Exa RAG Integration

Quick Reference

Essential Patterns

LangChain Retriever

from langchain_exa import ExaSearchRetriever

retriever = ExaSearchRetriever(
    exa_api_key="your-key",
    k=5,
    highlights=True
)

docs = retriever.invoke("latest AI research papers")

LlamaIndex Reader

from llama_index.readers.web import ExaReader

reader = ExaReader(api_key="your-key")
documents = reader.load_data(
    query="machine learning best practices",
    num_results=10
)

Vercel AI SDK Tool

import { exa } from "@agentic/exa";
import { createOpenAI } from "@ai-sdk/openai";
import { generateText } from "ai";

const result = await generateText({
  model: openai("gpt-4"),
  tools: { search: exa.searchAndContents },
  prompt: "Search for the latest TypeScript features",
});

OpenAI-Compatible Endpoint

from openai import OpenAI

client = OpenAI(
    base_url="https://api.exa.ai/v1",
    api_key="your-exa-key"
)

response = client.chat.completions.create(
    model="exa",
    messages=[{"role": "user", "content": "What are the latest AI trends?"}]
)

Integration Selection

Common Mistakes

1. Not using highlights for RAG - Full text wastes context; use highlights=True for relevant snippets
2. Missing source attribution - Always include result.url in citations for grounded responses
3. Ignoring summaries - summary=True provides concise context without full page overhead
4. Over-fetching results - Start with 3-5 results; more isn't always better for RAG quality
5. Not filtering domains - Use include_domains to limit to authoritative sources
6. Skipping date filters - For current events, always add start_published_date to avoid stale info
7. Forgetting async patterns - Use async retrievers in production for better throughput