement TradingCrew with trading_designer, strategy_validator, strategy_explainer - Add strategy parsing from natural language to strategy_config JSON - Update chat endpoint with CrewAI integration and conversation context - Add strategy validation logic - Add explanation generation for user-friendly responses - Add BotChatRequest/BotChatResponse schemas Fixes #6
109 lines
3.7 KiB
Python
109 lines
3.7 KiB
Python
from typing import Optional, List, Dict, Any
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import httpx
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from crewai import LLM
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class MiniMaxLLM(LLM):
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def __init__(self, api_key: str, model: str = "MiniMax-Text-01", **kwargs):
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super().__init__(**kwargs)
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self.api_key = api_key
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self.model = model
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self.base_url = "https://api.minimax.chat/v1"
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def _call(self, messages: List[Dict[str, str]], **kwargs) -> str:
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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payload = {
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"model": self.model,
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"messages": messages,
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"temperature": kwargs.get("temperature", 0.7),
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"max_tokens": kwargs.get("max_tokens", 2048),
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}
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with httpx.Client(timeout=60.0) as client:
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response = client.post(
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f"{self.base_url}/chat/completions",
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headers=headers,
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json=payload,
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)
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response.raise_for_status()
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return response.json()["choices"][0]["message"]["content"]
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def call(self, messages: List[Dict[str, str]], **kwargs) -> str:
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return self._call(messages, **kwargs)
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class MiniMaxConnector:
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def __init__(self, api_key: str, model: str = "MiniMax-Text-01"):
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self.api_key = api_key
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self.model = model
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def chat(self, messages: list[dict], **kwargs) -> str:
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formatted_messages = []
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for msg in messages:
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if isinstance(msg, dict):
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formatted_messages.append(
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{
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"role": msg.get("role", "user"),
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"content": msg.get("content", str(msg)),
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}
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)
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else:
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formatted_messages.append({"role": "user", "content": str(msg)})
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llm = MiniMaxLLM(api_key=self.api_key, model=self.model)
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return llm.call(formatted_messages, **kwargs)
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def parse_strategy(
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self, user_message: str, conversation_history: list[dict] = None
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) -> dict:
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system_prompt = """You are a trading strategy designer. Parse the user's natural language request into a JSON strategy_config object.
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Supported conditions (MVP):
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- price_drop: Token price drops by X% (requires: token, threshold_percent)
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- price_rise: Token price rises by X% (requires: token, threshold_percent)
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- volume_spike: Trading volume increases X% (requires: token, threshold_percent)
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- price_level: Price crosses above/below X (requires: token, price, direction)
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Output ONLY valid JSON with this schema:
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{
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"conditions": [
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{
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"type": "price_drop|price_rise|volume_spike|price_level",
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"params": {
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"token": "TOKEN_SYMBOL",
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"threshold_percent": number, // for price_drop, price_rise, volume_spike
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"price": number, // for price_level
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"direction": "above|below" // for price_level
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}
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}
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],
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"actions": [
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{
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"type": "buy|sell|notify",
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"params": {}
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}
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]
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}
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If the user wants a condition not in the supported list, ask for clarification.
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"""
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messages = [{"role": "system", "content": system_prompt}]
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if conversation_history:
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for msg in conversation_history:
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messages.append(
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{"role": msg.get("role", "user"), "content": msg.get("content", "")}
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)
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messages.append({"role": "user", "content": user_message})
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response = self.chat(messages)
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try:
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import json
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result = json.loads(response)
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return result
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except json.JSONDecodeError:
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return {"error": "Failed to parse strategy", "raw_response": response}
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