261 lines
10 KiB
Python
261 lines
10 KiB
Python
import os
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import re
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import tempfile
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from typing import Any
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import structlog
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from fastapi import HTTPException
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from pypdf import PdfReader
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from skyvern.config import settings
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from skyvern.forge.prompts import prompt_engine
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from skyvern.forge.sdk.api.llm.api_handler_factory import LLMAPIHandlerFactory
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from skyvern.forge.sdk.schemas.organizations import Organization
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from skyvern.schemas.workflows import WorkflowCreateYAMLRequest
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LOG = structlog.get_logger(__name__)
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class PDFImportService:
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@staticmethod
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def _sanitize_workflow_json(raw: dict[str, Any]) -> dict[str, Any]:
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"""Clean LLM JSON to match Skyvern schema conventions and avoid Jinja errors.
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- Replace Jinja refs like {{workflow.foo}} or {{parameters.foo}} with {{foo}}
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- Auto-populate block.parameter_keys with any referenced parameter keys
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- Ensure all block labels are unique by appending indices to duplicates
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"""
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def strip_prefixes(text: str) -> tuple[str, set[str]]:
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# Replace {{ workflow.xxx }} and {{ parameters.xxx }} with {{ xxx }}
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cleaned = text
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cleaned = re.sub(r"\{\{\s*workflow\.([a-zA-Z0-9_\.]+)\s*\}\}", r"{{ \1 }}", cleaned)
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cleaned = re.sub(r"\{\{\s*parameters\.([a-zA-Z0-9_\.]+)\s*\}\}", r"{{ \1 }}", cleaned)
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# Collect jinja variable names (take first segment before any dot)
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used: set[str] = set()
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for match in re.finditer(r"\{\{\s*([^\}\s\|]+)\s*[^}]*\}\}", cleaned):
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var = match.group(1)
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# Use base segment before dot to match parameter keys
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base = var.split(".")[0]
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used.add(base)
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return cleaned, used
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workflow_def = raw.get("workflow_definition", {})
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param_defs = workflow_def.get("parameters", []) or []
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param_keys = {p.get("key") for p in param_defs if isinstance(p, dict) and p.get("key")}
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blocks = workflow_def.get("blocks", []) or []
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# First pass: deduplicate block labels
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seen_labels: dict[str, int] = {}
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deduplicated_count = 0
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for blk in blocks:
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if not isinstance(blk, dict):
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continue
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label = blk.get("label", "")
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if not label:
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continue
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if label in seen_labels:
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# This label has been seen before, append index
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seen_labels[label] += 1
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new_label = f"{label}_{seen_labels[label]}"
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LOG.info(
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"Deduplicating block label",
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original_label=label,
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new_label=new_label,
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occurrence=seen_labels[label],
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)
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blk["label"] = new_label
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deduplicated_count += 1
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else:
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# First time seeing this label
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seen_labels[label] = 1
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if deduplicated_count > 0:
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LOG.info(
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"Deduplicated block labels",
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total_deduplicated=deduplicated_count,
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duplicate_labels=sorted([label for label, count in seen_labels.items() if count > 1]),
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)
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for blk in blocks:
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if not isinstance(blk, dict):
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continue
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referenced: set[str] = set()
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# Fields that commonly contain Jinja
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for field in [
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"url",
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"navigation_goal",
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"data_extraction_goal",
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"complete_criterion",
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"terminate_criterion",
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"title",
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]:
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val = blk.get(field)
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if isinstance(val, str):
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cleaned, used = strip_prefixes(val)
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blk[field] = cleaned
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referenced.update(used)
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# Ensure required fields for text_prompt blocks
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if blk.get("block_type") == "text_prompt":
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if not blk.get("prompt"):
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# Prefer an instruction-bearing field if present
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blk["prompt"] = (
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blk.get("navigation_goal")
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or blk.get("title")
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or blk.get("label")
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or "Provide the requested text response."
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)
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# Track jinja usage within the prompt
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prompt_val = blk.get("prompt")
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if isinstance(prompt_val, str):
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cleaned, used = strip_prefixes(prompt_val)
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blk["prompt"] = cleaned
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referenced.update(used)
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# parameter_keys should include only known parameter keys
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if param_keys:
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keys_to_include = sorted(k for k in referenced if k in param_keys)
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if keys_to_include:
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blk["parameter_keys"] = keys_to_include
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# Ensure engine where needed
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if blk.get("block_type") in {"navigation", "action", "extraction", "login", "file_download"}:
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blk.setdefault("engine", "skyvern-1.0")
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# Ensure url exists (can be empty string)
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if blk.get("block_type") in {"navigation", "action", "extraction", "file_download"}:
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if blk.get("url") is None:
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blk["url"] = ""
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return raw
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def extract_text_from_pdf(self, file_contents: bytes, file_name: str) -> str:
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"""Extract text from PDF file contents. Raises HTTPException if invalid."""
