OperationalError: (pyodbc.OperationalError) ('08001', '[08001]

[jyoti194]

System Info

latest version of langchain. python=3.11.4

Who can help?

@all

Information

• [x] The official example notebooks/scripts
• [x] My own modified scripts

Related Components

• [X] LLMs/Chat Models
• [ ] Embedding Models
• [ ] Prompts / Prompt Templates / Prompt Selectors
• [ ] Output Parsers
• [ ] Document Loaders
• [ ] Vector Stores / Retrievers
• [ ] Memory
• [ ] Agents / Agent Executors
• [X] Tools / Toolkits
• [X] Chains
• [ ] Callbacks/Tracing
• [ ] Async

Reproduction

from langchain.agents import * from langchain.llms import OpenAI from langchain.sql_database import SQLDatabase

from langchain.agents import create_sql_agent from langchain.agents.agent_toolkits import SQLDatabaseToolkit from langchain.sql_database import SQLDatabase from langchain.llms.openai import OpenAI from langchain.agents import AgentExecutor

from langchain.chat_models import ChatOpenAI

from secret_key import openapi_key

openapi_key = "######"

os.environ['OPENAI_API_KEY'] = openapi_key

def chat(question):

llm = OpenAI(temperature=0)
tools = load_tools(["llm-math"], llm=llm)

agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)

driver = 'ODBC Driver 17 for SQL Server'  
host = '####'
database = 'chatgpt'
user = 'rnd'
password = '###'
#db_uri = SQLDatabase.from_uri(f"mssql+pyodbc://{user}:{password}@{host}/{database}?driver={driver}")
db = SQLDatabase.from_uri(f"mssql+pyodbc://{user}:{password}@{host}/{database}?driver=ODBC+Driver+17+for+SQL+Server")

llm = ChatOpenAI(model_name="gpt-3.5-turbo",
                temperature=0, 
                max_tokens=1000)

# toolkit = SQLDatabaseToolkit(db=db)
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent_executor = create_sql_agent(
    llm=llm,
    toolkit=toolkit,
    verbose=True,
    reduce_k_below_max_tokens=True,
)

mrkl = initialize_agent(
    tools,
    ChatOpenAI(temperature=0),
    agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True,
    handle_parsing_errors=True,
)

return agent_executor.run(question)

Expected behavior

i need to connect to test server, but i'm getting an error while connecting to it, but is working fine on local server , been getting "OperationalError: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]Named Pipes Provider: Could not open a connection to SQL Server [53]. (53) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (53)') "

[dosubot[bot]]

🤖

Hello,

The error message you're encountering is related to the SQL Server connection and not directly to the LangChain framework. The error message suggests that the SQL Server instance you're trying to connect to is not found or not accessible. Here are a few things you could check:

1. Server Name: Make sure the server name is correct. It should be the same as the one you're using to connect to the local server.

2. Network Access: If the server is not on your local machine, make sure your network allows connections to the server. This could be a firewall issue or the server might not be set up to accept remote connections.

3. SQL Server Configuration: Check if the SQL Server is configured to allow remote connections. You can do this by opening SQL Server Management Studio -> Right click on the server node -> Properties -> Connections -> Check the box 'Allow remote connections to this server'.

4. ODBC Driver: Make sure you have the correct ODBC driver installed. The error message suggests you're using 'ODBC Driver 17 for SQL Server'. You can check this in the ODBC Data Source Administrator in your system settings.

5. Connection String: Your connection string seems to be correct, but just to confirm, it should be in the following format:

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

   Please replace username, password, hostname, and database with your actual values.

If you've checked all of these and you're still encountering the issue, it might be helpful to provide more information about your SQL Server setup. Are you able to connect to the server using other tools like SQL Server Management Studio or Azure Data Studio? Is the server hosted on your local network or is it a cloud instance (like Azure SQL Database)?

I hope this helps! Let me know if you have any other questions.

