exa-core-workflow-a

What this skill does

# Exa Core Workflow A — Neural Search

## Overview

Primary workflow for Exa: semantic web search using `search()` and `searchAndContents()`. Exa's neural search understands query meaning rather than matching keywords, making it ideal for research, RAG pipelines, and content discovery. This skill covers search types, content extraction, filtering, and categories.

## Prerequisites

- `exa-js` installed and `EXA_API_KEY` configured
- Understanding of neural vs keyword search tradeoffs

## Search Types

| Type | Latency | Best For |
|------|---------|----------|
| `auto` (default) | 300-1500ms | General queries; Exa picks best approach |
| `neural` | 500-2000ms | Conceptual/semantic queries |
| `keyword` | 200-500ms | Exact terms, names, URLs |
| `fast` | p50 < 425ms | Speed-critical applications |
| `instant` | < 150ms | Real-time autocomplete |
| `deep` | 2-5s | Maximum quality, light deep search |
| `deep-reasoning` | 5-15s | Complex research questions |

## Instructions

### Step 1: Basic Neural Search

```typescript
import Exa from "exa-js";

const exa = new Exa(process.env.EXA_API_KEY);

// Neural search: phrase your query as a statement, not a question
const results = await exa.search(
  "comprehensive guide to building production RAG systems",
  {
    type: "neural",
    numResults: 10,   // max 100 for neural/deep
  }
);

for (const r of results.results) {
  console.log(`[${r.score.toFixed(2)}] ${r.title} — ${r.url}`);
  console.log(`  Published: ${r.publishedDate || "unknown"}`);
}
```

### Step 2: Search with Content Extraction

```typescript
// searchAndContents returns page text, highlights, and/or summaries
const results = await exa.searchAndContents(
  "best practices for vector database selection",
  {
    type: "auto",
    numResults: 5,
    // Text: full page content as markdown
    text: { maxCharacters: 2000 },
    // Highlights: key excerpts relevant to a custom query
    highlights: {
      maxCharacters: 500,
      query: "comparison of vector databases",
    },
    // Summary: LLM-generated summary tailored to a query
    summary: { query: "which vector database should I choose?" },
  }
);

for (const r of results.results) {
  console.log(`## ${r.title}`);
  console.log(`Summary: ${r.summary}`);
  console.log(`Highlights: ${r.highlights?.join(" ... ")}`);
  console.log(`Full text: ${r.text?.substring(0, 300)}...`);
}
```

### Step 3: Date and Domain Filtering

```typescript
// Filter by publication date and restrict to specific domains
const results = await exa.searchAndContents(
  "TypeScript 5.5 new features",
  {
    type: "auto",
    numResults: 10,
    // Date filters use ISO 8601 format
    startPublishedDate: "2024-06-01T00:00:00.000Z",
    endPublishedDate: "2025-01-01T00:00:00.000Z",
    // Domain filters (up to 1200 domains each)
    includeDomains: ["devblogs.microsoft.com", "typescriptlang.org"],
    // Text content filters (1 string, max 5 words each)
    includeText: ["TypeScript"],
    text: true,
  }
);
```

### Step 4: Category-Scoped Search

```typescript
// Categories narrow results to specific content types
// Available: company, research paper, news, tweet, personal site,
//            financial report, people
const papers = await exa.searchAndContents(
  "attention mechanism improvements for long context LLMs",
  {
    type: "neural",
    numResults: 10,
    category: "research paper",
    text: { maxCharacters: 3000 },
    highlights: true,
  }
);

const companies = await exa.search(
  "AI infrastructure startup founded 2024",
  {
    type: "auto",
    numResults: 10,
    category: "company",
    // Note: company and people categories do NOT support date filters
  }
);
```

### Step 5: Content Freshness with LiveCrawl

```typescript
// Control whether Exa fetches fresh content or uses cache
const results = await exa.searchAndContents(
  "latest AI model releases this week",
  {
    numResults: 5,
    text: { maxCharacters: 1500 },
    // maxAgeHours controls freshness (replaces deprecated livecrawl)
    // 0 = always crawl fresh, -1 = never crawl, positive = max cache age
    livecrawl: "preferred",     // try fresh, fall back to cache
    livecrawlTimeout: 10000,    // 10s timeout for live crawling
  }
);
```

## Output

- Ranked search results with URLs, titles, scores, and published dates
- Optional text content, highlights, and summaries per result
- Results filtered by date range, domains, categories, and text content

## Error Handling

| Error | HTTP Code | Cause | Solution |
|-------|-----------|-------|----------|
| `INVALID_REQUEST_BODY` | 400 | Invalid parameter types | Check query is string, numResults is integer |
| `INVALID_NUM_RESULTS` | 400 | numResults > 100 with highlights | Reduce numResults or remove highlights |
| Empty results array | 200 | Date filter too narrow | Widen date range or remove filter |
| Low relevance scores | 200 | Keyword-style query | Rephrase as natural language statement |
| `FETCH_DOCUMENT_ERROR` | 422 | URL content unretrievable | Use `livecrawl: "fallback"` or try without text |

## Examples

### RAG Context Retrieval

```typescript
async function getRAGContext(question: string, maxResults = 5) {
  const results = await exa.searchAndContents(question, {
    type: "neural",
    numResults: maxResults,
    text: { maxCharacters: 2000 },
    highlights: { maxCharacters: 500, query: question },
  });

  return results.results.map((r, i) => ({
    source: `[${i + 1}] ${r.title} (${r.url})`,
    content: r.text,
    highlights: r.highlights,
  }));
}
```

## Resources

- [Exa Search Reference](https://docs.exa.ai/reference/search)
- [Exa Contents Retrieval](https://docs.exa.ai/reference/contents-retrieval)
- [Exa Search Types](https://docs.exa.ai/reference/search)

## Next Steps

For similarity search and advanced retrieval, see `exa-core-workflow-b`.