create_sql_agent

[GayatriSharma23]

Checked other resources

• I added a very descriptive title to this question.
• I searched the LangChain documentation with the integrated search.
• I used the GitHub search to find a similar question and didn't find it.

Commit to Help

• I commit to help with one of those options 👆

Example Code

from langchain.agents import create_sql_agent

db=SQLDatabase(engine)
llm=Ollama(model='llama3')
toolkit=SQLDatabaseToolkit(db,llm)
tools=SQLDatabaseToolkit.get_tools()
agent=create_sql_agent(llm==llm,toolkit=toolkit,verbose=True, 
                            agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
                            handle_parsing_errors=True,)
agent.invoke("How many rows are there in Table A")

Description

I am trying to create_sql_agent to create an agent that takes NL query and provide answer to it using information the connected database. But when I am using the above code I am getting invalid response with message invalid or incomplete response, if i don't give agent_type then I am getting error as invalid format missing Action after Thought

System Info

langchain==0.2.6

[dosubot[bot]]

Hey @GayatriSharma23! I'm here to help you with your issue. Let's work together to solve this problem!

To resolve the issues with creating an SQL agent using LangChain, you can follow these steps:

1. Correct the create_sql_agent Function Call: Ensure that the parameters passed to the create_sql_agent function are correct. Specifically, check the equality operator (==) used for llm and correct it to a single =.

2. Define the Prompt Template: Ensure that you have a proper prompt template defined for the agent. This template guides the agent on how to process the input and generate the SQL query.

Here is an updated version of your code with these corrections:

from langchain.agents import create_sql_agent
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
from langchain_community.llms.ollama import Ollama
from langchain_core.prompts import PromptTemplate
from langchain.agents.agent_types import AgentType

# Define the prompt template
template = '''Answer the following questions as best you can. You have access to the following tools:

{tools}

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin!

Question: {input}
Thought:{agent_scratchpad}'''

prompt = PromptTemplate.from_template(template)

# Initialize the database and language model
db = SQLDatabase(engine)
llm = Ollama(model='llama3')

# Create the toolkit
toolkit = SQLDatabaseToolkit(db, llm)

# Create the SQL agent
agent = create_sql_agent(
    llm=llm,
    toolkit=toolkit,
    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    prompt=prompt,
    verbose=True,
    handle_parsing_errors=True,
)

# Invoke the agent with a query
response = agent.invoke("How many rows are there in Table A")
print(response)

Ensure that you have the correct versions of the required packages installed and that your database connection is properly configured. This should help resolve the issues you are encountering with the SQL agent ^[1][2].

To continue talking to Dosu, mention @dosu.

[GayatriSharma23]

@dosu
i tried the code you have provided but it's not working out I am using this code
agent = create_sql_agent(
llm=llm,
toolkit=toolkit,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
prompt=prompt,
verbose=True,
handle_parsing_errors=True,
)
where my prompt is
from langchain_core.prompts import (
ChatPromptTemplate,
FewShotChatMessagePromptTemplate,
PromptTemplate,
)

from langchain.agents.mrkl import prompt as react_prompt

examples = [
{"input": "List all the characters in Anna Karenina",
"query": "SELECT DISTINCT characterNames FROM novels WHERE novelName = 'Anna Karenina'"},
]

system_prefix = """
You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct query to run, then look at the results of the query and
return the answer. Unless the user specifies a specific number of examples they wish to obtain.
You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for the relevant columns given the question.
You have access to tools for interacting with the database.
Only use the given tools. Only use the information returned by the tools to construct your final answer.
You MUST double check your query before executing it. If you get an error while executing a query, rewrite the query
and try again.
use the following tables {table_info} with {table_names}
DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.

If the question does not seem related to the database, just return "I don't know" as the answer.

"""

basic_suffix = """
Begin!

