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feat: integrate dots.ocr native prompt modes and 2-stage qa_pairs pipeline

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Prompt Modes Integration:
- Create local_runtime.py with DOTS_PROMPT_MAP
- Map OutputMode to native dots.ocr prompt modes (prompt_layout_all_en, prompt_ocr, etc.)
- Support dict_promptmode_to_prompt from dots.ocr with fallback prompts
- Add layout_only and region modes to OutputMode enum

2-Stage Q&A Pipeline:
- Create qa_builder.py for 2-stage qa_pairs generation
- Stage 1: PARSER (dots.ocr) → raw JSON via prompt_layout_all_en
- Stage 2: LLM (DAGI Router) → Q&A pairs via mode=qa_build
- Update endpoints.py to use 2-stage pipeline for qa_pairs mode
- Add ROUTER_BASE_URL and ROUTER_TIMEOUT to config

Updates:
- Update inference.py to use local_runtime with native prompts
- Update ollama_client.py to use same prompt map
- Add PROMPT_MODES.md documentation
This commit is contained in:
Apple
2025-11-16 04:24:03 -08:00
parent d474a085c3
commit be22752590
8 changed files with 714 additions and 44 deletions
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158
services/parser-service/PROMPT_MODES.md Normal file
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@@ -0,0 +1,158 @@
# Prompt Modes Integration - dots.ocr Native Modes
Цей документ описує інтеграцію режимів `OutputMode` в `parser-service` з нативними prompt-модами `dots.ocr`.
---
## 📋 Мапа режимів
### Базові режими
| OutputMode | dots.ocr Prompt Mode | Опис |
|------------|---------------------|------|
| `raw_json` | `prompt_layout_all_en` | Повний JSON (layout + контент) |
| `markdown` | `prompt_ocr` | Текст/таблиці в Markdown |
| `qa_pairs` | `prompt_layout_all_en` | Повний JSON, потім 2-й крок LLM |
| `chunks` | `prompt_layout_all_en` | Повний JSON для chunking |
| `layout_only` | `prompt_layout_only_en` | Тільки layout (bbox + категорії, без тексту) |
| `region` | `prompt_grounding_ocr` | Таргетований парсинг регіону |
---
## 🔄 Архітектура
### 1. Local Runtime (`local_runtime.py`)
Використовує нативні prompt-режими `dots.ocr` через `dict_promptmode_to_prompt`:
```python
from dots_ocr.utils.prompts import dict_promptmode_to_prompt
DOTS_PROMPT_MAP = {
"raw_json": "prompt_layout_all_en",
"markdown": "prompt_ocr",
"qa_pairs": "prompt_layout_all_en",
# ...
}
def _build_prompt(output_mode: str) -> str:
prompt_key = DOTS_PROMPT_MAP.get(output_mode, "prompt_layout_all_en")
return dict_promptmode_to_prompt[prompt_key]
```
**Fallback:** Якщо `dict_promptmode_to_prompt` недоступний, використовуються fallback-промпти.
---
### 2. Ollama Runtime (`ollama_client.py`)
Використовує ту саму мапу prompt-режимів, але з fallback-промптами (оскільки Ollama не має прямого доступу до `dict_promptmode_to_prompt`).
