[FEATURE] Add Prompt Engineering Layer for Improved LLM Extraction

api/main.py

@@ -1,12 +1,17 @@
 from contextlib import asynccontextmanager
 import os
+import uuid
+
+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from fastapi.exceptions import RequestValidationError
+from starlette.exceptions import HTTPException as StarletteHTTPException
+from starlette.middleware.base import BaseHTTPMiddleware
 
-from fastapi import FastAPI
 from api.routes import templates, forms
 from api.db.init_db import init_db
 from api.errors.handlers import register_exception_handlers
-from fastapi.middleware.cors import CORSMiddleware
-from api.routes import forms, templates
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
@@ -18,7 +23,17 @@ async def lifespan(app: FastAPI):
 
 app = FastAPI(lifespan=lifespan)
 
-register_exception_handlers(app)
+
+class RequestIDMiddleware(BaseHTTPMiddleware):
+    async def dispatch(self, request: Request, call_next):
+        request_id = str(uuid.uuid4())
+        request.state.request_id = request_id
+        response = await call_next(request)
+        response.headers["X-Request-ID"] = request_id
+        return response
+
+
+app.add_middleware(RequestIDMiddleware)
 
 default_origins = "http://127.0.0.1:5173"
 allowed_origins = [
@@ -35,5 +50,59 @@ async def lifespan(app: FastAPI):
     allow_headers=["*"],
 )
 
+@app.exception_handler(StarletteHTTPException)
+async def http_exception_handler(request: Request, exc: StarletteHTTPException):
+    return JSONResponse(
+        status_code=exc.status_code,
+        content={
+            "error": {
+                "type": "HTTPException",
+                "message": exc.detail,
+                "details": {}
+            }
+        },
+    )
+
+
+@app.exception_handler(RequestValidationError)
+async def validation_exception_handler(request: Request, exc: RequestValidationError):
+    formatted_errors = []
+
+    for err in exc.errors():
+        loc = err.get("loc", [])
+        field = loc[-1] if loc else "unknown"
+        issue = err.get("msg", "Invalid value")
+        expected = err.get("type", "")
+
+        formatted_errors.append({
+            "field": field,
+            "issue": issue,
+            "expected": expected
+        })
+
+    return JSONResponse(
+        status_code=422,
+        content={
+            "error": {
+                "type": "ValidationError",
+                "message": "Invalid request data",
+                "details": formatted_errors,
+            }
+        },
+    )
+
+@app.exception_handler(Exception)
+async def general_exception_handler(request: Request, exc: Exception):
+    return JSONResponse(
+        status_code=500,
+        content={
+            "error": {
+                "type": "InternalServerError",
+                "message": str(exc),
+                "details": {}
+            }
+        },
+    )
+
 app.include_router(templates.router)
-app.include_router(forms.router)
+app.include_router(forms.router)

api/schemas/forms.py

@@ -1,9 +1,15 @@
-from pydantic import BaseModel
+from pydantic import BaseModel, field_validator
 
 class FormFill(BaseModel):
     template_id: int
     input_text: str
 
+    @field_validator("input_text")
+    def validate_input_text(cls, value):
+        if not value or not value.strip():
+            raise ValueError("Input text cannot be empty")
+        return value
+
 
 class FormFillResponse(BaseModel):
     id: int

api/services/prompt_builder.py

@@ -0,0 +1,40 @@
+def build_extraction_prompt(input_text: str) -> str:
+    return f"""
+You are an AI system that extracts structured information from incident reports.
+
+Extract the following fields:
+- name
+- location
+- date (YYYY-MM-DD if possible)
+- incident_type
+- description
+
+Return ONLY valid JSON. Do not include any extra text.
+
+Format:
+{{
+  "name": "",
+  "location": "",
+  "date": "",
+  "incident_type": "",
+  "description": ""
+}}
+
+Example:
+
+Input:
+Fire reported near Central Park on Jan 5 involving a vehicle.
+
+Output:
+{{
+  "name": "",
+  "location": "Central Park",
+  "date": "2024-01-05",
+  "incident_type": "fire",
+  "description": "Fire involving a vehicle"
+}}
+
+Now extract from:
+
+{input_text}
+"""

src/llm.py

@@ -1,6 +1,7 @@
 import json
 import os
 import requests
+from api.services.prompt_builder import build_extraction_prompt
 from requests.exceptions import Timeout, RequestException
 
 
@@ -58,10 +59,21 @@ def main_loop(self):
             ollama_host = os.getenv("OLLAMA_HOST", "http://localhost:11434").rstrip("/")
             ollama_url = f"{ollama_host}/api/generate"
 
+            base_prompt = build_extraction_prompt(self._transcript_text)
+
+            prompt = f"""
+            {base_prompt}
+
+            Focus specifically on extracting the value for this field:
+            {field}
+
+            Return only the extracted value as a plain string. Do not return JSON.
+            """
+
             payload = {
                 "model": "mistral",
                 "prompt": prompt,
-                "stream": False,  # don't really know why --> look into this later.
+                "stream": False,  # streaming disabled; using single response mode
             }
 
             json_data = None