Hi All any one can tell me how i can build sql db agent with langflow thanks

[MuhammadAdil-Developer]

Feature Request

Hi All any one can tell me how i can build sql db agent with langflow thanks

Motivation

None

Your Contribution

None

[dosubot]

@MuhammadAdil-Developer Hello there! I'm here to help you out with any bugs, questions, or contributions you have in mind. Let's tackle this together! Dosu

To build an SQL DB agent with Langflow, you can use the SQLAgentComponent class. Here's a quick guide on how to do it:

1. Define the Configuration: Use the build_agent method to set up your SQL agent's configuration. This involves specifying the language model (LLM), the database URI, and any extra tools you might want to use.

2. Build the Agent: Use the build_agent method of the SQLAgentComponent class. You need to provide:

   • llm: An instance of a language model.
   • database_uri: The URI of the SQL database you want to connect to.
   • extra_tools: A list of additional tools if needed (optional).

Here's a code snippet to illustrate:

from langflow.custom import SQLAgentComponent
from langflow.field_typing import LanguageModel

# Initialize your language model (llm) and database URI
llm = ...  # Your language model instance
database_uri = "your_database_uri_here"

# Create an instance of SQLAgentComponent
sql_agent_component = SQLAgentComponent()

# Build the SQL agent
sql_agent = sql_agent_component.build_agent(llm=llm, database_uri=database_uri, extra_tools=[])

This will create an SQL agent using the specified language model and database connection ^[1][2].

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[MuhammadAdil-Developer]

@dosu Can you please provide me with the complete code if you know that shows connect an LLM (Language Learning Model) to my sql database and chat effectively with my SQL database? I would be very thankful to you. plzz!!!

[dosubot]

To connect an LLM to your SQL database and enable effective interaction, you can use the SQLAgentComponent from Langflow. Here's a complete example code to help you set this up:

from langchain.agents import AgentExecutor
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_community.utilities import SQLDatabase
from langflow.base.agents.agent import LCAgentComponent
from langflow.inputs import HandleInput, MessageTextInput

class SQLAgentComponent(LCAgentComponent):
    display_name = "SQLAgent"
    description = "Construct an SQL agent from an LLM and tools."
    name = "SQLAgent"

    inputs = [
        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
        MessageTextInput(name="database_uri", display_name="Database URI", required=True),
        HandleInput(
            name="extra_tools",
            display_name="Extra Tools",
            input_types=["Tool", "BaseTool"],
            is_list=True,
            advanced=True,
        ),
    ]

    def build_agent(self, llm, database_uri, extra_tools=None) -> AgentExecutor:
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        agent_args = self.get_agent_kwargs()
        agent_args["max_iterations"] = agent_args["agent_executor_kwargs"]["max_iterations"]
        del agent_args["agent_executor_kwargs"]["max_iterations"]
        return create_sql_agent(llm=llm, toolkit=toolkit, extra_tools=extra_tools or [], **agent_args)

# Example usage
# Initialize your language model (llm) and database URI
llm = ...  # Your language model instance
database_uri = "your_database_uri_here"

# Create an instance of SQLAgentComponent
sql_agent_component = SQLAgentComponent()

# Build the SQL agent
sql_agent = sql_agent_component.build_agent(llm=llm, database_uri=database_uri, extra_tools=[])

# Now you can use `sql_agent` to interact with your SQL database

This code sets up an SQL agent using a language model and a database URI. You can replace llm and database_uri with your specific language model instance and database connection string, respectively. This setup allows the agent to interact with your SQL database effectively ^[1][2].

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[MuhammadAdil-Developer]

@dosu. Can you provide me with code that includes an LLM instance? I want to use OpenAI's LLM and return responses properly. I don't want to use any tools right now; I just want to chat directly with the tables in my SQL database in a best way.

[dosubot]

To connect an OpenAI LLM to your SQL database and chat directly with the tables, you can use the OpenAIModelComponent to initialize the LLM and the SQLExecutorComponent to execute SQL queries. Here's a complete example:

from langflow.base.models.model import LCModelComponent
from langchain_openai import ChatOpenAI
from pydantic.v1 import SecretStr
from langflow.base.agents.agent import LCAgentComponent
from langflow.inputs import HandleInput, MessageTextInput
from langflow.base.langflow.components.data.sql_executor import SQLExecutorComponent

# Initialize the OpenAI language model
class OpenAIModelComponent(LCModelComponent):
    display_name = "OpenAI"
    description = "Generates text using OpenAI LLMs."
    icon = "OpenAI"

    def build_model(self) -> ChatOpenAI:
        openai_api_key = "your_openai_api_key_here"
        model_name = "gpt-3.5-turbo"  # Example model name
        return ChatOpenAI(api_key=SecretStr(openai_api_key), model=model_name)

# Initialize your language model
openai_model_component = OpenAIModelComponent()
llm = openai_model_component.build_model()

# SQL Executor to interact with the database
class SQLAgentComponent(LCAgentComponent):
    display_name = "SQLAgent"
    description = "Construct an SQL agent from an LLM and tools."
    name = "SQLAgent"

    inputs = [
        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
        MessageTextInput(name="database_uri", display_name="Database URI", required=True),
    ]

    def build_agent(self, llm, database_uri) -> SQLExecutorComponent:
        sql_executor = SQLExecutorComponent()
        return sql_executor.build(query="SELECT * FROM your_table_name", database_url=database_uri)

# Example usage
database_uri = "your_database_uri_here"

# Create an instance of SQLAgentComponent
sql_agent_component = SQLAgentComponent()

# Build the SQL agent
sql_agent = sql_agent_component.build_agent(llm=llm, database_uri=database_uri)

# Now you can use `sql_agent` to interact with your SQL database

Replace "your_openai_api_key_here", "gpt-3.5-turbo", "your_database_uri_here", and "your_table_name" with your actual OpenAI API key, desired model name, database URI, and table name, respectively. This setup allows you to interact directly with your SQL database using OpenAI's LLM without additional tools.

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[MuhammadAdil-Developer]

@dosu I want to connect to all the tables and data in the database so that if I ask anything from the entire DB, it gives me a proper and accurate answer. This is what I want. plz i dont want to connect a single table i want to connect whole sql db so..

[dosubot]

To connect an OpenAI LLM to interact with all tables and data in your entire SQL database, you can use the SQLGeneratorComponent from the Langflow library. This component allows you to generate SQL queries from natural language inputs. Here's how you can set it up:

1. Initialize the SQLDatabase: Create an instance of SQLDatabase that connects to your SQL database using the database URI.

2. Set up the Language Model (LLM): Use an appropriate language model that can process natural language inputs.

3. Configure the SQLGeneratorComponent: Use the SQLGeneratorComponent to build a configuration that includes your database, language model, and any specific prompt you want to use.

4. Generate SQL Queries: Use the invoke_chain method of SQLGeneratorComponent to generate SQL queries from natural language questions.

Here's a code snippet to illustrate the setup:

from langchain.sql_database import SQLDatabase
from langchain.base_language import BaseLanguageModel
from langflow.components.chains.SQLGenerator import SQLGeneratorComponent

# Initialize your SQL database connection
db_uri = "your_database_connection_string"
llm = BaseLanguageModel(your_language_model)

# Create an instance of SQLGeneratorComponent
sql_generator = SQLGeneratorComponent(
    input_value="Your natural language question here",
    llm=llm,
    db=SQLDatabase(db_uri),
    top_k=5,  # Number of results per select statement
    prompt="Your custom prompt with {question}"  # Optional
)

# Generate SQL query from natural language
query = sql_generator.invoke_chain()

print(query)

This setup will allow the LLM to generate SQL queries that can interact with all tables and data in your SQL database to provide accurate answers to any query. Make sure your prompt contains {question} if you are using a custom prompt. Additionally, ensure that the top_k parameter is set to a value greater than 0, as it specifies the number of results per select statement to return ^[1][2].

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[MuhammadAdil-Developer]

@ dosu i dont need to provide open ai or other llm key?

[MuhammadAdil-Developer]

from langchain.sql_database import SQLDatabase
from langchain.base_language import BaseLanguageModel
from langflow.components.chains.SQLGenerator import SQLGeneratorComponent

Initialize your SQL database connection

db_uri = "your_database_connection_string"
llm = BaseLanguageModel(your_language_model)

Create an instance of SQLGeneratorComponent

sql_generator = SQLGeneratorComponent(
input_value="Your natural language question here",
llm=llm,
db=SQLDatabase(db_uri),
top_k=5, # Number of results per select statement
prompt="Your custom prompt with {question}" # Optional
)

Generate SQL query from natural language

query = sql_generator.invoke_chain()

print(query)

plz add the llm configuration at this entire code i want to use open ai

[MuhammadAdil-Developer]

@dosu

from langchain.sql_database import SQLDatabase
from langchain.base_language import BaseLanguageModel
from langflow.components.chains.SQLGenerator import SQLGeneratorComponent

Initialize your SQL database connection
db_uri = "your_database_connection_string"
llm = BaseLanguageModel(your_language_model)

Create an instance of SQLGeneratorComponent
sql_generator = SQLGeneratorComponent(
input_value="Your natural language question here",
llm=llm,
db=SQLDatabase(db_uri),
top_k=5, # Number of results per select statement
prompt="Your custom prompt with {question}" # Optional
)

Generate SQL query from natural language
query = sql_generator.invoke_chain()

print(query)

plz add the llm configuration at this entire code i want to use open ai

[dosubot]

To configure the LLM to use OpenAI in your code, you need to initialize an OpenAI language model and pass it to the SQLGeneratorComponent. Here's how you can modify your code to include the OpenAI LLM configuration:

from langchain_community.utilities.sql_database import SQLDatabase
from langchain_core.models import OpenAI
from langflow.base.chains.model import SQLGeneratorComponent

# Initialize your OpenAI model
openai_llm = OpenAI(api_key="your_openai_api_key")

# Initialize your SQL database connection
db_uri = "your_database_connection_string"
sql_db = SQLDatabase(connection_string=db_uri)

# Create an instance of SQLGeneratorComponent
sql_generator = SQLGeneratorComponent()

# Build the component with the OpenAI LLM
query = sql_generator.build(
    input_value="Your natural language question here",
    llm=openai_llm,
    db=sql_db,
    top_k=5,  # Number of results per select statement
    prompt="Your custom prompt with {question}"  # Optional
)

print(query)

Make sure to replace "your_openai_api_key" and "your_database_connection_string" with your actual OpenAI API key and database connection string. This setup will allow the SQLGeneratorComponent to use OpenAI's language model for generating SQL queries from natural language input ^[1].

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[MuhammadAdil-Developer]

@dosu
i want to build with langflow not with langchain

[dosubot]

To build a solution using Langflow to connect an OpenAI LLM to your entire SQL database, you can use the SQLAgent class from Langflow. Here's how you can set it up:

1. Define the Language Model and Database URI: You need to provide an OpenAI language model (LLM) and the URI of your SQL database.

2. Create the SQL Agent: Use the SQLAgent class to construct an SQL agent. This class requires the LLM and the database URI as inputs.

3. Build the Agent: Call the from_toolkit_and_llm method to create an agent that can interact with your SQL database.

Here's a code snippet to illustrate the setup:

from langflow.interface.agents.custom import SQLAgent
from langchain_openai import ChatOpenAI
from pydantic.v1 import SecretStr

# Initialize the OpenAI language model
openai_api_key = "your_openai_api_key_here"
llm = ChatOpenAI(api_key=SecretStr(openai_api_key), model="gpt-3.5-turbo")

# Define your database URI
database_uri = "your_database_uri_here"

# Initialize the SQLAgent
sql_agent = SQLAgent.from_toolkit_and_llm(
    llm=llm,
    database_uri=database_uri
)

# Now you can use sql_agent to interact with your SQL database

Replace "your_openai_api_key_here" and "your_database_uri_here" with your actual OpenAI API key and database connection string. This setup allows you to connect an OpenAI LLM to your SQL database and interact with all tables and data using the Langflow framework ^[1][2].

