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Author: mdwasimshaikh2010-rgb

omnicare-engine/Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 8000
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

omnicare-engine/README.md

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+# OmniCare Engine
+
+Enterprise customer support engine integrating Claude, Llama, Mistral, RAG, Bedrock Agents, Guardrails, and Long-Term Memory (LTM).
+
+## Quickstart
+
+1. Run locally via Docker Compose:
+   ```bash
+   docker-compose up --build
+   ```
+
+2. Deploy to AWS ECR:
+   ```bash
+   chmod +x deploy.sh
+   ./deploy.sh
+   ```

omnicare-engine/app/__init__.py

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+import requests
+import os
+
+def async_llama_audit(user_id: str, prompt: str, response: str):
+    llama_url = os.getenv("LLAMA_HOST", "http://localhost:11434/api/generate")
+    payload = {
+        "model": "llama3",
+        "prompt": f"Audit transaction for user {user_id}.\nInput: {prompt}\nOutput: {response}",
+        "stream": False
+    }
+    try:
+        requests.post(llama_url, json=payload, timeout=2)
+    except Exception:
+        pass

omnicare-engine/app/audit.py

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+import boto3
+import os
+
+def get_user_ltm(user_id: str) -> dict:
+    dynamodb = boto3.resource('dynamodb', region_name=os.getenv('AWS_REGION', 'us-east-1'))
+    table = dynamodb.Table('OmniCare_UserMemory')
+    try:
+        response = table.get_item(Key={'userId': user_id})
+        return response.get('Item', {'preferred_color': 'Blue', 'vip_status': True})
+    except Exception:
+        return {'preferred_color': 'Unknown', 'vip_status': False}

omnicare-engine/app/ltm.py

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+from fastapi import FastAPI, BackgroundTasks
+from pydantic import BaseModel
+from app.routing import classify_intent
+from app.ltm import get_user_ltm
+from app.rag import retrieve_policy_context
+from app.orchestrator import generate_response
+from app.audit import async_llama_audit
+
+app = FastAPI(title="OmniCare Engine")
+
+class RequestModel(BaseModel):
+    user_id: str
+    prompt: str
+
+@app.get("/health")
+def health_check():
+    return {"status": "healthy"}
+
+@app.post("/chat")
+def process_request(data: RequestModel, background_tasks: BackgroundTasks):
+    intent = classify_intent(data.prompt)
+    ltm_data = get_user_ltm(data.user_id)
+    rag_data = retrieve_policy_context(data.prompt)
+    
+    answer = generate_response(data.prompt, ltm_data, rag_data)
+    
+    # Run self-hosted Llama audit asynchronously
+    background_tasks.add_task(async_llama_audit, data.user_id, data.prompt, answer)
+    
+    return {
+        "intent": intent,
+        "response": answer
+    }

omnicare-engine/app/main.py

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+import boto3
+import os
+
+def generate_response(user_prompt: str, ltm_context: dict, rag_context: str) -> str:
+    bedrock = boto3.client('bedrock-runtime', region_name=os.getenv('AWS_REGION', 'us-east-1'))
+    
+    system_context = f"User Memory (LTM): {ltm_context}\nPolicy Context (RAG): {rag_context}"
+    guardrail_id = os.getenv('BEDROCK_GUARDRAIL_ID')
+    
+    kwargs = {
+        "modelId": "anthropic.claude-3-5-sonnet-20240620-v1:0",
+        "messages": [{"role": "user", "content": [{"text": user_prompt}]}],
+        "system": [{"text": system_context}]
+    }
+    
+    if guardrail_id:
+        kwargs["guardrailConfig"] = {
+            "guardrailIdentifier": guardrail_id,
+            "guardrailVersion": "1"
+        }
+        
+    response = bedrock.converse(**kwargs)
+    return response['output']['message']['content'][0]['text']

omnicare-engine/app/orchestrator.py

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+from opensearchpy import OpenSearch
+import os
+
+def retrieve_policy_context(query: str) -> str:
+    host = os.getenv('OPENSEARCH_HOST')
+    if not host:
+        return "Standard 30-day return policy applies."
+    
+    client = OpenSearch(hosts=[{'host': host, 'port': 443}], use_ssl=True)
+    search_query = {"size": 1, "query": {"match": {"text": query}}}
+    try:
+        res = client.search(body=search_query, index="policies")
+        return res['hits']['hits'][0]['_source']['text']
+    except Exception:
+        return "Standard policy document context."

omnicare-engine/app/rag.py

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+import os
+from mistralai import Mistral
+
+def classify_intent(user_query: str) -> str:
+    api_key = os.getenv("MISTRAL_API_KEY")
+    if not api_key:
+        return "GENERAL"
+    client = Mistral(api_key=api_key)
+    prompt = f"Classify intent into strictly one word [REFUND, INVENTORY, GENERAL]: {user_query}"
+    response = client.chat.complete(
+        model="mistral-small-latest",
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return response.choices[0].message.content.strip().upper()

omnicare-engine/app/routing.py

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+#!/bin/bash
+set -e
+
+# Config settings
+AWS_REGION="us-east-1"
+AWS_ACCOUNT_ID=$(aws sts get-caller-identity --query Account --output text)
+ECR_REPO_NAME="omnicare-engine"
+IMAGE_TAG="latest"
+
+echo "=== Step 1: Authenticating Docker to AWS ECR ==="
+aws ecr get-login-password --region $AWS_REGION | docker login --username AWS --password-stdin $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com
+
+echo "=== Step 2: Creating ECR Repository (if not exists) ==="
+aws ecr describe-repositories --repository-names $ECR_REPO_NAME --region $AWS_REGION || \
+aws ecr create-repository --repository-name $ECR_REPO_NAME --region $AWS_REGION
+
+echo "=== Step 3: Building and Tagging Docker Image ==="
+docker build -t $ECR_REPO_NAME .
+docker tag $ECR_REPO_NAME:latest $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$ECR_REPO_NAME:$IMAGE_TAG
+
+echo "=== Step 4: Pushing Docker Image to ECR ==="
+docker push $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$ECR_REPO_NAME:$IMAGE_TAG
+
+echo "=== Success! Docker image deployed to ECR ==="
+echo "URI: $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$ECR_REPO_NAME:$IMAGE_TAG"