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feat: implement semantic cassette matching for o3 models

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Adds flexible cassette matching that ignores system prompt changes
for o3 models, preventing CI failures when prompts are updated.

Changes:
- Semantic matching: Only compares model name, user question, and core params
- Ignores: System prompts, conversation memory instructions, metadata
- Prevents cassette breaks when prompts change between code versions
- Added comprehensive tests for semantic matching behavior
- Created maintenance documentation (tests/CASSETTE_MAINTENANCE.md)

This solves the CI failure where o3-pro test cassettes would break
whenever system prompts or conversation memory format changed.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Fahad
2025-10-01 18:53:30 +04:00
parent cff6d8998f
commit 70fa088c32
3 changed files with 417 additions and 2 deletions
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231
tests/CASSETTE_MAINTENANCE.md Normal file
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# HTTP Cassette Testing - Maintenance Guide
## Overview
This project uses HTTP cassettes (recorded HTTP interactions) to test API integrations without making real API calls during CI. This document explains how the cassette system works and how to maintain it.
## How Cassette Matching Works
### Standard Matching (Non-o3 Models)
For most models, cassettes match requests using:
- HTTP method (GET, POST, etc.)
- Request path (/v1/chat/completions, etc.)
- **Exact hash of the request body**
If ANY part of the request changes, the hash changes and the cassette won't match.
### Semantic Matching (o3 Models)
**Problem**: o3 models use system prompts and conversation memory instructions that change frequently with code updates. Using exact hash matching would require re-recording cassettes after every prompt change.
**Solution**: o3 models use **semantic matching** that only compares:
- Model name (e.g., "o3-pro", "o3-mini")
- User's actual question (extracted from request)
- Core parameters (reasoning effort, temperature)
**Ignored fields** (can change without breaking cassettes):
- System prompts
- Conversation memory instructions
- Follow-up guidance text
- Token limits and other metadata
### Example
These two requests will match with semantic matching:
```json
// Request 1 - Old system prompt
{
"model": "o3-pro",
"reasoning": {"effort": "medium"},
"input": [{
"role": "user",
"content": [{
"text": "Old system prompt v1...\n\n=== USER REQUEST ===\nWhat is 2 + 2?\n=== END REQUEST ===\n\nOld instructions..."
}]
}]
}
// Request 2 - New system prompt (DIFFERENT)
{
"model": "o3-pro",
"reasoning": {"effort": "medium"},
"input": [{
"role": "user",
"content": [{
"text": "New system prompt v2...\n\n=== USER REQUEST ===\nWhat is 2 + 2?\n=== END REQUEST ===\n\nNew instructions..."
}]
}]
}
```
Both extract the same semantic content:
```json
{
"model": "o3-pro",
"reasoning": {"effort": "medium"},
"user_question": "What is 2 + 2?"
}
```
## When to Re-Record Cassettes
### You MUST re-record when:
1. **The user's test question changes**
- Example: Changing "What is 2 + 2?" to "What is 3 + 3?"
2. **Core parameters change**
- Model name changes (o3-pro → o3-mini)
- Reasoning effort changes (medium → high)
- Temperature changes
3. **For non-o3 models: ANY request body change**
### You DON'T need to re-record when (o3 models only):
1. **System prompts change**
- Semantic matching ignores these
2. **Conversation memory instructions change**
- Follow-up guidance text changes
- Token limit instructions change
3. **Response format instructions change**
- As long as the user's actual question stays the same
## How to Re-Record a Cassette
### Step 1: Delete the Old Cassette
```bash
rm tests/openai_cassettes/<cassette_name>.json
```
### Step 2: Run the Test with Real API Key
```bash
# Make sure you have a valid API key in .env
export OPENAI_API_KEY="your-real-key"
# Run the specific test
python -m pytest tests/test_o3_pro_output_text_fix.py -v
```
The test will:
1. Detect the missing cassette
2. Make a real API call
3. Record the interaction
4. Save it as a new cassette
### Step 3: Verify the Cassette Works in Replay Mode
```bash
# Test with dummy key (forces replay mode)
OPENAI_API_KEY="dummy-key" python -m pytest tests/test_o3_pro_output_text_fix.py -v
```
### Step 4: Commit the New Cassette
```bash
git add tests/openai_cassettes/<cassette_name>.json
git commit -m "chore: re-record cassette for <test_name>"
```
## Troubleshooting
### Error: "No matching interaction found"
**Cause**: The request body has changed in a way that affects the hash.
**For o3 models**: This should NOT happen due to semantic matching. If it does:
1. Check if the user question changed
2. Check if model name or reasoning effort changed
3. Verify semantic matching is working (run `test_cassette_semantic_matching.py`)
**For non-o3 models**: This is expected when request changes. Re-record the cassette.
**Solution**: Re-record the cassette following the steps above.
### Error: "Cassette file not found"
**Cause**: Cassette hasn't been recorded yet or was deleted.
**Solution**: Re-record the cassette with a real API key.
### CI Fails but Local Tests Pass
**Cause**:
1. You recorded with uncommitted code changes
2. CI is running different code than your local environment
**Solution**:
1. Commit all your changes first
2. Then re-record cassettes
3. Commit the cassettes
## Best Practices
### 1. Keep Test Questions Simple
- Use simple, stable questions like "What is 2 + 2?"
- Avoid questions that might elicit different responses over time
### 2. Document Cassette Recording Conditions
- Add comments in tests explaining when recorded
- Note any special setup required
### 3. Use Semantic Matching for Prompt-Heavy Tests
- If your test involves lots of system prompts, use o3 models
- Or extend semantic matching to other models if needed
### 4. Test Both Record and Replay Modes
- Always verify cassettes work in replay mode
- Ensure tests can record new cassettes when needed
### 5. Don't Commit Cassettes with Secrets
- The recording system sanitizes API keys automatically
- But double-check for any other sensitive data
## Implementation Details
### Semantic Matching Code
The semantic matching is implemented in `tests/http_transport_recorder.py`:
- `_is_o3_model_request()`: Detects o3 model requests
- `_extract_semantic_fields()`: Extracts only essential fields
- `_get_request_signature()`: Generates hash from semantic fields
### Adding Semantic Matching to Other Models
To add semantic matching for other models:
1. Update `_is_o3_model_request()` to include your model
2. Update `_extract_semantic_fields()` if needed
3. Add tests in `test_cassette_semantic_matching.py`
Example:
```python
def _is_o3_model_request(self, content_dict: dict) -> bool:
"""Check if this is an o3 or other semantic-matching model request."""
model = content_dict.get("model", "")
return model.startswith("o3") or model.startswith("gpt-5") # Add more models
```
## Questions?
If you encounter issues with cassette testing:
1. Check this guide first
2. Review existing cassette tests for examples
3. Run semantic matching tests to verify the system
4. Open an issue if you find a bug in the matching logic
## Related Files
- `tests/http_transport_recorder.py` - Cassette recording/replay implementation
- `tests/transport_helpers.py` - Helper functions for injecting transports
- `tests/test_cassette_semantic_matching.py` - Tests for semantic matching
- `tests/test_o3_pro_output_text_fix.py` - Example of cassette usage
- `tests/openai_cassettes/` - Directory containing recorded cassettes

