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Overview

LlamaIndex is a data framework for LLM applications, especially powerful for building RAG (Retrieval Augmented Generation) systems. LemonData works seamlessly with LlamaIndex’s OpenAI integration.

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Installation

pip install llama-index llama-index-llms-openai llama-index-embeddings-openai

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Basic Configuration

from llama_index.llms.openai import OpenAI
from llama_index.core import Settings

# Configure LLM
llm = OpenAI(
    model="gpt-4o",
    api_key="sk-your-lemondata-key",
    api_base="https://api.lemondata.cc/v1"
)

# Set as default
Settings.llm = llm

# Simple query
response = llm.complete("What is LemonData?")
print(response.text)

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Using Different Models

# OpenAI GPT-4o
gpt4 = OpenAI(
    model="gpt-4o",
    api_key="sk-your-key",
    api_base="https://api.lemondata.cc/v1"
)

# Anthropic Claude (via OpenAI-compatible endpoint)
claude = OpenAI(
    model="claude-sonnet-4-5",
    api_key="sk-your-key",
    api_base="https://api.lemondata.cc/v1"
)

# Google Gemini
gemini = OpenAI(
    model="gemini-2.5-flash",
    api_key="sk-your-key",
    api_base="https://api.lemondata.cc/v1"
)

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Chat Interface

from llama_index.core.llms import ChatMessage

messages = [
    ChatMessage(role="system", content="You are a helpful assistant."),
    ChatMessage(role="user", content="What is the capital of France?")
]

response = llm.chat(messages)
print(response.message.content)

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Streaming

# Streaming completion
for chunk in llm.stream_complete("Write a poem about AI"):
    print(chunk.delta, end="", flush=True)

# Streaming chat
for chunk in llm.stream_chat(messages):
    print(chunk.delta, end="", flush=True)

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Embeddings

from llama_index.embeddings.openai import OpenAIEmbedding

embed_model = OpenAIEmbedding(
    model="text-embedding-3-small",
    api_key="sk-your-lemondata-key",
    api_base="https://api.lemondata.cc/v1"
)

# Set as default
Settings.embed_model = embed_model

# Get embeddings
embeddings = embed_model.get_text_embedding("Hello, world!")
print(f"Embedding dimension: {len(embeddings)}")

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RAG with Documents

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Configure settings
Settings.llm = llm
Settings.embed_model = embed_model

# Load documents
documents = SimpleDirectoryReader("./data").load_data()

# Create index
index = VectorStoreIndex.from_documents(documents)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is in my documents?")
print(response)

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Chat Engine

# Create chat engine with memory
chat_engine = index.as_chat_engine(chat_mode="condense_question")

# Multi-turn conversation
response = chat_engine.chat("What is LemonData?")
print(response)

response = chat_engine.chat("How many models does it support?")
print(response)

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Async Usage

import asyncio

async def main():
    response = await llm.acomplete("Hello!")
    print(response.text)

asyncio.run(main())

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Environment Variables

For cleaner code, use environment variables: 
export OPENAI_API_KEY="sk-your-lemondata-key"
export OPENAI_API_BASE="https://api.lemondata.cc/v1"

from llama_index.llms.openai import OpenAI

# Will automatically use environment variables
llm = OpenAI(model="gpt-4o")

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Best Practices

Use faster models (GPT-4o-mini) for embedding and summarization tasks, reserve powerful models (GPT-4o, Claude) for final responses.
Adjust chunk size based on your document types. Smaller chunks for dense technical docs, larger for narrative content.
Enable LlamaIndex caching to avoid redundant API calls during development.