This is an example custom assistant that will help you complete the Python onboarding in VS Code. After trying it out, feel free to experiment with other blocks or create your own custom assistant.
ollama
ollama
ollama
You are a Python coding assistant. You should always try to - Use type hints consistently - Write concise docstrings on functions and classes - Follow the PEP8 style guide
Use Cargo to write a comprehensive suite of unit tests for this function
Please create a new PyTorch module following these guidelines:
- Include docstrings for the model class and methods
- Add type hints for all parameters
- Add basic validation in __init__
Create a new Next.js page based on the following description.
Create an exploratory data analysis workflow that includes:
Data Overview:
- Basic statistics (mean, median, std, quartiles)
- Missing values and data types
- Unique value distributions
Visualizations:
- Numerical: histograms, box plots
- Categorical: bar charts, frequency plots
- Relationships: correlation matrices
- Temporal patterns (if applicable)
Quality Assessment:
- Outlier detection
- Data inconsistencies
- Value range validation
Insights & Documentation:
- Key findings summary
- Data quality issues
- Variable relationships
- Next steps recommendations
- Reproducible Jupyter notebook
The user has provided the following information:
Design a RAG (Retrieval-Augmented Generation) system with:
Document Processing:
- Text extraction strategy
- Chunking approach with size and overlap parameters
- Metadata extraction and enrichment
- Document hierarchy preservation
Vector Store Integration:
- Embedding model selection and rationale
- Vector database architecture
- Indexing strategy
- Query optimization
Retrieval Strategy:
- Hybrid search (vector + keyword)
- Re-ranking methodology
- Metadata filtering capabilities
- Multi-query reformulation
LLM Integration:
- Context window optimization
- Prompt engineering for retrieval
- Citation and source tracking
- Hallucination mitigation strategies
Evaluation Framework:
- Retrieval relevance metrics
- Answer accuracy measures
- Ground truth comparison
- End-to-end benchmarking
Deployment Architecture:
- Caching strategies
- Scaling considerations
- Latency optimization
- Monitoring approach
The user's knowledge base has the following characteristics:
Review this API route for security vulnerabilities. Ask questions about the context, data flow, and potential attack vectors. Be thorough in your investigation.
Create a client component with the following functionality. If writing this as a server component is not possible, explain why.
No Data configured
npx -y tavily-mcp@0.1.4
npx -y @browsermcp/mcp@latest
npx -y @modelcontextprotocol/server-memory