63
tests/http_transport_recorder.py
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@@ -290,7 +290,12 @@ class ReplayTransport(httpx.MockTransport):
return None
def _get_request_signature(self, request: httpx.Request) -> str:
"""Generate signature for request matching."""
"""Generate signature for request matching.
Uses semantic matching for o3 models to avoid cassette breaks from prompt changes.
For o3 models, matches on model name and user prompt only, ignoring system prompts
that may change between code versions.
"""
# Use method, path, and content hash for matching
content = request.content
if hasattr(content, "read"):
@@ -305,7 +310,14 @@ class ReplayTransport(httpx.MockTransport):
try:
if content_str.strip():
content_dict = json.loads(content_str)
content_str = json.dumps(content_dict, sort_keys=True)
# For o3 models, use semantic matching to avoid cassette breaks
if self._is_o3_model_request(content_dict):
# Extract only the essential fields for matching
semantic_dict = self._extract_semantic_fields(content_dict)
content_str = json.dumps(semantic_dict, sort_keys=True)
else:
content_str = json.dumps(content_dict, sort_keys=True)
except json.JSONDecodeError:
# Not JSON, use as-is
pass
@@ -315,6 +327,50 @@ class ReplayTransport(httpx.MockTransport):
return f"{request.method}:{request.url.path}:{content_hash}"
def _is_o3_model_request(self, content_dict: dict) -> bool:
"""Check if this is an o3 model request."""
model = content_dict.get("model", "")
return model.startswith("o3")
def _extract_semantic_fields(self, content_dict: dict) -> dict:
"""Extract only semantic fields for matching, ignoring volatile prompts.
For o3 models, we want to match on:
- Model name
- User's actual question (last user message)
- Core parameters (temperature, reasoning effort)
We ignore:
- System prompts (change frequently with code updates)
- Conversation memory instructions (change with features)
"""
semantic = {
"model": content_dict.get("model"),
"reasoning": content_dict.get("reasoning"),
}
# Extract only the last user message (actual user question)
input_messages = content_dict.get("input", [])
if input_messages:
# Get the last user message content
last_msg = input_messages[-1]
if isinstance(last_msg, dict) and last_msg.get("role") == "user":
content = last_msg.get("content", [])
if isinstance(content, list) and len(content) > 0:
# Extract just the text from the last message
last_text = content[-1].get("text", "")
# Only include the actual question, not the system instructions
if "=== USER REQUEST ===" in last_text:
# Extract just the user question
parts = last_text.split("=== USER REQUEST ===")
if len(parts) > 1:
user_question = parts[1].split("=== END REQUEST ===")[0].strip()
semantic["user_question"] = user_question
else:
semantic["user_question"] = last_text
return semantic
def _get_saved_request_signature(self, saved_request: dict[str, Any]) -> str:
"""Generate signature for saved request."""
method = saved_request["method"]
@@ -323,6 +379,9 @@ class ReplayTransport(httpx.MockTransport):
# Hash the saved content
content = saved_request.get("content", "")
if isinstance(content, dict):
# Apply same semantic matching for o3 models
if self._is_o3_model_request(content):
content = self._extract_semantic_fields(content)
content_str = json.dumps(content, sort_keys=True)
else:
content_str = str(content)

