Data Framework Integration

API Gateway Proxy
for LlamaIndex

Connect LlamaIndex data framework with API Gateway proxy. Build powerful RAG applications with unified LLM access, intelligent caching, and comprehensive monitoring.

📊
Data Connectors
🔍
Vector Index
💬
Query Engine
🚀
Production Ready

Why Gateway with LlamaIndex?

Enterprise features for your RAG applications

🔌
Unified LLM Access
Single interface for all LLM providers. Connect OpenAI, Anthropic, local models through one gateway.
💾
Response Caching
Cache embeddings and query responses. Reduce costs and improve latency for repeated queries.
📈
Usage Analytics
Track token usage, query patterns, and costs across all LlamaIndex operations.
🔄
Auto Retries
Automatic retry with exponential backoff for failed LLM calls and embedding requests.
⚡
Rate Limiting
Protect against quota exhaustion. Implement per-user or global rate limits.
🎯
Fallback Support
Configure fallback models for high availability. Ensure RAG apps stay responsive.

Architecture Overview

How LlamaIndex connects through the gateway

📄
Documents
Data sources
🦙
LlamaIndex
Data framework
🚪
Gateway Proxy
Unified access
🤖
LLM Providers
AI models

Code Example

Quick start with LlamaIndex and Gateway

🦙 LlamaIndex with Gateway Python 
# Import LlamaIndex and Gateway components
from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms import GatewayLLM
from llama_index.embeddings import GatewayEmbedding

# Configure Gateway LLM
llm = GatewayLLM(
    endpoint="https://gateway.example.com",
    api_key="your-gateway-key",
    model="gpt-4",
    cache=True
)

# Configure Gateway Embeddings
embed_model = GatewayEmbedding(
    endpoint="https://gateway.example.com",
    model="text-embedding-3-small"
)

# Load documents and build index
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(
    documents,
    llm=llm,
    embed_model=embed_model
)

# Create query engine
query_engine = index.as_query_engine()

# Query your data
response = query_engine.query("What is the main topic?")
print(response)

Use Cases

Build powerful applications with LlamaIndex and Gateway

📚
Document Q&A
Build intelligent document search and question answering systems with semantic understanding.
RAG Semantic Search PDF
💬
Chat with Data
Create conversational interfaces that understand your data context and history.
Chatbot Memory Context
🔍
Knowledge Base
Build searchable knowledge bases with automatic indexing and retrieval.
Search Index Retrieval
📊
Data Analysis
Analyze structured and unstructured data with natural language queries.
Analytics SQL Pandas