---
### 3. 2-стадійний пайплайн `qa_pairs`
**Stage 1: PARSER (dots.ocr)**
- Використовує `prompt_layout_all_en` → повний JSON
**Stage 2: LLM (DAGI Router)**
- Бере parsed JSON
- Генерує Q&A пари через `qa_builder.py`
- Викликає DAGI Router з `mode="qa_build"`
```python
# Stage 1: PARSER
parsed_doc = parse_document_from_images(images, output_mode="qa_pairs")
# Stage 2: LLM
qa_pairs = await build_qa_pairs_via_router(parsed_doc, dao_id)
```
---
## 🎯 Використання
### Приклад 1: Raw JSON (layout + content)
```python
# POST /ocr/parse
{
"output_mode": "raw_json"
}
# Використовує: prompt_layout_all_en
# Результат: ParsedDocument з повним JSON
```
### Приклад 2: Markdown
```python
# POST /ocr/parse
{
"output_mode": "markdown"
}
# Використовує: prompt_ocr
# Результат: Markdown текст
```
### Приклад 3: Q&A Pairs (2-stage)
```python
# POST /ocr/parse_qa
{
"dao_id": "daarion"
}
# Stage 1: prompt_layout_all_en → ParsedDocument
# Stage 2: DAGI Router → QAPair[]
```
### Приклад 4: Layout Only
```python
# POST /ocr/parse
{
"output_mode": "layout_only"
}
# Використовує: prompt_layout_only_en
# Результат: ParsedDocument з тільки layout (без тексту)
```
---
## 🔧 Конфігурація
### Environment Variables
```bash
# DAGI Router (для qa_pairs 2-stage pipeline)
ROUTER_BASE_URL=http://router:9102
ROUTER_TIMEOUT=60
# dots.ocr model
PARSER_MODEL_NAME=rednote-hilab/dots.ocr
PARSER_DEVICE=cuda # or cpu, mps
```
---
## 📝 Примітки
1. **qa_pairs композитний режим**: У `dots.ocr` немає нативного `qa_pairs` prompt-моду. Тому використовується 2-стадійний пайплайн.
2. **Fallback промпти**: Якщо `dict_promptmode_to_prompt` недоступний, використовуються fallback-промпти з `local_runtime.py`.
3. **Ollama runtime**: Ollama використовує fallback-промпти, оскільки не має прямого доступу до `dict_promptmode_to_prompt`.
4. **DAGI Router integration**: Для `qa_pairs` потрібен доступ до DAGI Router з `mode="qa_build"` (потрібно додати в `router-config.yml`).
---
## 🚀 Наступні кроки
1. Додати `mode="qa_build"` в DAGI Router (`router-config.yml`)
2. Тестувати 2-стадійний пайплайн `qa_pairs`
3. Додати підтримку `region` mode (grounding OCR)

34
services/parser-service/app/api/endpoints.py
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@@ -21,6 +21,7 @@ from app.runtime.preprocessing import (
from app.runtime.postprocessing import (
build_chunks, build_qa_pairs, build_markdown
)
from app.runtime.qa_builder import build_qa_pairs_via_router
logger = logging.getLogger(__name__)
@@ -120,9 +121,27 @@ async def parse_document_endpoint(
elif output_mode == "markdown":
response_data["markdown"] = build_markdown(parsed_doc)
elif output_mode == "qa_pairs":
response_data["qa_pairs"] = build_qa_pairs(parsed_doc)
# 2-stage pipeline: PARSER → LLM (DAGI Router)
logger.info("Starting 2-stage Q&A pipeline: PARSER → LLM")
try:
qa_pairs = await build_qa_pairs_via_router(
parsed_doc=parsed_doc,
dao_id=dao_id or "daarion"
)
response_data["qa_pairs"] = qa_pairs
logger.info(f"Generated {len(qa_pairs)} Q&A pairs via DAGI Router")
except Exception as e:
logger.error(f"Q&A generation failed, falling back to simple extraction: {e}")
# Fallback to simple Q&A extraction
response_data["qa_pairs"] = build_qa_pairs(parsed_doc)
elif output_mode == "chunks":
response_data["chunks"] = build_chunks(parsed_doc, dao_id=dao_id)
elif output_mode == "layout_only":
# Return document with layout info only
response_data["document"] = parsed_doc
elif output_mode == "region":
# Region parsing (for future use)
response_data["document"] = parsed_doc
return ParseResponse(**response_data)
@@ -136,13 +155,20 @@ async def parse_document_endpoint(
@router.post("/parse_qa", response_model=ParseResponse)
async def parse_qa_endpoint(
file: Optional[UploadFile] = File(None),
doc_url: Optional[str] = Form(None)
doc_url: Optional[str] = Form(None),
dao_id: Optional[str] = Form(None)
):
"""Parse document and return Q&A pairs"""
"""
Parse document and return Q&A pairs (2-stage pipeline)
