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配置 SSOT（TOML + .env） 统一错误契约 Auth 与事务边界 Redis / Celery 可靠性:业务 Redis（DB/0）与 Celery broker/backend（DB/1）显式拆分；连接池、sync client 可观测性（OpenTelemetry + LGTM）
87 lines
2.3 KiB
Markdown
87 lines
2.3 KiB
Markdown
---
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title: Use LangCache for LLM Response Caching
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impact: HIGH
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impactDescription: Reduces LLM API costs by 50-90% for similar queries
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tags: langcache, llm, semantic-cache, embeddings, ai
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description: Use LangCache for LLM Response Caching
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alwaysApply: true
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---
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## Use LangCache for LLM Response Caching
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> **Note:** LangCache is currently in preview on Redis Cloud. Features and behavior may change.
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LangCache is a fully-managed semantic caching service on Redis Cloud that reduces LLM costs and latency.
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**How it works:**
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1. Your app sends a prompt to LangCache via `POST /v1/caches/{cacheId}/entries/search`
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2. LangCache generates an embedding and searches for similar cached responses
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3. If found (cache hit), returns the cached response instantly
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4. If not found (cache miss), your app calls the LLM and stores the response
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**Correct:** Use the LangCache Python SDK.
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```python
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from langcache import LangCache
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import os
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lang_cache = LangCache(
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server_url=f"https://{os.getenv('HOST')}",
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cache_id=os.getenv("CACHE_ID"),
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api_key=os.getenv("API_KEY")
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)
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# Search for cached response
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result = lang_cache.search(
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prompt="What is Redis?",
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similarity_threshold=0.9
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)
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if result:
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response = result[0]["response"]
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else:
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response = llm.generate("What is Redis?")
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# Store for future queries
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lang_cache.set(
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prompt="What is Redis?",
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response=response
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)
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```
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**LangCache REST API:**
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```bash
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# Search cache
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curl -X POST "https://$HOST/v1/caches/$CACHE_ID/entries/search" \
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-H "Authorization: Bearer $API_KEY" \
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-H "Content-Type: application/json" \
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-d '{"prompt": "What is Redis?"}'
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# Store a response
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curl -X POST "https://$HOST/v1/caches/$CACHE_ID/entries" \
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-H "Authorization: Bearer $API_KEY" \
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-H "Content-Type: application/json" \
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-d '{"prompt": "What is Redis?", "response": "Redis is an in-memory database..."}'
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```
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**With custom attributes for filtering:**
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```python
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# Store with attributes
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lang_cache.set(
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prompt="What is Redis?",
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response="Redis is an in-memory database...",
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attributes={"category": "database", "version": "v1"}
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)
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# Search with attribute filter
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result = lang_cache.search(
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prompt="Tell me about Redis",
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attributes={"category": "database"},
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similarity_threshold=0.9
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)
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```
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Reference: [LangCache Documentation](https://redis.io/docs/latest/develop/ai/langcache/)
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