Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
volcengine-tos-vectors-skills
Manage vector storage and similarity search using TOS Vectors service. Use when working with embeddings, semantic search, RAG systems, recommendation engines, or when the user mentions vector databases, similarity search, or TOS Vectors operations.
skill openclawclawhub Free
Manage vector storage and similarity search using TOS Vectors service. Use when working with embeddings, semantic search, RAG systems, recommendation engines, or when the user mentions vector databases, similarity search, or TOS Vectors operations.
โฌ 0 downloads โ
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- WORKFLOWS.md, REFERENCE.md, README.md, SKILL.md, scripts/search_vectors.py, scripts/insert_vectors.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.2
Provenance
- Publisher
- jneless
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
TOS Vectors Skill
Comprehensive skill for managing vector storage, indexing, and similarity search using the TOS Vectors service - a cloud-based vector database optimized for AI applications.
Initialize Client
import os import tos # Get credentials from environment ak = os.getenv('TOS_ACCESS_KEY') sk = os.getenv('TOS_SECRET_KEY') account_id = os.getenv('TOS_ACCOUNT_ID') # Configure endpoint and region endpoint = 'https://tosvectors-cn-beijing.volces.com' region = 'cn-beijing' # Create client client = tos.VectorClient(ak, sk, endpoint, region)
Basic Workflow
# 1. Create vector bucket (like a database) client.create_vector_bucket('my-vectors') # 2. Create vector index (like a table) client.create_index( account_id=account_id, vector_bucket_name='my-vectors', index_name='embeddings-768d', data_type=tos.DataType.DataTypeFloat32, dimension=768, distance_metric=tos.DistanceMetricType.DistanceMetricCosine ) # 3. Insert vectors vectors = [ tos.models2.Vector( key='doc-1', data=tos.models2.VectorData(float32=[0.1] * 768), metadata={'title': 'Document 1', 'category': 'tech'} ) ] client.put_vectors( vector_bucket_name='my-vectors', account_id=account_id, index_name='embeddings-768d', vectors=vectors ) # 4. Search similar vectors query_vector = tos.models2.VectorData(float32=[0.1] * 768) results = client.query_vectors( vector_bucket_name='my-vectors', account_id=account_id, index_name='embeddings-768d', query_vector=query_vector, top_k=5, return_distance=True, return_metadata=True )
Vector Bucket Management
Create Bucket client.create_vector_bucket(bucket_name) List Buckets result = client.list_vector_buckets(max_results=100) for bucket in result.vector_buckets: print(bucket.vector_bucket_name) Delete Bucket (must be empty) client.delete_vector_bucket(bucket_name, account_id)
Vector Index Management
Create Index client.create_index( account_id=account_id, vector_bucket_name=bucket_name, index_name='my-index', data_type=tos.DataType.DataTypeFloat32, dimension=128, distance_metric=tos.DistanceMetricType.DistanceMetricCosine ) List Indexes result = client.list_indexes(bucket_name, account_id) for index in result.indexes: print(f"{index.index_name}: {index.dimension}d")
Vector Data Operations
Insert Vectors (batch up to 500) vectors = [] for i in range(100): vector = tos.models2.Vector( key=f'vec-{i}', data=tos.models2.VectorData(float32=[...]), metadata={'category': 'example'} ) vectors.append(vector) client.put_vectors( vector_bucket_name=bucket_name, account_id=account_id, index_name=index_name, vectors=vectors ) Query Similar Vectors (KNN search) results = client.query_vectors( vector_bucket_name=bucket_name, account_id=account_id, index_name=index_name, query_vector=query_vector, top_k=10, filter={"$and": [{"category": "tech"}]}, # Optional metadata filter return_distance=True, return_metadata=True ) for vec in results.vectors: print(f"Key: {vec.key}, Distance: {vec.distance}") Get Vectors by Keys result = client.get_vectors( vector_bucket_name=bucket_name, account_id=account_id, index_name=index_name, keys=['vec-1', 'vec-2'], return_data=True, return_metadata=True ) Delete Vectors client.delete_vectors( vector_bucket_name=bucket_name, account_id=account_id, index_name=index_name, keys=['vec-1', 'vec-2'] )
1. Semantic Search
Build a semantic search system for documents: # Index documents for doc in documents: embedding = get_embedding(doc.text) # Your embedding model vector = tos.models2.Vector( key=doc.id, data=tos.models2.VectorData(float32=embedding), metadata={'title': doc.title, 'content': doc.text[:500]} ) vectors.append(vector) client.put_vectors( vector_bucket_name=bucket_name, account_id=account_id, index_name=index_name, vectors=vectors ) # Search query_embedding = get_embedding(user_query) results = client.query_vectors( vector_bucket_name=bucket_name, account_id=account_id, index_name=index_name, query_vector=tos.models2.VectorData(float32=query_embedding), top_k=5, return_metadata=True )
2. RAG (Retrieval Augmented Generation)
Retrieve relevant context for LLM prompts: # Retrieve relevant documents question_embedding = get_embedding(user_question) search_results = client.query_vectors( vector_bucket_name=bucket_name, account_id=account_id, index_name='knowledge-base', query_vector=tos.models2.VectorData(float32=question_embedding), top_k=3, return_metadata=True ) # Build context context = "\n\n".join([ v.metadata.get('content', '') for v in search_results.vectors ]) # Generate answer with LLM prompt = f"Context:\n{context}\n\nQuestion: {user_question}"
3. Recommendation System
Find similar items based on user preferences: # Query with metadata filtering results = client.query_vectors( vector_bucket_name=bucket_name, account_id=account_id, index_name='products', query_vector=user_preference_vector, top_k=10, filter={"$and": [{"category": "electronics"}, {"price_range": "mid"}]}, return_metadata=True )
Naming Conventions
Bucket names: 3-32 chars, lowercase letters, numbers, hyphens only Index names: 3-63 chars Vector keys: 1-1024 chars, use meaningful identifiers
Batch Operations
Insert up to 500 vectors per call Delete up to 100 vectors per call Use pagination for listing operations
Error Handling
try: result = client.create_vector_bucket(bucket_name) except tos.exceptions.TosClientError as e: print(f'Client error: {e.message}') except tos.exceptions.TosServerError as e: print(f'Server error: {e.code}, Request ID: {e.request_id}')
Performance Tips
Choose appropriate vector dimensions (balance accuracy vs performance) Use metadata filtering to reduce search space Use cosine similarity for normalized vectors Use Euclidean distance for absolute distances
Important Limits
Vector buckets: Max 100 per account Vector dimensions: 1-4096 Batch insert: 1-500 vectors per call Batch get/delete: 1-100 vectors per call Query TopK: 1-30 results
Additional Resources
For detailed API reference, see REFERENCE.md For complete workflows, see WORKFLOWS.md For example scripts, see the scripts/ directory
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs2 Scripts
- SKILL.md
- README.md
- REFERENCE.md
- WORKFLOWS.md
- scripts/insert_vectors.py
- scripts/search_vectors.py