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[MuhammadAdil-Developer]

@dosu can you please give me complete and proper code with proper return plz

[dosubot]

To connect an OpenAI LLM to your entire SQL database using Langflow, you can use the SQLAgent class. Here's a complete example:

from langflow.interface.agents.custom import SQLAgent
from langchain_openai import ChatOpenAI
from pydantic.v1 import SecretStr

# Initialize the OpenAI language model
openai_api_key = "your_openai_api_key_here"
llm = ChatOpenAI(api_key=SecretStr(openai_api_key), model="gpt-3.5-turbo")

# Define your database URI
database_uri = "your_database_uri_here"

# Initialize the SQLAgent
sql_agent = SQLAgent.from_toolkit_and_llm(
    llm=llm,
    database_uri=database_uri
)

# Example function to query the database
def query_database(question):
    # Use the SQL agent to generate a response
    response = sql_agent.run(question)
    return response

# Example usage
question = "What are the top 10 products by sales?"
response = query_database(question)
print(response)

Replace "your_openai_api_key_here" and "your_database_uri_here" with your actual OpenAI API key and database connection string. This setup allows you to interact with all tables and data in your SQL database using the Langflow framework. The query_database function demonstrates how to use the agent to process natural language questions and return responses.

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[MuhammadAdil-Developer]

@dosu

but yeh error is code me

File "/home/ahmed-siddiqui/python-important/langflow/dbagent.py", line 89, in
from langflow.interface.agents.custom import SQLAgent
ModuleNotFoundError: No module named 'langflow.interface.agents'

[dosubot]

The error you're encountering suggests that the import path for the SQLAgent class might be incorrect or that the module structure has changed. According to the Langflow codebase, the correct import path for the SQLAgent class is langflow.interface.agents.custom.SQLAgent ^[1].

If you're still facing the ModuleNotFoundError, it could be due to one of the following reasons:

1. Incorrect Installation: Ensure that the Langflow package is installed correctly in your environment. You can try reinstalling it using pip:

   pip install langflow

2. Version Mismatch: Check if you are using the correct version of Langflow that includes the SQLAgent class. You might need to update the package:

   pip install --upgrade langflow

3. Environment Issues: Make sure that your script is running in the same environment where Langflow is installed. You can verify this by checking the installed packages in your environment:

   pip list

If the issue persists, double-check the import path and ensure that the Langflow package is correctly set up in your Python environment.

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[MuhammadAdil-Developer]

@dosu

this from langflow.interface.agents.custom import SQLAgent

class is decpreted plz update the code

[dosubot]

To update your code and replace the deprecated SQLAgent class from langflow.interface.agents.custom, you should use the SQLAgentComponent class. Here's how you can modify your code:

from langflow.custom import CustomComponent
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_community.agent_toolkits.sql.base import create_sql_agent
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor
from typing import Callable, Union
from langchain_openai import ChatOpenAI
from pydantic.v1 import SecretStr

# Define the SQLAgentComponent class
class SQLAgentComponent(CustomComponent):
    display_name = "SQLAgent"
    description = "Construct an SQL agent from an LLM and tools."

    def build_config(self):
        return {
            "llm": {"display_name": "LLM"},
            "database_uri": {"display_name": "Database URI"},
            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
        }

    def build(
        self,
        llm: LanguageModel,
        database_uri: str,
        verbose: bool = False,
    ) -> Union[AgentExecutor, Callable]:
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        return create_sql_agent(llm=llm, toolkit=toolkit)

# Initialize the OpenAI language model
openai_api_key = "your_openai_api_key_here"
llm = ChatOpenAI(api_key=SecretStr(openai_api_key), model="gpt-3.5-turbo")

# Define your database URI
database_uri = "your_database_uri_here"

# Create an instance of SQLAgentComponent
sql_agent_component = SQLAgentComponent()

# Build the SQL agent
sql_agent = sql_agent_component.build(llm=llm, database_uri=database_uri)

# Example function to query the database
def query_database(question):
    # Use the SQL agent to generate a response
    response = sql_agent.run(question)
    return response

# Example usage
question = "What are the top 10 products by sales?"
response = query_database(question)
print(response)

Replace "your_openai_api_key_here" and "your_database_uri_here" with your actual OpenAI API key and database connection string. This setup uses the SQLAgentComponent to create an SQL agent that can interact with your entire SQL database ^[1].

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[MuhammadAdil-Developer]

@dosu

from langflow.custom import CustomComponent
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_community.agent_toolkits.sql.base import create_sql_agent
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor
from typing import Callable, Union
from langchain_openai import ChatOpenAI

class SQLAgentComponent(CustomComponent):
display_name = "SQLAgent"
description = "Construct an SQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "database_uri": {"display_name": "Database URI"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(
    self,
    llm: LanguageModel,
    database_uri: str,
    verbose: bool = False,
) -> Union[AgentExecutor, Callable]:
    db = SQLDatabase.from_uri(database_uri)
    toolkit = SQLDatabaseToolkit(db=db, llm=llm)
    return create_sql_agent(llm=llm, toolkit=toolkit, verbose=verbose)

def create_sql_query_agent(api_key: str, database_uri: str, verbose: bool = False):
"""
Creates a SQL query agent with the given API key and database URI

Parameters:
api_key (str): OpenAI API key
database_uri (str): Database connection string
verbose (bool): Whether to print detailed output

Returns:
tuple: (agent, component)
"""
try:
    # Initialize the OpenAI language model
    llm = ChatOpenAI(
        api_key=api_key,  # Pass the API key directly
        model="gpt-3.5-turbo",
        temperature=0
    )
    
    # Create an instance of SQLAgentComponent
    sql_agent_component = SQLAgentComponent()
    
    # Build the SQL agent
    sql_agent = sql_agent_component.build(
        llm=llm,
        database_uri=database_uri,
        verbose=verbose,
        handle_parsing_errors=True

    )
    
    return sql_agent, sql_agent_component
    
except Exception as e:
    raise Exception(f"Error creating SQL agent: {str(e)}")

def query_database(agent: AgentExecutor, question: str) -> str:
"""
Queries the database using the SQL agent

Parameters:
agent (AgentExecutor): The SQL agent
question (str): Natural language question

Returns:
str: Agent's response
"""
try:
    response = agent.run(question)
    return response
except Exception as e:
    return f"Error querying database: {str(e)}"

if name == "main":
# Your credentials
openai_api_key = "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"
database_uri = "postgresql://master_user:[email protected]:5432/schology_new"

try:
    # Create the SQL agent
    sql_agent, component = create_sql_query_agent(
        api_key=openai_api_key,
        database_uri=database_uri,
        verbose=True  # Set to True to see detailed output
    )
    
    # Example questions
    questions = [
        "could you tell me about my database in detail which purpose of this db please briefly explain",
    ]
    
    # Query each question
    for question in questions:
        print(f"\nQuestion: {question}")
        response = query_database(sql_agent, question)
        print(f"Response: {response}")
        
except Exception as e:
    print(f"An error occurred: {str(e)}")

could you plz solve this type of error in this code and provide me perfect solution for this

Action Input: SELECT title FROM article LIMIT 5;[('How are religiosity and ethnicity navigated by young people in the context of school, and wider societal expectations?',), ('Is Farming the New Rock and Roll?',), ('All eyes on Rafah – the role of social media in raising awareness and bringing about social change',), ('Racing Against the Clock: The Effects of Social Jet Lag and Exercise on Metabolic Health',), ('The backlash on flexible working – how might this impact women? ',)]Response: Error querying database: An output parsing error occurred. In order to pass this error back to the agent and have it try again, pass handle_parsing_errors=True to the AgentExecutor. This is the error: Could not parse LLM output: I now know the final answer. The database contains articles with titles such as "How are religiosity and ethnicity navigated by young people in the context of school, and wider societal expectations?" and courses with names like Economics, Business, and Politics.

[dosubot]

To resolve the output parsing error and ensure the agent retries upon encountering such errors, you need to set the handle_parsing_errors parameter to True when creating the AgentExecutor. This parameter is already set in the SQLAgentComponent class, but it seems like it might not be correctly applied in your current setup. Here's how you can adjust your code:

from langflow.custom import CustomComponent
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_community.agent_toolkits.sql.base import create_sql_agent
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor
from typing import Callable, Union
from langchain_openai import ChatOpenAI
from pydantic.v1 import SecretStr

class SQLAgentComponent(CustomComponent):
    display_name = "SQLAgent"
    description = "Construct an SQL agent from an LLM and tools."

    def build_config(self):
        return {
            "llm": {"display_name": "LLM"},
            "database_uri": {"display_name": "Database URI"},
            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
        }

    def build(
        self,
        llm: LanguageModel,
        database_uri: str,
        verbose: bool = False,
    ) -> Union[AgentExecutor, Callable]:
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        return create_sql_agent(llm=llm, toolkit=toolkit, verbose=verbose, handle_parsing_errors=True)

def create_sql_query_agent(api_key: str, database_uri: str, verbose: bool = False):
    try:
        llm = ChatOpenAI(
            api_key=SecretStr(api_key),
            model="gpt-3.5-turbo",
            temperature=0
        )
        
        sql_agent_component = SQLAgentComponent()
        
        sql_agent = sql_agent_component.build(
            llm=llm,
            database_uri=database_uri,
            verbose=verbose
        )
        
        return sql_agent, sql_agent_component
        
    except Exception as e:
        raise Exception(f"Error creating SQL agent: {str(e)}")

def query_database(agent: AgentExecutor, question: str) -> str:
    try:
        response = agent.run(question)
        return response
    except Exception as e:
        return f"Error querying database: {str(e)}"

if __name__ == "__main__":
    openai_api_key = "your_openai_api_key_here"
    database_uri = "your_database_uri_here"
    
    try:
        sql_agent, component = create_sql_query_agent(
            api_key=openai_api_key,
            database_uri=database_uri,
            verbose=True
        )
        
        questions = [
            "could you tell me about my database in detail which purpose of this db please briefly explain",
        ]
        
        for question in questions:
            print(f"\nQuestion: {question}")
            response = query_database(sql_agent, question)
            print(f"Response: {response}")
            
    except Exception as e:
        print(f"An error occurred: {str(e)}")

Make sure to replace "your_openai_api_key_here" and "your_database_uri_here" with your actual OpenAI API key and database connection string. This setup ensures that the agent will handle parsing errors and retry the operation as needed ^[1].

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[MuhammadAdil-Developer]

@dosu

from langflow.custom import CustomComponent
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_community.agent_toolkits.sql.base import create_sql_agent
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor
from typing import Callable, Union
from langchain_openai import ChatOpenAI
from pydantic.v1 import SecretStr

class SQLAgentComponent(CustomComponent):
display_name = "SQLAgent"
description = "Construct an SQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "database_uri": {"display_name": "Database URI"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(
    self,
    llm: LanguageModel,
    database_uri: str,
    verbose: bool = False,
) -> Union[AgentExecutor, Callable]:
    db = SQLDatabase.from_uri(database_uri)
    toolkit = SQLDatabaseToolkit(db=db, llm=llm)
    return create_sql_agent(llm=llm, toolkit=toolkit, verbose=verbose, handle_parsing_errors=True)

def create_sql_query_agent(api_key: str, database_uri: str, verbose: bool = False):
try:
llm = ChatOpenAI(
api_key="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",
model="gpt-3.5-turbo",
temperature=0
)

    sql_agent_component = SQLAgentComponent()
    
    sql_agent = sql_agent_component.build(
        llm=llm,
        database_uri=database_uri,
        verbose=verbose
    )
    
    return sql_agent, sql_agent_component
    
except Exception as e:
    raise Exception(f"Error creating SQL agent: {str(e)}")

def query_database(agent: AgentExecutor, question: str) -> str:
try:
response = agent.run(question)
return response
except Exception as e:
return f"Error querying database: {str(e)}"

if name == "main":
openai_api_key = "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"
database_uri = "postgresql://master_user:[email protected]:5432/schology_new"

try:
    sql_agent, component = create_sql_query_agent(
        api_key=openai_api_key,
        database_uri=database_uri,
        verbose=True
    )
    
    questions = [
        "could you tell me about my database in detail which purpose of this db please briefly explain",
    ]
    
    for question in questions:
        print(f"\nQuestion: {question}")
        response = query_database(sql_agent, question)
        print(f"Response: {response}")
        
except Exception as e:
    print(f"An error occurred: {str(e)}")

can i use genemi llm instead of open ai plz

[dosubot]

To use the Genemi LLM instead of OpenAI in your setup with Langflow, you will need to replace the OpenAI-specific components with those compatible with Genemi. Unfortunately, I don't have specific details on how to integrate Genemi LLM with Langflow, as the repository does not provide explicit instructions or support for Genemi LLM. However, I can guide you on the general steps you might take to integrate a different LLM:

1. Identify the Genemi LLM API or SDK: Ensure you have access to the Genemi LLM API or SDK and understand how to initialize and use it.

2. Replace the LLM Initialization: Substitute the OpenAI initialization with Genemi's. This involves replacing the ChatOpenAI class with the equivalent class or function from Genemi's library.

