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AI_Agent_Framework.md

Engineering My Data-Centric AI Solution

CDA-NAMESPACE by David Cannan

Lets build on the initial state I've been using, by expanding my favorite tools as a framework with additional functionalities and improving the robustness of the deployment. Here, we'll focus on the following new aspects:

  1. Advanced Data Processing with LLMs
  2. Integrating Additional AI Services
  3. Enhanced Security and Compliance
  4. Scalable Deployment
  5. Real-Time Monitoring and Alerting

Table of Contents

  1. Introduction
  2. Requirements
  3. Architecture Overview
  4. Setting Up the Environment
  5. Advanced Data Processing with LLMs
  6. Integrating Additional AI Services
  7. Enhanced Security and Compliance
  8. Scalable Deployment
  9. Real-Time Monitoring and Alerting
  10. Example Workflow Expansion

1. Introduction

This guide extends the initial framework by incorporating advanced data processing, additional AI services, enhanced security measures, scalable deployment practices, and real-time monitoring. These improvements ensure that the LLM agent framework remains robust, secure, and capable of handling increasing workloads.

2. Requirements

  • Existing Setup: As described in the initial state.
  • New Tools and Libraries:
    • OpenAI GPT-4: For advanced language processing.
    • Prometheus and Grafana: For monitoring and alerting.
    • OAuth 2.0: For secure authentication.

3. Architecture Overview

The extended architecture will include:

  • LLM Agents with Advanced Processing: Utilizing GPT-4 for more complex tasks.
  • AI Services Integration: Incorporating additional AI services such as image recognition or sentiment analysis.
  • Enhanced Security: Implementing OAuth 2.0 and secure storage practices.
  • Scalable Deployment: Using Kubernetes for scaling.
  • Real-Time Monitoring: Employing Prometheus and Grafana for comprehensive monitoring.

4. Setting Up the Environment

We will build on the existing Docker Compose setup and introduce Kubernetes for scalability and Prometheus/Grafana for monitoring.

Kubernetes Setup

  1. Install Kubernetes: Follow the official documentation to set up a Kubernetes cluster.
  2. Deploy MinIO, Weaviate, and LangChain to Kubernetes: 
    apiVersion: apps/v1
kind: Deployment
metadata:
  name: minio
spec:
  replicas: 1
  selector:
    matchLabels:
      app: minio
  template:
    metadata:
      labels:
        app: minio
    spec:
      containers:
      - name: minio
        image: minio/minio
        args:
        - server
        - /data
        env:
        - name: MINIO_ACCESS_KEY
          value: "your-access-key"
        - name: MINIO_SECRET_KEY
          value: "your-secret-key"
        ports:
        - containerPort: 9000

5. Advanced Data Processing with LLMs

5.1. Utilizing GPT-4 for Advanced Tasks

Integrate OpenAI’s GPT-4 for tasks that require sophisticated language understanding.

Example: Advanced Processing Agent

import openai
from minio import Minio
import weaviate

class AdvancedLLMProcessingAgent:
    def __init__(self, minio_client, weaviate_client, openai_api_key):
        self.minio_client = minio_client
        self.weaviate_client = weaviate_client
        openai.api_key = openai_api_key

    def process_data(self, bucket_name, object_name):
        data = self.minio_client.get_object(bucket_name, object_name).read()
        processed_data = openai.Completion.create(
            model="gpt-4",
            prompt=data.decode('utf-8'),
            max_tokens=1000
        )
        self.weaviate_client.batch.create(processed_data['choices'][0]['text'])
        return processed_data['choices'][0]['text']

# Initialize clients
minio_client = Minio("play.min.io", access_key="your-access-key", secret_key="your-secret-key", secure=True)
weaviate_client = weaviate.Client("http://localhost:8080")
agent = AdvancedLLMProcessingAgent(minio_client, weaviate_client, "your-openai-api-key")
agent.process_data("cda-datasets", "example-object")

6. Integrating Additional AI Services

6.1. Image Recognition Service

Integrate an image recognition service to handle image data processing.

Example: Image Recognition Agent

import requests
from minio import Minio

class ImageRecognitionAgent:
    def __init__(self, minio_client, image_recognition_api_url):
        self.minio_client = minio_client
        self.image_recognition_api_url = image_recognition_api_url

    def recognize_image(self, bucket_name, object_name):
        data = self.minio_client.get_object(bucket_name, object_name).read()
        response = requests.post(self.image_recognition_api_url, files={"file": data})
        return response.json()

# Initialize clients
minio_client = Minio("play.min.io", access_key="your-access-key", secret_key="your-secret-key", secure=True)
agent = ImageRecognitionAgent(minio_client, "https://example.com/image-recognition")
result = agent.recognize_image("cda-datasets", "example-image")
print(result)

7. Enhanced Security and Compliance

7.1. Implementing OAuth 2.0

Ensure secure authentication using OAuth 2.0 for all services.

