66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
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import json
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import ai_client
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import mma_prompts
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import aggregate
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import summarize
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from pathlib import Path
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def generate_tracks(user_request: str, project_config: dict, file_items: list[dict]) -> list[dict]:
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"""
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Tier 1 (Strategic PM) call.
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Analyzes the project state and user request to generate a list of Tracks.
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"""
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# 1. Build Repository Map (Summary View)
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repo_map = summarize.build_summary_markdown(file_items)
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# 2. Construct Prompt
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system_prompt = mma_prompts.PROMPTS.get("tier1_epic_init")
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user_message = (
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f"### USER REQUEST:
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{user_request}
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"
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f"### REPOSITORY MAP:
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{repo_map}
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"
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"Please generate the implementation tracks for this request."
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)
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# 3. Call Tier 1 Model (Strategic - Pro)
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# Note: We use gemini-1.5-pro or similar high-reasoning model for Tier 1
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response = ai_client.send(
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md_content="", # We pass everything in user_message for clarity
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user_message=user_message,
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system_prompt=system_prompt,
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model_name="gemini-1.5-pro" # Strategic Tier
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)
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# 4. Parse JSON Output
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try:
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# The prompt asks for a JSON array. We need to extract it if the AI added markdown blocks.
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json_match = response.strip()
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if "```json" in json_match:
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json_match = json_match.split("```json")[1].split("```")[0].strip()
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elif "```" in json_match:
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json_match = json_match.split("```")[1].split("```")[0].strip()
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tracks = json.loads(json_match)
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return tracks
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except Exception as e:
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print(f"Error parsing Tier 1 response: {e}")
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print(f"Raw response: {response}")
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return []
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if __name__ == "__main__":
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# Quick CLI test
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import project_manager
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proj = project_manager.load_project("manual_slop.toml")
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flat = project_manager.flat_config(proj)
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file_items = aggregate.build_file_items(Path("."), flat.get("files", {}).get("paths", []))
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print("Testing Tier 1 Track Generation...")
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tracks = generate_tracks("Implement a basic unit test for the ai_client.py module.", flat, file_items)
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print(json.dumps(tracks, indent=2))
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