f172f7d4aa
- Introduced new skills tools: `skills_categories`, `skills_list`, and `skill_view` in `model_tools.py`, allowing for better organization and access to skill-related functionalities. - Updated `toolsets.py` to include a new `skills` toolset, providing a dedicated space for skill tools. - Enhanced `batch_runner.py` to recognize and validate skills tools during batch processing. - Added comprehensive tool definitions for skills tools, ensuring compatibility with OpenAI's expected format. - Created new shell script `test_skills_kimi.sh` for testing skills tool functionality with Kimi K2.5. - Added example skill files demonstrating the structure and usage of skills within the Hermes-Agent framework, including `SKILL.md` for example and audiocraft skills. - Improved documentation for skills tools and their integration into the existing tool framework, ensuring clarity for future development and usage.
182 lines
3.5 KiB
Markdown
182 lines
3.5 KiB
Markdown
# Pinecone Deployment Guide
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Production deployment patterns for Pinecone.
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## Serverless vs Pod-based
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### Serverless (Recommended)
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```python
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from pinecone import Pinecone, ServerlessSpec
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pc = Pinecone(api_key="your-key")
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# Create serverless index
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pc.create_index(
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name="my-index",
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dimension=1536,
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metric="cosine",
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spec=ServerlessSpec(
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cloud="aws", # or "gcp", "azure"
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region="us-east-1"
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)
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)
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```
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**Benefits:**
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- Auto-scaling
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- Pay per usage
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- No infrastructure management
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- Cost-effective for variable load
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**Use when:**
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- Variable traffic
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- Cost optimization important
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- Don't need consistent latency
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### Pod-based
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```python
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from pinecone import PodSpec
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pc.create_index(
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name="my-index",
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dimension=1536,
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metric="cosine",
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spec=PodSpec(
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environment="us-east1-gcp",
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pod_type="p1.x1", # or p1.x2, p1.x4, p1.x8
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pods=2, # Number of pods
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replicas=2 # High availability
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)
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)
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```
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**Benefits:**
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- Consistent performance
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- Predictable latency
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- Higher throughput
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- Dedicated resources
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**Use when:**
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- Production workloads
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- Need consistent p95 latency
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- High throughput required
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## Hybrid search
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### Dense + Sparse vectors
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```python
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# Upsert with both dense and sparse vectors
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index.upsert(vectors=[
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{
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"id": "doc1",
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"values": [0.1, 0.2, ...], # Dense (semantic)
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"sparse_values": {
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"indices": [10, 45, 123], # Token IDs
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"values": [0.5, 0.3, 0.8] # TF-IDF/BM25 scores
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},
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"metadata": {"text": "..."}
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}
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])
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# Hybrid query
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results = index.query(
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vector=[0.1, 0.2, ...], # Dense query
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sparse_vector={
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"indices": [10, 45],
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"values": [0.5, 0.3]
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},
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top_k=10,
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alpha=0.5 # 0=sparse only, 1=dense only, 0.5=balanced
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)
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```
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**Benefits:**
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- Best of both worlds
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- Semantic + keyword matching
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- Better recall than either alone
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## Namespaces for multi-tenancy
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```python
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# Separate data by user/tenant
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index.upsert(
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vectors=[{"id": "doc1", "values": [...]}],
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namespace="user-123"
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)
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# Query specific namespace
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results = index.query(
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vector=[...],
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namespace="user-123",
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top_k=5
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)
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# List namespaces
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stats = index.describe_index_stats()
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print(stats['namespaces'])
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```
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**Use cases:**
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- Multi-tenant SaaS
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- User-specific data isolation
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- A/B testing (prod/staging namespaces)
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## Metadata filtering
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### Exact match
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```python
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results = index.query(
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vector=[...],
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filter={"category": "tutorial"},
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top_k=5
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)
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```
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### Range queries
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```python
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results = index.query(
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vector=[...],
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filter={"price": {"$gte": 100, "$lte": 500}},
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top_k=5
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)
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```
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### Complex filters
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```python
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results = index.query(
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vector=[...],
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filter={
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"$and": [
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{"category": {"$in": ["tutorial", "guide"]}},
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{"difficulty": {"$lte": 3}},
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{"published": {"$gte": "2024-01-01"}}
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]
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},
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top_k=5
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)
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```
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## Best practices
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1. **Use serverless for development** - Cost-effective
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2. **Switch to pods for production** - Consistent performance
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3. **Implement namespaces** - Multi-tenancy
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4. **Add metadata strategically** - Enable filtering
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5. **Use hybrid search** - Better quality
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6. **Batch upserts** - 100-200 vectors per batch
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7. **Monitor usage** - Check Pinecone dashboard
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8. **Set up alerts** - Usage/cost thresholds
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9. **Regular backups** - Export important data
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10. **Test filters** - Verify performance
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## Resources
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- **Docs**: https://docs.pinecone.io
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- **Console**: https://app.pinecone.io
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