50 lines
1.6 KiB
Markdown
50 lines
1.6 KiB
Markdown
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# Context Extraction
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How code context is extracted and prepared for LLM optimization prompts.
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## Context Types
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### Single-File Context (`SingleOptimizerContext`)
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Used when the function to optimize lives in a single file:
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- Extracts the function source code
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- Collects helper functions and class definitions
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- Formats as system prompt + user prompt
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### Multi-File Context (`MultiOptimizerContext`)
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Used when the function spans or depends on multiple files:
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- Collects code from multiple source files
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- Manages file-path-annotated code blocks
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## `BaseOptimizerContext` (`optimizer_context.py`)
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Abstract base class for all context types:
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### Factory Method
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`get_dynamic_context()` — dispatches to `SingleOptimizerContext` or `MultiOptimizerContext` based on the input.
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### Prompt Construction
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- `get_system_prompt(python_version_str)` — builds system prompt with language version
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- `get_user_prompt(dependency_code, line_profiler_results)` — builds user prompt with code and optional profiler data
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### LLM Response Parsing
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- `extract_code_and_explanation_from_llm_res(content)` — parses markdown code blocks from LLM output, extracts code and explanation text
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- `parse_and_generate_candidate_schema()` — converts extracted code into `OptimizeResponseItemSchema`
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- `is_valid_code()` — validates the extracted code is syntactically correct
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## Code Formatting
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LLM responses use markdown code blocks with file path annotations:
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```
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\`\`\`python:path/to/file.py
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# optimized code here
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\`\`\`
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```
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The context extraction system both generates this format (for prompts) and parses it (from responses).
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