enhanced_app.py

"""
Enhanced Flask app with integrated guardrails system.

This module demonstrates how to integrate the guardrails system
with the existing Flask API endpoints.
"""

# ...existing code...

from dotenv import load_dotenv
from flask import Flask, jsonify, render_template, request

# Load environment variables from .env file
load_dotenv()

app = Flask(__name__)


@app.route("/")
def index():
    """
    Renders the chat interface.
    """
    return render_template("chat.html")


@app.route("/health")
def health():
    """
    Health check endpoint.
    """
    return jsonify({"status": "ok"}), 200


@app.route("/chat", methods=["POST"])
def chat():
    """
    Enhanced endpoint for conversational RAG interactions with guardrails.

    Accepts JSON requests with user messages and returns AI-generated
    responses with comprehensive validation and safety checks.
    """
    try:
        # Validate request contains JSON data
        if not request.is_json:
            return (
                jsonify(
                    {
                        "status": "error",
                        "message": "Content-Type must be application/json",
                    }
                ),
                400,
            )

        data = request.get_json()

        # Validate required message parameter
        message = data.get("message")
        if message is None:
            return (
                jsonify({"status": "error", "message": "message parameter is required"}),
                400,
            )

        if not isinstance(message, str) or not message.strip():
            return (
                jsonify({"status": "error", "message": "message must be a non-empty string"}),
                400,
            )

        # Extract optional parameters
        conversation_id = data.get("conversation_id")
        include_sources = data.get("include_sources", True)
        include_debug = data.get("include_debug", False)
        enable_guardrails = data.get("enable_guardrails", True)

        # Initialize enhanced RAG pipeline components
        try:
            from src.config import COLLECTION_NAME, VECTOR_DB_PERSIST_PATH
            from src.embedding.embedding_service import EmbeddingService
            from src.llm.llm_service import LLMService
            from src.rag.enhanced_rag_pipeline import EnhancedRAGPipeline
            from src.rag.rag_pipeline import RAGPipeline
            from src.rag.response_formatter import ResponseFormatter
            from src.search.search_service import SearchService
            from src.vector_store.vector_db import VectorDatabase

            # Initialize services
            vector_db = VectorDatabase(VECTOR_DB_PERSIST_PATH, COLLECTION_NAME)
            embedding_service = EmbeddingService()
            search_service = SearchService(vector_db, embedding_service)

            # Initialize LLM service from environment
            llm_service = LLMService.from_environment()

            # Initialize base RAG pipeline
            base_rag_pipeline = RAGPipeline(search_service, llm_service)

            # Initialize enhanced pipeline with guardrails if enabled
            if enable_guardrails:
                # Configure guardrails for production use
                guardrails_config = {
                    "min_confidence_threshold": 0.7,
                    "strict_mode": False,
                    "enable_response_enhancement": True,
                    "log_all_results": True,
                }
                rag_pipeline = EnhancedRAGPipeline(base_rag_pipeline, guardrails_config)
            else:
                rag_pipeline = base_rag_pipeline

            # Initialize response formatter
            formatter = ResponseFormatter()

        except ValueError as e:
            return (
                jsonify(
                    {
                        "status": "error",
                        "message": f"LLM service configuration error: {str(e)}",
                        "details": (
                            "Please ensure OPENROUTER_API_KEY or GROQ_API_KEY " "environment variables are set"
                        ),
                    }
                ),
                503,
            )
        except Exception as e:
            return (
                jsonify(
                    {
                        "status": "error",
                        "message": f"Service initialization failed: {str(e)}",
                    }
                ),
                500,
            )

        # Generate RAG response with enhanced validation
        rag_response = rag_pipeline.generate_answer(message.strip())

        # Format response for API with guardrails information
        if include_sources:
            formatted_response = formatter.format_api_response(rag_response, include_debug)

            # Add guardrails information if available
            if hasattr(rag_response, "guardrails_approved"):
                formatted_response["guardrails"] = {
                    "approved": rag_response.guardrails_approved,
                    "confidence": rag_response.guardrails_confidence,
                    "safety_passed": rag_response.safety_passed,
                    "quality_score": rag_response.quality_score,
                    "warnings": getattr(rag_response, "guardrails_warnings", []),
                    "fallbacks": getattr(rag_response, "guardrails_fallbacks", []),
                }
        else:
            formatted_response = formatter.format_chat_response(rag_response, conversation_id, include_sources=False)

        return jsonify(formatted_response)

    except Exception as e:
        return (
            jsonify({"status": "error", "message": f"Chat request failed: {str(e)}"}),
            500,
        )


@app.route("/chat/health", methods=["GET"])
def chat_health():
    """
    Health check endpoint for enhanced RAG chat functionality.

