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IDs are now deterministic hashes based on request content, and timestamps are normalized to constants, eliminating spurious changes when re-recording tests. ## Changes - Updated `inference_recorder.py` to normalize IDs and timestamps during recording - Added `scripts/normalize_recordings.py` utility to re-normalize existing recordings - Created documentation in `tests/integration/recordings/README.md` - Normalized 350 existing recording files
43 lines
1.3 KiB
JSON
43 lines
1.3 KiB
JSON
{
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"request": {
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"method": "POST",
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"url": "https://api.fireworks.ai/inference/v1/v1/completions",
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"headers": {},
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"body": {
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"model": "accounts/fireworks/models/llama-v3p1-8b-instruct",
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"prompt": "Respond to this question and explain your answer. Complete the sentence using one word: Roses are red, violets are ",
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"stream": false,
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"extra_body": {}
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"endpoint": "/v1/completions",
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"model": "accounts/fireworks/models/llama-v3p1-8b-instruct"
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"response": {
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"body": {
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"__type__": "openai.types.completion.Completion",
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"__data__": {
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"id": "rec-d45ca9107508",
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"choices": [
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{
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"finish_reason": "length",
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"index": 0,
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"logprobs": null,
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"text": "4. At the beginning of the year, a woman has $5,000"
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}
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],
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"created": 0,
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"model": "accounts/fireworks/models/llama-v3p1-8b-instruct",
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"object": "text_completion",
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"system_fingerprint": null,
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"usage": {
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"completion_tokens": 16,
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"prompt_tokens": 25,
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"total_tokens": 41,
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"completion_tokens_details": null,
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"prompt_tokens_details": null
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}
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},
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"is_streaming": false
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}
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}
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