Example: OAuth 2.0 Configuration

version: '3.8'
services:
  auth:
    image: oauth2-proxy/oauth2-proxy
    environment:
      OAUTH2_PROXY_PROVIDER: "google"
      OAUTH2_PROXY_CLIENT_ID: "your-client-id"
      OAUTH2_PROXY_CLIENT_SECRET: "your-client-secret"
      OAUTH2_PROXY_COOKIE_SECRET: "your-cookie-secret"
    ports:
      - "4180:4180"

8. Scalable Deployment

8.1. Using Kubernetes

Deploy the entire stack on Kubernetes for scalability.

Kubernetes Deployment Example

apiVersion: apps/v1
kind: Deployment
metadata:
  name: advanced-llm-agent
spec:
  replicas: 3
  selector:
    matchLabels:
      app: advanced-llm-agent
  template:
    metadata:
      labels:
        app: advanced-llm-agent
    spec:
      containers:
      - name: advanced-llm-agent
        image: your-username/advanced-llm-agent:latest
        ports:
        - containerPort: 5000

9. Real-Time Monitoring and Alerting

9.1. Setting Up Prometheus and Grafana

Monitor your infrastructure in real-time with Prometheus and Grafana.

Prometheus Configuration

global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'kubernetes'
    kubernetes_sd_configs:
      - role: node

Grafana Setup

  1. Deploy Grafana: 
    docker run -d --name=grafana -p 3000:3000 grafana/grafana
  2. Configure Grafana to use Prometheus as a data source.

10. Example Workflow Expansion

Expanded Workflow: Data Ingestion, Advanced Processing, and Image Recognition

  1. Data Ingestion Agent: Fetch data from a URL and store it in MinIO.
  2. Advanced Processing Agent: Retrieve data from MinIO, process it using GPT-4, and store results in Weaviate.
  3. Image Recognition Agent: Handle image data processing using an image recognition API.
  4. Notification Agent: Send notifications upon completion.

Example Workflow Script (continued)

from minio import Minio
import openai
import requests
import weaviate

class AdvancedLLMProcessingAgent:
    def __init__(self, minio_client, weaviate_client, openai_api_key):
        self.minio_client = minio_client
        self.weaviate_client = weaviate_client
        openai.api_key = openai_api_key

    def process_data(self, bucket_name, object_name):
        data = self.minio_client.get_object(bucket_name, object_name).read()
        processed_data = openai.Completion.create(
            model="gpt-4",
            prompt=data.decode('utf-8'),
            max_tokens=1000
        )
        self.weaviate_client.batch.create(processed_data['choices'][0]['text'])
        return processed_data['choices'][0]['text']

class ImageRecognitionAgent:
    def __init__(self, minio_client, image_recognition_api_url):
        self.minio_client = minio_client
        self.image_recognition_api_url = image_recognition_api_url

    def recognize_image(self, bucket_name, object_name):
        data = self.minio_client.get_object(bucket_name, object_name).read()
        response = requests.post(self.image_recognition_api_url, files={"file": data})
        return response.json()

# Initialize clients
minio_client = Minio("play.min.io", access_key="your-access-key", secret_key="your-secret-key", secure=True)
weaviate_client = weaviate.Client("http://localhost:8080")

# Create agents
llm_agent = AdvancedLLMProcessingAgent(minio_client, weaviate_client, "your-openai-api-key")
image_agent = ImageRecognitionAgent(minio_client, "https://example.com/image-recognition")

# Ingest and process data
def ingest_and_process_data(data_url, bucket_name, object_name):
    # Fetch data from URL and store it in MinIO
    response = requests.get(data_url)
    minio_client.put_object(bucket_name, object_name, response.content, len(response.content))
    
    # Process data using GPT-4
    processed_data = llm_agent.process_data(bucket_name, object_name)
    
    # If the data is an image, recognize image using the Image Recognition Agent
    if object_name.endswith(('.png', '.jpg', '.jpeg')):
        image_recognition_result = image_agent.recognize_image(bucket_name, object_name)
        return {
            "processed_data": processed_data,
            "image_recognition_result": image_recognition_result
        }
    
    return {
        "processed_data": processed_data
    }

# Example usage
result = ingest_and_process_data("https://example.com/data", "cda-datasets", "example-object")
print(result)

Kubernetes Deployment

Next, we will set up the Kubernetes deployment for scalability and reliability.

