Enterprise Integration Blueprints

Production Integration & Deployment Guides

Step-by-step engineering tutorials, infrastructure deployment scripts, and architectural blueprints for deploying AIConnect AI agents, speech engines, and vector RAG pipelines into production.

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Featured Integration Blueprints

Edge Deployment02 — Local AI Agents

Deploying Air-Gapped Local AI Agents with Ollama & sqlite-vec

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Configure offline desktop AI agents operating completely on local hardware. Covers quantized GGUF model ingestion with Ollama, compiling C-based sqlite-vec extensions for vector search, and OS automation safety limits.

Implementation Steps

Step 1: Install Ollama runtime and download GGUF 4-bit quantized model (llama3.3:8b-instruct-q4_K_M).

Step 2: Initialize embedded SQLite database and enable the sqlite-vec extension module.

Step 3: Construct a LangGraph state machine to perform zero-cloud local RAG synthesis.

Production Configuration / Code Snippet

# 1. Pull Quantized Model locally
ollama pull llama3.3:8b-instruct-q4_K_M
ollama pull nomic-embed-text

# 2. Verify local C vector extension in Python
import sqlite3, sqlite_vec
db = sqlite3.connect("local_vault.db")
db.enable_load_extension(True)
sqlite_vec.load(db)
print("✓ sqlite-vec loaded successfully.")

DevOps & IaC04 — AWS AI Cloud Automation

Configuring Bedrock Action Groups with AWS Lambda & Terraform

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Connect Amazon Bedrock Agents to serverless Boto3 Lambda execution handlers defined by OpenAPI 3.0 schemas. Automate CloudWatch log insights queries and Terraform code patching.

Implementation Steps

Step 1: Upload OpenAPI 3.0 schema YAML defining agent action parameters to Amazon S3.

Step 2: Provision AWS Lambda action execution handler with least-privilege IAM permissions.

Step 3: Link Bedrock Agent with Anthropic Claude 3.5 Sonnet foundation model backbone.

Production Configuration / Code Snippet

# Terraform AWS Bedrock Agent Action Group Definition
resource "aws_bedrockagent_agent_action_group" "devops_actions" {
  action_group_name          = "DevOpsRemediationGroup"
  agent_id                   = aws_bedrockagent_agent.devops_agent.id
  agent_version              = "DRAFT"
  action_group_executor {
    lambda = aws_lambda_function.boto3_remediator.arn
  }
  api_schema {
    s3 {
      s3_bucket_name = "company-agent-schemas"
      s3_object_key  = "openapi-devops.yaml"
    }
  }
}

Speech & Voice01 — Whisper & Call Center AI

Setting Up Real-Time Whisper Streaming on AWS EKS with TensorRT-LLM

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Deploy auto-scaling NVIDIA GPU worker nodes on Amazon EKS running TensorRT-LLM compiled Whisper v3 model engines. Connect WebSocket PCM 16kHz audio streams with PyAnnote diarization.

Implementation Steps

Step 1: Compile HuggingFace Whisper v3 into TensorRT-LLM engine binaries with FP16 precision.

Step 2: Deploy Kubernetes DaemonSet with NVIDIA Container Toolkit on g5.xlarge instances.

Step 3: Expose WebSocket ALB load balancer with TLS termination and sticky session routing.

Production Configuration / Code Snippet

# Kubernetes Deployment snippet for TensorRT-LLM Whisper
apiVersion: apps/v1
kind: Deployment
metadata:
  name: whisper-tensorrt-worker
spec:
  replicas: 4
  template:
    spec:
      containers:
      - name: whisper-engine
        image: 123456789012.dkr.ecr.us-east-1.amazonaws.com/whisper-trt:v3
        resources:
          limits:
            nvidia.com/gpu: 1

Data Engineering06 — Enterprise ETL & RAG Pipelines

Building Enterprise Vector RAG Pipelines with PySpark & OpenSearch Serverless

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Ingest terabyte-scale unstructured documents using serverless AWS Glue PySpark jobs. Extract semantic vector embeddings via Bedrock Titan Text Embeddings v2 and populate OpenSearch hybrid BM25/k-NN indices.

Implementation Steps

Step 1: Configure S3 Iceberg data lake event triggers streaming into Amazon Kinesis Data Streams.

Step 2: Execute AWS Glue PySpark job for parallel document chunking and vector batch creation.

Step 3: Populate OpenSearch Serverless k-NN collection and wire Cohere cross-encoder reranking.

Production Configuration / Code Snippet

# PySpark AWS Glue OpenSearch Serverless Writer
from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet("s3://data-lake/processed-chunks/")

# Write embeddings into OpenSearch Serverless Vector Collection
df.write \
  .format("org.opensearch.spark.sql") \
  .option("opensearch.nodes", "https://xyz.us-east-1.aoss.amazonaws.com") \
  .option("opensearch.resource", "enterprise-vector-index") \
  .mode("append") \
  .save()