Using create_sql_query_chain with some models generates incorrect SQL what am i doing wrong?

[RollsChris]

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Example Code

import os
from langchain_openai import AzureChatOpenAI
llm = AzureChatOpenAI(
    temperature=0,
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

from langchain.chains import create_sql_query_chain

chain = create_sql_query_chain(llm, db)
response = chain.invoke({"question": "How many artists are there"})

Description

The output of the above code using gpt-4o-mini:

```sql
SQLQuery: SELECT COUNT(*) AS "ArtistCount" FROM "Artist";```

which when run against the db fails with:

```": syntax error
[SQL: ```sql
SQLQuery: SELECT COUNT(*) AS "ArtistCount" FROM "Artist";```]

The generated SQL Prompt is:

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 SQLite. You can order the results to return the most informative data in the database.
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.
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.
Pay attention to use date('now') function to get the current date, if the question involves "today".

Use the following format:

Question: Question here
SQLQuery: SQL Query to run
SQLResult: Result of the SQLQuery
Answer: Final answer here

Only use the following tables:
{table_info}

Question: {input}

Please note i do get a successful query to run from gpt-3.5-turbo

Thanks for any help given

System Info

langchain==0.3.0
langchain-anthropic==0.2.1
langchain-chroma==0.1.4
langchain-community==0.3.0
langchain-core==0.3.2
langchain-openai==0.2.0
langchain-text-splitters==0.3.0
langchainhub==0.1.21

System: Docker devcontainer mcr.microsoft.com/devcontainers/miniconda:1-3

python --version = 3.12.4

[dosubot[bot]]

Hey there, @RollsChris! I'm here to help you out with any bugs, questions, or contributions while you wait for a human maintainer. Let's dive in and tackle this issue together!

I found a similar solved discussion that might help you with your issue ^[1].

The error you're encountering is due to the SQL query being wrapped in triple backticks, which is not valid SQL syntax. To resolve this issue, you need to ensure that the query generated by the LLM does not include these backticks. Here’s how you can modify your code to handle this:

1. Modify the QuerySQLDataBaseTool class to clean up the query:
   You can create a custom tool that extends QuerySQLDataBaseTool and overrides the _run method to clean up the query before execution.

from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool

class CustomQuerySQLDataBaseTool(QuerySQLDataBaseTool):
    def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> Union[str, Sequence[Dict[str, Any]], Result]:
        # Clean up the query by removing triple backticks
        cleaned_query = query.strip("```sql\n").strip("\n```")
        return self.db.run_no_throw(cleaned_query)

2. Use the custom tool in your chain:
   Replace the QuerySQLDataBaseTool with your custom tool in the chain.

import os
from dotenv import load_dotenv
from langchain_community.utilities import SQLDatabase
from langchain_openai import AzureChatOpenAI
from langchain.chains import create_sql_query_chain

load_dotenv()

db_url = f"sqlite:///{os.getenv('DB_PATH')}"
db = SQLDatabase.from_uri(db_url)

llm = AzureChatOpenAI(
    temperature=0,
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

# Use the custom tool
execute_query = CustomQuerySQLDataBaseTool(db=db)
write_query = create_sql_query_chain(llm, db)
chain = write_query | execute_query

response = chain.invoke({"question": "How many artists are there"})
print(response)

This should ensure that the SQL query generated by the LLM is correctly formatted and executable by the SQLite database.

Additionally, make sure you are using the correct configurations and settings for SQLite databases to avoid syntax errors. Refer to the MYSQL_PROMPT template and other configurations mentioned in the LangChain documentation ^[1].

To continue talking to Dosu, mention @dosu.

[dosubot[bot]]

Different models return different SQL outputs because they interpret the input query differently based on their training data, architecture, and the specific prompt used. To help mitigate this issue, you can use the SQLDatabaseChain from the LangChain library, which includes features like a query checker to correct invalid SQL queries and the ability to customize prompts to guide the model's behavior.

Here is an example of how to use the SQLDatabaseChain with a query checker:

from langchain_openai import OpenAI
from langchain_community.utilities import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain

db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db")
llm = OpenAI(temperature=0, verbose=True)

# Initialize the SQLDatabaseChain with the query checker enabled
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, use_query_checker=True)

# Run a query
result = db_chain.run("How many albums by Aerosmith?")
print(result)

Additionally, you can improve SQL query generation by making the prompt specific to the SQL dialect, formatting schema information into the prompt, and using few-shot examples to assist the model. Here is an example of setting up a few-shot prompt:

from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate

examples = [
    {"input": "List all artists.", "query": "SELECT * FROM Artist;"},
    {"input": "Find all albums for the artist 'AC/DC'.", "query": "SELECT * FROM Album WHERE ArtistId = (SELECT ArtistId FROM Artist WHERE Name = 'AC/DC');"},
    # Add more examples as needed
]

example_prompt = PromptTemplate.from_template("User input: {input}\nSQL query: {query}")
prompt = FewShotPromptTemplate(
    examples=examples[:5],
    example_prompt=example_prompt,
    prefix="You are a SQLite expert. Given an input question, create a syntactically correct SQLite query to run. Unless otherwise specified, do not return more than {top_k} rows.\n\nHere is the relevant table info: {table_info}\n\nBelow are a number of examples of questions and their corresponding SQL queries.",
    suffix="User input: {input}\nSQL query: ",
    input_variables=["input", "top_k", "table_info"],
)

print(prompt.format(input="How many artists are there?", top_k=3, table_info="foo"))

For more advanced use cases, such as working with vector databases, LangChain also provides the VectorSQLDatabaseChain. This chain includes a specialized output parser (VectorSQLOutputParser) that can handle vector-specific SQL commands and embeddings.