Stage 1: PARSER (dots.ocr) → raw JSON
Stage 2: LLM (DAGI Router) → Q&A pairs
"""
return await parse_document_endpoint(
file=file,
doc_url=doc_url,
output_mode="qa_pairs"
output_mode="qa_pairs",
dao_id=dao_id
)

4
services/parser-service/app/core/config.py
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@@ -43,6 +43,10 @@ class Settings(BaseSettings):
# Ollama configuration (if RUNTIME_TYPE=ollama)
OLLAMA_BASE_URL: str = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
# DAGI Router configuration (for qa_pairs 2-stage pipeline)
ROUTER_BASE_URL: str = os.getenv("ROUTER_BASE_URL", "http://router:9102")
ROUTER_TIMEOUT: int = int(os.getenv("ROUTER_TIMEOUT", "60"))
class Config:
env_file = ".env"
case_sensitive = True

35
services/parser-service/app/runtime/inference.py
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@@ -11,6 +11,7 @@ from PIL import Image
from app.schemas import ParsedDocument, ParsedPage, ParsedBlock, BBox
from app.runtime.model_loader import get_model
from app.runtime.local_runtime import parse_document_with_local
from app.runtime.preprocessing import (
convert_pdf_to_images, load_image, prepare_images_for_model
)
@@ -26,7 +27,7 @@ logger = logging.getLogger(__name__)
async def parse_document_with_ollama(
images: List[Image.Image],
output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks"] = "raw_json",
output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks", "layout_only", "region"] = "raw_json",
doc_id: Optional[str] = None,
doc_type: Literal["pdf", "image"] = "image"
) -> ParsedDocument:
@@ -106,7 +107,7 @@ async def parse_document_with_ollama(
def parse_document_from_images(
images: List[Image.Image],
output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks"] = "raw_json",
output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks", "layout_only", "region"] = "raw_json",
doc_id: Optional[str] = None,
doc_type: Literal["pdf", "image"] = "image"
) -> ParsedDocument:
@@ -146,33 +147,19 @@ def parse_document_from_images(
if not prepared_images:
raise ValueError("No valid images to process")
# Process with model
# Process with model using local_runtime (with native dots.ocr prompts)
pages_data = []
for idx, image in enumerate(prepared_images, start=1):
try:
# Prepare inputs for model
inputs = model["processor"](images=image, return_tensors="pt")
# Convert image to bytes for local_runtime
import io
buf = io.BytesIO()
image.convert("RGB").save(buf, format="PNG")
image_bytes = buf.getvalue()
# Move inputs to device
device = model["device"]
if device != "cpu":
inputs = {k: v.to(device) if isinstance(v, torch.Tensor) else v
for k, v in inputs.items()}
# Generate output
with torch.no_grad():
outputs = model["model"].generate(
**inputs,
max_new_tokens=2048, # Adjust based on model capabilities
do_sample=False # Deterministic output
)
# Decode output
generated_text = model["processor"].decode(
outputs[0],
skip_special_tokens=True
)
# Use local_runtime with native prompt modes
generated_text = parse_document_with_local(image_bytes, output_mode)
logger.debug(f"Model output for page {idx}: {generated_text[:100]}...")

273
services/parser-service/app/runtime/local_runtime.py Normal file
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@@ -0,0 +1,273 @@
"""
Local runtime for dots.ocr model with native prompt modes
Maps OutputMode to dots.ocr prompt modes using dict_promptmode_to_prompt
"""
import os
import tempfile
import logging
from typing import Literal, Optional
import torch
from transformers import AutoModelForVision2Seq, AutoProcessor
from qwen_vl_utils import process_vision_info
from app.core.config import settings
logger = logging.getLogger(__name__)
# Try to import dots.ocr prompt dictionary
try:
from dots_ocr.utils.prompts import dict_promptmode_to_prompt
DOTS_PROMPTS_AVAILABLE = True
except ImportError:
logger.warning(
"dots_ocr.utils.prompts not available. "
"Using fallback prompts. Install dots.ocr package for native prompt modes."
