135 lines
3.6 KiB
Markdown
135 lines
3.6 KiB
Markdown
# Protocol Bicorder Analysis Workflow
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This directory contains scripts for analyzing protocols using the Protocol Bicorder framework with LLM assistance.
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## Scripts
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1. **bicorder_batch.py** - **[RECOMMENDED]** Process entire CSV with one command
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2. **bicorder_analyze.py** - Prepares CSV with gradient columns
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3. **bicorder_query.py** - Queries LLM for each gradient value and updates CSV (each query is a new chat)
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## Quick Start (Recommended)
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### Process All Protocols with One Command
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```bash
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python3 bicorder_batch.py protocols_edited.csv -o analysis_output.csv
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```
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This will:
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1. Create the analysis CSV with gradient columns
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2. For each protocol row, query all gradients (each query is a new chat with full protocol context)
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3. Update the CSV automatically with the results
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4. Show progress and summary
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### Common Options
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```bash
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# Process only rows 1-5 (useful for testing)
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python3 bicorder_batch.py protocols_edited.csv -o analysis_output.csv --start 1 --end 5
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# Use specific LLM model
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python3 bicorder_batch.py protocols_edited.csv -o analysis_output.csv -m mistral
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# Add analyst metadata
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python3 bicorder_batch.py protocols_edited.csv -o analysis_output.csv \
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-a "Your Name" -s "Your analytical standpoint"
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```
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---
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## Manual Workflow (Advanced)
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### Step 1: Prepare the Analysis CSV
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Create a CSV with empty gradient columns:
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```bash
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python3 bicorder_analyze.py protocols_edited.csv -o analysis_output.csv
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```
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Optional: Add analyst metadata:
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```bash
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python3 bicorder_analyze.py protocols_edited.csv -o analysis_output.csv \
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-a "Your Name" -s "Your analytical standpoint"
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```
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### Step 2: Query Gradients for a Protocol Row
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Query all gradients for a specific protocol:
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```bash
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python3 bicorder_query.py analysis_output.csv 1
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```
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- Replace `1` with the row number you want to analyze
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- Each gradient is queried in a new chat with full protocol context
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- Each response is automatically parsed and written to the CSV
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- Progress is shown for each gradient
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Optional: Specify a model:
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```bash
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python3 bicorder_query.py analysis_output.csv 1 -m mistral
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```
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### Step 3: Repeat for All Protocols
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For each protocol in your CSV:
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```bash
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python3 bicorder_query.py analysis_output.csv 1
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python3 bicorder_query.py analysis_output.csv 2
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python3 bicorder_query.py analysis_output.csv 3
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# ... and so on
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# OR: Use bicorder_batch.py to automate all of this!
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```
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## Architecture
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### How It Works
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Each gradient query is sent to the LLM as a **new, independent chat**. Every query includes:
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- The protocol descriptor (name)
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- The protocol description
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- The gradient definition (left term, right term, and their descriptions)
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- Instructions to rate 1-9
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This approach:
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- **Simplifies the code** - No conversation state management
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- **Prevents bias** - Each evaluation is independent, not influenced by previous responses
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- **Enables parallelization** - Queries could theoretically run concurrently
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- **Makes debugging easier** - Each query/response pair is self-contained
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## Tips
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### Dry Run Mode
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Test prompts without calling the LLM:
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```bash
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python3 bicorder_query.py analysis_output.csv 1 --dry-run
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```
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This shows you exactly what prompt will be sent for each gradient, including the full protocol context.
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### Check Your Progress
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View completed values:
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```bash
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python3 -c "
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import csv
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with open('analysis_output.csv') as f:
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reader = csv.DictReader(f)
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for i, row in enumerate(reader, 1):
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empty = sum(1 for k, v in row.items() if 'vs' in k and not v)
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print(f'Row {i}: {empty}/23 gradients empty')
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"
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```
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### Batch Processing
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Use the `bicorder_batch.py` script (see Quick Start section above) for processing multiple protocols.
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