LangChain also provides specific tools for interacting with SQL databases, such as QuerySQLDataBaseTool, InfoSQLDatabaseTool, and ListSQLDatabaseTool. These tools can be used to execute queries, get metadata about the database, and list table names, respectively. Additionally, the QuerySQLCheckerTool can be used to check the correctness of a SQL query before execution ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[RollsChris]

you have shown me code for a deprecated methos, run on db_chain.run

[dosubot[bot]]

The db_chain.run method is deprecated. Instead, you should use the from_llm class method to instantiate the SQLDatabaseChain. Here is how you can do it:

from langchain_openai import OpenAI
from langchain_community.utilities import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain

# Initialize the database and language model
db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db")
llm = OpenAI(temperature=0, verbose=True)

# Create the SQLDatabaseChain using the from_llm class method
db_chain = SQLDatabaseChain.from_llm(llm=llm, db=db, verbose=True, use_query_checker=True)

# Run a query
result = db_chain({"question": "How many albums by Aerosmith?"})
print(result)

This approach ensures that you are using the latest recommended method for running queries with SQLDatabaseChain ^[1].

To continue talking to Dosu, mention @dosu.

[RollsChris]

could explain to me a bit about create_sql_agent

[dosubot[bot]]

The create_sql_agent function constructs a SQL agent from a language model (LLM) and a toolkit or database. Here is a brief explanation of its parameters and functionality:

Parameters:

• llm: The language model to use for the agent.
• toolkit: An optional SQLDatabaseToolkit for the agent to use. Either toolkit or db must be provided.
• agent_type: Specifies the type of agent. Options include "tool-calling", "openai-tools", "openai-functions", or "zero-shot-react-description". Defaults to "zero-shot-react-description".
• callback_manager: Deprecated. Use agent_executor_kwargs to pass callbacks.
• prefix: Optional prompt prefix string.
• suffix: Optional prompt suffix string.
• format_instructions: Optional formatting instructions.
• input_variables: Deprecated.
• top_k: Number of rows to query by default.
• max_iterations: Maximum number of iterations for the agent.
• max_execution_time: Maximum execution time for the agent.
• early_stopping_method: Method for early stopping.
• verbose: Verbosity of the agent executor.
• agent_executor_kwargs: Additional arguments for the AgentExecutor.
• extra_tools: Additional tools for the agent.
• db: An optional SQLDatabase to create a SQLDatabaseToolkit. Either db or toolkit must be provided.
• prompt: Optional complete agent prompt.
• kwargs: Additional arbitrary arguments.

Functionality:

The function creates an AgentExecutor with the specified agent type and configuration. It supports different agent types and configurations, allowing for flexible integration with SQL databases and language models.

Example Usage:

from langchain_openai import ChatOpenAI
from langchain_community.agent_toolkits import create_sql_agent
from langchain_community.utilities import SQLDatabase

db = SQLDatabase.from_uri("sqlite:///Chinook.db")
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
agent_executor = create_sql_agent(llm, db=db, agent_type="tool-calling", verbose=True)

This example demonstrates how to create a SQL agent using a language model and a SQL database.

Unit Test Example:

from langchain_community.agent_toolkits import SQLDatabaseToolkit, create_sql_agent
from langchain_community.utilities.sql_database import SQLDatabase
from tests.unit_tests.llms.fake_llm import FakeLLM

def test_create_sql_agent() -> None:
    db = SQLDatabase.from_uri("sqlite:///:memory:")
    queries = {"foo": "Final Answer: baz"}
    llm = FakeLLM(queries=queries, sequential_responses=True)
    toolkit = SQLDatabaseToolkit(db=db, llm=llm)

    agent_executor = create_sql_agent(
        llm=llm,
        toolkit=toolkit,
    )

    assert agent_executor.run("hello") == "baz"

This unit test demonstrates how to create a SQL agent using a fake language model and an in-memory SQLite database, ensuring that the agent can execute a query and return the expected result.

Additional Context:

Building a Q&A system over SQL data involves converting user questions into SQL queries, executing those queries, and then using the results to answer the questions. This process can be implemented using both chains and agents. Agents offer more flexibility, such as the ability to query the database multiple times to refine the answer and handle errors by regenerating queries. When creating such systems, it is crucial to consider security best practices, such as minimizing database connection permissions and potentially adding human approval steps before executing queries ^[1][2][3].

To continue talking to Dosu, mention @dosu.