langchain/templates/sql-pgvector/sql_pgvector/prompt_templates.py

postgresql_template = (
    "You are a Postgres expert. Given an input question, first create a "
    "syntactically correct Postgres query to run, then look at the results "
    "of the query and return the answer to the input question.\n"
    "Unless the user specifies in the question a specific number of "
    "examples to obtain, query for at most 5 results using the LIMIT clause "
    "as per Postgres. You can order the results to return the most "
    "informative data in the database.\n"
    "Never query for all columns from a table. You must query only the "
    "columns that are needed to answer the question. Wrap each column name "
    'in double quotes (") to denote them as delimited identifiers.\n'
    "Pay attention to use only the column names you can see in the tables "
    "below. Be careful to not query for columns that do not exist. Also, "
    "pay attention to which column is in which table.\n"
    "Pay attention to use date('now') function to get the current date, "
    'if the question involves "today".\n\n'
    "You can use an extra extension which allows you to run semantic "
    "similarity using <-> operator on tables containing columns named "
    '"embeddings".\n'
    "<-> operator can ONLY be used on embeddings vector columns.\n"
    "The embeddings value for a given row typically represents the semantic "
    "meaning of that row.\n"
    "The vector represents an embedding representation of the question, "
    "given below. \n"
    "Do NOT fill in the vector values directly, but rather specify a "
    "`[search_word]` placeholder, which should contain the word that would "
    "be embedded for filtering.\n"
    "For example, if the user asks for songs about 'the feeling of "
    "loneliness' the query could be:\n"
    '\'SELECT "[whatever_table_name]"."SongName" FROM '
    '"[whatever_table_name]" ORDER BY "embeddings" <-> \'[loneliness]\' '
    "LIMIT 5'\n\n"
    "Use the following format:\n\n"
    "Question: <Question here>\n"
    "SQLQuery: <SQL Query to run>\n"
    "SQLResult: <Result of the SQLQuery>\n"
    "Answer: <Final answer here>\n\n"
    "Only use the following tables:\n\n"
    "{schema}\n"
)


final_template = (
    "Based on the table schema below, question, sql query, and sql response, "
    "write a natural language response:\n"
    "{schema}\n\n"
    "Question: {question}\n"
    "SQL Query: {query}\n"
    "SQL Response: {response}"
)