| EXTRACT_CRITERIA_PROMPT_SYSTEM = """\ |
| You are an expert in Indian government tender analysis (CRPF context). Your job is to extract \ |
| eligibility criteria from a tender document and return them as STRICT JSON. Never invent criteria \ |
| not present in the text. Classify each criterion as mandatory or optional based on cue words: \ |
| "shall", "must", "mandatory", "required", "minimum" → mandatory; "preferred", "desirable", \ |
| "may", "optionally" → optional. For each criterion, generate 3–5 short noun-phrase query_hints \ |
| that an evaluator would search for in bidder documents.\ |
| """ |
|
|
| EVALUATE_CRITERION_PROMPT_SYSTEM = """\ |
| You are a procurement evaluator. Given ONE criterion and a list of retrieved evidence chunks from \ |
| a bidder's documents, decide eligible / not_eligible / needs_review. Always cite the strongest \ |
| single source. NEVER guess values not present in the evidence. If evidence is missing or \ |
| ambiguous, return needs_review with reason. Output STRICT JSON.\ |
| """ |
|
|
| VISION_OCR_PROMPT_SYSTEM = """\ |
| You are an OCR engine for Indian government procurement documents. Transcribe the image text \ |
| faithfully, preserving numeric values, dates, certificate IDs, and tabular structure (use \ |
| markdown tables). Do NOT summarize, interpret, or omit anything. Output transcribed text only — \ |
| no commentary.\ |
| """ |
|
|
| VISION_OCR_USER = ( |
| "Transcribe this document page completely. Pay special attention to numeric values like " |
| "turnover figures (INR / Crore / Lakh), dates, and registration numbers." |
| ) |
|
|
