mirror of
https://github.com/Mintplex-Labs/langchain-python.git
synced 2026-07-22 01:05:52 -04:00
Harrison Chase
ec727bf166
Currently the chain is getting the column names and types on the one side and the example rows on the other. It is easier for the llm to read the table information if the column name and examples are shown together so that it can easily understand to which columns do the examples refer to. For an instantiation of this, please refer to the changes in the `sqlite.ipynb` notebook. Also changed `eval` for `ast.literal_eval` when interpreting the results from the sample row query since it is a better practice. --------- Co-authored-by: Francisco Ingham <> --------- Co-authored-by: Francisco Ingham <fpingham@gmail.com>
41 lines
1.6 KiB
Python
41 lines
1.6 KiB
Python
# flake8: noqa
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from langchain.prompts.base import CommaSeparatedListOutputParser
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from langchain.prompts.prompt import PromptTemplate
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_DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results using the LIMIT clause. You can order the results by a relevant column to return the most interesting examples in the database.
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Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
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Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
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Use the following format:
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Question: "Question here"
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SQLQuery: "SQL Query to run"
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SQLResult: "Result of the SQLQuery"
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Answer: "Final answer here"
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Only use the tables listed below.
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{table_info}
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Question: {input}"""
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PROMPT = PromptTemplate(
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input_variables=["input", "table_info", "dialect", "top_k"],
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template=_DEFAULT_TEMPLATE,
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)
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_DECIDER_TEMPLATE = """Given the below input question and list of potential tables, output a comma separated list of the table names that may be necessary to answer this question.
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Question: {query}
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Table Names: {table_names}
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Relevant Table Names:"""
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DECIDER_PROMPT = PromptTemplate(
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input_variables=["query", "table_names"],
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template=_DECIDER_TEMPLATE,
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output_parser=CommaSeparatedListOutputParser(),
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)
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