Kubernetes Deployment Configuration

  1. Create Kubernetes Deployment YAML files for each service.
  2. Deploy services to Kubernetes.

Example: MinIO Deployment YAML

apiVersion: apps/v1
kind: Deployment
metadata:
  name: minio
spec:
  replicas: 1
  selector:
    matchLabels:
      app: minio
  template:
    metadata:
      labels:
        app: minio
    spec:
      containers:
      - name: minio
        image: minio/minio
        args:
        - server
        - /data
        env:
        - name: MINIO_ACCESS_KEY
          value: "your-access-key"
        - name: MINIO_SECRET_KEY
          value: "your-secret-key"
        ports:
        - containerPort: 9000
---
apiVersion: v1
kind: Service
metadata:
  name: minio
spec:
  ports:
  - port: 9000
    targetPort: 9000
  selector:
    app: minio

Example: Weaviate Deployment YAML

apiVersion: apps/v1
kind: Deployment
metadata:
  name: weaviate
spec:
  replicas: 1
  selector:
    matchLabels:
      app: weaviate
  template:
    metadata:
      labels:
        app: weaviate
    spec:
      containers:
      - name: weaviate
        image: semitechnologies/weaviate
        env:
        - name: QUERY_DEFAULTS_LIMIT
          value: "20"
        - name: AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED
          value: "true"
        ports:
        - containerPort: 8080
---
apiVersion: v1
kind: Service
metadata:
  name: weaviate
spec:
  ports:
  - port: 8080
    targetPort: 8080
  selector:
    app: weaviate

Example: LangChain Deployment YAML

apiVersion: apps/v1
kind: Deployment
metadata:
  name: langchain
spec:
  replicas: 1
  selector:
    matchLabels:
      app: langchain
  template:
    metadata:
      labels:
        app: langchain
    spec:
      containers:
      - name: langchain
        image: your-username/langchain:latest
        ports:
        - containerPort: 5000
---
apiVersion: v1
kind: Service
metadata:
  name: langchain
spec:
  ports:
  - port: 5000
    targetPort: 5000
  selector:
    app: langchain

Example: Advanced LLM Agent Deployment YAML

apiVersion: apps/v1
kind: Deployment
metadata:
  name: advanced-llm-agent
spec:
  replicas: 3
  selector:
    matchLabels:
      app: advanced-llm-agent
  template:
    metadata:
      labels:
        app: advanced-llm-agent
    spec:
      containers:
      - name: advanced-llm-agent
        image: your-username/advanced-llm-agent:latest
        ports:
        - containerPort: 5000
---
apiVersion: v1
kind: Service
metadata:
  name: advanced-llm-agent
spec:
  ports:
  - port: 5000
    targetPort: 5000
  selector:
    app: advanced-llm-agent

Real-Time Monitoring and Alerting

Prometheus and Grafana Setup

  1. Deploy Prometheus: 
    apiVersion: apps/v1
kind: Deployment
metadata:
  name: prometheus
spec:
  replicas: 1
  selector:
    matchLabels:
      app: prometheus
  template:
    metadata:
      labels:
        app: prometheus
    spec:
      containers:
      - name: prometheus
        image: prom/prometheus
        ports:
        - containerPort: 9090

apiVersion: v1 kind: Service metadata: name: prometheus spec: ports:

  • port: 9090 targetPort: 9090 selector: app: prometheus

2. **Deploy Grafana**:
   ```yaml
   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: grafana
   spec:
     replicas: 1
     selector:
       matchLabels:
         app: grafana
     template:
       metadata:
         labels:
           app: grafana
       spec:
         containers:
         - name: grafana
           image: grafana/grafana
           ports:
           - containerPort: 3000
---
apiVersion: v1
kind: Service
metadata:
  name: grafana
spec:
  ports:
  - port: 3000
    targetPort: 3000
  selector:
    app: grafana
  1. Configure Grafana to use Prometheus as a data source:
    • Access Grafana at http://<your-grafana-ip>:3000.
    • Add Prometheus as a data source by providing the Prometheus service URL (http://prometheus:9090).

By following these steps, you can extend the initial LLM agent framework with advanced data processing, additional AI services, enhanced security, scalable deployment, and real-time monitoring. This setup ensures a robust, secure, and scalable infrastructure capable of handling complex workflows and large-scale data processing tasks.

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