Sources

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

---

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

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

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

[jyoti194]

Hi @dosu-bot @dosu-bot , thank you for response. Yes I'm able to connect to the server with SQL Server Management Studio, and I'm also able to connect to the same server using pyodbc.connect but when I'm trying with connection string in sqlalchemy I'm getting the above mentioned error.

[dosubot[bot]]

🤖

Hello,

From the context provided, it doesn't seem like there are any known issues or limitations with SQLAlchemy or pyodbc in the LangChain framework that could be causing this error. The error message you're seeing is typically associated with a problem in the connection string or network issues.

The connection string you're using seems to be correct. However, there are a few things you could try:

1. Quoting the driver name: Sometimes, the driver name in the connection string needs to be URL encoded or surrounded by quotes. You could try changing your connection string to:

mssql+pyodbc://username:password@hostname/database?driver={ODBC Driver 17 for SQL Server}

2. Using a DSN (Data Source Name): Instead of specifying the driver and other details in the connection string, you could set up a DSN through the ODBC Data Source Administrator in your operating system, and then use that DSN in your connection string:

mssql+pyodbc://username:password@DSN

3. Checking the network protocol: The error message mentions Named Pipes, which is one of the network protocols that SQL Server can use. You could try forcing the use of TCP/IP, which is typically more reliable, by adding tcp: before the server name in your connection string:

mssql+pyodbc://username:password@tcp:hostname/database?driver={ODBC Driver 17 for SQL Server}

If none of these suggestions work, it would be helpful if you could provide the exact code you're using to initialize the SQLRecordManager in LangChain, as well as any other relevant code. This could help identify if there's a problem in the way you're using SQLAlchemy or pyodbc with LangChain.

I hope this helps! Let me know if you have any other questions or if there's anything else I can do to assist you.

Sources

• libs/langchain/langchain/indexes/_sql_record_manager.py

---

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

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

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

[jyoti194]

hi @dosu-bot , can you explain about Using a DSN (Data Source Name)(2nd point), this another simple code which i was using to test with test server from sqlalchemy import create_engine

server = '-------'

database = 'HR_Git'

database = 'it_feb' username = 'sa' password = '------' # Removed the space

Define the connection string

connection_string = f'mssql+pyodbc://{username}:{password}@{server}:1433/{database}?driver=ODBC+Driver+17+for+SQL+Server'

connection_string = f"mssql+pyodbc://username:password@tcp:hostname/database?driver={'ODBC Driver 17 for SQL Server'}"

engine = create_engine(connection_string)

result = engine.execute('SELECT * FROM mst_Area')

for row in result: print(row)

engine.dispose()

note: i have just removed server details and password

Here is the code which is working with pyodbc.connect

import pyodbc

cnxn = pyodbc.connect( driver='{SQL Server}', host='------', database='HR_Git', user='sa', password='------' )

print(cnxn)

cursor = cnxn.cursor() cursor.execute('Select * from careers')

cursor.execute('Select * from dbo.AttendanceReason')

for row in cursor: print('row = %r' %(row,))

[dosubot[bot]]

🤖

Hello,

To connect to a SQL Server using SQLAlchemy in the LangChain framework using a DSN (Data Source Name), you can modify the SQLRecordManager class's __init__ method in the _sql_record_manager.py file to accept a DSN and use it to create the SQLAlchemy engine.

Here's how you can do it:

from sqlalchemy import create_engine, URL
from sqlalchemy.orm import sessionmaker
from typing import Optional, Dict, Any, Union

class SQLRecordManager(RecordManager):
    """A SQL Alchemy based implementation of the record manager."""

    def __init__(
        self,
        namespace: str,
        *,
        engine: Optional[Engine] = None,
        db_url: Union[None, str, URL] = None,
        dsn: Optional[str] = None,
        engine_kwargs: Optional[Dict[str, Any]] = None,
    ) -> None:
        """Initialize the SQLRecordManager.

