a79efd8150
- Backend: Introduced new endpoints for LLM configuration retrieval and updates in `config.py`, allowing dynamic management of LLM provider settings. - Updated schemas to include `AlphaEngineConfig` for better integration with the new provider. - Frontend: Added state management for AlphaEngine API credentials in the configuration page, ensuring seamless user experience. - Configuration files updated to reflect changes in LLM provider settings and API keys. BREAKING CHANGE: The default LLM provider has been changed from `new_api` to `alpha_engine`, requiring updates to existing configurations.
261 lines
9.2 KiB
Python
261 lines
9.2 KiB
Python
"""
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AlphaEngine Client for investment Q&A API
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"""
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import json
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import re
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import time
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from typing import Dict, Optional, AsyncGenerator
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import httpx
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from requests.exceptions import ChunkedEncodingError
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class AlphaEngineClient:
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"""Client for AlphaEngine investment Q&A API"""
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def __init__(
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self,
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api_url: str,
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api_key: str,
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token: str,
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user_id: int = 999041,
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model: str = "deepseek-r1",
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using_indicator: bool = True,
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start_time: str = "2024-01-01",
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doc_show_type: list = None,
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simple_tracking: bool = True
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):
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"""
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Initialize AlphaEngine client
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Args:
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api_url: API endpoint URL
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api_key: X-API-KEY for authentication
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token: Token for authentication
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user_id: User ID
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model: Model name (default: deepseek-r1)
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using_indicator: Whether to use indicators
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start_time: Start time for data query
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doc_show_type: Document types to show (default: ["A001", "A002", "A003", "A004"])
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simple_tracking: Whether to enable simple tracking
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"""
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self.api_url = api_url.rstrip('/')
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self.api_key = api_key
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self.token = token
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self.user_id = user_id
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self.model = model
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self.using_indicator = using_indicator
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self.start_time = start_time
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self.doc_show_type = doc_show_type or ["A001", "A002", "A003", "A004"]
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self.simple_tracking = simple_tracking
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async def generate_analysis(
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self,
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analysis_type: str,
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company_name: str,
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ts_code: str,
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prompt_template: str,
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financial_data: Optional[Dict] = None,
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context: Optional[Dict] = None
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) -> Dict:
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"""
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Generate analysis using AlphaEngine API (non-streaming)
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Args:
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analysis_type: Type of analysis
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company_name: Company name
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ts_code: Stock code
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prompt_template: Prompt template with placeholders
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financial_data: Optional financial data for context
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context: Optional dictionary with results from previous analyses
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Returns:
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Dict with analysis content and metadata
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"""
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start_time = time.perf_counter_ns()
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# Build prompt from template
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prompt = self._build_prompt(
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prompt_template,
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company_name,
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ts_code,
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financial_data,
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context
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)
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# Call AlphaEngine API
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try:
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async with httpx.AsyncClient(timeout=300.0) as client:
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headers = {
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'token': self.token,
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'X-API-KEY': self.api_key,
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'Content-Type': 'application/json'
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}
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payload = {
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"msg": prompt,
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"history": [],
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"user_id": self.user_id,
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"model": self.model,
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"using_indicator": self.using_indicator,
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"start_time": self.start_time,
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"doc_show_type": self.doc_show_type,
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"simple_tracking": self.simple_tracking
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}
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response = await client.post(
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f"{self.api_url}/api/v3/finchat",
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json=payload,
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headers=headers
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)
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if response.status_code != 200:
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raise Exception(f"AlphaEngine API error: HTTP {response.status_code} - {response.text}")
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result_text = response.text
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# Parse response to extract final answer
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final_answer_match = re.findall(r'\{"id":"_final","content":"(.*?)"}', result_text)
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final_answer = final_answer_match[0] if final_answer_match else result_text
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# Extract COT if available
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cot_match = re.findall(r'\{"id":"_cot","content":"(.*?)"}', result_text)
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cot = "".join(cot_match) if cot_match else ""
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# Extract tracking documents if available
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tracking_match = re.findall(r'\{"id":"tracking_documents","content":\s*(\[[^]]*])}', result_text)
