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* feat(gateway): skill-aware slash commands, paginated /commands, Telegram 100-cap Map active skills to Telegram's slash command menu so users can discover and invoke skills directly. Three changes: 1. Telegram menu now includes active skill commands alongside built-in commands, capped at 100 entries (Telegram Bot API limit). Overflow commands remain callable but hidden from the picker. Logged at startup when cap is hit. 2. New /commands [page] gateway command for paginated browsing of all commands + skills. /help now shows first 10 skill commands and points to /commands for the full list. 3. When a user types a slash command that matches a disabled or uninstalled skill, they get actionable guidance: - Disabled: 'Enable it with: hermes skills config' - Optional (not installed): 'Install with: hermes skills install official/<path>' Built on ideas from PR #3921 by @kshitijk4poor. * chore: move 21 niche skills to optional-skills Move specialized/niche skills from built-in (skills/) to optional (optional-skills/) to reduce the default skill count. Users can install them with: hermes skills install official/<category>/<name> Moved skills (21): - mlops: accelerate, chroma, faiss, flash-attention, hermes-atropos-environments, huggingface-tokenizers, instructor, lambda-labs, llava, nemo-curator, pinecone, pytorch-lightning, qdrant, saelens, simpo, slime, tensorrt-llm, torchtitan - research: domain-intel, duckduckgo-search - devops: inference-sh cli Built-in skills: 96 → 75 Optional skills: 22 → 43 * fix: only include repo built-in skills in Telegram menu, not user-installed User-installed skills (from hub or manually added) stay accessible via /skills and by typing the command directly, but don't get registered in the Telegram slash command picker. Only skills whose SKILL.md is under the repo's skills/ directory are included in the menu. This keeps the Telegram menu focused on the curated built-in set while user-installed skills remain discoverable through /skills and /commands.
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Pinecone Deployment Guide
Production deployment patterns for Pinecone.
Serverless vs Pod-based
Serverless (Recommended)
from pinecone import Pinecone, ServerlessSpec
pc = Pinecone(api_key="your-key")
# Create serverless index
pc.create_index(
name="my-index",
dimension=1536,
metric="cosine",
spec=ServerlessSpec(
cloud="aws", # or "gcp", "azure"
region="us-east-1"
)
)
Benefits:
- Auto-scaling
- Pay per usage
- No infrastructure management
- Cost-effective for variable load
Use when:
- Variable traffic
- Cost optimization important
- Don't need consistent latency
Pod-based
from pinecone import PodSpec
pc.create_index(
name="my-index",
dimension=1536,
metric="cosine",
spec=PodSpec(
environment="us-east1-gcp",
pod_type="p1.x1", # or p1.x2, p1.x4, p1.x8
pods=2, # Number of pods
replicas=2 # High availability
)
)
Benefits:
- Consistent performance
- Predictable latency
- Higher throughput
- Dedicated resources
Use when:
- Production workloads
- Need consistent p95 latency
- High throughput required
Hybrid search
Dense + Sparse vectors
# Upsert with both dense and sparse vectors
index.upsert(vectors=[
{
"id": "doc1",
"values": [0.1, 0.2, ...], # Dense (semantic)
"sparse_values": {
"indices": [10, 45, 123], # Token IDs
"values": [0.5, 0.3, 0.8] # TF-IDF/BM25 scores
},
"metadata": {"text": "..."}
}
])
# Hybrid query
results = index.query(
vector=[0.1, 0.2, ...], # Dense query
sparse_vector={
"indices": [10, 45],
"values": [0.5, 0.3]
},
top_k=10,
alpha=0.5 # 0=sparse only, 1=dense only, 0.5=balanced
)
Benefits:
- Best of both worlds
- Semantic + keyword matching
- Better recall than either alone
Namespaces for multi-tenancy
# Separate data by user/tenant
index.upsert(
vectors=[{"id": "doc1", "values": [...]}],
namespace="user-123"
)
# Query specific namespace
results = index.query(
vector=[...],
namespace="user-123",
top_k=5
)
# List namespaces
stats = index.describe_index_stats()
print(stats['namespaces'])
Use cases:
- Multi-tenant SaaS
- User-specific data isolation
- A/B testing (prod/staging namespaces)
Metadata filtering
Exact match
results = index.query(
vector=[...],
filter={"category": "tutorial"},
top_k=5
)
Range queries
results = index.query(
vector=[...],
filter={"price": {"$gte": 100, "$lte": 500}},
top_k=5
)
Complex filters
results = index.query(
vector=[...],
filter={
"$and": [
{"category": {"$in": ["tutorial", "guide"]}},
{"difficulty": {"$lte": 3}},
{"published": {"$gte": "2024-01-01"}}
]
},
top_k=5
)
Best practices
- Use serverless for development - Cost-effective
- Switch to pods for production - Consistent performance
- Implement namespaces - Multi-tenancy
- Add metadata strategically - Enable filtering
- Use hybrid search - Better quality
- Batch upserts - 100-200 vectors per batch
- Monitor usage - Check Pinecone dashboard
- Set up alerts - Usage/cost thresholds
- Regular backups - Export important data
- Test filters - Verify performance
Resources
- Docs: https://docs.pinecone.io
- Console: https://app.pinecone.io