        This class serves as a manager persistence layer that uses an SQL
        backend to track upserted records. You should specify either a db_url
        to create an engine or provide an existing engine.

        Args:
            namespace: The namespace associated with this record manager.
            engine: An already existing SQL Alchemy engine.
                Default is None.
            db_url: A database connection string used to create
                an SQL Alchemy engine. Default is None.
            dsn: A DSN used to create an SQL Alchemy engine. Default is None.
            engine_kwargs: Additional keyword arguments
                to be passed when creating the engine. Default is an empty dictionary.

        Raises:
            ValueError: If both db_url and engine are provided or neither.
            AssertionError: If something unexpected happens during engine configuration.
        """
        super().__init__(namespace=namespace)
        if db_url is None and engine is None and dsn is None:
            raise ValueError("Must specify either db_url, dsn or engine")
        if (db_url is not None and engine is not None) or (dsn is not None and (engine is not None or db_url is not None)):
            raise ValueError("Must specify either db_url, dsn or engine, not multiple")

        if db_url:
            _kwargs = engine_kwargs or {}
            _engine = create_engine(db_url, **_kwargs)
        elif dsn:
            _kwargs = engine_kwargs or {}
            _engine = create_engine(f"mssql+pyodbc:///?odbc_connect={dsn}", **_kwargs)
        elif engine:
            _engine = engine
        else:
            raise AssertionError("Something went wrong with configuration of engine.")

        self.engine = _engine
        self.dialect = _engine.dialect.name
        self.session_factory = sessionmaker(bind=self.engine)

In the above code, we added a new argument dsn to the __init__ method. If a DSN is provided, we use it to create the SQLAlchemy engine with the mssql+pyodbc dialect. The DSN is URL-encoded and passed to the odbc_connect parameter in the connection string.

Please note that the DSN should be URL-encoded. You can use Python's urllib.parse.quote_plus function to URL-encode the DSN.

Also, make sure that the DSN is correctly configured in your system's ODBC Data Source Administrator and that the SQL Server you're trying to connect to allows connections through DSN.

As for your question about whether the LangChain framework currently supports connecting to a SQL Server using SQLAlchemy with a DSN, I wasn't able to find an answer within the repository. However, the modification I suggested should allow you to use a DSN to connect to a SQL Server using SQLAlchemy in the LangChain framework.

I hope this helps! Let me know if you have any other questions.

Sources

• libs/langchain/langchain/indexes/_sql_record_manager.py

---

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

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

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

[jyoti194]

@dosu-bot , can you explain how DSN will be working, do we need to create a system DSN in both local system and server system with same name and configurate it? does it require any modifications?

hoe to modify this "mssql+pyodbc://username:password@DSN" string

[dosubot[bot]]

🤖

Hello,

To connect to a SQL Server using SQLAlchemy in the LangChain framework with a DSN, you can use the from_uri class method of the SQLDatabase class. This method takes a database_uri parameter, which should be your connection string.

Here is an example of how you can use it:

from langchain.utilities.sql_database import SQLDatabase

dsn = "your_dsn"
username = "your_username"
password = "your_password"
connection_string = f"mssql+pyodbc://{username}:{password}@{dsn}"

db = SQLDatabase.from_uri(database_uri=connection_string)

A DSN (Data Source Name) is a data structure that contains the information about a specific database that an Open Database Connectivity (ODBC) driver needs to connect to it. The DSN contains database name, directory, database driver, UserID, password, and other info. In the connection string "mssql+pyodbc://username:password@DSN", 'username' and 'password' should be replaced with your actual username and password, and 'DSN' should be replaced with the name of your DSN.

You can find more information about the SQLDatabase class and its from_uri method in the LangChain repository at these locations:

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

Please let me know if you have any other questions or if there's anything else I can help with.

Sources

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

---

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

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

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

[jyoti194]

Hi @dosu-bot , do i need to create a DSN ie. system DSN in test server? or local machine?