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"""
Tests for cassette semantic matching to prevent breaks from prompt changes.
This validates that o3 model cassettes match on semantic content (model + user question)
rather than exact request bodies, preventing cassette breaks when system prompts change.
"""
import hashlib
import json
import pytest
from tests.http_transport_recorder import ReplayTransport
class TestCassetteSemanticMatching:
"""Test that cassette matching is resilient to prompt changes."""
@pytest.fixture
def dummy_cassette(self, tmp_path):
"""Create a minimal dummy cassette file."""
cassette_file = tmp_path / "dummy.json"
cassette_file.write_text(json.dumps({"interactions": []}))
return cassette_file
def test_o3_model_semantic_matching(self, dummy_cassette):
"""Test that o3 models use semantic matching."""
transport = ReplayTransport(str(dummy_cassette))
# Two requests with same user question but different system prompts
request1_body = {
"model": "o3-pro",
"reasoning": {"effort": "medium"},
"input": [
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "System prompt v1...\n\n=== USER REQUEST ===\nWhat is 2 + 2?\n=== END REQUEST ===\n\nMore instructions...",
}
],
}
],
}
request2_body = {
"model": "o3-pro",
"reasoning": {"effort": "medium"},
"input": [
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "System prompt v2 (DIFFERENT)...\n\n=== USER REQUEST ===\nWhat is 2 + 2?\n=== END REQUEST ===\n\nDifferent instructions...",
}
],
}
],
}
# Extract semantic fields - should be identical
semantic1 = transport._extract_semantic_fields(request1_body)
semantic2 = transport._extract_semantic_fields(request2_body)
assert semantic1 == semantic2, "Semantic fields should match despite different prompts"
assert semantic1["user_question"] == "What is 2 + 2?"
assert semantic1["model"] == "o3-pro"
assert semantic1["reasoning"] == {"effort": "medium"}
# Generate signatures - should be identical
content1 = json.dumps(semantic1, sort_keys=True)
content2 = json.dumps(semantic2, sort_keys=True)
hash1 = hashlib.md5(content1.encode()).hexdigest()
hash2 = hashlib.md5(content2.encode()).hexdigest()
assert hash1 == hash2, "Hashes should match for same semantic content"
def test_non_o3_model_exact_matching(self, dummy_cassette):
"""Test that non-o3 models still use exact matching."""
transport = ReplayTransport(str(dummy_cassette))
request_body = {
"model": "gpt-4",
"messages": [{"role": "user", "content": "test"}],
}
# Should not use semantic matching
assert not transport._is_o3_model_request(request_body)
def test_o3_mini_semantic_matching(self, dummy_cassette):
"""Test that o3-mini also uses semantic matching."""
transport = ReplayTransport(str(dummy_cassette))
request_body = {
"model": "o3-mini",
"reasoning": {"effort": "low"},
"input": [
{
"role": "user",
"content": [
{"type": "input_text", "text": "System...\n\n=== USER REQUEST ===\nTest\n=== END REQUEST ==="}
],
}
],
}
assert transport._is_o3_model_request(request_body)
semantic = transport._extract_semantic_fields(request_body)
assert semantic["model"] == "o3-mini"
assert semantic["user_question"] == "Test"
def test_o3_without_request_markers(self, dummy_cassette):
"""Test o3 requests without REQUEST markers fall back to full text."""
transport = ReplayTransport(str(dummy_cassette))
request_body = {
"model": "o3-pro",
"reasoning": {"effort": "medium"},
"input": [{"role": "user", "content": [{"type": "input_text", "text": "Just a simple question"}]}],
}
semantic = transport._extract_semantic_fields(request_body)
assert semantic["user_question"] == "Just a simple question"