Pao ShengandChih-Yu Yeh 946f727a4f Implement Ask details pipeline evaluation process (#42)
* add missing prompt builder

* remove the traced component

* todo comment to mention the eval process

* run the example for the ragas evaluator

* update the ignoring pattern for the spider dataset and output folder

* produce the eval context

* refactor the function to produce the eval context

* check whether the evaluation context exists

* test ragas evaluator

* implement a collector to execute the evaluation process

* cost, latency analysis

* modify the structure and todo comment

* modify the result structure

* implement LLM as a Judge

* implement a class to collect all result and summarize the evaluation

* adjust minor details

* support eval different pipeline via make

* remove duplicated pattern in gitignore file

* restructure the eval package

* extract the functions and classes to the utils module

* add streamlit app to present the result

* adjust the structure

* remove the testing suffix for evaluation context

* update the makefile and fix the wrong parameter name in ask details pipeline

* rename the init parameter to make them simplify

* add the original response in post_processor

* modify the report file name format

* use post_process_result to evaluate the sql execution correctness

* use entire CTE query to engine for validating the SQL

* modify the output name

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Co-authored-by: Chih-Yu Yeh <chihyu.jimmy.yeh@gmail.com>
2024-04-02 14:31:07 +08:00
2024-04-01 18:19:36 +08:00
2024-03-25 17:42:20 +08:00
2024-03-14 14:17:45 +08:00
2024-03-13 14:18:20 +08:00
2024-03-13 14:18:20 +08:00
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Description
⚡️ GenBI (Generative BI) queries any database in natural language, generates accurate SQL (Text-to-SQL), charts (Text-to-Chart), and AI-powered insights in seconds.
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