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LOG.info("Extracting text from PDF", filename=file_name)
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# Save the uploaded file to a temporary location
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with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
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temp_file.write(file_contents)
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temp_file_path = temp_file.name
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try:
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reader = PdfReader(temp_file_path)
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sop_text = ""
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for page_num, page in enumerate(reader.pages, 1):
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page_text = page.extract_text() or ""
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sop_text += page_text + "\n"
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LOG.debug("Extracted text from page", page=page_num, text_length=len(page_text))
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LOG.info("PDF text extraction complete", total_text_length=len(sop_text))
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if not sop_text.strip():
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raise HTTPException(status_code=400, detail="No readable content found in the PDF.")
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return sop_text
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except Exception as e:
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LOG.warning(
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"Failed to read/extract text from PDF",
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filename=file_name,
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error=str(e),
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)
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raise HTTPException(status_code=400, detail="Invalid or unreadable PDF file.") from e
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finally:
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# Clean up the temporary file
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os.unlink(temp_file_path)
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async def create_workflow_from_sop_text(self, sop_text: str, organization: Organization) -> dict[str, Any]:
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"""Convert SOP text to workflow definition using LLM (does not create the workflow)."""
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# Load and render the prompt template
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prompt = prompt_engine.load_prompt(
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"build-workflow-from-pdf",
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sop_text=sop_text,
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)
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# Use the LLM to convert SOP to workflow
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llm_key = settings.LLM_KEY or "gpt-4o-mini"
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LOG.info(
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"Calling LLM to convert SOP to workflow",
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llm_key=llm_key,
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prompt_length=len(prompt),
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sop_text_length=len(sop_text),
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sop_chars_sent=len(sop_text),
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organization_id=organization.organization_id,
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)
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llm_api_handler = LLMAPIHandlerFactory.get_llm_api_handler(llm_key)
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response = await llm_api_handler(
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prompt=prompt,
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prompt_name="sop_to_workflow_conversion",
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organization_id=organization.organization_id,
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parameters={"max_completion_tokens": 32768}, # Override the default 4096 limit for PDF conversion
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)
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LOG.info(
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"LLM response received",
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response_type=type(response),
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response_keys=list(response.keys()) if isinstance(response, dict) else None,
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organization_id=organization.organization_id,
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)
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# The LLM API handler automatically parses JSON responses
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# The response should be a dict with the workflow structure
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if not isinstance(response, dict):
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LOG.error(
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"LLM returned non-dict response",
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response_type=type(response),
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response=str(response)[:500],
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organization_id=organization.organization_id,
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)
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raise HTTPException(status_code=422, detail="LLM returned invalid response format - expected JSON object")
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# Validate that it has the required structure
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if "workflow_definition" not in response:
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LOG.error(
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"LLM response missing workflow_definition",
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response_keys=list(response.keys()),
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organization_id=organization.organization_id,
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)
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raise HTTPException(status_code=422, detail="LLM response missing 'workflow_definition' field")
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if "blocks" not in response.get("workflow_definition", {}):
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LOG.error(
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"LLM workflow_definition missing blocks",
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workflow_def_keys=list(response.get("workflow_definition", {}).keys()),
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organization_id=organization.organization_id,
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)
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raise HTTPException(status_code=422, detail="LLM workflow definition missing 'blocks' field")
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try:
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# Sanitize LLM output for Jinja and required fields before validation
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response = self._sanitize_workflow_json(response)
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workflow_create_request = WorkflowCreateYAMLRequest.model_validate(response)
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LOG.info(
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"Workflow JSON validated successfully",
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title=response.get("title"),
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block_count=len(response.get("workflow_definition", {}).get("blocks", [])),
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organization_id=organization.organization_id,
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)
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except Exception as e:
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LOG.error(
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"Failed to validate workflow request",
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error=str(e),
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error_type=type(e).__name__,
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organization_id=organization.organization_id,
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exc_info=True,
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)
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raise HTTPException(
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status_code=422,
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detail=f"Failed to validate workflow structure: {e!s}",
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) from e
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# Return the validated request as a dict (caller will create the workflow)
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return workflow_create_request.model_dump(by_alias=True)
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pdf_import_service = PDFImportService()
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