)
DOTS_PROMPTS_AVAILABLE = False
dict_promptmode_to_prompt = {}
# Map OutputMode to dots.ocr native prompt modes
DOTS_PROMPT_MAP = {
"raw_json": "prompt_layout_all_en", # Full JSON (layout + content)
"markdown": "prompt_ocr", # Content-oriented OCR (Markdown)
"qa_pairs": "prompt_layout_all_en", # Full JSON, then 2nd step LLM
"chunks": "prompt_layout_all_en", # Full JSON for chunking
"layout_only": "prompt_layout_only_en", # Layout only (bbox + categories, no text)
"region": "prompt_grounding_ocr", # Targeted region parsing (grounding)
}
# Fallback prompts if dict_promptmode_to_prompt is not available
FALLBACK_PROMPTS = {
"prompt_layout_all_en": (
"You are a document OCR and layout parser. "
"Extract all text, tables, formulas, and layout into a clean JSON structure with fields like "
"`blocks`, `tables`, `reading_order`, including bounding boxes and page numbers. "
"Respond with JSON only, no explanations."
),
"prompt_ocr": (
"You are a document OCR and layout parser. "
"Extract the document as Markdown, preserving headings, paragraphs, and tables. "
"Tables should be proper GitHub-flavored Markdown tables. "
"Respond with Markdown as plain text."
),
"prompt_layout_only_en": (
"You are a document layout parser. "
"Extract only the layout structure (bounding boxes, block types, reading order) "
"without the text content. "
"Respond with JSON containing only layout information (bbox, type, reading_order)."
),
"prompt_grounding_ocr": (
"You are a document OCR assistant for targeted region parsing. "
"Extract text and layout for the specified region of the document. "
"Respond with JSON containing the parsed content for the region."
),
}
# Global model instance
_model: Optional[dict] = None
_processor: Optional[object] = None
# Model configuration
MODEL_PATH = settings.PARSER_MODEL_NAME
DEVICE = settings.PARSER_DEVICE
DTYPE = torch.bfloat16 if DEVICE != "cpu" else torch.float32
MAX_NEW_TOKENS = int(os.getenv("DOTS_OCR_MAX_NEW_TOKENS", "24000"))
def load_model():
"""Load dots.ocr model with lazy initialization"""
global _model, _processor
if _model is not None and _processor is not None:
return _model, _processor
logger.info(f"Loading dots.ocr model: {MODEL_PATH}")
logger.info(f"Device: {DEVICE}")
try:
model = AutoModelForVision2Seq.from_pretrained(
MODEL_PATH,
attn_implementation="flash_attention_2",
torch_dtype=DTYPE,
device_map="auto",
trust_remote_code=True,
low_cpu_mem_usage=True,
)
processor = AutoProcessor.from_pretrained(
MODEL_PATH,
trust_remote_code=True
)
if DEVICE == "cuda" and torch.cuda.is_available():
model.to("cuda")
elif DEVICE == "mps" and hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
model.to("mps")
_model = model
_processor = processor
logger.info(f"Model loaded successfully on {DEVICE}")
return _model, _processor
except Exception as e:
logger.error(f"Failed to load model: {e}", exc_info=True)
raise
def get_model():
"""Get loaded model instance"""
if _model is None or _processor is None:
return load_model()
return _model, _processor
def _build_prompt(output_mode: str) -> str:
"""
Build prompt for dots.ocr based on OutputMode
Args:
output_mode: One of "raw_json", "markdown", "qa_pairs", "chunks", "layout_only", "region"
Returns:
Prompt string for dots.ocr
"""
prompt_key = DOTS_PROMPT_MAP.get(output_mode, "prompt_layout_all_en")
# Try to use native dots.ocr prompts
if DOTS_PROMPTS_AVAILABLE and prompt_key in dict_promptmode_to_prompt:
prompt = dict_promptmode_to_prompt[prompt_key]
logger.debug(f"Using native dots.ocr prompt: {prompt_key}")
return prompt
# Fallback to our prompts
if prompt_key in FALLBACK_PROMPTS:
logger.debug(f"Using fallback prompt: {prompt_key}")
return FALLBACK_PROMPTS[prompt_key]
# Ultimate fallback
logger.warning(f"Unknown prompt key: {prompt_key}, using default")
return FALLBACK_PROMPTS["prompt_layout_all_en"]
def _build_messages(image_path: str, prompt: str) -> list:
