ydy0615
2abf276d10
This commit refactors the LLM integration to use Ollama's native Python client instead of OpenAI-compatible API, while fixing critical template syntax errors and improving project structure. Key changes: - Replace openai package with ollama package in backend requirements - Rewrite llm.py to use ollama.AsyncClient for direct Ollama API calls - Update main.py to use non-streaming Ollama responses with thinking extraction - Fix template syntax error in MilkdownEditor.vue (GhostTextOverlay component tags) - Fix string截取错误 by using slice() instead of substring() - Add src/utils/api.js and src/utils/config.js for shared configuration - Add CORS middleware to FastAPI backend - Update prompt.py with clearer instructions for continuation generation - Add comprehensive README.md documentation BREAKING CHANGE: Environment variables OLLAMA_BASE_URL changed to OLLAMA_HOST (remove /v1/ suffix)
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
import os
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import json
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import ollama
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from typing import AsyncGenerator
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OLLAMA_MODEL = os.getenv('OLLAMA_MODEL', 'gpt-oss:120b')
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OLLAMA_HOST = os.getenv('OLLAMA_BASE_URL', 'http://192.168.0.120:11434')
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# 移除 /v1/ 后缀（如果有的话），因为 Ollama Python 包使用原生 API
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if OLLAMA_HOST.endswith('/v1/'):
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OLLAMA_HOST = OLLAMA_HOST[:-4]
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elif OLLAMA_HOST.endswith('/v1'):
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OLLAMA_HOST = OLLAMA_HOST[:-3]
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os.environ['OLLAMA_HOST'] = OLLAMA_HOST
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print(f"[LLM] Ollama host: {OLLAMA_HOST}")
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print(f"[LLM] Model: {OLLAMA_MODEL}")
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client = ollama.AsyncClient(host=OLLAMA_HOST)
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async def stream_openai(prompt: str) -> AsyncGenerator[str, None]:
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print(f"[LLM] Calling Ollama API with prompt length: {len(prompt)}")
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try:
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print(f"[LLM] Awaiting client.chat...")
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stream = await client.chat(
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model=OLLAMA_MODEL,
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messages=[{'role': 'user', 'content': prompt}],
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stream=True,
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options={
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'num_predict': 8192,
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'temperature': 0.2,
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}
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)
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print(f"[LLM] Got stream object, starting iteration...")
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chunk_count = 0
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async for chunk in stream:
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if chunk['message'] and chunk['message']['content']:
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content = chunk['message']['content']
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chunk_count += 1
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print(f"[LLM] Chunk {chunk_count}: {content}")
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yield json.dumps({"content": content})
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print(f"[LLM] Stream complete, total chunks: {chunk_count}")
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except Exception as e:
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error_msg = f"Error: {str(e)}"
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print(f"[LLM] Error: {error_msg}")
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import traceback
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traceback.print_exc()
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yield json.dumps({"error": str(e)})
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