[dosubot[bot]]

🤖

Hello,

Based on the context provided, it's clear that the LangChain framework does support SQLAlchemy. However, it's not explicitly mentioned whether it supports SQL Server connections using SQLAlchemy.

As for your question about where to create the DSN, it depends on where your application is running. If your application is running on your local machine and needs to connect to the SQL Server, you should create the DSN on your local machine. If your application is running on the test server and needs to connect to the SQL Server, you should create the DSN on the test server.

Here is a general example of how you might create an engine with SQLAlchemy to connect to a SQL Server using a DSN:

from sqlalchemy import create_engine

engine = create_engine("mssql+pyodbc://<DSN>")

In the above code, replace <DSN> with the name of your DSN.

Please note that you'll need to have the appropriate ODBC driver installed for SQL Server on the machine where you're running your application. Also, the pyodbc package must be installed as it's required to connect to SQL Server using SQLAlchemy.

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

Sources

• libs/langchain/langchain/indexes/_sql_record_manager.py

---

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

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

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

[jyoti194]

Hi dosu-bot , my password is "root@123" does having a @ in real password as an "@" symbol/ special character in it, and so this interferes with the latter connection string?

[dosubot[bot]]

🤖

Hi there,

The "@" symbol in your password should not inherently cause issues with your connection string. However, it's possible that the way the connection string is being parsed could lead to problems if it's not properly encoded.

To help you further, could you please provide more details? Specifically, it would be helpful to know:

1. The exact error message you're receiving, if any.
2. The version of LangChain you're using.
3. The code snippet where you're constructing your connection string.

This information will help me understand the issue better and provide a more accurate solution. Thanks!

---

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

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

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

[jyoti194]

@dosu-bot,

1. this the error "sqlalchemy.exc.OperationalError: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]TCP Provider: A non-recoverable error occurred during a database lookup.\r\n (11003) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (11003)')"

2. langchain information Name: langchain Version: 0.0.306 Summary: Building applications with LLMs through composability Home-page: https://github.com/langchain-ai/langchain Author: Author-email: License: MIT Location: C:\Users\rndbcpsoft\AppData\Local\Programs\Python\Python311\Lib\site-packages Requires: aiohttp, anyio, dataclasses-json, jsonpatch, langsmith, numexpr, numpy, pydantic, PyYAML, requests, SQLAlchemy, tenacity Required-by: langchain-experimental

3. code snippet import sqlalchemy

from sqlalchemy import create_engine

server = '------'

server = '-------'

database = 'HR_Git'

database = 'it_feb' username = 'sa' password = 'root@123'

Define the connection string

connection_string = f'mssql+pyodbc://{username}:{password}@{server}:1433/{database}?driver=ODBC+Driver+17+for+SQL+Server'

connection_string = f'mssql+pyodbc://username:password@sql'

connection_string = f"mssql+pyodbc://username:password@hostname/database?driver={'ODBC Driver 17 for SQL Server'}"

engine = create_engine(connection_string)

result = engine.execute('SELECT * FROM dbo.mst_Area')

for row in result: print(row)

engine.dispose()

note: i just removed the server name and password

[dosubot[bot]]

🤖

Hello,

The error message you're seeing is typically associated with network-related issues or incorrect server details, rather than an issue with the password. However, it's good to ensure that your password is correctly encoded.

In your connection string, if your password contains special characters like "@", you should URL encode it. In Python, you can use the urllib.parse.quote_plus function to URL encode your password. Here's an example:

from urllib.parse import quote_plus

password = quote_plus("your_password_with_@_symbol")

Then, you can use this encoded password in your connection string.

However, based on the error message you provided, it seems like the issue might be related to the server not being found or not accessible, or SQL Server not being configured to allow remote connections. I would recommend checking the following:

1. Ensure that your server name, port, and database name are correct.
2. Check if your SQL Server instance is running and network services are enabled.
3. Make sure your SQL Server is configured to allow remote connections.
4. If you're trying to connect from a different network, check if there are any firewalls that might be blocking the connection.

