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Update README.md
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README.md
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@@ -40,14 +40,14 @@ Then, construct the prompt template like so:
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def prompt(nlq, cols):
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return f"""Honeycomb is an observability platform that allows you to write queries to inspect trace data. You are an assistant that takes a natural language query (NLQ) and a list of valid columns and produce a Honeycomb query.
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### Instruction:
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NLQ: "{nlq}"
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Columns: {cols}
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### Response:
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"""
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def prompt_tok(nlq, cols):
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_p = prompt(nlq, cols)
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@@ -72,9 +72,9 @@ print(nlq, '\n', out)
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This will give you a prediction that looks like this:
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Alternatively, you can play with this model on Replicate: [hamelsmu/honeycomb-2](https://replicate.com/hamelsmu/honeycomb-2)
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def prompt(nlq, cols):
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return f"""Honeycomb is an observability platform that allows you to write queries to inspect trace data. You are an assistant that takes a natural language query (NLQ) and a list of valid columns and produce a Honeycomb query.
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### Instruction:
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NLQ: "{nlq}"
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Columns: {cols}
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### Response:
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"""
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def prompt_tok(nlq, cols):
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_p = prompt(nlq, cols)
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This will give you a prediction that looks like this:
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```md
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"{'breakdowns': ['exception.message', 'exception.type'], 'calculations': [{'op': 'COUNT'}], 'filters': [{'column': 'exception.message', 'op': 'exists'}, {'column': 'exception.type', 'op': 'exists'}], 'orders': [{'op': 'COUNT', 'order': 'descending'}], 'time_range': 7200}"
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```
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Alternatively, you can play with this model on Replicate: [hamelsmu/honeycomb-2](https://replicate.com/hamelsmu/honeycomb-2)
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