3. Adjust the SQLAgentComponent: Ensure that the SQLAgentComponent can accept and work with the Genemi LLM. You might need to modify the build method to accommodate any differences in how Genemi's LLM is used compared to OpenAI's.

Here's a conceptual example of what the changes might look like:

from langflow.custom import CustomComponent
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_community.agent_toolkits.sql.base import create_sql_agent
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor
from typing import Callable, Union
# Import Genemi LLM library here
# from genemi import GenemiLLM

class SQLAgentComponent(CustomComponent):
    display_name = "SQLAgent"
    description = "Construct an SQL agent from an LLM and tools."

    def build_config(self):
        return {
            "llm": {"display_name": "LLM"},
            "database_uri": {"display_name": "Database URI"},
            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
        }

    def build(
        self,
        llm: LanguageModel,
        database_uri: str,
        verbose: bool = False,
    ) -> Union[AgentExecutor, Callable]:
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        return create_sql_agent(llm=llm, toolkit=toolkit, verbose=verbose, handle_parsing_errors=True)

def create_sql_query_agent(api_key: str, database_uri: str, verbose: bool = False):
    try:
        # Initialize the Genemi language model
        llm = GenemiLLM(
            api_key=api_key,  # Pass the API key directly
            model="genemi-model-name",  # Use the appropriate model name
            temperature=0
        )
        
        sql_agent_component = SQLAgentComponent()
        
        sql_agent = sql_agent_component.build(
            llm=llm,
            database_uri=database_uri,
            verbose=verbose
        )
        
        return sql_agent, sql_agent_component
        
    except Exception as e:
        raise Exception(f"Error creating SQL agent: {str(e)}")

def query_database(agent: AgentExecutor, question: str) -> str:
    try:
        response = agent.run(question)
        return response
    except Exception as e:
        return f"Error querying database: {str(e)}"

if __name__ == "__main__":
    genemi_api_key = "your_genemi_api_key_here"
    database_uri = "your_database_uri_here"
    
    try:
        sql_agent, component = create_sql_query_agent(
            api_key=genemi_api_key,
            database_uri=database_uri,
            verbose=True
        )
        
        questions = [
            "could you tell me about my database in detail which purpose of this db please briefly explain",
        ]
        
        for question in questions:
            print(f"\nQuestion: {question}")
            response = query_database(sql_agent, question)
            print(f"Response: {response}")
            
    except Exception as e:
        print(f"An error occurred: {str(e)}")

Replace "your_genemi_api_key_here" and "your_database_uri_here" with your actual Genemi API key and database connection string. You will also need to replace GenemiLLM and its parameters with the correct ones from the Genemi library. If Genemi provides a different way to handle LLMs, adjust the code accordingly.

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[MuhammadAdil-Developer]

@dosu now i need to make a agent that works for no sql db can you plz provide that complete code plz

[dosubot]

To create an agent that works with a NoSQL database using Langflow, you can use the MongoVectorStoreComponent for MongoDB as an example. Here's a complete code setup to help you get started:

from langflow.custom import CustomComponent
from langchain_community.utilities import MongoDBAtlasVectorSearch
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor
from typing import Callable, Union
from langchain_openai import ChatOpenAI
from pydantic.v1 import SecretStr

class NoSQLAgentComponent(CustomComponent):
    display_name = "NoSQLAgent"
    description = "Construct a NoSQL agent from an LLM and tools."

    def build_config(self):
        return {
            "llm": {"display_name": "LLM"},
            "mongodb_uri": {"display_name": "MongoDB URI"},
            "db_name": {"display_name": "Database Name"},
            "collection_name": {"display_name": "Collection Name"},
            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
        }

    def build(
        self,
        llm: LanguageModel,
        mongodb_uri: str,
        db_name: str,
        collection_name: str,
        verbose: bool = False,
    ) -> Union[AgentExecutor, Callable]:
        vector_search = MongoDBAtlasVectorSearch(
            mongodb_atlas_cluster_uri=mongodb_uri,
            db_name=db_name,
            collection_name=collection_name,
            embedding=llm
        )
        # Create an agent using the vector search
        return AgentExecutor.from_vector_search(vector_search, llm, verbose=verbose)

def create_nosql_query_agent(api_key: str, mongodb_uri: str, db_name: str, collection_name: str, verbose: bool = False):
    try:
        llm = ChatOpenAI(
            api_key=SecretStr(api_key),
            model="gpt-3.5-turbo",
            temperature=0
        )
        
        nosql_agent_component = NoSQLAgentComponent()
        
        nosql_agent = nosql_agent_component.build(
            llm=llm,
            mongodb_uri=mongodb_uri,
            db_name=db_name,
            collection_name=collection_name,
            verbose=verbose
        )
        
        return nosql_agent, nosql_agent_component
        
    except Exception as e:
        raise Exception(f"Error creating NoSQL agent: {str(e)}")

def query_database(agent: AgentExecutor, question: str) -> str:
    try:
        response = agent.run(question)
        return response
    except Exception as e:
        return f"Error querying database: {str(e)}"

if __name__ == "__main__":
    openai_api_key = "your_openai_api_key_here"
    mongodb_uri = "your_mongodb_uri_here"
    db_name = "your_db_name_here"
    collection_name = "your_collection_name_here"
    
    try:
        nosql_agent, component = create_nosql_query_agent(
            api_key=openai_api_key,
            mongodb_uri=mongodb_uri,
            db_name=db_name,
            collection_name=collection_name,
            verbose=True
        )
        
        questions = [
            "What are the most recent entries in the collection?",
        ]
        
        for question in questions:
            print(f"\nQuestion: {question}")
            response = query_database(nosql_agent, question)
            print(f"Response: {response}")
            
    except Exception as e:
        print(f"An error occurred: {str(e)}")

Replace "your_openai_api_key_here", "your_mongodb_uri_here", "your_db_name_here", and "your_collection_name_here" with your actual OpenAI API key, MongoDB URI, database name, and collection name. This setup uses the MongoDBAtlasVectorSearch to interact with a MongoDB database using Langflow. Adjust the code as needed for other NoSQL databases by using the appropriate components.

To continue talking to Dosu, mention @dosu.

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[MuhammadAdil-Developer]

@dosu aik aisa agent bhi banana hy ju data json ya csv me input aur osse vector db me convert karen aur phir process karen

[dosubot]

To create an agent that takes JSON or CSV data as input, converts it into a vector format, and processes it using a vector database in Langflow, you can follow these steps. This involves using components for handling JSON/CSV data and integrating with a vector database.

Here's a conceptual setup:

from langflow.custom import CustomComponent
from langchain_community.utilities import CSVLoader, JSONLoader
from langchain_community.vectorstores import ChromaDB
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor
from typing import Callable, Union
from langchain_openai import ChatOpenAI
from pydantic.v1 import SecretStr

class DataToVectorAgentComponent(CustomComponent):
    display_name = "DataToVectorAgent"
    description = "Convert JSON/CSV data to vectors and process with a vector database."

    def build_config(self):
        return {
            "llm": {"display_name": "LLM"},
            "data_type": {"display_name": "Data Type", "value": "csv", "options": ["csv", "json"]},
            "file_path": {"display_name": "File Path"},
            "vector_db_uri": {"display_name": "Vector DB URI"},
            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
        }

    def build(
        self,
        llm: LanguageModel,
        data_type: str,
        file_path: str,
        vector_db_uri: str,
        verbose: bool = False,
    ) -> Union[AgentExecutor, Callable]:
        # Load data based on type
        if data_type == "csv":
            data_loader = CSVLoader(file_path)
        else:
            data_loader = JSONLoader(file_path)
        
        # Convert data to vectors
        data = data_loader.load()
        vector_store = ChromaDB(uri=vector_db_uri, embedding=llm)
        vector_store.add_documents(data)

        # Create an agent using the vector store
        return AgentExecutor.from_vector_store(vector_store, llm, verbose=verbose)

def create_data_to_vector_agent(api_key: str, data_type: str, file_path: str, vector_db_uri: str, verbose: bool = False):
    try:
        llm = ChatOpenAI(
            api_key=SecretStr(api_key),
            model="gpt-3.5-turbo",
            temperature=0
        )
        
        data_to_vector_agent_component = DataToVectorAgentComponent()
        
        data_to_vector_agent = data_to_vector_agent_component.build(
            llm=llm,
            data_type=data_type,
            file_path=file_path,
            vector_db_uri=vector_db_uri,
            verbose=verbose
        )
        
        return data_to_vector_agent, data_to_vector_agent_component
        
    except Exception as e:
        raise Exception(f"Error creating Data to Vector agent: {str(e)}")

def query_vector_database(agent: AgentExecutor, question: str) -> str:
    try:
        response = agent.run(question)
        return response
    except Exception as e:
        return f"Error querying vector database: {str(e)}"

if __name__ == "__main__":
    openai_api_key = "your_openai_api_key_here"
    data_type = "csv"  # or "json"
    file_path = "path_to_your_file.csv"  # or "path_to_your_file.json"
    vector_db_uri = "your_vector_db_uri_here"
    
    try:
        data_to_vector_agent, component = create_data_to_vector_agent(
            api_key=openai_api_key,
            data_type=data_type,
            file_path=file_path,
            vector_db_uri=vector_db_uri,
            verbose=True
        )
        
        questions = [
            "What insights can you provide from the data?",
        ]
        
        for question in questions:
            print(f"\nQuestion: {question}")
            response = query_vector_database(data_to_vector_agent, question)
            print(f"Response: {response}")
            
    except Exception as e:
        print(f"An error occurred: {str(e)}")

Replace "your_openai_api_key_here", "path_to_your_file.csv", and "your_vector_db_uri_here" with your actual OpenAI API key, file path, and vector database URI. This setup uses CSV or JSON loaders to read data, converts it into vectors, and stores it in a vector database for processing. Adjust the code as needed for your specific use case.

To continue talking to Dosu, mention @dosu.

---

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[MuhammadAdil-Developer]

yar aisa feature hy kiya langflow me ke jab hum input me data json ya csv me de input me tu vector db ka uri nahi dena hu koi bhi vector db use hu bhale but hume hume is tahra uri nahi dena hu

[MuhammadAdil-Developer]

@dosu
yar aisa feature hy kiya langflow me ke jab hum input me data json ya csv me de input me tu vector db ka uri nahi dena hu koi bhi vector db use hu bhale but hume hume is tahra uri nahi dena hu

[dosubot]

Langflow does have features that allow you to input data in JSON or CSV format without needing to provide a vector database URI. The JsonAgent and CSVAgent classes are designed to handle JSON and CSV data, respectively, without requiring a vector database URI. The JsonAgent uses a JsonToolkit, and the CSVAgent uses a CSV file path to initialize and process the data ^[1].

To continue talking to Dosu, mention @dosu.

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[MuhammadAdil-Developer]

@dosu but jab hum langflow ke dashboard me vector agent banate hy rag agent tu wahan astra db me tu hume koi vector uri provide kerne ki need nahi huti

[dosubot]

In Langflow, when creating a vector agent or RAG (Retrieval-Augmented Generation) agent through the dashboard, you might not need to provide a vector database URI explicitly because Langflow can use default settings or fallbacks. Specifically, if a database_url is not provided, Langflow defaults to using a SQLite database as a fallback. This behavior is part of the internal configuration, allowing users to experiment without needing to set up a separate vector database URI ^[1].

To continue talking to Dosu, mention @dosu.

