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## Summary Implements Phase 3 of the AUDIT-2026-05-20 findings: - 29 FIX-severity items across API, web, infra, and lambdas - 7 accessibility NITs (aria-labels, document titles) - 4 code quality NITs (deduplication, constants extraction) Key changes: - API: N+1 fix, pagination clamping, idempotent transitions, upload confirm endpoint, revision TotalBidAmount carry-forward - Web: confirmation dialogs, currency formatting, error states, date range filters, document titles - Infra: S3 CORS lockdown, API Gateway throttling, AOSS network policy fix, CI concurrency - Lambdas: skip empty suggestions, remove status side-effect - Scripts: post-deploy health check ## Test plan - [x] tsc --noEmit (web + infra) - [x] dotnet build (api) - [x] ruff check + format (lambdas) - [x] Cross-review via orchestrator (no blockers) [skip deploy]
340 lines
11 KiB
Python
340 lines
11 KiB
Python
"""Proposal System - Suggestion Engine Lambda.
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Queries Bedrock Knowledge Base for similar proposals and invokes Claude
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to generate line item suggestions for new proposals.
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"""
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import json
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import logging
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import os
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import time
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import boto3
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import httpx
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logger = logging.getLogger(__name__)
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logger.setLevel(os.environ.get("LOG_LEVEL", "INFO"))
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KNOWLEDGE_BASE_ID = os.environ.get("KNOWLEDGE_BASE_ID", "")
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MODEL_ID = os.environ.get("MODEL_ID", "us.anthropic.claude-sonnet-4-5-20250929-v1:0")
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API_BASE_URL = os.environ.get("API_BASE_URL", "")
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INTERNAL_API_KEY_SECRET_ARN = os.environ.get("INTERNAL_API_KEY_SECRET_ARN", "")
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bedrock_agent = boto3.client("bedrock-agent-runtime")
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bedrock_runtime = boto3.client("bedrock-runtime")
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secrets_client = boto3.client("secretsmanager")
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_cached_api_key: str | None = None
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def _get_api_key() -> str:
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global _cached_api_key
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if _cached_api_key is None:
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if INTERNAL_API_KEY_SECRET_ARN:
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resp = secrets_client.get_secret_value(SecretId=INTERNAL_API_KEY_SECRET_ARN)
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_cached_api_key = resp["SecretString"]
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else:
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_cached_api_key = ""
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return _cached_api_key
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def handler(event, context):
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batch_item_failures = []
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for record in event.get("Records", []):
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try:
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body = json.loads(record["body"])
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payload = body.get("payload", body)
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proposal_id = payload["proposalId"]
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trigger = payload.get("trigger", "generate")
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process_suggestion(proposal_id, trigger)
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except Exception as e:
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logger.error("Failed to process record %s: %s", record.get("messageId"), e)
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batch_item_failures.append({"itemIdentifier": record["messageId"]})
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return {"batchItemFailures": batch_item_failures}
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def process_suggestion(proposal_id: str, trigger: str):
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proposal = fetch_proposal(proposal_id)
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if not proposal:
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logger.warning("Proposal %s not found", proposal_id)
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return
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scope = proposal.get("refinedScope") or proposal.get("scopeOfWork", "")
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category = proposal.get("serviceCategory", "")
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priority = proposal.get("priority", "")
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existing_items = fetch_line_items(proposal_id)
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similar_proposals = retrieve_similar(scope, category)
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suggested_items = generate_line_items(scope, category, priority, similar_proposals)
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if not suggested_items and not existing_items:
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logger.warning(
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"No suggestions generated and no existing items for %s, skipping status update",
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proposal_id,
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)
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return
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post_line_items(proposal_id, suggested_items, existing_items)
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store_similar_references(proposal_id, similar_proposals)
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def fetch_line_items(proposal_id: str) -> list[dict]:
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try:
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resp = _retry_request(
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"GET",
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f"{API_BASE_URL}/api/proposals/{proposal_id}/line-items",
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headers=_api_headers(),
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)
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if resp.status_code == 200:
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return resp.json()
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except Exception as e:
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logger.error("Error fetching line items: %s", e)
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return []
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def fetch_proposal(proposal_id: str) -> dict | None:
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try:
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resp = _retry_request(
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"GET",
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f"{API_BASE_URL}/api/proposals/{proposal_id}",
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headers=_api_headers(),
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)
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if resp.status_code == 200:
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return resp.json()
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except Exception as e:
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logger.error("Error fetching proposal: %s", e)
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return None
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def retrieve_similar(scope: str, category: str) -> list[dict]:
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if not KNOWLEDGE_BASE_ID:
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logger.info("No Knowledge Base configured, skipping retrieval")
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return []
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try:
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filter_config = (
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{"equals": {"key": "service_category", "value": category}}
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if category
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else None
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)
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params = {
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"knowledgeBaseId": KNOWLEDGE_BASE_ID,
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"retrievalQuery": {"text": scope},
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"retrievalConfiguration": {
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"vectorSearchConfiguration": {
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"numberOfResults": 10,
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}
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},
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}
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if filter_config:
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params["retrievalConfiguration"]["vectorSearchConfiguration"]["filter"] = (
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filter_config
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)
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response = bedrock_agent.retrieve(**params)
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results = []
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for result in response.get("retrievalResults", []):
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content = result.get("content", {}).get("text", "")
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score = result.get("score", 0.0)
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metadata = result.get("metadata", {})
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source_uri = result.get("location", {}).get("s3Location", {}).get("uri", "")
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results.append(
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{
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"content": content,
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"score": score,
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"metadata": metadata,
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"sourceUri": source_uri,
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}
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)
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return results
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except Exception as e:
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logger.error("Error retrieving from KB: %s", e)
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return []
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def generate_line_items(
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scope: str,
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category: str,
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priority: str,
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similar_proposals: list[dict],
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) -> list[dict]:
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context_block = ""
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if similar_proposals:
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context_block = "Here are similar historical proposals and their line items for reference:\n\n"
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for i, sp in enumerate(similar_proposals[:5], 1):
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context_block += (
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f"--- Similar Proposal {i} (relevance: {sp['score']:.2f}) ---\n"
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)
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context_block += sp["content"] + "\n\n"
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prompt = f"""You are a construction/facilities proposal estimator for Sea Haven Industries.
