4211e587ee
- Add qa_models.py with TextQAItem, GenTextQARequest, QAPair, ImageQAItem, GenImageQARequest, ImageQAPair, TextQAResponse, ImageQAResponse - Implement gen_text_qa(): batch-formats triples into a single prompt, calls llm.chat(), parses JSON array via extract_json() - Implement gen_image_qa(): downloads cropped image from source-data bucket, base64-encodes inline (data URI), builds multimodal message, calls llm.chat_vision(), parses JSON; image_path preserved on ImageQAPair - Replace qa.py stub with full router: POST /qa/gen-text and /qa/gen-image using Depends(get_llm_client) and Depends(get_storage_client) - 15 new tests (8 service + 7 router), 53/53 total passing
107 lines
3.4 KiB
Python
107 lines
3.4 KiB
Python
import base64
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from app.clients.llm.base import LLMClient
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from app.clients.storage.base import StorageClient
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from app.core.config import get_config
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from app.core.json_utils import extract_json
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from app.core.logging import get_logger
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from app.models.qa_models import (
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GenImageQARequest,
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GenTextQARequest,
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ImageQAPair,
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ImageQAResponse,
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QAPair,
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TextQAResponse,
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)
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logger = get_logger(__name__)
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_DEFAULT_TEXT_PROMPT = (
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"请根据以下知识三元组生成问答对，以 JSON 数组格式返回，每条包含 question 和 answer 字段。\n\n"
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"三元组列表：\n{triples_text}"
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)
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_DEFAULT_IMAGE_PROMPT = (
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"请根据图片内容和以下四元组信息生成问答对，以 JSON 数组格式返回，每条包含 question 和 answer 字段。"
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)
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async def gen_text_qa(req: GenTextQARequest, llm: LLMClient) -> TextQAResponse:
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cfg = get_config()
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model = req.model or cfg["models"]["default_text"]
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# Format all triples + source snippets into a single batch prompt
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triple_lines: list[str] = []
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for item in req.items:
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triple_lines.append(
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f"({item.subject}, {item.predicate}, {item.object}) — 来源: {item.source_snippet}"
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)
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triples_text = "\n".join(triple_lines)
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prompt_template = req.prompt_template or _DEFAULT_TEXT_PROMPT
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if "{triples_text}" in prompt_template:
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prompt = prompt_template.format(triples_text=triples_text)
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else:
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prompt = prompt_template + "\n\n" + triples_text
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messages = [{"role": "user", "content": prompt}]
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raw = await llm.chat(model, messages)
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logger.info("gen_text_qa", extra={"items": len(req.items), "model": model})
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items_raw = extract_json(raw)
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pairs = [QAPair(question=item["question"], answer=item["answer"]) for item in items_raw]
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return TextQAResponse(pairs=pairs)
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async def gen_image_qa(
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req: GenImageQARequest,
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llm: LLMClient,
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storage: StorageClient,
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) -> ImageQAResponse:
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cfg = get_config()
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bucket = cfg["storage"]["buckets"]["source_data"]
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model = req.model or cfg["models"]["default_vision"]
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prompt = req.prompt_template or _DEFAULT_IMAGE_PROMPT
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pairs: list[ImageQAPair] = []
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for item in req.items:
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# Download cropped image bytes from storage
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image_bytes = await storage.download_bytes(bucket, item.cropped_image_path)
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# Base64 encode inline for multimodal message
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b64 = base64.b64encode(image_bytes).decode()
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image_data_url = f"data:image/jpeg;base64,{b64}"
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# Build quad info text
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quad_text = f"{item.subject} — {item.predicate} — {item.object}"
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if item.qualifier:
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quad_text += f" ({item.qualifier})"
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image_url", "image_url": {"url": image_data_url}},
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{"type": "text", "text": f"{prompt}\n\n{quad_text}"},
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],
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}
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]
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raw = await llm.chat_vision(model, messages)
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logger.info("gen_image_qa", extra={"path": item.cropped_image_path, "model": model})
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items_raw = extract_json(raw)
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for qa in items_raw:
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pairs.append(
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ImageQAPair(
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question=qa["question"],
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answer=qa["answer"],
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image_path=item.cropped_image_path,
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)
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)
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return ImageQAResponse(pairs=pairs)
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