Question: {input}
Thought: I should look at the tables in the database to see what I can query. Then I should query the schema of the most relevant tables.
{agent_scratchpad}

"""

example_prompt = ChatPromptTemplate.from_messages(
messages=[
('human', "{input}"),
('ai', "{query}")
]
)

few_shot_prompt = FewShotChatMessagePromptTemplate(
examples=examples,
example_prompt=example_prompt,
input_variables=["input",
"agent_scratchpad"],
)

format_instructions = f"{react_prompt.FORMAT_INSTRUCTIONS}\n "
f"Here are some examples of user inputs and "
f"their corresponding SQL queries:\n"

template = "\n\n".join(
[
system_prefix,
"{tools}",
format_instructions,
few_shot_prompt.format(),
basic_suffix
]
)

prompt = PromptTemplate.from_template(template=template)
in this 90% of time i am getting Invalid response Missing action after thought, and if agent produces correct response then it's not giving answer in natural language it just provide answer till agent action i.e. sql result not the final NL response

[dosubot[bot]]

To address the issues you're encountering with the SQL agent using LangChain, follow these steps:

1. Correct the create_sql_agent Function Call: Ensure that the parameters passed to the create_sql_agent function are correct. Specifically, check the equality operator (==) used for llm and correct it to a single =.

2. Prompt Configuration: Ensure that you have a proper prompt template defined for the agent. This template guides the agent on how to process the input and generate the SQL query.

3. Handling Missing Actions: The error "Invalid response: Missing action after thought" indicates that the agent's response is not following the expected format. Ensure that the prompt template is correctly guiding the agent to produce the required "Action" and "Action Input" steps.

4. Natural Language Response: To ensure the agent provides a natural language response, you need to include a step in the prompt that instructs the agent to convert the SQL result into a natural language answer.

Here is an updated version of your code with these corrections:

from langchain.agents import create_sql_agent
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
from langchain_community.llms.ollama import Ollama
from langchain_core.prompts import PromptTemplate
from langchain.agents.agent_types import AgentType

# Define the prompt template
template = '''Answer the following questions as best you can. You have access to the following tools:

{tools}

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin!

Question: {input}
Thought:{agent_scratchpad}'''

prompt = PromptTemplate.from_template(template)

# Initialize the database and language model
db = SQLDatabase(engine)
llm = Ollama(model='llama3')

# Create the toolkit
toolkit = SQLDatabaseToolkit(db, llm)

# Create the SQL agent
agent = create_sql_agent(
    llm=llm,
    toolkit=toolkit,
    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    prompt=prompt,
    verbose=True,
    handle_parsing_errors=True,
)

# Invoke the agent with a query
response = agent.invoke("How many rows are there in Table A")
print(response)

Additionally, ensure that your prompt template includes instructions for the agent to provide a final natural language response. Here is an example of how you can modify the prompt to include this step:

basic_suffix = """
    Begin!

    Question: {input}
    Thought: I should look at the tables in the database to see what I can query. Then I should query the schema of the most relevant tables.
    {agent_scratchpad}
    Thought: I now know the final answer
    Final Answer: the final answer to the original input question
"""

template = "\n\n".join(
    [
        system_prefix,
        "{tools}",
        format_instructions,
        few_shot_prompt.format(),
        basic_suffix
    ]
)

prompt = PromptTemplate.from_template(template=template)

By following these steps and ensuring the correct configuration, you can resolve issues with your SQL agent and improve the quality of the natural language responses ^[1][2].

To continue talking to Dosu, mention @dosu.

[GayatriSharma23]

@dosu
I made changes in basic_suffix as mentioned above it's giving natural language response but along with the response I am also getting:
Answer: There are 45 customers in customer table. Invalid Format: Missing 'Action:' after 'Thought'
Another issue that I am facing is that everytime my agent produces different response to the same question asked, I have kept the LLM temperature as 0

[dosubot[bot]]

To address the issues you're facing:

1. Handling "Invalid Format: Missing 'Action:' after 'Thought'" Error:
   Ensure that the agent's response strictly follows the required format. The error indicates that the response is missing the "Action:" line after the "Thought:" line. Here is an example of the expected format:

   Thought: I need to find the number of rows in the table.
   Action: search
   Action Input: SELECT COUNT(*) FROM TableA
   

   Ensure your prompt template guides the agent to produce this format.

