SELECT target_id, reference_id, distancetype, CAST(distance AS DECIMAL(36,8)) AS distanceFROM TD_VECTORDISTANCE (ON target_mobile_data_dense AS TargetTableON ref_mobile_data_dense AS ReferenceTable DIMENSIONUSINGTargetIDColumn('userid')TargetFeatureColumns('CallDuration','DataCounter','SMS')RefIDColumn('userid')RefFeatureColumns('CallDuration','DataCounter','SMS')DistanceMeasure('cosine')TopK(2)) AS dt ORDER BY 3,1,2,4;

Target_IDReference_ID DistanceType Distance-------------------------------------------15cosine 0.4548651817cosine 0.3260481525cosine 0.0260892327cosine 0.0079760935cosine 0.0241505437cosine 0.0033733845cosine 0.4382224347cosine 0.31184844

# Import require libfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.vectorstores import FAISS

syntax_1 = """Syntax Description : TD_VectorDistance () ...

Example :TD_VectorDistance ( ... )"""

syntax_2 = """..."""

syntax_3 = """..."""

embedding_function = OpenAIEmbeddings(openai_api_key=os.getenv("OPENAI_API"))

technical_list = [syntax_1, syntax_2, syntax_3, ..syntax_n]         db = FAISS.from_texts(technical_list, embedding_function) 

db.similarity_search("Calculate similarity")[0]

from langchain.tools import BaseToolfrom typing import Optionalfrom langchain.callback_manager import CallbackManagerForToolRun, AsyncCallbackManagerForToolRun

# Define retrieverretriever = db.as_retriever()

# Define customize toolclass TeradataSearchTool(BaseTool):name = "teradata_search_tool"description = "Input to this tool is a keyword such as binning or bucketing, similarity, moving average. Output is an instruction on how to use Teradata Syntax with examples to improve queries."

def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:"""Use the tool."""global retrieverrelevant_doc = retriever.get_relevant_documents(query)if len(relevant_doc) == 0 or len(query) == 0:return "There are no Teradata syntax examples to be used in this scenario."else:return relevant_doc[0].page_content

async def _arun(self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:"""Use the tool asynchronously."""raise NotImplementedError("custom_search does not support async")

# Init teradata search toolteradata_search_tool = TeradataSearchTool()

# Step 4. Create Agent Executor sql_agent = create_sql_agent(llm=model,toolkit=toolkit,verbose=True,agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,extra_tools=[teradata_search_tool],prefix=prefix,suffix=suffix)

agent_executor.run("Identify user similarities by analyzing the 'UserHistory' table using 'UserHistoryReference' as the reference table, focusing on attributes CallDuration, DataCounter, and SMS")