update
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@@ -135,18 +135,19 @@ function decisionMaker(state::T1, additionalinfo, text2textInstructLLM::Function
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systemmsg =
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"""
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You are a helpful assistant that find the data from a database to satisfy the user's query.
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You are a helpful assistant that find the data from a database to satisfy the user's question.
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You are also eager to improve your helpfulness.
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For your information:
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- Observation: Result of the immediately preceding action
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At each round of conversation, you will be given the following information:
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User Query: ...
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Closest SQL: The closest known SQL for this query
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Your work progress: your progress so far
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Evaluation: Evaluation of the immediately preceding action and observation
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Suggestion: Suggestion for the immediately preceding action and observation
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table_schema: schema of tables in the database
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most_relevant_SQL: The closest known SQL for this question
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query_result_of_most_relevant_SQL: the query result when executing the most_relevant_SQL against a database.
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progress: your progress so far
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evaluation: Evaluation of the immediately preceding action and observation
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suggestion: Suggestion for the immediately preceding action and observation
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You must follow the following guidelines:
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- Keep SQL queries focused only on the provided information.
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@@ -207,11 +208,24 @@ function decisionMaker(state::T1, additionalinfo, text2textInstructLLM::Function
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context =
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"""
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<context>
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<table_schema>
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$(additionalinfo[:tablelist])
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Closest SQL: $similarSQL_
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Your work progress: $workprogress
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Evaluation: $(state[:evaluation])
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Suggestion: $(state[:suggestion])
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</table_schema>
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<most_relevant_SQL>
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$similarSQL_
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</most_relevant_SQL>
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<query_result_of_most_relevant_SQL>
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winery: Chateau Montelena, wine_name: The Montelena Estate Cabernet Sauvignon, wine_id: 97264f71-007c-4cce-a3fe-2cc88fba4d05, vintage: 2017, region: Napa Valley, country: United States, wine_type: red, grape: Cabernet Sauvignon, serving_temperature: 15 to 18 Celsius, sweetness: 1, intensity: 5, tannin: 4, acidity: 4, tasting_notes: oak, vanilla, tobacco, blackberry, plum, black cherry, leather, earthy, smoke, price: 19.95, currency: USD
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</query_result_of_most_relevant_SQL>
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<progress>
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$workprogress
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</progress>
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<evaluation>
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$(state[:evaluation])
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</evaluation>
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<suggestion>
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$(state[:suggestion])
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</suggestion>
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P.S. $errornote
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</context>
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"""
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@@ -230,7 +244,7 @@ function decisionMaker(state::T1, additionalinfo, text2textInstructLLM::Function
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response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
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think, response = GeneralUtils.extractthink(response)
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#[WORKING] check for tasting_notes occurs AFTER where in the sql
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# LLM tends to generate observation given that it is in the input
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response =
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@@ -640,6 +654,7 @@ function evaluator(state::T1, additionalinfo, text2textInstructLLM::Function, ll
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At each round of conversation, you will be given the following information:
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Trajectory: A history of how you worked on the question chronologically.
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table_schema: schema of tables in the database
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You must follow the following guidelines:
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- When the search returns no result, validate whether the SQL query makes sense before accepting it as a valid answer.
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@@ -696,7 +711,9 @@ function evaluator(state::T1, additionalinfo, text2textInstructLLM::Function, ll
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context =
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"""
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<context>
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<table_schema>
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$(additionalinfo[:tablelist])
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</table_schema>
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P.S. $errornote
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</context>
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"""
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@@ -1171,7 +1188,6 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
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context = Dict(
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:tablelist =>
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"""
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Here are SQL that used to create tables in the database:
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create table customer (
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customer_id uuid primary key default gen_random_uuid (),
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customer_firstname varchar(128),
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