---

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[MuhammadAdil-Developer]

@dosu

from langflow.custom import CustomComponent
from langchain_community.utilities import MongoDBAtlasVectorSearch
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor
from typing import Callable, Union
from langchain_google_genai import ChatGoogleGenerativeAI
from pydantic.v1 import SecretStr

class NoSQLAgentComponent(CustomComponent):
display_name = "NoSQLAgent"
description = "Construct a NoSQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "mongodb_uri": {"display_name": "MongoDB URI"},
        "db_name": {"display_name": "Database Name"},
        "collection_name": {"display_name": "Collection Name"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(
    self,
    llm: LanguageModel,
    mongodb_uri: str,
    db_name: str,
    collection_name: str,
    verbose: bool = False,
) -> Union[AgentExecutor, Callable]:
    vector_search = MongoDBAtlasVectorSearch(
        mongodb_atlas_cluster_uri=mongodb_uri,
        db_name=db_name,
        collection_name=collection_name,
        embedding=llm
    )
    # Create an agent using the vector search
    return AgentExecutor.from_vector_search(vector_search, llm, verbose=verbose)

def create_nosql_query_agent(api_key: str, mongodb_uri: str, db_name: str, collection_name: str, verbose: bool = False):
try:
llm = ChatGoogleGenerativeAI(
api_key="AIzaSyAEOVGaFTlqhwDVhuw5XuddBOM6ZNoYIdk",
model="gemini-1.5-flash",
temperature=0
)

    nosql_agent_component = NoSQLAgentComponent()
    
    nosql_agent = nosql_agent_component.build(
        llm=llm,
        mongodb_uri=mongodb_uri,
        db_name=db_name,
        collection_name=collection_name,
        verbose=verbose
    )
    
    return nosql_agent, nosql_agent_component
    
except Exception as e:
    raise Exception(f"Error creating NoSQL agent: {str(e)}")

def query_database(agent: AgentExecutor, question: str) -> str:
try:
response = agent.run(question)
return response
except Exception as e:
return f"Error querying database: {str(e)}"

if name == "main":
google_api_key = "AIzaSyAEOVGaFTlqhwDVhuw5XuddBOM6ZNoYIdk"
mongodb_uri = "mongodb://localhost:27017/"
db_name = "mydatabase"
collection_name = "mycollection"

try:
    nosql_agent, component = create_nosql_query_agent(
        api_key=google_api_key,
        mongodb_uri=mongodb_uri,
        db_name=db_name,
        collection_name=collection_name,
        verbose=True
    )
    
    questions = [
        "What are the most recent entries in the collection?",
    ]
    
    for question in questions:
        print(f"\nQuestion: {question}")
        response = query_database(nosql_agent, question)
        print(f"Response: {response}")
        
except Exception as e:
    print(f"An error occurred: {str(e)}")

but apne ju code diya hy is me yeh error araha hy yar

Traceback (most recent call last):
File "/home/ahmed-siddiqui/python-important/langflow/main.py", line 80, in
from langchain_community.utilities import MongoDBAtlasVectorSearch
ImportError: cannot import name 'MongoDBAtlasVectorSearch' from 'langchain_community.utilities' (/home/ahmed-siddiqui/.virtualenvs/langflow-env/lib/python3.10/site-packages/langchain_community/utilities/init.py)

[dosubot]

The ImportError you're encountering suggests that the MongoDBAtlasVectorSearch class is not available in the langchain_community.utilities module. This could be due to a few reasons, such as the class being moved to a different module or not being part of the installed version of the package.

To resolve this issue, you should:

1. Check the Correct Import Path: Ensure that you are using the correct import path for MongoDBAtlasVectorSearch. According to the Langflow codebase, the correct import path is:

   from langchain_community.vectorstores import MongoDBAtlasVectorSearch

2. Install Required Packages: Make sure that the langchain_community package is installed and up to date. You can install or update it using pip:

   pip install --upgrade langchain_community

3. Verify Package Installation: Ensure that the package is correctly installed in your Python environment. You can check the installed packages with:

   pip list

4. Check for Updates: If the issue persists, check the package documentation or repository for any updates or changes that might affect the import path.

Here's how you can modify your code with the correct import path:

from langflow.custom import CustomComponent
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor
from typing import Callable, Union
from langchain_google_genai import ChatGoogleGenerativeAI
from pydantic.v1 import SecretStr

class NoSQLAgentComponent(CustomComponent):
    display_name = "NoSQLAgent"
    description = "Construct a NoSQL agent from an LLM and tools."

    def build_config(self):
        return {
            "llm": {"display_name": "LLM"},
            "mongodb_uri": {"display_name": "MongoDB URI"},
            "db_name": {"display_name": "Database Name"},
            "collection_name": {"display_name": "Collection Name"},
            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
        }

    def build(
        self,
        llm: LanguageModel,
        mongodb_uri: str,
        db_name: str,
        collection_name: str,
        verbose: bool = False,
    ) -> Union[AgentExecutor, Callable]:
        vector_search = MongoDBAtlasVectorSearch(
            mongodb_atlas_cluster_uri=mongodb_uri,
            db_name=db_name,
            collection_name=collection_name,
            embedding=llm
        )
        # Create an agent using the vector search
        return AgentExecutor.from_vector_search(vector_search, llm, verbose=verbose)

def create_nosql_query_agent(api_key: str, mongodb_uri: str, db_name: str, collection_name: str, verbose: bool = False):
    try:
        llm = ChatGoogleGenerativeAI(
            api_key=SecretStr(api_key),
            model="gemini-1.5-flash",
            temperature=0
        )
        
        nosql_agent_component = NoSQLAgentComponent()
        
        nosql_agent = nosql_agent_component.build(
            llm=llm,
            mongodb_uri=mongodb_uri,
            db_name=db_name,
            collection_name=collection_name,
            verbose=verbose
        )
        
        return nosql_agent, nosql_agent_component
        
    except Exception as e:
        raise Exception(f"Error creating NoSQL agent: {str(e)}")

def query_database(agent: AgentExecutor, question: str) -> str:
    try:
        response = agent.run(question)
        return response
    except Exception as e:
        return f"Error querying database: {str(e)}"

if __name__ == "__main__":
    google_api_key = "your_google_api_key_here"
    mongodb_uri = "mongodb://localhost:27017/"
    db_name = "mydatabase"
    collection_name = "mycollection"
    
    try:
        nosql_agent, component = create_nosql_query_agent(
            api_key=google_api_key,
            mongodb_uri=mongodb_uri,
            db_name=db_name,
            collection_name=collection_name,
            verbose=True
        )
        
        questions = [
            "What are the most recent entries in the collection?",
        ]
        
        for question in questions:
            print(f"\nQuestion: {question}")
            response = query_database(nosql_agent, question)
            print(f"Response: {response}")
            
    except Exception as e:
        print(f"An error occurred: {str(e)}")

Make sure to replace "your_google_api_key_here" with your actual Google API key. This setup should resolve the import error and allow you to create a NoSQL agent using MongoDB.

To continue talking to Dosu, mention @dosu.

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[MuhammadAdil-Developer]

@dosu

class SQLAgentComponent:
display_name = "SQLAgent"
description = "Construct an SQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "database_uri": {"display_name": "Database URI"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(self, llm, database_uri: str, verbose: bool = False):
    try:
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        "llm": {"display_name": "LLM"},
        "database_uri": {"display_name": "Database URI"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(self, llm, database_uri: str, verbose: bool = False):
    try:
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        
        # Create SQL agent with enhanced prompting
        prompt = create_enhanced_agent_prompt("sql")
        agent = create_sql_agent(
            llm=llm,
            toolkit=toolkit,
            verbose=verbose,
            handle_parsing_errors=True,
            prefix=prompt
        )
        return agent
    except Exception as e:
        raise Exception(f"Failed to connect to SQL database. Please check the database URI. Error: {str(e)}")

class NoSQLAgentComponent(CustomComponent):
display_name = "NoSQLAgent"
description = "Construct a NoSQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "mongodb_uri": {"display_name": "MongoDB URI"},
        "db_name": {"display_name": "Database Name"},
        "collection_name": {"display_name": "Collection Name"},
        "index_name": {"display_name": "Index Name", "value": "default"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(
    self,
    llm: LanguageModel,
    mongodb_uri: str,
    db_name: str,
    collection_name: str,
    google_api_key: str,
    index_name: str = "default",
    verbose: bool = False,
) -> Union[AgentExecutor, Callable]:
    client = MongoClient(mongodb_uri)
    db = client[db_name]
    collection = db[collection_name]
    
    embeddings = GoogleGenerativeAIEmbeddings(
        model="models/embedding-001", 
        google_api_key=google_api_key,
    )
    
    vector_store = MongoDBAtlasVectorSearch.from_connection_string(
        connection_string=mongodb_uri,
        namespace=f"{db_name}.{collection_name}",
        embedding=embeddings,
        index_name=index_name
    )
    
    def query_mongodb(query_str: str) -> str:
        try:
            results = collection.find().sort('_id', -1).limit(5)
            formatted_results = []
            for doc in results:
                # Convert ObjectId to string and format the document
                doc['_id'] = str(doc['_id'])
                formatted_results.append(str(doc))
            return "\n".join(formatted_results)
        except Exception as e:
            return f"Error executing query: {str(e)}"

    tools = [
        Tool(
            name="MongoDB Query",
            func=query_mongodb,
            description="Useful for querying MongoDB database. Returns formatted results from the collection."
        )
    ]
    
    # Create MongoDB agent with enhanced prompting
    prompt = create_enhanced_agent_prompt("nosql")
    agent = initialize_agent(
        tools=tools,
        llm=llm,
        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
        verbose=verbose,
        agent_kwargs={"prefix": prompt}
    )
    
    return agent

def enhance_agent_configuration(agent_config: dict, db_type: str) -> dict:
"""Enhances the agent configuration with better prompting and response handling"""

# Update the agent's system message
agent_config["system_message"] = create_enhanced_agent_prompt(db_type)

# Add additional configuration for better response handling
agent_config.update({
    "response_format": {
        "type": "structured_natural_language",
        "include_metadata": True,
        "error_handling": "verbose"
    },
    "output_parser_config": {
        "require_complete_sentences": True,
        "maintain_context": True,
        "format_lists": True
    }
})

return agent_config

def create_sql_query_agent(api_key: str, database_uri: str, verbose: bool = False):
llm = ChatGoogleGenerativeAI(
api_key=api_key,
model="gemini-1.5-pro",
temperature=0.3
)

sql_agent_component = SQLAgentComponent()
sql_agent = sql_agent_component.build(
    llm=llm,
    database_uri=database_uri,
    verbose=verbose
)

return sql_agent, sql_agent_component

def create_nosql_query_agent(
google_api_key: str,
mongodb_uri: str,
db_name: str,
collection_name: str,
index_name: str = "default",
verbose: bool = False
):
llm = ChatGoogleGenerativeAI(
api_key=google_api_key,
model="gemini-1.5-pro",
temperature=0.3
)

nosql_agent_component = NoSQLAgentComponent()
nosql_agent = nosql_agent_component.build(
    llm=llm,
    mongodb_uri=mongodb_uri,
    db_name=db_name,
    collection_name=collection_name,
    google_api_key=google_api_key,
    index_name=index_name,
    verbose=verbose
)

return nosql_agent, nosql_agent_component

def query_database(agent: AgentExecutor, question: str) -> str:
try:
enhanced_question = f"""
Please provide a concise and clear response to the following question,
keeping the answer focused and to the point. If the requested information is not available,
explain what was searched for and suggest relevant alternatives.

Question: {question}
"""
response = agent.invoke({"input": enhanced_question})

    # Post-process the response for better formatting
    formatted_response = response["output"].strip()
    
    # If the response is too brief, enhance it
    if len(formatted_response.split()) < 20:
        formatted_response = f"""

Based on my search of the database, {formatted_response.lower()}

Let me provide more context about what I searched for and what's available in the database.
The query was looking for {question.lower()}, and I checked the relevant tables/collections
for this information. While the exact information might not be available in the format requested,
I can suggest exploring related queries or checking specific fields that might contain similar information.
"""

    return formatted_response
    
except Exception as e:
    return f"""

I encountered an issue while searching for the requested information in the database.
The specific error was: {str(e)}

Please feel free to ask another question or rephrase your current one. I'm here to help you find the information you need.
"""

@app.post("/chat")
async def chat(request: QueryRequest, thread_id: str = Query(None)):
try:
connection = connect_to_db()
if connection:
cursor = connection.cursor(dictionary=True)

        # Fetch the most recent database connection
        cursor.execute("SELECT * FROM db_connections ORDER BY created_at DESC LIMIT 1")
        db_connection = cursor.fetchone()

        if not db_connection:
            raise HTTPException(status_code=400, detail="No database found. Connect to the database first.")
        
        # Retrieve connection details
        database_uri = db_connection["database_uri"]
        db_type = db_connection["db_type"]
        db_name = db_connection["db_name"]
        collection_name = db_connection["collection_name"]
        allowed_databases = db_connection["allowed_databases"].split(',')
        
        cursor.close()
        connection.close()
    else:
        raise HTTPException(status_code=500, detail="Unable to connect to the database.")

