Merge branch 'main' into dependabot/pip/ai/generative-ai-service/hr-goal-alignment/files/protobuf-5.29.5

Author: AlexanderHodicke

ai/gen-ai-agents/standard_ai_agent/README.md

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+# Generative AI Chatbot
+
+This repository contains a Flask-based chatbot application utilizing Oracle's Generative AI agent.
+
+## Getting Started
+
+1. **Clone the Repository**:
+```bash
+git clone [your-repository-link]
+cd [your-repository-folder]
+```
+
+2. **Configuration**:
+- Open the Python file and replace the `AGENT_ENDPOINT_ID` with your Oracle Generative AI agent endpoint OCID:
+
+```python
+AGENT_ENDPOINT_ID = "your-agent-endpoint-ocid-here"
+```
+
+3. **Install Dependencies**:
+```bash
+pip install -r requirements.txt
+```
+
+4. **Run the Application**:
+```bash
+python [your-main-file].py
+```
+
+## Usage
+
+Access the chatbot via your browser:
+```
+http://localhost:5000
+```
+
+Enter messages in the web interface to interact with your generative AI agent.
+
+## Requirements
+- Python 3.8+
+- OCI SDK
+- Flask
+- qrcode
+
+Ensure your OCI configuration is properly set in `~/.oci/config`.
+