2. Consistent Responses:
   Since you have set the LLM temperature to 0, the responses should be deterministic. However, if the agent still produces different responses, ensure that the prompt and the agent's configuration are consistent. Here is an updated version of your code to ensure the correct format and consistent responses:

   from langchain.agents import create_sql_agent
   from langchain_community.utilities import SQLDatabase
   from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
   from langchain_community.llms.ollama import Ollama
   from langchain_core.prompts import PromptTemplate
   from langchain.agents.agent_types import AgentType
   
   # Define the prompt template
   template = '''Answer the following questions as best you can. You have access to the following tools:
   
   {tools}
   
   Use the following format:
   
   Question: the input question you must answer
   Thought: you should always think about what to do
   Action: the action to take, should be one of [{tool_names}]
   Action Input: the input to the action
   Observation: the result of the action
   ... (this Thought/Action/Action Input/Observation can repeat N times)
   Thought: I now know the final answer
   Final Answer: the final answer to the original input question
   
   Begin!
   
   Question: {input}
   Thought:{agent_scratchpad}'''
   
   prompt = PromptTemplate.from_template(template)
   
   # Initialize the database and language model
   db = SQLDatabase(engine)
   llm = Ollama(model='llama3', temperature=0)  # Ensure temperature is set to 0
   
   # Create the toolkit
   toolkit = SQLDatabaseToolkit(db, llm)
   
   # Create the SQL agent
   agent = create_sql_agent(
       llm=llm,
       toolkit=toolkit,
       agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
       prompt=prompt,
       verbose=True,
       handle_parsing_errors=True,
   )
   
   # Invoke the agent with a query
   response = agent.invoke("How many rows are there in Table A")
   print(response)

   Ensure that the prompt template includes instructions for the agent to provide a final natural language response and follows the required format strictly. This should help in resolving the "Invalid Format: Missing 'Action:' after 'Thought'" error and ensure consistent responses from the agent ^[1][2].

To continue talking to Dosu, mention @dosu.

[jjfeore]

Another issue that I am facing is that everytime my agent produces different response to the same question asked, I have kept the LLM temperature as 0

LLMs typically perfectly deterministic, even with temperature set to 0. You can improve their determinism a bit more by adding the "seed" parameter, if your LLM of choice supports it. When you're using hosted models, you might also still see variation in behavior with a set seed and a temperature of 0, due to small changes in the model's backend and small updates to the model version. For instance, in the case of OpenAI, they return a "system_fingerprint" with the response (to indicate the specific model variation used) and you can only be assured of deterministic behavior when the seed is the same, system_fingerprint is the same, and temperature is 0.

[amsmota]

Hi all, I'm relatively new to langchain/langchain_community andI have exactly the same issue except that I'm using an openai-based model. I'm also using the "miracle" prompts from langchain that are exactly the same.

Besides the "invalid or incomplete response" and "invalid format missing Action after Thought" that happens most of times, like 90% of the times, the data that is returned, when it actually returns data, it's fabricated data, completly different from what it has in the db. It initially retrieves the sql_db_list_tables with fabricated data (coming from the LLM I think) and then retrieves the correct list of tables from the db, but then it uses the fabricated table with a fabricated schema in the next steps, and even if the query is correct the result is also fabricated .

I did saw that in past versions there was a return_direct that would make the queries directly to the dab instead of the LLM, but that flag does not exist in version 0.2.6...

Issue: #4208
PR: #864

Anybody have expereince this and hopefully solved it?

Thanks.

[NicolasCampana]

Hi there @amsmota , did you figure out?
Did you follow the LC tutorials on SQL? https://python.langchain.com/v0.2/docs/tutorials/sql_qa/

Regards,

[grappa-ytannous]

+1