If you continue to experience issues, please provide more details about your environment (without sharing any sensitive information), such as the version of SQL Server you're using, the network setup, and the exact code snippet (with sensitive details redacted) for creating the SQLAlchemy engine.

I hope this helps! Let me know if you have any other questions.

Sources

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

---

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

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

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

[jyoti194]

Hi @dosu-bot , I'm able to connect to the test server, the issue was with the special character only and after using from urllib.parse import quote_plus

password = quote_plus("your_passwordwith@_symbol")

this I'm able to connect. i have another question, my db is very huge so, as i'm running a query im getting tocke error or parsing error, instead of working on entire db can i work on SQL View? how to do that?

[dosubot[bot]]

🤖

Hello,

To work with SQL Views in the LangChain framework, you can use the SQLDatabaseSequentialChain class. This class is designed to handle large databases by determining which tables to use based on the query.

Here's how you can use it:

from langchain_experimental.sql import SQLDatabaseSequentialChain
from langchain.llms import OpenAI, SQLDatabase

# Initialize your database
db = SQLDatabase(...)

# Initialize your language model
llm = OpenAI(...)

# Create the SQLDatabaseSequentialChain
chain = SQLDatabaseSequentialChain.from_llm(llm, db)

# Use the chain to process your query
result = chain({"query": "your query here"})

In the context of SQL Views, you can treat them as regular tables. When you create a view in your SQL database, it should appear in the list of tables that the SQLDatabaseSequentialChain class uses to determine which tables to use based on the query.

Please note that the SQLDatabaseSequentialChain class uses the decider_chain to determine which tables to use based on the query. The decider_chain is an instance of the LLMChain class that uses a language model to predict the table names based on the query. If your SQL Views have names that the language model can predict based on the query, they should be included in the tables used by the SQLDatabaseSequentialChain class.

The SQLDatabaseSequentialChain class does not directly determine which tables to use based on the query. Instead, it uses the table_names_to_use field from the input dictionary if it is provided. If table_names_to_use is not provided, it does not specify any tables to use.

This is evident from the create_sql_query_chain function, which creates a chain that generates SQL queries. In the inputs dictionary, the table_info key is associated with a lambda function that calls the get_table_info method of the db object (an instance of SQLDatabase). This method is called with the table_names argument set to the value of table_names_to_use from the input dictionary (x).

Here is the relevant code:

inputs = {
    ...
    "table_info": lambda x: db.get_table_info(
        table_names=x.get("table_names_to_use")
    ),
    ...
}

This means that the tables to use are determined by the table_names_to_use field in the input dictionary. If this field is not provided, the get_table_info method will not receive any specific table names to use.

I hope this helps! Let me know if you have any other questions.

Best, Dosu

Sources

• libs/experimental/langchain_experimental/sql/base.py
• libs/langchain/langchain/chains/sql_database/query.py

---

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

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

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

[jyoti194]

Hi dosu-bot , can you link any work done related to this, i need to understand it in detail, thank you.

[dosubot[bot]]

🤖

Hello,

The SQLDatabaseSequentialChain class in the LangChain framework is designed to interact with SQL databases. It first determines which tables to use based on the query, and then uses those tables to call the normal SQL database chain. This is particularly useful when the number of tables in the database is large.

To specify SQL Views for it to use, you would need to ensure that the views are present in the database and are accessible by the SQLDatabase object. The SQLDatabaseSequentialChain class does not directly interact with the views or tables. Instead, it uses the SQLDatabase object, which should have the necessary permissions to access the views or tables.