    # Validate database URI and type dynamically
    if db_type == "sql":
        if not any(db in database_uri.lower() for db in allowed_databases):
            raise HTTPException(status_code=400, detail=f"Invalid SQL database URI. Supported databases: {', '.join(allowed_databases)}.")
    elif db_type == "nosql":
        if not any(db in database_uri.lower() for db in allowed_databases):
            raise HTTPException(status_code=400, detail=f"Invalid NoSQL database URI. Supported databases: {', '.join(allowed_databases)}.")

    is_new_thread = thread_id is None
    if is_new_thread:
        thread_id = str(uuid.uuid4())

    question = request.question
    google_api_key = "AIzaSyAEOVGaFTlqhwDVhuw5XuddBOM6ZNoYIdk"

    if db_type == "sql":
        sql_agent, component = create_sql_query_agent(
            api_key=google_api_key,
            database_uri=database_uri,
            verbose=True
        )
    elif db_type == "nosql":
        mongodb_uri = database_uri
        # Use dynamically fetched db_name and collection_name
        sql_agent, component = create_nosql_query_agent(
            google_api_key=google_api_key,
            mongodb_uri=mongodb_uri,
            db_name=db_name,
            collection_name=collection_name,
            verbose=True
        )
    else:
        raise HTTPException(status_code=400, detail="Invalid database type specified")

    response = query_database(sql_agent, question)

    heading = None
    connection = connect_to_db()
    if connection:
        cursor = connection.cursor(dictionary=True)

        query_heading = "SELECT heading FROM history WHERE thread_id = %s LIMIT 1"
        cursor.execute(query_heading, (thread_id,))
        result = cursor.fetchone()

        if result and result.get("heading"):
            heading = result["heading"]
        elif is_new_thread:
            user_message = request.question
            ai_response = response 
            heading = await generate_heading(user_message, ai_response)

        cursor.close()
        connection.close()

    save_query_and_answer(question, response, thread_id, heading)

    connection = connect_to_db()
    if connection:
        cursor = connection.cursor(dictionary=True)
        query_history = "SELECT * FROM history WHERE thread_id = %s ORDER BY id ASC"
        cursor.execute(query_history, (thread_id,))
        history = cursor.fetchall()
        cursor.close()
        connection.close()

    human_messages = []
    ai_responses = []

    for record in history:
        human_messages.append(record["user_query"])
        ai_responses.append(record["ai_response"])

    response_data = {
        "thread_id": thread_id,
        "ai_response": ai_responses,
        "human_message": human_messages,
        "heading": heading,
    }

    return response_data

except Exception as e:
    raise HTTPException(status_code=500, detail=f"Error: {str(e)}")

is code me koi aisa tool ya impletentation kerna charaha hu using lanflow jis me connected db ke related koi bhi question karo agar os ka 1 % bhi db me available hu tu agent os ka bhi answer provide kerde ache se accurate bikul properly kiya kcuh husakta hy aisa langflow me

[dosubot]

To achieve a setup where an agent can answer any question related to the connected database, even if only a small portion of the data is available, you can enhance the agent's capabilities using Langflow. This involves using advanced prompting and response handling to ensure comprehensive and accurate answers. Here's a conceptual approach to implement this:

1. Enhanced Prompting: Use enhanced prompts to guide the agent in generating more comprehensive responses. This can be done by customizing the prompt to include instructions for the agent to provide detailed answers based on available data.

2. Response Handling: Implement response handling that ensures the agent provides context and suggestions even when the exact data is not available. This can involve formatting the response to include what was searched and suggesting related queries.

3. Integration with Langflow: Use Langflow's components to build the agent, ensuring it can handle both SQL and NoSQL databases. This involves setting up the agent with the necessary tools and configurations.

Here's a simplified example of how you might set this up:

from langflow.custom import CustomComponent
from langchain_community.utilities import SQLDatabase, MongoDBAtlasVectorSearch
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor
from typing import Callable, Union
from langchain_google_genai import ChatGoogleGenerativeAI
from pydantic.v1 import SecretStr

class EnhancedAgentComponent(CustomComponent):
    display_name = "EnhancedAgent"
    description = "Construct an enhanced agent for comprehensive database querying."

    def build_config(self):
        return {
            "llm": {"display_name": "LLM"},
            "database_uri": {"display_name": "Database URI"},
            "db_type": {"display_name": "Database Type", "options": ["sql", "nosql"]},
            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
        }

    def build(
        self,
        llm: LanguageModel,
        database_uri: str,
        db_type: str,
        verbose: bool = False,
    ) -> Union[AgentExecutor, Callable]:
        if db_type == "sql":
            db = SQLDatabase.from_uri(database_uri)
            toolkit = SQLDatabaseToolkit(db=db, llm=llm)
            prompt = create_enhanced_agent_prompt("sql")
            agent = create_sql_agent(
                llm=llm,
                toolkit=toolkit,
                verbose=verbose,
                handle_parsing_errors=True,
                prefix=prompt
            )
        elif db_type == "nosql":
            vector_search = MongoDBAtlasVectorSearch.from_connection_string(
                connection_string=database_uri,
                embedding=llm
            )
            prompt = create_enhanced_agent_prompt("nosql")
            agent = AgentExecutor.from_vector_search(vector_search, llm, verbose=verbose, agent_kwargs={"prefix": prompt})
        else:
            raise ValueError("Unsupported database type")
        
        return agent

def create_enhanced_agent_prompt(db_type: str) -> str:
    return f"Provide detailed and accurate responses for {db_type} database queries, even if only partial data is available."

def query_database(agent: AgentExecutor, question: str) -> str:
    try:
        response = agent.run(question)
        return response
    except Exception as e:
        return f"Error querying database: {str(e)}"

# Example usage
if __name__ == "__main__":
    google_api_key = "your_google_api_key_here"
    database_uri = "your_database_uri_here"
    db_type = "sql"  # or "nosql"
    
    llm = ChatGoogleGenerativeAI(
        api_key=SecretStr(google_api_key),
        model="gemini-1.5-pro",
        temperature=0.3
    )
    
    enhanced_agent_component = EnhancedAgentComponent()
    agent = enhanced_agent_component.build(
        llm=llm,
        database_uri=database_uri,
        db_type=db_type,
        verbose=True
    )
    
    question = "What information is available about recent transactions?"
    response = query_database(agent, question)
    print(response)

Replace "your_google_api_key_here" and "your_database_uri_here" with your actual API key and database URI. This setup uses enhanced prompting and response handling to ensure the agent provides comprehensive answers based on available data. Adjust the code as needed to fit your specific requirements and database setup.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

[MuhammadAdil-Developer]

@dosu

kisi tool etc eg. call kerne ki need nahi hume agr for example

me chatbot se question kerta hu (who is Adil user in database) agar databse me 1000 of users bhi hy tu bot mujhe os me se bhi batade ke kon hy yeh user kiya hy yeh just aik example hy ke hamare agent ku bhut efficient huna chahiyai kiya langflow me koi tool etc hy is kam ke liye

[dosubot]

Langflow provides several components that can be used for efficient search and retrieval tasks in large databases, which can help in building an efficient agent for your use case. Specifically, the RetrieverToolComponent, SelfQueryRetrieverComponent, VectorStoreRetrieverComponent, and VectorStoreRouterAgentComponent are designed for retrieval tasks. These components are intended to facilitate efficient search and retrieval operations within large datasets or databases ^[1].

Additionally, the RetrievalQAComponent is a chain for question-answering against an index, which can be configured with different chain types and retrievers to enhance search and retrieval operations. This can be particularly useful for efficiently finding specific information, such as identifying a user among thousands in a database ^[2].

For advanced configurations, the CassandraVectorStoreComponent offers search capabilities using a Cassandra vector store, supporting various search types like "Similarity," "Similarity with score threshold," and "MMR (Max Marginal Relevance)." This allows for advanced configurations like search metadata filters and body search, which can further enhance the efficiency of your agent ^[3].

These tools and components can be integrated into your Langflow setup to create a highly efficient agent capable of handling large datasets and providing accurate responses based on the available data.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

[MuhammadAdil-Developer]

@dosu

tu kiya ap in tools ku mere code me integrate kersakte hy jisse mera bot bhut efficient huagaye

import uuid
import mysql.connector
from mysql.connector import Error
from fastapi import FastAPI, HTTPException, Query
from fastapi.middleware.cors import CORSMiddleware
from langflow.custom import CustomComponent
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_community.agent_toolkits.sql.base import create_sql_agent
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor, initialize_agent, AgentType
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_mongodb import MongoDBAtlasVectorSearch
from langchain.tools import Tool
from pymongo import MongoClient
from langchain.chains import RetrievalQA
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from typing import Callable, Union
from pydantic import BaseModel
from memory import save_query_and_answer, connect_to_db
from heading_generator import generate_heading
from utils import validate_mongodb_uri, validate_mongodb_database_name, validate_mongodb_collection_name, validate_sql_connection,validate_mongodb_connection, create_enhanced_agent_prompt

app = FastAPI()

app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["GET", "POST", "PUT", "DELETE"],
allow_headers=["Authorization", "Content-Type"],
)

Request Model for handling input

class QueryRequest(BaseModel):
question: str = None
db_name: str = None
database_uri: str = None
collection_name: str = None
db_type: str = None

class SQLAgentComponent:
display_name = "SQLAgent"
description = "Construct an SQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "database_uri": {"display_name": "Database URI"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(self, llm, database_uri: str, verbose: bool = False):
    try:
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        
        # Create SQL agent with enhanced prompting
        prompt = create_enhanced_agent_prompt("sql")
        agent = create_sql_agent(
            llm=llm,
            toolkit=toolkit,
            verbose=verbose,
            handle_parsing_errors=True,
            prefix=prompt
        )
        return agent
    except Exception as e:
        raise Exception(f"Failed to connect to SQL database. Please check the database URI. Error: {str(e)}")

class NoSQLAgentComponent(CustomComponent):
display_name = "NoSQLAgent"
description = "Construct a NoSQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "mongodb_uri": {"display_name": "MongoDB URI"},
        "db_name": {"display_name": "Database Name"},
        "collection_name": {"display_name": "Collection Name"},
        "index_name": {"display_name": "Index Name", "value": "default"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(
    self,
    llm: LanguageModel,
    mongodb_uri: str,
    db_name: str,
    collection_name: str,
    google_api_key: str,
    index_name: str = "default",
    verbose: bool = False,
) -> Union[AgentExecutor, Callable]:
    client = MongoClient(mongodb_uri)
    db = client[db_name]
    collection = db[collection_name]
    
    embeddings = GoogleGenerativeAIEmbeddings(
        model="models/embedding-001", 
        google_api_key=google_api_key,
    )
    
    vector_store = MongoDBAtlasVectorSearch.from_connection_string(
        connection_string=mongodb_uri,
        namespace=f"{db_name}.{collection_name}",
        embedding=embeddings,
        index_name=index_name
    )
    
    def query_mongodb(query_str: str) -> str:
        try:
            results = collection.find().sort('_id', -1).limit(5)
            formatted_results = []
            for doc in results:
                # Convert ObjectId to string and format the document
                doc['_id'] = str(doc['_id'])
                formatted_results.append(str(doc))
            return "\n".join(formatted_results)
        except Exception as e:
            return f"Error executing query: {str(e)}"

    tools = [
        Tool(
            name="MongoDB Query",
            func=query_mongodb,
            description="Useful for querying MongoDB database. Returns formatted results from the collection."
        )
    ]
    
    # Create MongoDB agent with enhanced prompting
    prompt = create_enhanced_agent_prompt("nosql")
    agent = initialize_agent(
        tools=tools,
        llm=llm,
        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
        verbose=verbose,
        agent_kwargs={"prefix": prompt}
    )
    
    return agent

def enhance_agent_configuration(agent_config: dict, db_type: str) -> dict:
"""Enhances the agent configuration with better prompting and response handling"""

# Update the agent's system message
agent_config["system_message"] = create_enhanced_agent_prompt(db_type)

# Add additional configuration for better response handling
agent_config.update({
    "response_format": {
        "type": "structured_natural_language",
        "include_metadata": True,
        "error_handling": "verbose"
    },
    "output_parser_config": {
        "require_complete_sentences": True,
        "maintain_context": True,
        "format_lists": True
    }
})

return agent_config

def create_sql_query_agent(api_key: str, database_uri: str, verbose: bool = False):
llm = ChatGoogleGenerativeAI(
api_key=api_key,
model="gemini-1.5-pro",
temperature=0.3
)

sql_agent_component = SQLAgentComponent()
sql_agent = sql_agent_component.build(
    llm=llm,
    database_uri=database_uri,
    verbose=verbose
)

return sql_agent, sql_agent_component

def create_nosql_query_agent(
google_api_key: str,
mongodb_uri: str,
db_name: str,
collection_name: str,
index_name: str = "default",
verbose: bool = False
):
llm = ChatGoogleGenerativeAI(
api_key=google_api_key,
model="gemini-1.5-pro",
temperature=0.3
)

nosql_agent_component = NoSQLAgentComponent()
nosql_agent = nosql_agent_component.build(
    llm=llm,
    mongodb_uri=mongodb_uri,
    db_name=db_name,
    collection_name=collection_name,
    google_api_key=google_api_key,
    index_name=index_name,
    verbose=verbose
)

return nosql_agent, nosql_agent_component

def query_database(agent: AgentExecutor, question: str) -> str:
try:
enhanced_question = f"""
Please provide a concise and clear response to the following question,
keeping the answer focused and to the point. If the requested information is not available,
explain what was searched for and suggest relevant alternatives.

