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