Here's an example of how to use the SQLDatabaseSequentialChain class:

from langchain_experimental.sql import SQLDatabaseSequentialChain
from langchain.llms import OpenAI, SQLDatabase
from langchain.schema import BasePromptTemplate

# Initialize the database connection
db = SQLDatabase(...)

# Initialize the language model
llm = OpenAI(...)

# Define the prompts
query_prompt = BasePromptTemplate(...)
decider_prompt = BasePromptTemplate(...)

# Create the SQLDatabaseSequentialChain
chain = SQLDatabaseSequentialChain.from_llm(llm, db, query_prompt, decider_prompt)

# Use the chain
result = chain({"query": "your query here"})

In the above example, replace the ... with the appropriate values for your setup. The query_prompt and decider_prompt are instances of BasePromptTemplate that define how the language model should be prompted.

The SQLDatabaseSequentialChain class determines which tables to use based on the query by using the decider_chain attribute, which is an instance of LLMChain. The decider_chain takes the query and a list of table names as input, and predicts which tables should be used. This is done in the _call method of the SQLDatabaseSequentialChain class.

In the context shared, the SQLDatabase class has a view_support parameter in its constructor. If view_support is set to True, the SQLDatabase instance will include views in its list of usable tables. This means that the SQLDatabaseSequentialChain can use SQL views as well as tables when executing queries. The get_usable_table_names method of the SQLDatabase class will return a list of all table names and view names that can be used, depending on the view_support parameter.

For more details, you can refer to the following files in the repository:

• libs/experimental/langchain_experimental/sql/base.py
• libs/experimental/tests/integration_tests/chains/test_sql_database.py
• libs/langchain/langchain/utilities/sql_database.py

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

Best, Dosu

Sources

• libs/experimental/langchain_experimental/sql/base.py
• libs/experimental/tests/integration_tests/chains/test_sql_database.py
• libs/langchain/langchain/utilities/sql_database.py

---

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

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

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

[devstein]

Hi @jyoti194 I'm the creator of Dosu. We're working on improving the types of request Dosu can handle.

What are you looking for when you ask Dosu for

can you link any work done related to this, i need to understand it in detail, thank you.

Are you looking for merged PRs? Or something else?

Also, any thoughts/comments/feedback is super valuable!

[jyoti194]

Hi @devstein @dosu , i was trying to get assistant on, if there is any related work which can help me in understanding things lot better.

Dosu was good with giving responses based on the information provided.

i have another question, I'm trying to doing a project to create a chatbot using langchain and chatgpt openai, on the data is from sql server management studio, I'm able to create a chatbot with a small database with the above code i have provided, but if the database is huge like around 5gb, for such i,m getting error like " token limit error" or "parsing errors" how to solve it or deal with the dataset?

here is the code snipet: from langchain.agents import * from langchain.llms import OpenAI from langchain.sql_database import SQLDatabase

from langchain.agents import create_sql_agent from langchain.agents.agent_toolkits import SQLDatabaseToolkit from langchain.sql_database import SQLDatabase from langchain.llms.openai import OpenAI from langchain.agents import AgentExecutor

from langchain.chat_models import ChatOpenAI

from secret_key import openapi_key openapi_key = "######"

os.environ['OPENAI_API_KEY'] = openapi_key

def chat(question):

llm = OpenAI(temperature=0) tools = load_tools(["llm-math"], llm=llm)

agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)

driver = 'ODBC Driver 17 for SQL Server'
host = '####' database = 'chatgpt' user = 'rnd' password = '###'

db_uri = SQLDatabase.from_uri(f"mssql+pyodbc://{user}:{password}@{host}/{database}?driver={driver}")

db = SQLDatabase.from_uri(f"mssql+pyodbc://{user}:{password}@{host}/{database}?driver=ODBC+Driver+17+for+SQL+Server")

llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, max_tokens=1000)

toolkit = SQLDatabaseToolkit(db=db)

toolkit = SQLDatabaseToolkit(db=db, llm=llm) agent_executor = create_sql_agent( llm=llm, toolkit=toolkit, verbose=True, reduce_k_below_max_tokens=True, )

mrkl = initialize_agent( tools, ChatOpenAI(temperature=0), agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True, handle_parsing_errors=True, )

return agent_executor.run(question)

Thank you.

