Python
# Basic RAG pipeline setup
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Context-aware chunking for industrial reports
splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    separators=["\n\n", "\n", "Shift:", "Equipment:", " ", ""]
)

# Vector store with retrieval optimization
vectorstore = Chroma(
    embedding_function=OpenAIEmbeddings(),
    collection_metadata={"hnsw:space": "cosine"}
)

1. Design Architecture

   • Define agent responsibilities and communication patterns
   • Establish data flow between components
   • Plan token usage and latency requirements

2. Build RAG Foundation

   • Implement context-aware document chunking
   • Set up vector database with appropriate embeddings
   • Create retrieval evaluation metrics (Hit Rate, MRR)

3. Orchestrate Agents

   • Implement ReAct or Plan-and-Execute patterns
   • Define JSON schemas for tool interactions
   • Add episodic memory for recurring issues

4. Integrate Safety Measures

   • Add prompt injection guards
   • Implement data validation layers
   • Create audit trails for AI decisions

5. Optimize for Production

   • Profile token usage and costs
   • Implement caching strategies
   • Add monitoring and alerting

Progress:

• Architecture defined
• RAG pipeline implemented
• Agent orchestration working
• Safety measures in place
• Production optimized

Example 1: Industrial Query Processing Input: "Why did Fleet A have 20% more downtime than Fleet B last week?" Output:

Python
# Agent breaks down into sub-queries
queries = [
    "Fleet A downtime events last week",
    "Fleet B downtime events last week", 
    "Common failure patterns across fleets"
]
# Retrieves relevant maintenance logs, compares patterns
# Returns grounded analysis with specific equipment issues

Example 2: Multi-Agent Function Schema Input: Agent needs to query production data Output:

JSON
{
  "name": "query_production_metrics",
  "description": "Query production database for specific metrics",
  "parameters": {
    "type": "object",
    "properties": {
      "fleet_id": {"type": "string"},
      "date_range": {"type": "object"},
      "metric_types": {"type": "array", "items": {"type": "string"}}
    }
  }
}