# Exa Architecture Variants

> Choose and implement Exa validated architecture blueprints for different scales. Use when designing new Exa integrations, choosing between monolith/service/microservice architectures, or planning migration paths for Exa applications. Trigger with phrases like "exa architecture", "exa blueprint", "how to structure exa", "exa project layout", "exa microservice".

- Skill: `micsapp/exa-architecture-variants` (Agent Skill)
- Install (CLI): `npx skillmds@latest add micsapp/exa-architecture-variants`
- Raw SKILL.md: https://api.skillmd.com/api/skills/micsapp/exa-architecture-variants/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: micsapp (https://skillmd.com/u/micsapp)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/micsapp/exa-architecture-variants

---

# Exa Architecture Variants

## Overview
Deployment architectures for Exa neural search at different scales. Exa's search-and-contents model supports everything from simple search features to full RAG pipelines and semantic knowledge bases.

## Prerequisites
- Exa API configured
- Clear search use case defined
- Infrastructure for chosen architecture tier

## Instructions

### Step 1: Direct Search Integration (Simple)

**Best for:** Adding search to an existing app, < 1K queries/day.

```
User Query -> Backend -> Exa Search API -> Format Results -> User
```

```python
from exa_py import Exa
exa = Exa(api_key=os.environ["EXA_API_KEY"])

@app.route('/search')
def search():
    query = request.args.get('q')
    results = exa.search_and_contents(
        query, num_results=5, text={"max_characters": 1000}  # 1000: 1 second in ms
    )
    return jsonify([{
        "title": r.title, "url": r.url, "snippet": r.text[:200]  # HTTP 200 OK
    } for r in results.results])
```

### Step 2: Cached Search with Semantic Layer (Moderate)

**Best for:** High-traffic search, 1K-50K queries/day, content aggregation.

```
User Query -> Cache Check -> (miss) -> Exa API -> Cache Store -> User
                  |                                    
                  v (hit)                              
              Cached Results -> User
```

```python
class CachedExaSearch:
    def __init__(self, exa_client, redis_client, ttl=600):  # 600: timeout: 10 minutes
        self.exa = exa_client
        self.cache = redis_client
        self.ttl = ttl

    def search(self, query: str, **kwargs):
        key = self._cache_key(query, **kwargs)
        cached = self.cache.get(key)
        if cached:
            return json.loads(cached)
        results = self.exa.search_and_contents(query, **kwargs)
        serialized = self._serialize(results)
        self.cache.setex(key, self.ttl, json.dumps(serialized))
        return serialized
```

### Step 3: RAG Pipeline with Exa as Knowledge Source (Scale)

**Best for:** AI-powered apps, 50K+ queries/day, LLM-augmented answers.

```
User Query -> Query Planner -> Exa Search -> Content Extraction
                                                  |
                                                  v
                                          Vector Store (cache)
                                                  |
                                                  v
                                    LLM Generation with Context -> User
```

```python
class ExaRAGPipeline:
    def __init__(self, exa, llm, vector_store):
        self.exa = exa
        self.llm = llm
        self.vectors = vector_store

    async def answer(self, question: str) -> dict:
        # 1. Search for relevant content
        results = self.exa.search_and_contents(
            question, num_results=5, text={"max_characters": 3000},  # 3000: 3 seconds in ms
            highlights=True
        )
        # 2. Store in vector cache for future queries
        for r in results.results:
            self.vectors.upsert(r.url, r.text, {"title": r.title})
        # 3. Generate answer with citations
        context = "\n\n".join([f"[{i+1}] {r.text}" for i, r in enumerate(results.results)])
        answer = await self.llm.generate(
            f"Based on the following sources, answer: {question}\n\n{context}"
        )
        return {"answer": answer, "sources": [r.url for r in results.results]}
```

## Decision Matrix

| Factor | Direct | Cached | RAG Pipeline |
|--------|--------|--------|--------------|
| Volume | < 1K/day | 1K-50K/day | 50K+/day |
| Latency | 1-3s | 50ms (cached) | 3-8s |
| Use Case | Simple search | Content aggregation | AI-powered answers |
| Complexity | Low | Medium | High |

## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| Slow search in UI | No caching | Add result cache with TTL |
| Stale cached results | Long TTL | Reduce TTL for time-sensitive queries |
| RAG hallucination | Poor source selection | Use highlights, increase num_results |
| High API costs | No query deduplication | Cache layer deduplicates identical queries |

## Examples


**Basic usage**: Apply exa architecture variants to a standard project setup with default configuration options.

**Advanced scenario**: Customize exa architecture variants for production environments with multiple constraints and team-specific requirements.

## Resources
- [Exa API Docs](https://docs.exa.ai)
- [Exa RAG Guide](https://docs.exa.ai/reference/rag)

## Output

- Configuration files or code changes applied to the project
- Validation report confirming correct implementation
- Summary of changes made and their rationale