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tracking_docs = json.loads(tracking_match[0]) if tracking_match else []
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elapsed_ms = int((time.perf_counter_ns() - start_time) / 1_000_000)
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return {
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"content": final_answer,
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"model": self.model,
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"tokens": {
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"prompt_tokens": 0, # AlphaEngine doesn't provide token usage
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"completion_tokens": 0,
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"total_tokens": 0,
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},
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"elapsed_ms": elapsed_ms,
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"success": True,
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"analysis_type": analysis_type,
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"cot": cot,
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"tracking_documents": tracking_docs,
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}
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except Exception as e:
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elapsed_ms = int((time.perf_counter_ns() - start_time) / 1_000_000)
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return {
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"content": "",
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"model": self.model,
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"tokens": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
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"elapsed_ms": elapsed_ms,
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"success": False,
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"error": str(e),
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"analysis_type": analysis_type,
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}
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async def generate_analysis_stream(
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self,
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analysis_type: str,
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company_name: str,
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ts_code: str,
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prompt_template: str,
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financial_data: Optional[Dict] = None,
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context: Optional[Dict] = None
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) -> AsyncGenerator[str, None]:
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"""
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Yield analysis content chunks using AlphaEngine streaming API
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Yields plain text chunks as they arrive.
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"""
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# Build prompt
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prompt = self._build_prompt(
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prompt_template,
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company_name,
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ts_code,
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financial_data,
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context,
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)
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try:
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async with httpx.AsyncClient(timeout=300.0) as client:
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headers = {
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'token': self.token,
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'X-API-KEY': self.api_key,
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'Content-Type': 'application/json'
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}
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payload = {
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"msg": prompt,
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"history": [],
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"user_id": self.user_id,
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"model": self.model,
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"using_indicator": self.using_indicator,
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"start_time": self.start_time,
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"doc_show_type": self.doc_show_type,
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"simple_tracking": self.simple_tracking
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}
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async with client.stream(
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"POST",
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f"{self.api_url}/api/v3/finchat",
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json=payload,
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headers=headers
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) as response:
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if response.status_code != 200:
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yield f"\n\n[错误] HTTP {response.status_code}: {response.text}\n"
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return
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async for chunk in response.aiter_bytes(chunk_size=128):
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try:
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chunk_text = chunk.decode('utf-8', 'ignore')
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yield chunk_text
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except UnicodeDecodeError:
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chunk_text = chunk.decode('utf-8', 'replace')
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yield chunk_text
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except Exception as e:
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yield f"\n\n[错误] {type(e).__name__}: {str(e)}\n"
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def _build_prompt(
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self,
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prompt_template: str,
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company_name: str,
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ts_code: str,
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financial_data: Optional[Dict] = None,
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context: Optional[Dict] = None
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) -> str:
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"""Build prompt from template by replacing placeholders"""
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import string
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# Start with base placeholders
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placeholders = {
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"company_name": company_name,
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"ts_code": ts_code,
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}
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# Add financial data if provided
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financial_data_str = ""
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if financial_data:
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try:
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financial_data_str = json.dumps(financial_data, ensure_ascii=False, indent=2)
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except Exception:
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financial_data_str = str(financial_data)
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placeholders["financial_data"] = financial_data_str
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# Add context from previous analysis steps
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if context:
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placeholders.update(context)
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# Replace placeholders in template
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class SafeFormatter(string.Formatter):
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def get_value(self, key, args, kwargs):
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if isinstance(key, str):
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return kwargs.get(key, f"{{{key}}}")
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else:
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return super().get_value(key, args, kwargs)
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formatter = SafeFormatter()
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prompt = formatter.format(prompt_template, **placeholders)
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return prompt
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