Backend Automation Using LlamaIndex

For Python backend services

You are a LlamaIndex backend automation agent.

Your role is to create intelligent data pipelines, retrieval systems, and AI automation services.

Responsibilities:

- Build document ingestion pipelines.
- Create indexes from structured and unstructured data.
- Implement query engines.
- Manage embeddings and vector databases.
- Automate backend workflows.
- Connect APIs and enterprise systems.

Architecture Requirements:

Use:
- LlamaIndex Data Connectors
- Vector Indexes
- Query Engines
- Agents
- Tool Calling
- Memory Management

Always consider:
- Performance
- Security
- Data privacy
- Error handling
- Scalability

When designing solutions provide:

1. Architecture Overview
2. Components
3. Data Flow
4. API Design
5. Implementation Steps
6. Optimization Recommendations

LlamaIndex Document Processing Agent Prompt

You are an enterprise document intelligence agent powered by LlamaIndex.

Your tasks:

- Ingest documents from multiple sources.
- Clean and preprocess text.
- Generate embeddings.
- Create searchable indexes.
- Answer user queries.

Supported Sources:
- PDFs
- Websites
- Databases
- Cloud storage
- Internal documents

Pipeline:

Input Data
↓
Document Loader
↓
Text Processing
↓
Embedding Generation
↓
Vector Storage
↓
Retriever
↓
LLM Response

Rules:
- Preserve document meaning.
- Remove duplicate information.
- Maintain metadata.
- Return traceable answers.

Backend Automation Architecture

Client Request
      │
      ▼
Backend API
      │
      ▼
 LlamaIndex Workflow
      │
 ┌────┴─────────────┐
 │                  │
 ▼                  ▼
Database        Documents
 │                  │
 ▼                  ▼
Retriever     Vector Store
      │
      ▼
LLM
      │
      ▼
Automated Response

Installing LlamaIndex

pip install llama-index

For OpenAI:

pip install llama-index-llms-openai

For vector databases:

pip install llama-index-vector-stores-chroma

Project Structure

backend/
│
├── app.py
├── workflows.py
├── agents.py
├── database.py
├── documents/
├── api/
└── index/

Loading Business Documents

from llama_index.core import SimpleDirectoryReader

documents = SimpleDirectoryReader(
    "./documents"
).load_data()

Creating an Index

from llama_index.core import VectorStoreIndex

index = VectorStoreIndex.from_documents(documents)

The index enables semantic search over enterprise content.

Building a Query Engine

query_engine = index.as_query_engine()

response = query_engine.query(
    "Summarize last month's sales report."
)

print(response)

Automating Database Queries

Example:

from sqlalchemy import create_engine

engine = create_engine(
    "postgresql://user:password@localhost/company"
)

LlamaIndex can translate natural language into SQL queries.

Example:

“Show customers with unpaid invoices.”

Generated workflow:

Natural Language
       │
       ▼
SQL Generator
       │
       ▼
Database
       │
       ▼
Results

Automating API Calls

Example tool:

import requests

def get_weather(city):

    return requests.get(
        f"https://api.weatherapi.com/{city}"
    ).json()

The AI agent can determine when to invoke the API automatically.

Workflow Automation

Example pipeline:

Invoice Uploaded
        │
        ▼
OCR
        │
        ▼
Information Extraction
        │
        ▼
Accounting Database
        │
        ▼
Approval Workflow
        │
        ▼
Email Notification

Creating AI Agents

Example:

from llama_index.core.agent.workflow import FunctionAgent

agent = FunctionAgent(
    tools=[get_weather]
)

Multi-Step Automation

Example workflow:

User Request
      │
      ▼
Agent
      │
      ├─────────────► CRM
      │
      ├─────────────► ERP
      │
      ├─────────────► Inventory
      │
      └─────────────► Email Service

Background Task Automation

Example:

Schedule Job

↓

Read Emails

↓

Summarize

↓

Categorize

↓

Store Results

FastAPI Integration

from fastapi import FastAPI

app = FastAPI()

@app.post("/ask")
async def ask(question: str):

    return {
        "answer": str(
            query_engine.query(question)
        )
    }

Example Enterprise Workflow

Employee Request

↓

Authentication

↓

LlamaIndex Agent

↓

Retrieve Company Policies

↓

Search Knowledge Base

↓

Query HR Database

↓

Generate Response

↓

Log Activity