"""Build messages for dots.ocr model"""
return [
{
"role": "user",
"content": [
{"type": "image", "image": image_path},
{"type": "text", "text": prompt},
],
}
]
def _generate_from_path(
image_path: str,
output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks", "layout_only", "region"]
) -> str:
"""
Generate output from image path using dots.ocr model
Args:
image_path: Path to image file
output_mode: Output mode (maps to dots.ocr prompt mode)
Returns:
Generated text from model
"""
model, processor = get_model()
prompt = _build_prompt(output_mode)
messages = _build_messages(image_path, prompt)
# Apply chat template
text = processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
# Process vision info
image_inputs, video_inputs = process_vision_info(messages)
# Prepare inputs
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
# Move to device
device = DEVICE
if device == "cuda" and torch.cuda.is_available():
inputs = {k: v.to("cuda") if isinstance(v, torch.Tensor) else v
for k, v in inputs.items()}
elif device == "mps" and hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
inputs = {k: v.to("mps") if isinstance(v, torch.Tensor) else v
for k, v in inputs.items()}
# Generate
with torch.inference_mode():
generated_ids = model.generate(
**inputs,
max_new_tokens=MAX_NEW_TOKENS,
)
# Trim input tokens
generated_ids_trimmed = [
out_ids[len(in_ids):]
for in_ids, out_ids in zip(inputs["input_ids"], generated_ids)
]
# Decode
output_text = processor.batch_decode(
generated_ids_trimmed,
skip_special_tokens=True,
clean_up_tokenization_spaces=False,
)
return output_text[0]
def parse_document_with_local(
image_bytes: bytes,
output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks", "layout_only", "region"] = "raw_json"
) -> str:
"""
Parse document from image bytes using local dots.ocr model
Args:
image_bytes: Image bytes (PNG/JPEG)
output_mode: Output mode (maps to dots.ocr prompt mode)
- raw_json: Full JSON (layout + content) via prompt_layout_all_en
- markdown: Markdown text via prompt_ocr
- qa_pairs: Full JSON (same as raw_json), then 2nd step LLM
- chunks: Full JSON for chunking
- layout_only: Layout only (bbox + categories) via prompt_layout_only_en
- region: Targeted region parsing via prompt_grounding_ocr
Returns:
Generated text from model (JSON or Markdown depending on mode)
Note:
For "qa_pairs" mode, this returns full JSON.
The 2nd step (LLM Q&A generation) should be done separately.
"""
# Save to temporary file
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as f:
tmp_path = f.name
f.write(image_bytes)
try:
return _generate_from_path(tmp_path, output_mode)
finally:
try:
os.remove(tmp_path)
except OSError:
pass

54
services/parser-service/app/runtime/ollama_client.py
View File

@@ -20,32 +20,56 @@ class OutputMode(str, Enum):
raw_json = "raw_json"
markdown = "markdown"
qa_pairs = "qa_pairs"
chunks = "chunks"
layout_only = "layout_only"
region = "region"
def build_prompt(mode: OutputMode) -> str:
"""Build prompt for Ollama based on output mode"""
if mode == OutputMode.raw_json:
return (
"""
Build prompt for Ollama based on output mode
Maps to dots.ocr native prompt modes
"""
# Map to dots.ocr prompt modes (same as local_runtime)
prompt_map = {
OutputMode.raw_json: "prompt_layout_all_en",
OutputMode.markdown: "prompt_ocr",
OutputMode.qa_pairs: "prompt_layout_all_en", # Full JSON, then 2nd step LLM
OutputMode.chunks: "prompt_layout_all_en",
OutputMode.layout_only: "prompt_layout_only_en",
OutputMode.region: "prompt_grounding_ocr",
}
prompt_key = prompt_map.get(mode, "prompt_layout_all_en")
# Fallback prompts (same as local_runtime)
fallback_prompts = {
"prompt_layout_all_en": (
"You are a document OCR and layout parser. "
"Extract all text, tables, formulas, and layout into a clean JSON structure with fields like "
"`blocks`, `tables`, `reading_order`, including bounding boxes and page numbers. "
"Respond with JSON only, no explanations."
