Great question, Rahul! Caching in the context of Azure AI (especially when using **RAG pipelines with Azure OpenAI + Azure AI Search**) can **dramatically reduce latency and cost**, especially for frequently repeated or similar queries.
Here’s how you can cache both:
---
## ✅ 1. **Caching Embedded Text (Documents)**
Since embeddings are expensive to compute and documents rarely change often, caching them is **critical**.
### 💡 Strategy: Use a persistent storage for document embeddings
### 🔧 How:
* Use **Azure Blob Storage**, **Azure SQL**, or **Cosmos DB** to **store each document's embedding** with a content hash (MD5/SHA256) as the key.
* Before calling Azure OpenAI embedding API, **check if hash exists**.
* If yes → retrieve the cached embedding.
* If no → call API, then store.
```python
import hashlib
def get_or_generate_embedding(doc_text):
content_hash = hashlib.sha256(doc_text.encode()).hexdigest()
cached = db.get_embedding_by_hash(content_hash)
if cached:
return cached
else:
embedding = call_openai_embedding(doc_text)
db.save_embedding(content_hash, embedding)
return embedding
```
> 🔐 You can even pre-compute and persist embeddings as part of your Logic App ingestion pipeline.
---
## ✅ 2. **Caching Query Embeddings and Search Results**
This is useful when:
* Users repeat similar questions often.
* You want to avoid repeated vector searches.
### 🔧 How:
1. **Hash the user query** → e.g., SHA256 of lowercase query string.
2. Store:
* The **embedding** (for reuse)
* The **top N search results** from Azure AI Search as JSON
3. Use **Redis**, Cosmos DB, or Blob as cache store with TTL (e.g., 6 hours).
### Example:
```python
def search_with_cache(query_text):
query_hash = hashlib.sha256(query_text.lower().encode()).hexdigest()
cached = redis.get(f"search:{query_hash}")
if cached:
return json.loads(cached)
# Not in cache, do embedding + search
query_embedding = get_or_generate_embedding(query_text)
results = azure_ai_vector_search(query_embedding)
# Cache results for later
redis.setex(f"search:{query_hash}", 6 * 3600, json.dumps(results))
return results
```
---
## 🔄 TTL and Invalidation Strategy
| Data | TTL suggestion | Invalidation case |
| -------------------- | -------------------------- | ------------------------------- |
| Document embeddings | No expiry (immutable docs) | On document update |
| Search query results | 6–24 hours | Rarely — maybe on index refresh |
---
## 📦 Where to Store Cached Data?
| Option | Use for | Notes |
| --------------- | --------------------------- | ----------------------- |
| Azure Redis | Fastest real-time caching | Supports TTL, in-memory |
| Azure Cosmos DB | Persistent embedding store | For doc-level cache |
| Azure Blob | Embeddings + metadata files | Low-cost for bulk data |
---
## 🧠 Bonus: Use Cache for Grounded Response
If you're using a frontend agent (e.g., chatbot or API app), you can even cache the final GPT response based on prompt hash.
---
## 🚀 Result
By caching:
* 🧠 **Embeddings** — you avoid duplicate calls to Azure OpenAI
* 🔍 **Search results** — you reduce load on Azure AI Search
* 💬 **Responses** — you accelerate latency and reduce token cost
---
Let me know if you want to integrate this into your existing **Logic App + Azure Function** pipeline, or if you want to see a Redis + Python codebase sample!
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