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text_2_sql/autogen/Iteration 5 - Agentic Vector Based Text2SQL.ipynb

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@@ -29,7 +29,7 @@
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"import dotenv\n",
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"import logging\n",
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"from autogen_agentchat.task import Console\n",
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"from agentic_text_2_sql import AgenticText2Sql"
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"from autogen_text_2_sql import AutoGenText2Sql"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"agentic_text_2_sql = AgenticText2Sql(target_engine=\"TSQL\", engine_specific_rules=\"Use TOP X to limit the number of rows returned instead of LIMIT X. NEVER USE LIMIT X as it produces a syntax error.\").agentic_flow"
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"agentic_text_2_sql = AutoGenText2Sql(target_engine=\"TSQL\", engine_specific_rules=\"Use TOP X to limit the number of rows returned instead of LIMIT X. NEVER USE LIMIT X as it produces a syntax error.\").agentic_flow"
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]
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},
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{

text_2_sql/autogen/src/autogen/autogen_text_2_sql.py

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@@ -2,15 +2,15 @@
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# Licensed under the MIT License.
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from autogen_agentchat.task import TextMentionTermination, MaxMessageTermination
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from autogen_agentchat.teams import SelectorGroupChat
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from llm_model_creator import LLMModelCreator
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from llm_agent_creator import LLMAgentCreator
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from creators.llm_model_creator import LLMModelCreator
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from creators.llm_agent_creator import LLMAgentCreator
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import logging
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from custom_agents.sql_query_cache_agent import SqlQueryCacheAgent
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import json
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import os
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class AgenticText2Sql:
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class AutoGenText2Sql:
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def __init__(self, target_engine: str, engine_specific_rules: str):
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self.use_query_cache = False
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self.pre_run_query_cache = False
@@ -135,7 +135,7 @@ def agentic_flow(self):
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allow_repeated_speaker=False,
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model_client=LLMModelCreator.get_model("4o-mini"),
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termination_condition=self.termination_condition,
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selector_func=AgenticText2Sql.selector,
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selector_func=AutoGenText2Sql.selector,
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)
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return agentic_flow

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