)
elif mode == OutputMode.markdown:
return (
),
"prompt_ocr": (
"You are a document OCR and layout parser. "
"Extract the document as Markdown, preserving headings, paragraphs, and tables. "
"Tables should be proper GitHub-flavored Markdown tables. "
"Respond with Markdown as plain text."
)
elif mode == OutputMode.qa_pairs:
return (
"You are a document OCR and knowledge extraction assistant. "
"Read the document and output a JSON array of Q&A pairs covering the key information. "
"Each item should be {\"question\": ..., \"answer\": ..., \"page\": ..., \"section\": ...}. "
"Respond with JSON only, no explanations."
)
return "You are a document OCR assistant. Extract text."
),
"prompt_layout_only_en": (
"You are a document layout parser. "
"Extract only the layout structure (bounding boxes, block types, reading order) "
"without the text content. "
"Respond with JSON containing only layout information (bbox, type, reading_order)."
),
"prompt_grounding_ocr": (
"You are a document OCR assistant for targeted region parsing. "
"Extract text and layout for the specified region of the document. "
"Respond with JSON containing the parsed content for the region."
),
}
return fallback_prompts.get(prompt_key, fallback_prompts["prompt_layout_all_en"])
async def call_ollama_vision(

198
services/parser-service/app/runtime/qa_builder.py Normal file
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@@ -0,0 +1,198 @@
"""
Q&A Builder - 2-stage pipeline for qa_pairs mode
Stage 1: PARSER (dots.ocr) → raw JSON
Stage 2: LLM (DAGI Router) → Q&A pairs
"""
import json
import logging
from typing import List, Dict, Any, Optional
import httpx
from app.schemas import QAPair, ParsedDocument
from app.core.config import settings
logger = logging.getLogger(__name__)
async def build_qa_pairs_via_router(
parsed_doc: ParsedDocument,
dao_id: str = "daarion"
) -> List[QAPair]:
"""
2-stage pipeline: Generate Q&A pairs from parsed document using DAGI Router
Args:
parsed_doc: ParsedDocument from dots.ocr (stage 1)
dao_id: DAO identifier
Returns:
List of QAPair objects
"""
# Build prompt for LLM
prompt = _build_qa_prompt(parsed_doc)
# Prepare payload for DAGI Router
payload = {
"mode": "qa_build", # New mode in Router
"dao_id": dao_id,
"user_id": "parser-service",
"payload": {
"instruction": prompt,
"parsed_document": parsed_doc.model_dump(mode="json"),
},
}
# Call DAGI Router
router_url = f"{settings.ROUTER_BASE_URL.rstrip('/')}/route"
logger.info(f"Calling DAGI Router for Q&A generation: {router_url}")
try:
async with httpx.AsyncClient(timeout=settings.ROUTER_TIMEOUT) as client:
resp = await client.post(router_url, json=payload)
resp.raise_for_status()
data = resp.json()
# Extract response text
text = data.get("data", {}).get("text", "")
if not text:
logger.warning("Empty response from DAGI Router")
return []
# Parse JSON response
qa_pairs = _parse_qa_response(text, parsed_doc)
logger.info(f"Generated {len(qa_pairs)} Q&A pairs")
return qa_pairs
except httpx.HTTPError as e:
logger.error(f"DAGI Router HTTP error: {e}")
raise RuntimeError(f"DAGI Router API error: {e}") from e
except Exception as e:
logger.error(f"Failed to build Q&A pairs: {e}", exc_info=True)
raise RuntimeError(f"Q&A generation failed: {e}") from e
def _build_qa_prompt(parsed_doc: ParsedDocument) -> str:
"""
Build prompt for Q&A generation from parsed document
Args:
parsed_doc: ParsedDocument with structured content
Returns:
Prompt string for LLM
"""
# Extract text content from document (first 5000 chars to avoid token limits)
text_content = []
for page in parsed_doc.pages:
for block in page.blocks:
if block.text:
text_content.append(f"[Page {page.page_num}] {block.text}")
document_text = "\n\n".join(text_content[:50]) # Limit to first 50 blocks
if len(document_text) > 5000:
document_text = document_text[:5000] + "..."