Question: {question}
"""
response = agent.invoke({"input": enhanced_question})

    # Post-process the response for better formatting
    formatted_response = response["output"].strip()
    
    # If the response is too brief, enhance it
    if len(formatted_response.split()) < 20:
        formatted_response = f"""

Based on my search of the database, {formatted_response.lower()}

Let me provide more context about what I searched for and what's available in the database.
The query was looking for {question.lower()}, and I checked the relevant tables/collections
for this information. While the exact information might not be available in the format requested,
I can suggest exploring related queries or checking specific fields that might contain similar information.
"""

    return formatted_response
    
except Exception as e:
    return f"""

I encountered an issue while searching for the requested information in the database.
The specific error was: {str(e)}

Please feel free to ask another question or rephrase your current one. I'm here to help you find the information you need.
"""

[dosubot]

To integrate Langflow's tools for efficient data retrieval into your existing code, you can enhance your SQL and NoSQL agent components by incorporating retrieval components like RetrieverToolComponent or SelfQueryRetrieverComponent. These components are designed to improve search and retrieval operations, making your bot more efficient in handling large datasets.

Here's how you can modify your code to include these components:

1. Enhance SQL and NoSQL Agents: Use retrieval components to improve the efficiency of your agents. This involves setting up the agents to use advanced retrieval techniques.

2. Integrate Retrieval Components: Add components like RetrieverToolComponent to your agents to facilitate better data retrieval.

Here's a conceptual example of how you might integrate these components:

from langflow.custom import CustomComponent
from langchain_community.utilities import SQLDatabase, MongoDBAtlasVectorSearch
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor, initialize_agent, AgentType
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.tools import Tool
from pymongo import MongoClient
from typing import Callable, Union
from pydantic import BaseModel
from utils import create_enhanced_agent_prompt

class EnhancedSQLAgentComponent(CustomComponent):
    display_name = "EnhancedSQLAgent"
    description = "Construct an enhanced SQL agent with retrieval capabilities."

    def build(self, llm, database_uri: str, verbose: bool = False):
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        
        # Use a retriever tool for enhanced data retrieval
        retriever_tool = RetrieverToolComponent()
        retriever = retriever_tool.build(llm=llm, database_uri=database_uri)
        
        prompt = create_enhanced_agent_prompt("sql")
        agent = create_sql_agent(
            llm=llm,
            toolkit=toolkit,
            retriever=retriever,
            verbose=verbose,
            handle_parsing_errors=True,
            prefix=prompt
        )
        return agent

class EnhancedNoSQLAgentComponent(CustomComponent):
    display_name = "EnhancedNoSQLAgent"
    description = "Construct an enhanced NoSQL agent with retrieval capabilities."

    def build(
        self,
        llm: LanguageModel,
        mongodb_uri: str,
        db_name: str,
        collection_name: str,
        google_api_key: str,
        index_name: str = "default",
        verbose: bool = False,
    ) -> Union[AgentExecutor, Callable]:
        client = MongoClient(mongodb_uri)
        db = client[db_name]
        collection = db[collection_name]
        
        embeddings = GoogleGenerativeAIEmbeddings(
            model="models/embedding-001", 
            google_api_key=google_api_key,
        )
        
        vector_store = MongoDBAtlasVectorSearch.from_connection_string(
            connection_string=mongodb_uri,
            namespace=f"{db_name}.{collection_name}",
            embedding=embeddings,
            index_name=index_name
        )
        
        # Use a retriever tool for enhanced data retrieval
        retriever_tool = RetrieverToolComponent()
        retriever = retriever_tool.build(llm=llm, mongodb_uri=mongodb_uri)
        
        prompt = create_enhanced_agent_prompt("nosql")
        agent = initialize_agent(
            tools=[retriever],
            llm=llm,
            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
            verbose=verbose,
            agent_kwargs={"prefix": prompt}
        )
        
        return agent

# Example usage
if __name__ == "__main__":
    google_api_key = "your_google_api_key_here"
    database_uri = "your_database_uri_here"
    db_type = "sql"  # or "nosql"
    
    llm = ChatGoogleGenerativeAI(
        api_key=google_api_key,
        model="gemini-1.5-pro",
        temperature=0.3
    )
    
    if db_type == "sql":
        agent_component = EnhancedSQLAgentComponent()
    else:
        agent_component = EnhancedNoSQLAgentComponent()
    
    agent = agent_component.build(
        llm=llm,
        database_uri=database_uri,
        verbose=True
    )
    
    question = "Who is Adil user in the database?"
    response = query_database(agent, question)
    print(response)

Replace "your_google_api_key_here" and "your_database_uri_here" with your actual API key and database URI. This setup uses retrieval components to enhance the efficiency of your agents, allowing them to provide more accurate and comprehensive responses based on the available data. Adjust the code as needed to fit your specific requirements and database setup.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

[MuhammadAdil-Developer]

@dosu see if you can update this code in this code please.

class SQLAgentComponent:
display_name = "SQLAgent"
description = "Construct an SQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "database_uri": {"display_name": "Database URI"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(self, llm, database_uri: str, verbose: bool = False):
    try:
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        
        # Create SQL agent with enhanced prompting
        prompt = create_enhanced_agent_prompt("sql")
        agent = create_sql_agent(
            llm=llm,
            toolkit=toolkit,
            verbose=verbose,
            handle_parsing_errors=True,
            prefix=prompt
        )
        return agent
    except Exception as e:
        raise Exception(f"Failed to connect to SQL database. Please check the database URI. Error: {str(e)}")

class NoSQLAgentComponent(CustomComponent):
display_name = "NoSQLAgent"
description = "Construct a NoSQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "mongodb_uri": {"display_name": "MongoDB URI"},
        "db_name": {"display_name": "Database Name"},
        "collection_name": {"display_name": "Collection Name"},
        "index_name": {"display_name": "Index Name", "value": "default"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(
    self,
    llm: LanguageModel,
    mongodb_uri: str,
    db_name: str,
    collection_name: str,
    google_api_key: str,
    index_name: str = "default",
    verbose: bool = False,
) -> Union[AgentExecutor, Callable]:
    client = MongoClient(mongodb_uri)
    db = client[db_name]
    collection = db[collection_name]
    
    embeddings = GoogleGenerativeAIEmbeddings(
        model="models/embedding-001", 
        google_api_key=google_api_key,
    )
    
    vector_store = MongoDBAtlasVectorSearch.from_connection_string(
        connection_string=mongodb_uri,
        namespace=f"{db_name}.{collection_name}",
        embedding=embeddings,
        index_name=index_name
    )
    
    def query_mongodb(query_str: str) -> str:
        try:
            results = collection.find().sort('_id', -1).limit(5)
            formatted_results = []
            for doc in results:
                # Convert ObjectId to string and format the document
                doc['_id'] = str(doc['_id'])
                formatted_results.append(str(doc))
            return "\n".join(formatted_results)
        except Exception as e:
            return f"Error executing query: {str(e)}"

    tools = [
        Tool(
            name="MongoDB Query",
            func=query_mongodb,
            description="Useful for querying MongoDB database. Returns formatted results from the collection."
        )
    ]
    
    # Create MongoDB agent with enhanced prompting
    prompt = create_enhanced_agent_prompt("nosql")
    agent = initialize_agent(
        tools=tools,
        llm=llm,
        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
        verbose=verbose,
        agent_kwargs={"prefix": prompt}
    )
    
    return agent

def enhance_agent_configuration(agent_config: dict, db_type: str) -> dict:
"""Enhances the agent configuration with better prompting and response handling"""

# Update the agent's system message
agent_config["system_message"] = create_enhanced_agent_prompt(db_type)

# Add additional configuration for better response handling
agent_config.update({
    "response_format": {
        "type": "structured_natural_language",
        "include_metadata": True,
        "error_handling": "verbose"
    },
    "output_parser_config": {
        "require_complete_sentences": True,
        "maintain_context": True,
        "format_lists": True
    }
})

return agent_config

def create_sql_query_agent(api_key: str, database_uri: str, verbose: bool = False):
llm = ChatGoogleGenerativeAI(
api_key=api_key,
model="gemini-1.5-pro",
temperature=0.3
)

sql_agent_component = SQLAgentComponent()
sql_agent = sql_agent_component.build(
    llm=llm,
    database_uri=database_uri,
    verbose=verbose
)

return sql_agent, sql_agent_component

def create_nosql_query_agent(
google_api_key: str,
mongodb_uri: str,
db_name: str,
collection_name: str,
index_name: str = "default",
verbose: bool = False
):
llm = ChatGoogleGenerativeAI(
api_key=google_api_key,
model="gemini-1.5-pro",
temperature=0.3
)

nosql_agent_component = NoSQLAgentComponent()
nosql_agent = nosql_agent_component.build(
    llm=llm,
    mongodb_uri=mongodb_uri,
    db_name=db_name,
    collection_name=collection_name,
    google_api_key=google_api_key,
    index_name=index_name,
    verbose=verbose
)

return nosql_agent, nosql_agent_component

def query_database(agent: AgentExecutor, question: str) -> str:
try:
enhanced_question = f"""
Please provide a concise and clear response to the following question,
keeping the answer focused and to the point. If the requested information is not available,
explain what was searched for and suggest relevant alternatives.

Question: {question}
"""
response = agent.invoke({"input": enhanced_question})

    # Post-process the response for better formatting
    formatted_response = response["output"].strip()
    
    # If the response is too brief, enhance it
    if len(formatted_response.split()) < 20:
        formatted_response = f"""

Based on my search of the database, {formatted_response.lower()}

Let me provide more context about what I searched for and what's available in the database.
The query was looking for {question.lower()}, and I checked the relevant tables/collections
for this information. While the exact information might not be available in the format requested,
I can suggest exploring related queries or checking specific fields that might contain similar information.
"""

    return formatted_response
    
except Exception as e:
    return f"""

I encountered an issue while searching for the requested information in the database.
The specific error was: {str(e)}

Please feel free to ask another question or rephrase your current one. I'm here to help you find the information you need.
"""

@app.post("/chat")
async def chat(request: QueryRequest, thread_id: str = Query(None)):
try:
connection = connect_to_db()
if connection:
cursor = connection.cursor(dictionary=True)

        # Fetch the most recent database connection
        cursor.execute("SELECT * FROM db_connections ORDER BY created_at DESC LIMIT 1")
        db_connection = cursor.fetchone()

        if not db_connection:
            raise HTTPException(status_code=400, detail="No database found. Connect to the database first.")
        
        # Retrieve connection details
        database_uri = db_connection["database_uri"]
        db_type = db_connection["db_type"]
        db_name = db_connection["db_name"]
        collection_name = db_connection["collection_name"]
        allowed_databases = db_connection["allowed_databases"].split(',')
        
        cursor.close()
        connection.close()
    else:
        raise HTTPException(status_code=500, detail="Unable to connect to the database.")

