SQLDatabaseChain sharing data that's not in database

[vignxs]

Issue you'd like to raise.

Okay I have tried both SQL database chain and create SQL agent from agents and what i have seen is that after run and it returns zero records from the database and LLM gives the output from its own. So basically I have only one fruit in my database and if I ask what fruit contains vitamin c which is not in the database and it fetch me the result from its own knowledge base. I tried adding the instructions in the prompt like if query return zero records or observation is empty or the result of the SQL. Query is empty, Do not share anything on your own And just convey the message to the user that for your certain filter criteria that is no records Try different query or elaborate your request but it doesn't seem to be working. is there anyting we can do to fix it?

Suggestion:

No response

[dosubot[bot]]

🤖

Hello @vignxs,

I'm here to assist you with your issue. I'm an AI bot designed to help solve bugs, answer questions, and guide you in becoming a contributor while you're waiting for a human maintainer.

Based on your description, it seems like you want the SQLDatabaseChain to not provide information from its own knowledge base when the SQL query returns zero records. Instead, you want it to notify the user to try a different query or elaborate their request.

There was a similar issue raised in the past (#5188) where the solution was to set return_direct = True when creating the SQLDatabaseChain. However, this might not completely solve your problem as it doesn't provide a custom message to the user.

To achieve your desired behavior, you can modify the run method in the SQLDatabase class. Currently, the run method returns an empty string when no records are returned from the SQL query. You can modify this to return a custom message instead. Here's how you can do it:

def run(
    self,
    command: str,
    fetch: Union[Literal["all"], Literal["one"]] = "all",
) -> str:
    """Execute a SQL command and return a string representing the results.
    If the statement returns rows, a string of the results is returned.
    If the statement returns no rows, a custom message is returned.
    """
    result = self._execute(command, fetch)
    # Convert columns values to string to avoid issues with sqlalchemy
    # truncating text
    res = [
        tuple(truncate_word(c, length=self._max_string_length) for c in r.values())
        for r in result
    ]
    if not res:
        return "No records found. Please try a different query or elaborate your request."
    else:
        return str(res)

In this modification, when no records are found, the method will return the message "No records found. Please try a different query or elaborate your request." instead of an empty string. This will notify the user to try a different query or elaborate their request when the SQL query returns zero records.

You can find the run method in the SQLDatabase class in the sql_database.py file.

I hope this helps! If you have any other questions or need further clarification, feel free to ask.

Best, Dosu

Sources

• Answer from the SQLDatabaseChain does not output when using the Agent 'conversational-react-description'
• libs/langchain/langchain/utilities/sql_database.py

---

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[vignxs]

@dosu-bot what return_direct will do?

[dosubot[bot]]

🤖

Based on the provided context, there is no return_direct parameter in the SQLDatabaseChain class in the LangChain framework. The parameters accepted by the SQLDatabaseChain class are engine, schema, metadata, ignore_tables, include_tables, sample_rows_in_table_info, indexes_in_table_info, custom_table_info, view_support, and max_string_length.

However, the return_direct parameter is used in the VectorSQLDatabaseChain class within the LangChain framework. It is used in the _call method of the VectorSQLDatabaseChain class to determine whether to return the result of the SQL query directly or not. If return_direct is set to True, the final result will be the direct result of the SQL query.

Here is the relevant code snippet from the VectorSQLDatabaseChain class:

if self.return_direct:
    final_result = result

This information is based on the context provided and the source code found in the following files:

• libs/langchain/langchain/utilities/sql_database.py
• libs/experimental/langchain_experimental/sql/base.py
• libs/experimental/langchain_experimental/sql/vector_sql.py

Please let me know if you need further clarification or have any other questions.

Sources

• libs/langchain/langchain/utilities/sql_database.py
• libs/experimental/langchain_experimental/sql/base.py
• libs/experimental/langchain_experimental/sql/vector_sql.py
• libs/experimental/langchain_experimental/retrievers/vector_sql_database.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[vignxs]

@dosu-bot, how can I change the run function in agents for create_sql_agent?

[dosubot[bot]]

🤖

Sure, I can guide you on how to modify the run function in the SQLDatabase class to return a custom message when no records are found in the database.

Currently, the run function in the SQLDatabase class returns an empty string when no records are found in the database. This is evident from the following code snippet:

def run(
    self,
    command: str,
    fetch: Union[Literal["all"], Literal["one"]] = "all",
) -> str:
    """Execute a SQL command and return a string representing the results.
    If the statement returns rows, a string of the results is returned.
    If the statement returns no rows, an empty string is returned.
    """
    result = self._execute(command, fetch)
    # Convert columns values to string to avoid issues with sqlalchemy
    # truncating text
    res = [
        tuple(truncate_word(c, length=self._max_string_length) for c in r.values())
        for r in result
    ]
    if not res:
        return ""
    else:
        return str(res)

To change this behavior, you can modify the run function as follows:

def run(
    self,
    command: str,
    fetch: Union[Literal["all"], Literal["one"]] = "all",
) -> str:
    """Execute a SQL command and return a string representing the results.
    If the statement returns rows, a string of the results is returned.
    If the statement returns no rows, a custom message is returned.
    """
    result = self._execute(command, fetch)
    # Convert columns values to string to avoid issues with sqlalchemy
    # truncating text
    res = [
        tuple(truncate_word(c, length=self._max_string_length) for c in r.values())
        for r in result
    ]
    if not res:
        return "No records found in the database."
    else:
        return str(res)

In the above code, the line return "No records found in the database." is added to return a custom message when no records are found in the database.

