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| 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 |
| 主题 | 适用场景 | 参考文档 |
|---|---|---|
| LangChain | 使用LangChain构建RAG链 | langchain.md |
| LlamaIndex | 将Exa用作LlamaIndex数据源 | llamaindex.md |
| Vercel AI SDK | 为Next.js AI应用添加网页搜索功能 | vercel-ai.md |
| MCP & 工具 | Claude MCP服务器、OpenAI工具、函数调用 | mcp-tools.md |
from langchain_exa import ExaSearchRetriever
retriever = ExaSearchRetriever(
exa_api_key="your-key",
k=5,
highlights=True
)
docs = retriever.invoke("latest AI research papers")from langchain_exa import ExaSearchRetriever
retriever = ExaSearchRetriever(
exa_api_key="your-key",
k=5,
highlights=True
)
docs = retriever.invoke("latest AI research papers")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
)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
)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",
});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",
});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?"}]
)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?"}]
)| 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 |
| 框架 | 最佳适用场景 | 核心特性 |
|---|---|---|
| LangChain | 复杂链、Agent | ExaSearchRetriever、工具集成 |
| LlamaIndex | 文档索引、问答系统 | ExaReader、查询引擎 |
| Vercel AI SDK | Next.js应用、流式处理 | 工具定义、边缘就绪 |
| OpenAI兼容 | 直接替换方案 | 代码改动最小 |
| Claude MCP | Claude桌面端、Claude代码工具 | 原生工具调用 |
highlights=Trueresult.urlsummary=Trueinclude_domainsstart_published_datehighlights=Trueresult.urlsummary=Trueinclude_domainsstart_published_date