    # Validate database URI and type dynamically
    if db_type == "sql":
        if not any(db in database_uri.lower() for db in allowed_databases):
            raise HTTPException(status_code=400, detail=f"Invalid SQL database URI. Supported databases: {', '.join(allowed_databases)}.")
    elif db_type == "nosql":
        if not any(db in database_uri.lower() for db in allowed_databases):
            raise HTTPException(status_code=400, detail=f"Invalid NoSQL database URI. Supported databases: {', '.join(allowed_databases)}.")

    is_new_thread = thread_id is None
    if is_new_thread:
        thread_id = str(uuid.uuid4())

    question = request.question
    google_api_key = "AIzaSyAEOVGaFTlqhwDVhuw5XuddBOM6ZNoYIdk"

    if db_type == "sql":
        sql_agent, component = create_sql_query_agent(
            api_key=google_api_key,
            database_uri=database_uri,
            verbose=True
        )
    elif db_type == "nosql":
        mongodb_uri = database_uri
        # Use dynamically fetched db_name and collection_name
        sql_agent, component = create_nosql_query_agent(
            google_api_key=google_api_key,
            mongodb_uri=mongodb_uri,
            db_name=db_name,
            collection_name=collection_name,
            verbose=True
        )
    else:
        raise HTTPException(status_code=400, detail="Invalid database type specified")

    response = query_database(sql_agent, question)

    heading = None
    connection = connect_to_db()
    if connection:
        cursor = connection.cursor(dictionary=True)

        query_heading = "SELECT heading FROM history WHERE thread_id = %s LIMIT 1"
        cursor.execute(query_heading, (thread_id,))
        result = cursor.fetchone()

        if result and result.get("heading"):
            heading = result["heading"]
        elif is_new_thread:
            user_message = request.question
            ai_response = response 
            heading = await generate_heading(user_message, ai_response)

        cursor.close()
        connection.close()

    save_query_and_answer(question, response, thread_id, heading)

    connection = connect_to_db()
    if connection:
        cursor = connection.cursor(dictionary=True)
        query_history = "SELECT * FROM history WHERE thread_id = %s ORDER BY id ASC"
        cursor.execute(query_history, (thread_id,))
        history = cursor.fetchall()
        cursor.close()
        connection.close()

    human_messages = []
    ai_responses = []

    for record in history:
        human_messages.append(record["user_query"])
        ai_responses.append(record["ai_response"])

    response_data = {
        "thread_id": thread_id,
        "ai_response": ai_responses,
        "human_message": human_messages,
        "heading": heading,
    }

    return response_data

except Exception as e:
    raise HTTPException(status_code=500, detail=f"Error: {str(e)}")

[dosubot]

To enhance your code with Langflow's retrieval capabilities, you can integrate components like RetrieverToolComponent or SelfQueryRetrieverComponent to improve the efficiency of your SQL and NoSQL agents. Here's how you can update your code:

1. Integrate Retrieval Components: Add retrieval components to your agents to facilitate better data retrieval.

2. Enhance SQL and NoSQL Agents: Use these components to improve the efficiency of your agents.

Here's an updated version of your code with these enhancements:

from langflow.custom import CustomComponent
from langchain_community.utilities import SQLDatabase, MongoDBAtlasVectorSearch
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor, initialize_agent, AgentType
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.tools import Tool
from pymongo import MongoClient
from typing import Callable, Union
from pydantic import BaseModel
from utils import create_enhanced_agent_prompt

class EnhancedSQLAgentComponent(CustomComponent):
    display_name = "EnhancedSQLAgent"
    description = "Construct an enhanced SQL agent with retrieval capabilities."

    def build(self, llm, database_uri: str, verbose: bool = False):
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        
        # Use a retriever tool for enhanced data retrieval
        retriever_tool = RetrieverToolComponent()
        retriever = retriever_tool.build(llm=llm, database_uri=database_uri)
        
        prompt = create_enhanced_agent_prompt("sql")
        agent = create_sql_agent(
            llm=llm,
            toolkit=toolkit,
            retriever=retriever,
            verbose=verbose,
            handle_parsing_errors=True,
            prefix=prompt
        )
        return agent

class EnhancedNoSQLAgentComponent(CustomComponent):
    display_name = "EnhancedNoSQLAgent"
    description = "Construct an enhanced NoSQL agent with retrieval capabilities."

    def build(
        self,
        llm: LanguageModel,
        mongodb_uri: str,
        db_name: str,
        collection_name: str,
        google_api_key: str,
        index_name: str = "default",
        verbose: bool = False,
    ) -> Union[AgentExecutor, Callable]:
        client = MongoClient(mongodb_uri)
        db = client[db_name]
        collection = db[collection_name]
        
        embeddings = GoogleGenerativeAIEmbeddings(
            model="models/embedding-001", 
            google_api_key=google_api_key,
        )
        
        vector_store = MongoDBAtlasVectorSearch.from_connection_string(
            connection_string=mongodb_uri,
            namespace=f"{db_name}.{collection_name}",
            embedding=embeddings,
            index_name=index_name
        )
        
        # Use a retriever tool for enhanced data retrieval
        retriever_tool = RetrieverToolComponent()
        retriever = retriever_tool.build(llm=llm, mongodb_uri=mongodb_uri)
        
        prompt = create_enhanced_agent_prompt("nosql")
        agent = initialize_agent(
            tools=[retriever],
            llm=llm,
            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
            verbose=verbose,
            agent_kwargs={"prefix": prompt}
        )
        
        return agent

# Example usage
if __name__ == "__main__":
    google_api_key = "your_google_api_key_here"
    database_uri = "your_database_uri_here"
    db_type = "sql"  # or "nosql"
    
    llm = ChatGoogleGenerativeAI(
        api_key=google_api_key,
        model="gemini-1.5-pro",
        temperature=0.3
    )
    
    if db_type == "sql":
        agent_component = EnhancedSQLAgentComponent()
    else:
        agent_component = EnhancedNoSQLAgentComponent()
    
    agent = agent_component.build(
        llm=llm,
        database_uri=database_uri,
        verbose=True
    )
    
    question = "Who is Adil user in the database?"
    response = query_database(agent, question)
    print(response)

Replace "your_google_api_key_here" and "your_database_uri_here" with your actual API key and database URI. This setup uses retrieval components to enhance the efficiency of your agents, allowing them to provide more accurate and comprehensive responses based on the available data. Adjust the code as needed to fit your specific requirements and database setup.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

[MuhammadAdil-Developer]

@dosu

but is code me tu yeh error araha hy yar boss

{
"detail": "Error: Failed to connect to SQL database. Please check the database URI. Error: name 'RetrieverToolComponent' is not defined"
}

[dosubot]

To resolve the error "name 'RetrieverToolComponent' is not defined," you need to ensure that the RetrieverToolComponent is correctly imported in your code. According to the Langflow codebase, RetrieverToolComponent should be imported from the appropriate module. Here's how you can fix the import:

1. Import the RetrieverToolComponent: Add the correct import statement at the beginning of your script.

Here's how you can update your code:

from langflow.custom import CustomComponent
from langchain_community.utilities import SQLDatabase, MongoDBAtlasVectorSearch
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor, initialize_agent, AgentType
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.tools import Tool
from pymongo import MongoClient
from typing import Callable, Union
from pydantic import BaseModel
from utils import create_enhanced_agent_prompt
from langflow.components.tools import RetrieverToolComponent  # Add this import

class EnhancedSQLAgentComponent(CustomComponent):
    display_name = "EnhancedSQLAgent"
    description = "Construct an enhanced SQL agent with retrieval capabilities."

    def build(self, llm, database_uri: str, verbose: bool = False):
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        
        # Use a retriever tool for enhanced data retrieval
        retriever_tool = RetrieverToolComponent()
        retriever = retriever_tool.build(llm=llm, database_uri=database_uri)
        
        prompt = create_enhanced_agent_prompt("sql")
        agent = create_sql_agent(
            llm=llm,
            toolkit=toolkit,
            retriever=retriever,
            verbose=verbose,
            handle_parsing_errors=True,
            prefix=prompt
        )
        return agent

# Continue with the rest of your code...

Make sure that the import path for RetrieverToolComponent is correct and that the module is available in your environment. This should resolve the error and allow your code to run correctly.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

[MuhammadAdil-Developer]

@dosu

class SQLAgentComponent:
display_name = "SQLAgent"
description = "Construct an SQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "database_uri": {"display_name": "Database URI"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(self, llm, database_uri: str, verbose: bool = False):
    try:
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        
        # Use a retriever tool for enhanced data retrieval
        retriever_tool = RetrieverToolComponent()
        retriever = retriever_tool.build(llm=llm, database_uri=database_uri)
        
        prompt = create_enhanced_agent_prompt("sql")
        agent = create_sql_agent(
            llm=llm,
            toolkit=toolkit,
            retriever=retriever,
            verbose=verbose,
            handle_parsing_errors=True,
            prefix=prompt
        )

        return agent
    except Exception as e:
        raise Exception(f"Failed to connect to SQL database. Please check the database URI. Error: {str(e)}")

class NoSQLAgentComponent(CustomComponent):
display_name = "NoSQLAgent"
description = "Construct a NoSQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "mongodb_uri": {"display_name": "MongoDB URI"},
        "db_name": {"display_name": "Database Name"},
        "collection_name": {"display_name": "Collection Name"},
        "index_name": {"display_name": "Index Name", "value": "default"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(
    self,
    llm: LanguageModel,
    mongodb_uri: str,
    db_name: str,
    collection_name: str,
    google_api_key: str,
    index_name: str = "default",
    verbose: bool = False,
) -> Union[AgentExecutor, Callable]:
    client = MongoClient(mongodb_uri)
    db = client[db_name]
    collection = db[collection_name]
    
    embeddings = GoogleGenerativeAIEmbeddings(
        model="models/embedding-001", 
        google_api_key=google_api_key,
    )
    
    vector_store = MongoDBAtlasVectorSearch.from_connection_string(
        connection_string=mongodb_uri,
        namespace=f"{db_name}.{collection_name}",
        embedding=embeddings,
        index_name=index_name
    )

    
    def query_mongodb(query_str: str) -> str:
        try:
            results = collection.find().sort('_id', -1).limit(5)
            formatted_results = []
            for doc in results:
                # Convert ObjectId to string and format the document
                doc['_id'] = str(doc['_id'])
                formatted_results.append(str(doc))
            return "\n".join(formatted_results)
        except Exception as e:
            return f"Error executing query: {str(e)}"

    tools = [
        Tool(
            name="MongoDB Query",
            func=query_mongodb,
            description="Useful for querying MongoDB database. Returns formatted results from the collection."
        )
    ]
    
    # Use a retriever tool for enhanced data retrieval
    retriever_tool = RetrieverToolComponent()
    retriever = retriever_tool.build(llm=llm, mongodb_uri=mongodb_uri)

    # Create MongoDB agent with enhanced prompting
    prompt = create_enhanced_agent_prompt("nosql")
    agent = initialize_agent(
        tools=tools,
        llm=llm,
        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
        verbose=verbose,
        agent_kwargs={"prefix": prompt}
    )
    
    return agent

[dosubot]

To resolve the error regarding the RetrieverToolComponent not being defined, you need to ensure that it is correctly imported into your code. Here's how you can update your code to include the necessary import statement:

1. Import the RetrieverToolComponent: Add the correct import statement at the beginning of your script.

Here's the updated code with the necessary import:

from langflow.custom import CustomComponent
from langchain_community.utilities import SQLDatabase, MongoDBAtlasVectorSearch
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor, initialize_agent, AgentType
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.tools import Tool
from pymongo import MongoClient
from typing import Callable, Union
from utils import create_enhanced_agent_prompt
from langflow.components.tools import RetrieverToolComponent  # Ensure this import is correct

class SQLAgentComponent:
    display_name = "SQLAgent"
    description = "Construct an SQL agent from an LLM and tools."

    def build_config(self):
        return {
            "llm": {"display_name": "LLM"},
            "database_uri": {"display_name": "Database URI"},
            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
        }

    def build(self, llm, database_uri: str, verbose: bool = False):
        try:
            db = SQLDatabase.from_uri(database_uri)
            toolkit = SQLDatabaseToolkit(db=db, llm=llm)
            
            # Use a retriever tool for enhanced data retrieval
            retriever_tool = RetrieverToolComponent()
            retriever = retriever_tool.build(llm=llm, database_uri=database_uri)
            
            prompt = create_enhanced_agent_prompt("sql")
            agent = create_sql_agent(
                llm=llm,
                toolkit=toolkit,
                retriever=retriever,
                verbose=verbose,
                handle_parsing_errors=True,
                prefix=prompt
            )

            return agent
        except Exception as e:
            raise Exception(f"Failed to connect to SQL database. Please check the database URI. Error: {str(e)}")

class NoSQLAgentComponent(CustomComponent):
    display_name = "NoSQLAgent"
    description = "Construct a NoSQL agent from an LLM and tools."

    def build_config(self):
        return {
            "llm": {"display_name": "LLM"},
            "mongodb_uri": {"display_name": "MongoDB URI"},
            "db_name": {"display_name": "Database Name"},
            "collection_name": {"display_name": "Collection Name"},
            "index_name": {"display_name": "Index Name", "value": "default"},
            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
        }

    def build(
        self,
        llm: LanguageModel,
        mongodb_uri: str,
        db_name: str,
        collection_name: str,
        google_api_key: str,
        index_name: str = "default",
        verbose: bool = False,
    ) -> Union[AgentExecutor, Callable]:
        client = MongoClient(mongodb_uri)
        db = client[db_name]
        collection = db[collection_name]
        
        embeddings = GoogleGenerativeAIEmbeddings(
            model="models/embedding-001", 
            google_api_key=google_api_key,
        )
        
        vector_store = MongoDBAtlasVectorSearch.from_connection_string(
            connection_string=mongodb_uri,
            namespace=f"{db_name}.{collection_name}",
            embedding=embeddings,
            index_name=index_name
        )

        def query_mongodb(query_str: str) -> str:
            try:
                results = collection.find().sort('_id', -1).limit(5)
                formatted_results = []
                for doc in results:
                    # Convert ObjectId to string and format the document
                    doc['_id'] = str(doc['_id'])
                    formatted_results.append(str(doc))
                return "\n".join(formatted_results)
            except Exception as e:
                return f"Error executing query: {str(e)}"

        tools = [
            Tool(
                name="MongoDB Query",
                func=query_mongodb,
                description="Useful for querying MongoDB database. Returns formatted results from the collection."
            )
        ]
        
        # Use a retriever tool for enhanced data retrieval
        retriever_tool = RetrieverToolComponent()
        retriever = retriever_tool.build(llm=llm, mongodb_uri=mongodb_uri)

        # Create MongoDB agent with enhanced prompting
        prompt = create_enhanced_agent_prompt("nosql")
        agent = initialize_agent(
            tools=tools,
            llm=llm,
            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
            verbose=verbose,
            agent_kwargs={"prefix": prompt}
        )
        
        return agent

Ensure that the import path for RetrieverToolComponent is correct and that the module is available in your environment. This should resolve the error and allow your code to run correctly.