    Returns the status of all RAG pipeline components including guardrails.
    """
    try:
        from src.config import COLLECTION_NAME, VECTOR_DB_PERSIST_PATH
        from src.embedding.embedding_service import EmbeddingService
        from src.llm.llm_service import LLMService
        from src.rag.enhanced_rag_pipeline import EnhancedRAGPipeline
        from src.rag.rag_pipeline import RAGPipeline
        from src.search.search_service import SearchService
        from src.vector_store.vector_db import VectorDatabase

        # Initialize services
        vector_db = VectorDatabase(VECTOR_DB_PERSIST_PATH, COLLECTION_NAME)
        embedding_service = EmbeddingService()
        search_service = SearchService(vector_db, embedding_service)
        llm_service = LLMService.from_environment()

        # Initialize enhanced pipeline
        base_rag_pipeline = RAGPipeline(search_service, llm_service)
        enhanced_pipeline = EnhancedRAGPipeline(base_rag_pipeline)

        # Get comprehensive health status
        health_status = enhanced_pipeline.get_health_status()

        return jsonify(
            {
                "status": "healthy",
                "components": health_status,
                "timestamp": health_status.get("timestamp", "unknown"),
            }
        )

    except ValueError as e:
        # Specific handling for LLM configuration errors
        return (
            jsonify(
                {
                    "status": "error",
                    "message": f"LLM configuration error: {str(e)}",
                    "health": {
                        "pipeline_status": "unhealthy",
                        "components": {
                            "llm_service": {
                                "status": "unconfigured",
                                "error": str(e),
                            }
                        },
                    },
                }
            ),
            503,
        )
    except Exception as e:
        return (
            jsonify(
                {
                    "status": "unhealthy",
                    "error": str(e),
                    "components": {"error": "Failed to initialize components"},
                }
            ),
            500,
        )


@app.route("/guardrails/validate", methods=["POST"])
def validate_response():
    """
    Standalone endpoint for validating responses with guardrails.

    Allows testing of guardrails validation without full RAG pipeline.
    """
    try:
        if not request.is_json:
            return (
                jsonify(
                    {
                        "status": "error",
                        "message": "Content-Type must be application/json",
                    }
                ),
                400,
            )

        data = request.get_json()

        # Validate required parameters
        response_text = data.get("response")
        query_text = data.get("query")
        sources = data.get("sources", [])

        if not response_text or not query_text:
            return (
                jsonify(
                    {
                        "status": "error",
                        "message": "response and query parameters are required",
                    }
                ),
                400,
            )

        # Initialize enhanced pipeline for validation
        from src.config import COLLECTION_NAME, VECTOR_DB_PERSIST_PATH
        from src.embedding.embedding_service import EmbeddingService
        from src.llm.llm_service import LLMService
        from src.rag.enhanced_rag_pipeline import EnhancedRAGPipeline
        from src.rag.rag_pipeline import RAGPipeline
        from src.search.search_service import SearchService
        from src.vector_store.vector_db import VectorDatabase

        # Initialize services
        vector_db = VectorDatabase(VECTOR_DB_PERSIST_PATH, COLLECTION_NAME)
        embedding_service = EmbeddingService()
        search_service = SearchService(vector_db, embedding_service)
        llm_service = LLMService.from_environment()

        # Initialize enhanced pipeline
        base_rag_pipeline = RAGPipeline(search_service, llm_service)
        enhanced_pipeline = EnhancedRAGPipeline(base_rag_pipeline)

        # Perform validation
        validation_result = enhanced_pipeline.validate_response_only(response_text, query_text, sources)

        return jsonify({"status": "success", "validation": validation_result})

    except Exception as e:
        return (
            jsonify({"status": "error", "message": f"Validation failed: {str(e)}"}),
            500,
        )


if __name__ == "__main__":
    app.run(debug=True, host="0.0.0.0", port=8080)