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Based on the scope of work and similar historical proposals, generate a detailed list of line items
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with quantities, units, and estimated pricing.
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Service Category: {category}
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Priority: {priority}
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Scope of Work:
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{scope}
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{context_block}
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Generate line items as a JSON array. Each item should have:
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- description: clear description of the work/material
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- quantity: numeric quantity
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- unit: unit of measurement (e.g., "sq ft", "hours", "each", "linear ft")
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- unitPrice: price per unit in dollars (or null if lump sum)
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- totalPrice: total price for this line item in dollars
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- pricingMode: "UnitPrice" if unit price provided, "TotalPrice" if lump sum
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Respond ONLY with the JSON array, no additional text."""
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try:
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response = bedrock_runtime.invoke_model(
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modelId=MODEL_ID,
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contentType="application/json",
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accept="application/json",
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body=json.dumps(
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{
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"anthropic_version": "bedrock-2023-05-31",
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"max_tokens": 4096,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.3,
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}
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),
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)
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response_body = json.loads(response["body"].read())
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content = response_body["content"][0]["text"]
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content = content.strip()
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if content.startswith("```"):
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content = content.split("\n", 1)[1]
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content = content.rsplit("```", 1)[0]
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line_items = json.loads(content)
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return line_items if isinstance(line_items, list) else []
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except Exception as e:
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logger.error("Error generating line items: %s", e)
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return []
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def post_line_items(proposal_id: str, items: list[dict], existing_items: list[dict]):
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if not items and not existing_items:
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return
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line_items_payload = []
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# Preserve non-AI items (Manual, Vendor, Historical)
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preserved = [li for li in existing_items if li.get("source") != "AI"]
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for i, li in enumerate(preserved):
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line_items_payload.append(
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{
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"id": li.get("id"),
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"description": li["description"],
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"quantity": float(li.get("quantity", 1)),
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"unit": li.get("unit", "each"),
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"unitPrice": li.get("unitPrice"),
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"totalPrice": float(li.get("totalPrice", 0)),
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"pricingMode": li.get("pricingMode", "TotalPrice"),
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"sortOrder": i + 1,
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"source": li.get("source", "Manual"),
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}
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)
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# Add new AI-generated items after preserved ones
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offset = len(line_items_payload)
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for i, item in enumerate(items):
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pricing_mode = item.get("pricingMode", "TotalPrice")
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if pricing_mode not in ("UnitPrice", "TotalPrice", "Both"):
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pricing_mode = "UnitPrice" if item.get("unitPrice") else "TotalPrice"
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line_items_payload.append(
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{
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"id": None,
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"description": item["description"],
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"quantity": float(item.get("quantity", 1)),
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"unit": item.get("unit", "each"),
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"unitPrice": item.get("unitPrice"),
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"totalPrice": float(item.get("totalPrice", 0)),
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"pricingMode": pricing_mode,
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"sortOrder": offset + i + 1,
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"source": "AI",
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}
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)
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try:
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resp = _retry_request(
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"PUT",
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f"{API_BASE_URL}/api/proposals/{proposal_id}/line-items",
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json={"lineItems": line_items_payload},
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headers=_api_headers(),
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timeout=15,
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)
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if resp.status_code not in (200, 201):
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logger.error(
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"Failed to post line items: %s %s", resp.status_code, resp.text
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)
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except Exception as e:
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logger.error("Error posting line items: %s", e)
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def store_similar_references(proposal_id: str, similar_proposals: list[dict]):
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if not similar_proposals:
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return
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for sp in similar_proposals[:5]:
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source_uri = sp.get("sourceUri", "")
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library_item_id = source_uri.split("/")[-1] if source_uri else ""
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if not library_item_id:
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continue
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try:
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_retry_request(
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"POST",
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f"{API_BASE_URL}/api/proposals/{proposal_id}/similar-references",
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json={
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"referencedLibraryItemId": library_item_id,
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"similarityScore": sp["score"],
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},
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headers=_api_headers(),
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)
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except Exception as e:
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logger.error("Error storing similar reference: %s", e)
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def _api_headers() -> dict:
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headers = {"Content-Type": "application/json"}
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api_key = _get_api_key()
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if api_key:
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headers["X-Internal-Api-Key"] = api_key
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return headers
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def _retry_request(
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method: str, url: str, *, max_retries: int = 3, **kwargs
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) -> httpx.Response:
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kwargs.setdefault("timeout", 10)
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for attempt in range(max_retries):
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try:
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resp = httpx.request(method, url, **kwargs)
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if resp.status_code < 500:
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return resp
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except (httpx.ConnectError, httpx.ReadTimeout, httpx.WriteTimeout) as exc:
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if attempt == max_retries - 1:
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raise
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logger.warning(
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"Retryable error (attempt %d/%d): %s", attempt + 1, max_retries, exc
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)
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time.sleep(min(2**attempt, 4))
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return resp # type: ignore[possibly-undefined]
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