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[MuhammadAdil-Developer]

@dosu

class SQLAgentComponent:
display_name = "SQLAgent"
description = "Construct an SQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "database_uri": {"display_name": "Database URI"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(self, llm, database_uri: str, verbose: bool = False):
    try:
        db = SQLDatabase.from_uri(database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
        
        # Use a retriever tool for enhanced data retrieval
        retriever_tool = RetrieverToolComponent()
        retriever = retriever_tool.build(llm=llm, database_uri=database_uri)
        
        prompt = create_enhanced_agent_prompt("sql")
        agent = create_sql_agent(
            llm=llm,
            toolkit=toolkit,
            retriever=retriever,
            verbose=verbose,
            handle_parsing_errors=True,
            prefix=prompt
        )

        return agent
    except Exception as e:
        raise Exception(f"Failed to connect to SQL database. Please check the database URI. Error: {str(e)}")

class NoSQLAgentComponent(CustomComponent):
display_name = "NoSQLAgent"
description = "Construct a NoSQL agent from an LLM and tools."

def build_config(self):
    return {
        "llm": {"display_name": "LLM"},
        "mongodb_uri": {"display_name": "MongoDB URI"},
        "db_name": {"display_name": "Database Name"},
        "collection_name": {"display_name": "Collection Name"},
        "index_name": {"display_name": "Index Name", "value": "default"},
        "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
    }

def build(
    self,
    llm: LanguageModel,
    mongodb_uri: str,
    db_name: str,
    collection_name: str,
    google_api_key: str,
    index_name: str = "default",
    verbose: bool = False,
) -> Union[AgentExecutor, Callable]:
    client = MongoClient(mongodb_uri)
    db = client[db_name]
    collection = db[collection_name]
    
    embeddings = GoogleGenerativeAIEmbeddings(
        model="models/embedding-001", 
        google_api_key=google_api_key,
    )
    
    vector_store = MongoDBAtlasVectorSearch.from_connection_string(
        connection_string=mongodb_uri,
        namespace=f"{db_name}.{collection_name}",
        embedding=embeddings,
        index_name=index_name
    )

    
    def query_mongodb(query_str: str) -> str:
        try:
            results = collection.find().sort('_id', -1).limit(5)
            formatted_results = []
            for doc in results:
                # Convert ObjectId to string and format the document
                doc['_id'] = str(doc['_id'])
                formatted_results.append(str(doc))
            return "\n".join(formatted_results)
        except Exception as e:
            return f"Error executing query: {str(e)}"

    tools = [
        Tool(
            name="MongoDB Query",
            func=query_mongodb,
            description="Useful for querying MongoDB database. Returns formatted results from the collection."
        )
    ]
    
    # Use a retriever tool for enhanced data retrieval
    retriever_tool = RetrieverToolComponent()
    retriever = retriever_tool.build(llm=llm, mongodb_uri=mongodb_uri)

    # Create MongoDB agent with enhanced prompting
    prompt = create_enhanced_agent_prompt("nosql")
    agent = initialize_agent(
        tools=tools,
        llm=llm,
        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
        verbose=verbose,
        agent_kwargs={"prefix": prompt}
    )
    
    return agent

def enhance_agent_configuration(agent_config: dict, db_type: str) -> dict:
"""Enhances the agent configuration with better prompting and response handling"""

# Update the agent's system message
agent_config["system_message"] = create_enhanced_agent_prompt(db_type)

# Add additional configuration for better response handling
agent_config.update({
    "response_format": {
        "type": "structured_natural_language",
        "include_metadata": True,
        "error_handling": "verbose"
    },
    "output_parser_config": {
        "require_complete_sentences": True,
        "maintain_context": True,
        "format_lists": True
    }
})

return agent_config

def create_sql_query_agent(api_key: str, database_uri: str, verbose: bool = False):
llm = ChatGoogleGenerativeAI(
api_key=api_key,
model="gemini-1.5-pro",
temperature=0.3
)

sql_agent_component = SQLAgentComponent()
sql_agent = sql_agent_component.build(
    llm=llm,
    database_uri=database_uri,
    verbose=verbose
)

return sql_agent, sql_agent_component

def create_nosql_query_agent(
google_api_key: str,
mongodb_uri: str,
db_name: str,
collection_name: str,
index_name: str = "default",
verbose: bool = False
):
llm = ChatGoogleGenerativeAI(
api_key=google_api_key,
model="gemini-1.5-pro",
temperature=0.3
)

nosql_agent_component = NoSQLAgentComponent()
nosql_agent = nosql_agent_component.build(
    llm=llm,
    mongodb_uri=mongodb_uri,
    db_name=db_name,
    collection_name=collection_name,
    google_api_key=google_api_key,
    index_name=index_name,
    verbose=verbose
)

return nosql_agent, nosql_agent_component

def query_database(agent: AgentExecutor, question: str) -> str:
try:
enhanced_question = f"""
Please provide a concise and clear response to the following question,
keeping the answer focused and to the point. If the requested information is not available,
explain what was searched for and suggest relevant alternatives.

    Question: {question}

"""
response = agent.invoke({"input": enhanced_question})

    # Post-process the response for better formatting
    formatted_response = response["output"].strip()
    
    # If the response is too brief, enhance it
    if len(formatted_response.split()) < 20:
        formatted_response = f"""

Based on my search of the database, {formatted_response.lower()}

Let me provide more context about what I searched for and what's available in the database.
The query was looking for {question.lower()}, and I checked the relevant tables/collections
for this information. While the exact information might not be available in the format requested,
I can suggest exploring related queries or checking specific fields that might contain similar information.
"""

    return formatted_response
    
except Exception as e:
    return f"""

I encountered an issue while searching for the requested information in the database.
The specific error was: {str(e)}

Please feel free to ask another question or rephrase your current one. I'm here to help you find the information you need.
"""

can you solve this code in this code plz

{
"detail": "Error: Failed to connect to SQL database. Please check the database URI. Error: RetrieverToolComponent.build() missing 3 required positional arguments: 'retriever', 'name', and 'description'"
}

[dosubot]

To resolve the error "RetrieverToolComponent.build() missing 3 required positional arguments: 'retriever', 'name', and 'description'," you need to ensure that when you call the build method of the RetrieverToolComponent, you provide the required arguments: retriever, name, and description.

Here's how you can update your code to fix this issue:

1. Provide the Required Arguments: Ensure that you pass the necessary arguments to the build method of RetrieverToolComponent.

Here's the updated code:

from langflow.custom import CustomComponent
from langchain_community.utilities import SQLDatabase, MongoDBAtlasVectorSearch
from langflow.field_typing import LanguageModel
from langchain.agents import AgentExecutor, initialize_agent, AgentType
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.tools import Tool
from pymongo import MongoClient
from typing import Callable, Union
from utils import create_enhanced_agent_prompt
from langflow.components.tools import RetrieverToolComponent  # Ensure this import is correct

class SQLAgentComponent:
    display_name = "SQLAgent"
    description = "Construct an SQL agent from an LLM and tools."

    def build_config(self):
        return {
            "llm": {"display_name": "LLM"},
            "database_uri": {"display_name": "Database URI"},
            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
        }

    def build(self, llm, database_uri: str, verbose: bool = False):
        try:
            db = SQLDatabase.from_uri(database_uri)
            toolkit = SQLDatabaseToolkit(db=db, llm=llm)
            
            # Use a retriever tool for enhanced data retrieval
            retriever_tool = RetrieverToolComponent()
            retriever = retriever_tool.build(
                retriever=toolkit,  # Assuming toolkit acts as a retriever
                name="SQLRetriever",
                description="Retrieves data from SQL database"
            )
            
            prompt = create_enhanced_agent_prompt("sql")
            agent = create_sql_agent(
                llm=llm,
                toolkit=toolkit,
                retriever=retriever,
                verbose=verbose,
                handle_parsing_errors=True,
                prefix=prompt
            )

            return agent
        except Exception as e:
            raise Exception(f"Failed to connect to SQL database. Please check the database URI. Error: {str(e)}")

class NoSQLAgentComponent(CustomComponent):
    display_name = "NoSQLAgent"
    description = "Construct a NoSQL agent from an LLM and tools."

    def build_config(self):
        return {
            "llm": {"display_name": "LLM"},
            "mongodb_uri": {"display_name": "MongoDB URI"},
            "db_name": {"display_name": "Database Name"},
            "collection_name": {"display_name": "Collection Name"},
            "index_name": {"display_name": "Index Name", "value": "default"},
            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
        }

    def build(
        self,
        llm: LanguageModel,
        mongodb_uri: str,
        db_name: str,
        collection_name: str,
        google_api_key: str,
        index_name: str = "default",
        verbose: bool = False,
    ) -> Union[AgentExecutor, Callable]:
        client = MongoClient(mongodb_uri)
        db = client[db_name]
        collection = db[collection_name]
        
        embeddings = GoogleGenerativeAIEmbeddings(
            model="models/embedding-001", 
            google_api_key=google_api_key,
        )
        
        vector_store = MongoDBAtlasVectorSearch.from_connection_string(
            connection_string=mongodb_uri,
            namespace=f"{db_name}.{collection_name}",
            embedding=embeddings,
            index_name=index_name
        )

        def query_mongodb(query_str: str) -> str:
            try:
                results = collection.find().sort('_id', -1).limit(5)
                formatted_results = []
                for doc in results:
                    # Convert ObjectId to string and format the document
                    doc['_id'] = str(doc['_id'])
                    formatted_results.append(str(doc))
                return "\n".join(formatted_results)
            except Exception as e:
                return f"Error executing query: {str(e)}"

        tools = [
            Tool(
                name="MongoDB Query",
                func=query_mongodb,
                description="Useful for querying MongoDB database. Returns formatted results from the collection."
            )
        ]
        
        # Use a retriever tool for enhanced data retrieval
        retriever_tool = RetrieverToolComponent()
        retriever = retriever_tool.build(
            retriever=vector_store,  # Assuming vector_store acts as a retriever
            name="NoSQLRetriever",
            description="Retrieves data from NoSQL database"
        )

        # Create MongoDB agent with enhanced prompting
        prompt = create_enhanced_agent_prompt("nosql")
        agent = initialize_agent(
            tools=tools,
            llm=llm,
            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
            verbose=verbose,
            agent_kwargs={"prefix": prompt}
        )
        
        return agent

Ensure that the retriever argument in the build method of RetrieverToolComponent is correctly set to an object that can act as a retriever, such as toolkit or vector_store. Adjust the code as needed based on your specific setup and available components.

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[MuhammadAdil-Developer]

@dosu

kiya me is code me sql server ka db bhi use kersakta hu is me ?? ya nahi

[dosubot]

Yes, you can use a SQL Server database with this code. To do so, ensure that your SQLDatabase connection string is correctly configured for SQL Server. You will need to use the appropriate connection string format for SQL Server, which typically includes the server name, database name, and authentication details. Here's an example of how you might set up the connection string for SQL Server:

database_uri = "mssql+pyodbc://username:password@server/database?driver=ODBC+Driver+17+for+SQL+Server"

Make sure you have the necessary Python packages installed, such as pyodbc and sqlalchemy, to facilitate the connection to SQL Server. Adjust the connection string with your actual server details, username, and password. This setup should allow you to use SQL Server with the existing code structure.

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