prompt = (
"Тобі дається результат OCR-документу у JSON-форматі (layout + текст).\n"
"Твоє завдання: побудувати список запитань/відповідей, які покривають ключову "
"інформацію цього документу.\n\n"
"Формат відповіді — СУВОРО JSON-масив об'єктів:\n"
"[\n"
' {"question": "...", "answer": "...", "source_page": <int|null>, "confidence": <float|null>},\n'
" ...\n"
"]\n\n"
"Вимоги:\n"
"- Формулюй питання українською.\n"
"- Відповіді мають базуватись на тексті документа (не вигадуй).\n"
"- Якщо можна визначити номер сторінки — заповни поле source_page.\n"
"- Не додавай ніякого пояснення поза JSON.\n"
"- Мінімум 5-10 Q&A пар, максимум 20.\n\n"
f"Документ:\n{document_text}\n\n"
"Відповідь (тільки JSON):"
)
return prompt
def _parse_qa_response(text: str, parsed_doc: ParsedDocument) -> List[QAPair]:
"""
Parse LLM response into QAPair objects
Args:
text: Response text from LLM (should be JSON)
parsed_doc: Original parsed document (for page numbers)
Returns:
List of QAPair objects
"""
# Try to extract JSON from response
text_clean = text.strip()
# Remove markdown code blocks if present
if text_clean.startswith("```"):
lines = text_clean.split("\n")
text_clean = "\n".join(lines[1:-1]) if len(lines) > 2 else text_clean
# Try to parse as JSON
try:
qa_data = json.loads(text_clean)
if not isinstance(qa_data, list):
logger.warning(f"Expected list, got {type(qa_data)}")
return []
# Convert to QAPair objects
qa_pairs = []
for item in qa_data:
if not isinstance(item, dict):
continue
question = item.get("question", "").strip()
answer = item.get("answer", "").strip()
if not question or not answer:
continue
# Extract page number
source_page = item.get("source_page")
if source_page is None:
# Try to infer from answer text
source_page = _infer_page_number(answer, parsed_doc)
qa_pairs.append(QAPair(
question=question,
answer=answer,
source_page=source_page or 1,
confidence=item.get("confidence")
))
return qa_pairs
except json.JSONDecodeError as e:
logger.warning(f"Failed to parse JSON response: {e}")
logger.debug(f"Response text: {text_clean[:500]}")
return []
def _infer_page_number(text: str, parsed_doc: ParsedDocument) -> Optional[int]:
"""
Try to infer page number from text content
Args:
text: Answer text
parsed_doc: Parsed document
Returns:
Page number or None
"""
# Simple heuristic: check if text appears in any page
text_lower = text.lower()
for page in parsed_doc.pages:
for block in page.blocks:
if block.text and text_lower in block.text.lower():
return page.page_num
return None

2
services/parser-service/app/schemas.py
View File

@@ -115,7 +115,7 @@ class ParsedDocument(BaseModel):
class ParseRequest(BaseModel):
"""Parse request"""
doc_url: Optional[str] = Field(None, description="Document URL")
output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks"] = Field(
output_mode: Literal["raw_json", "markdown", "qa_pairs", "chunks", "layout_only", "region"] = Field(
"raw_json", description="Output mode"
)
dao_id: Optional[str] = Field(None, description="DAO ID")