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feat: add sql completions powered by LLM (#1514)
* feat: add sql completions powered by LLM * fix linter * fix linter * address comment
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42 changes: 42 additions & 0 deletions
42
querybook/server/lib/ai_assistant/prompts/sql_complete_prompt.py
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from langchain.prompts import PromptTemplate | ||
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prompt_template = """You are an expert in the {dialect} SQL dialect, skilled in providing precise SQL code completions. | ||
Your task is to complete the SQL query based on the given context. | ||
<Prefix><FILL_ME><Suffix> | ||
===Table Schemas | ||
{table_schemas} | ||
===Response Guidelines: | ||
1. Analyze the partial query and table schemas to understand the context and determine the query's goal. | ||
2. Identify the relevant tables and columns necessary for the query. | ||
3. Replace <FILL_ME> with appropriate SQL code, or leave it empty if no completion is needed. | ||
4. Make sure the completion does not overlap with the prefix or suffix. | ||
5. Respond in JSON format | ||
===Response Format: | ||
{{ | ||
"completion": "the SQL code to replace <FILL_ME>, if any" | ||
}} | ||
===Example | ||
Input: | ||
sele<FILL_ME> from some_table | ||
Reasoning: | ||
The prefix "sele" suggests that the query is likely a SELECT statement. The table schemas indicate the available columns. The completion should be a list of columns to select from the table "some_table". | ||
As it already has a partial query, the completion should be starting from "ct" to complete the word "select", and then followed by the columns to select. | ||
Output: | ||
{{ | ||
"completion": "ct *" | ||
}} | ||
===Input | ||
{prefix}<FILL_ME>{suffix} | ||
""" | ||
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SQL_AUTOCOMPLETE_PROMPT = PromptTemplate.from_template(prompt_template) |
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15 changes: 0 additions & 15 deletions
15
querybook/webapp/hooks/queryEditor/extensions/useCopilotExtension.ts
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