exa-rag

name	exa-rag
description	Build RAG pipelines with Exa.ai for real-time web retrieval. Use when building retrieval-augmented generation, integrating Exa with LangChain, LlamaIndex, Vercel AI SDK, or implementing AI agents with web search capabilities. Triggers on: RAG pipeline, retrieval augmented generation, Exa LangChain, Exa LlamaIndex, ExaSearchRetriever, ExaSearchResults, Exa MCP, Exa tool calling, Claude tool use, AI agent web search, grounded generation, citation generation, fact checking, hallucination detection, OpenAI compatibility, chat completions.
license	MIT
metadata	{"author":"ejirocodes","version":"1.0.0"}

Exa RAG Integration

Quick Reference

Topic	When to Use	Reference
LangChain	Building RAG chains with LangChain	langchain.md
LlamaIndex	Using Exa as a LlamaIndex data source	llamaindex.md
Vercel AI SDK	Adding web search to Next.js AI apps	vercel-ai.md
MCP & Tools	Claude MCP server, OpenAI tools, function calling	mcp-tools.md

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

Framework	Best For	Key Feature
LangChain	Complex chains, agents	ExaSearchRetriever, tool integration
LlamaIndex	Document indexing, Q&A	ExaReader, query engines
Vercel AI SDK	Next.js apps, streaming	Tool definitions, edge-ready
OpenAI Compat	Drop-in replacement	Minimal code changes
Claude MCP	Claude Desktop, Claude Code	Native tool calling

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