ai/gen-ai-agents/standard_ai_agent/chat_agent.py

@@ -0,0 +1,234 @@
+from __future__ import annotations
+import time
+
+import json
+import logging
+from pathlib import Path
+from typing import Any, Dict, Optional
+import base64
+from io import BytesIO
+import qrcode
+import requests
+
+
+import oci
+from flask import Flask, jsonify, request, send_from_directory
+from flask_cors import CORS
+from oci.generative_ai_agent_runtime.models import ChatDetails, CreateSessionDetails, FunctionCallingPerformedAction
+
+# ---------------------------------------------------------------------------
+# Configuration UPDATE with your endpoint OCID and your Service Endpoint
+# ---------------------------------------------------------------------------
+
+AGENT_ENDPOINT_ID = "ocid1.genaiagentendpoint.oc1.eu-frankfurt-1."
+SERVICE_ENDPOINT = "https://agent-runtime.generativeai.eu-frankfurt-1.oci.oraclecloud.com"
+PROFILE_NAME = "DEFAULT"  # profile in ~/.oci/config
+
+logging.basicConfig(format="%(asctime)s %(levelname)s: %(message)s", level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class ChatAgent:
+
+    def __init__(self) -> None:
+        cfg = oci.config.from_file("~/.oci/config", profile_name=PROFILE_NAME)
+        self.client = oci.generative_ai_agent_runtime.GenerativeAiAgentRuntimeClient(
+            config=cfg,
+            service_endpoint=SERVICE_ENDPOINT,
+            retry_strategy=oci.retry.NoneRetryStrategy(),
+            timeout=(10, 240),
+        )
+        self.session_id: Optional[str] = None
+        self.current_user: Optional[str] = None
+    # ------------------------------------------------------------------
+    def _create_session(self, *, user: str) -> str:
+        details = CreateSessionDetails(
+            display_name=f"Flask-session for {user}",
+            description="Session created by flask_chat_server.py",
+        )
+        resp = self.client.create_session(details, AGENT_ENDPOINT_ID)
+        self.session_id = resp.data.id
+        self.current_user = user
+        logger.debug("Created new session %s for %s", self.session_id, user)
+        return self.session_id
+
+    # ------------------------------------------------------------------
+    # QR
+    @staticmethod
+    def generate_qr_code(url: str, size: int = 200) -> dict:
+        """Generates a QR code for the given URL and returns it as a base64-encoded PNG."""
+        qr = qrcode.QRCode(box_size=10, border=2)
+        qr.add_data(url)
+        qr.make(fit=True)
+        img = qr.make_image(fill="black", back_color="white").resize((size, size))
+        buffer = BytesIO()
+        img.save(buffer, format="PNG")
+        b64 = base64.b64encode(buffer.getvalue()).decode()
+        return {"image_base64": b64}
+    
+    # ------------------------------------------------------------------
+    @staticmethod
+    def _extract_text(message_obj: Any) -> str:
+        try:
+            content_list = getattr(message_obj, "content", None)
+            if content_list:
+                first = content_list[0] if isinstance(content_list, list) else content_list
+                return getattr(first, "text", str(first))
+            return str(message_obj)
+        except Exception as exc:
+            logger.warning("Failed to extract text: %s", exc)
+            return str(message_obj)
+
+    def convert_sql_to_human_readable(self, sql_output: str, user: str) -> str:
+        """Convert SQL output to human readable format using OCI LLM."""
+        try:
+            if len(sql_output) > 1000:
+                return f"Hi {user}, the data you requested is quite large. Could you please specify what specific information you're looking for? This will help me provide you with a more focused and useful response."
+
+            prompt = f"""Please convert this SQL output into a friendly, human-readable format. 
+            Address the user as '{user}' once and present the information in a clear, conversational way.
+            If it's a list or table, format it nicely Please do not provide in the answer SQL query that was passed only human nice language. 
+            Here's the SQL output:
+            {sql_output}"""
+
+            chat_details = ChatDetails(
+                user_message=prompt,
+                session_id=self.session_id,
+                should_stream=False
+            )
+            reply = self.client.chat(AGENT_ENDPOINT_ID, chat_details)
+            return self._extract_text(reply.data.message)
+
+        except Exception as e:
+            logger.error(f"Error converting SQL output: {e}")
+            return f"Hi {user}, I encountered an error while processing your data. Please try again."
+
+    def _is_sql_output(self, text: str) -> bool:
+        """Check if the text appears to be SQL output."""
+        sql_indicators = [
+            "SELECT", "FROM", "WHERE", "JOIN", "GROUP BY", "ORDER BY",
+            "INSERT INTO", "UPDATE", "DELETE FROM", "CREATE TABLE"
+        ]
+        return any(indicator in text.upper() for indicator in sql_indicators)
+
+    # ------------------------------------------------------------------
+    def send(self, *, prompt: str, user: str, max_tokens: Optional[int] = None) -> str:
+        if self.session_id is None or self.current_user != user:
+            self._create_session(user=user)
+
+        weather_response = self.process_weather_query(prompt)
+        if weather_response:
+            return weather_response
+
+        filter_conditions: Dict[str, Any] = {
+            "filterConditions": [
+                {
+                    "field": "person",
+                    "field_type": "list_of_string",
+                    "operation": "contains",
+                    "value": user,
+                }
+            ]
+        }
+
+        logger.info("Applying RAG filter with author='%s'", user)
+
+        tool_params: Dict[str, str] = {"rag": json.dumps(filter_conditions)}
+        if max_tokens is not None:
+            tool_params["max_tokens"] = str(max_tokens)
+
+        chat_details = ChatDetails(
+            user_message=prompt,
+            session_id=self.session_id,
+            should_stream=False,
+            tool_parameters=tool_params,
+        )
+        reply = self.client.chat(AGENT_ENDPOINT_ID, chat_details)
+        data = reply.data
+
+        response_text = self._extract_text(data.message)
+
+        if self._is_sql_output(response_text):
+            return self.convert_sql_to_human_readable(response_text, user)
+
+        if getattr(data, "required_actions", None):
+            action = data.required_actions[0]
+            func_call = action.function_call
+            args = json.loads(func_call.arguments)
+            if func_call.name == "generateQrCode":
+                result = self.generate_qr_code(**args)
+                performed = FunctionCallingPerformedAction(
+                    action_id=action.action_id,
+                    function_call_output=json.dumps(result)
+                )
+                final_reply = self.client.chat(
+                    AGENT_ENDPOINT_ID,
+                    ChatDetails(
+                        session_id=self.session_id,
+                        user_message="",
+                        performed_actions=[performed],
+                        should_stream=False,
+                        tool_parameters=tool_params,
+                    )
+                )
+
+                raw = self._extract_text(final_reply.data.message)
+                payload = result["image_base64"]
+                if payload in raw:
+                    raw = raw.replace(payload, "").strip()
+                if not raw:
+                    raw = "Here's your QR code!"
+                return {
+                    "message": raw,
+                    "qr_image_base64": payload
+                }
+
+        return self._extract_text(reply.data.message)
+
+
+# ---------------------------------------------------------------------------
+# Flask wiring
+# ---------------------------------------------------------------------------
+
+app = Flask(__name__)
+CORS(app)
+chat_agent = ChatAgent()
+
+@app.route("/")
+def index():
+    return send_from_directory(Path(app.root_path), "index.html")
+
+@app.route("/config.js")
+def config_js():
+    return send_from_directory(Path(app.root_path), "config.js")
+
+@app.route("/chat", methods=["POST"])
+def chat() -> tuple[Any, int] | Any:
+    data = request.get_json(silent=True) or {}
+    prompt_raw = str(data.get("message", "").strip())
+    user_raw = str(data.get("user", "anonymous").strip() or "anonymous")
+
+    #user = user_raw[0].upper() + user_raw[1:] if user_raw else user_raw
+    user = user_raw
+    max_tokens_raw = data.get("max_tokens")
+
+    if not prompt_raw:
+        return jsonify({"error": "Missing 'message'"}), 400
+
+    try:
+        max_tokens = int(max_tokens_raw) if max_tokens_raw is not None else None
+    except (TypeError, ValueError):
+        return jsonify({"error": "'max_tokens' must be an integer"}), 400
+
+    try:
+        reply = chat_agent.send(prompt=prompt_raw, user=user, max_tokens=max_tokens)
+        if isinstance(reply, dict):
+            return jsonify({"user": user, **reply})
+        else:
+            return jsonify({"user": user, "message": reply})
+    except Exception as exc:
+        logger.exception("/chat failed: %s", exc)
+        return jsonify({"error": str(exc)}), 500
+
+
+if __name__ == "__main__":
+    app.run(debug=True, port=5000)