Please note that you need to replace "No records found in the database." with your custom message.

I hope this helps! If you have any other questions, feel free to ask.

Sources

• libs/langchain/langchain/utilities/sql_database.py

---

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If you want to continue the conversation, start your reply with @dosu-bot.

[vignxs]

@dosu-bot, I'm trying to use the Router using Lcel and can I use sql chain and llm chains?

from langchain.chat_models import ChatOpenAI from langchain.schema.output_parser import StrOutputParser from langchain.schema.runnable import RunnableBranch

general_prompt = PromptTemplate.from_template( "You are a helpful assistant. Answer the question as accurately as you can.\n\n{input}" ) prompt_branch = RunnableBranch( (lambda x: x["topic"] == "math", math_prompt), (lambda x: x["topic"] == "physics", physics_prompt), general_prompt, )

from typing import Literal

from langchain.output_parsers.openai_functions import PydanticAttrOutputFunctionsParser from langchain.pydantic_v1 import BaseModel from langchain.utils.openai_functions import convert_pydantic_to_openai_function

class TopicClassifier(BaseModel): "Classify the topic of the user question"

topic: Literal["math", "physics", "general"]
"The topic of the user question. One of 'math', 'physics' or 'general'."

classifier_function = convert_pydantic_to_openai_function(TopicClassifier) llm = ChatOpenAI().bind( functions=[classifier_function], function_call={"name": "TopicClassifier"} ) parser = PydanticAttrOutputFunctionsParser( pydantic_schema=TopicClassifier, attr_name="topic" ) classifier_chain = llm | parser

from operator import itemgetter

from langchain.schema.output_parser import StrOutputParser from langchain.schema.runnable import RunnablePassthrough

final_chain = ( RunnablePassthrough.assign(topic=itemgetter("input") | classifier_chain) | prompt_branch | ChatOpenAI() | StrOutputParser() )

[dosubot[bot]]

🤖

To integrate an SQL chain with your current LangChain setup, you can use the SQLDatabaseChain or SQLDatabaseSequentialChain classes from the langchain_experimental.sql module.

Here's an example of how you might integrate an SQL chain into your current setup:

from langchain_experimental.sql import SQLDatabaseChain
from langchain.sql import SQLDatabase

# Initialize your SQL database
db = SQLDatabase(...)  # replace with your actual database connection parameters

# Initialize your SQL chain
sql_chain = SQLDatabaseChain.from_llm(llm, db)

# Add the SQL chain to your final chain
final_chain = (
    RunnablePassthrough.assign(topic=itemgetter("input") | classifier_chain | sql_chain)
    | prompt_branch
    | ChatOpenAI()
    | StrOutputParser()
)

In this example, SQLDatabaseChain.from_llm(llm, db) creates an SQL chain from your existing LLM chain and an SQL database. The SQL chain is then added to your final chain.

Please note that you'll need to replace ... with the actual parameters for your SQL database connection. Also, remember to handle the SQL chain's output appropriately in your final chain.

This is a basic example and your actual implementation may vary based on the specifics of your project. For more information, you can refer to the SQLDatabaseChain class documentation.

Please be aware of the security warning in the class docstring: the database connection should use credentials that are narrowly-scoped to only include the permissions this chain needs. Failure to do so may result in data corruption or loss, since this chain may attempt commands like DROP TABLE or INSERT if appropriately prompted.

Sources

• libs/experimental/langchain_experimental/sql/init.py
• libs/experimental/langchain_experimental/sql/base.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[vignxs]

@dosu-bot, how can use tracers elapsed time with sqldatabsechain?

[dosubot[bot]]

🤖

Sure, I can guide you on how to implement tracers to measure the elapsed time for the SQLDatabaseChain class in the LangChain framework.

You can create a new class that inherits from the BaseTracer class, similar to the FunctionCallbackHandler class. This new class would override the necessary methods to handle the start, end, and error events of the SQLDatabaseChain class.

Here is a basic example:

from langchain.schema.callbacks.tracers import BaseTracer
from langchain.schema.run import Run
from typing import Callable, Any

class SQLDatabaseChainTracer(BaseTracer):
    """Tracer for the SQLDatabaseChain class."""

    name: str = "sql_database_chain_tracer"

    def __init__(self, function: Callable[[str], None], **kwargs: Any) -> None:
        super().__init__(**kwargs)
        self.function_callback = function

    def _on_chain_start(self, run: Run) -> None:
        # Implement your logic here
        pass

    def _on_chain_end(self, run: Run) -> None:
        # Implement your logic here
        pass

    def _on_chain_error(self, run: Run) -> None:
        # Implement your logic here
        pass

In the _on_chain_start, _on_chain_end, and _on_chain_error methods, you would implement your logic to measure the elapsed time and handle the events of the SQLDatabaseChain class.

Please note that this is a basic example and you might need to adjust it according to your specific needs. You can refer to the stdout.py file in the LangChain repository for more details on how to implement a tracer.

As for the structure and functionality of the SQLDatabaseChain class, I wasn't able to find specific information within the repository. If you could provide more details about what you're looking for, I might be able to give a more precise answer.

Sources

• libs/langchain/langchain/schema/callbacks/tracers/stdout.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.