[dosubot[bot]]

🤖

Hello,

Thank you for reaching out and providing detailed information about your issue. Based on the context provided, it seems like you're encountering "token limit error" and "parsing errors" when working with a larger SQL Server database. Here are some suggestions on how you can handle these errors:

1. Token Limit Error: This error occurs when the number of tokens in your request exceeds the maximum limit set by the OpenAI model. In your case, you've set max_tokens=1000 in your ChatOpenAI model. However, if your database is large and the generated tokens exceed this limit, you'll encounter this error.

   To resolve this, you can increase the max_tokens value. However, be aware that increasing this limit might also increase the time it takes to generate a response. Here's how you can do it:

   llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, max_tokens=2000)  # Increase max_tokens value

   Another solution is to set reduce_k_below_max_tokens=True to reduce the token length. This will ensure that the number of tokens generated does not exceed the maximum limit. You've already set this in your code, which is good.

2. Parsing Errors: These errors might occur if there's an issue with the data in your SQL Server database. To handle these errors, you can add error handling in your code to catch and handle any exceptions that might occur due to parsing errors. Here's how you can do it:

   try:
      return agent_executor.run(question)
   except Exception as e:
      print(f"Error: {e}")
      return None

   Remember to replace the print(f"Error: {e}") line with your own error handling logic.

3. Working with a Larger Database: If your database is large, you might want to consider only including the tables that you want. This can help reduce the number of tokens generated and prevent the "token limit error". Here's how you can do it:

   include_tables=['table1', 'table2']  # Include only the tables you want
   db = SQLDatabase.from_uri(f"mssql+pyodbc://{user}:{password}@{host}/{database}?driver=ODBC+Driver+17+for+SQL+Server", include_tables=include_tables)

These solutions are based on similar issues that were solved in the LangChain repository (Issue #2333 and Issue #3670).

I hope this helps! If you have any other questions or if the issue persists, please feel free to ask.

Best, Dosu

Sources

• openai.error.InvalidRequestError: This model's maximum context length is 4097 tokens, however you requested 11836 tokens (11580 in your prompt; 256 for the completion). Please reduce your prompt; or completion length.
• Feedback error while running langchain
• libs/langchain/langchain/chat_models/litellm.py
• libs/langchain/langchain/llms/openai.py
• libs/langchain/langchain/chat_models/openai.py

---

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

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

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

[jyoti194]

@dosu , instead of tables like here

include_tables=['table1', 'table2'] # Include only the tables you want db = SQLDatabase.from_uri(f"mssql+pyodbc://{user}:{password}@{host}/{database}?driver=ODBC+Driver+17+for+SQL+Server", include_tables=include_tables)

can i use view directly?

[dosubot[bot]]

🤖

Hello,

Based on the issues and solutions found in the LangChain repository, here are some suggestions to handle the "token limit error" and "parsing errors" you're encountering:

1. Token Limit Error: This error occurs when the number of tokens generated exceeds the maximum limit set. In your code, the max_tokens attribute is used to set the maximum number of tokens to generate. If you're encountering a token limit error, you may need to increase this limit. However, be aware that the OpenAI API has a maximum limit of 4096 tokens for gpt-3.5-turbo model. If your conversation has more tokens than this, you'll need to truncate, shrink or otherwise reduce your text until it fits. Here is how you can set it:

llm.max_tokens = 4096  # Set the maximum number of tokens to generate

2. Parsing Errors: Parsing errors typically occur when there's an issue with the format or structure of your input data. Ensure that your SQL Server database data is correctly formatted and structured for use with the OpenAI ChatGPT model. The data should be in the form of a list of messages, where each message is a dictionary with a 'role' (which can be 'system', 'user', or 'assistant') and 'content' (the content of the message from that role).

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who won the world series in 2020?"},
]

3. Error Handling: The _create_retry_decorator function in the provided context is used to handle various types of errors by retrying the operation. If you're encountering specific errors not included in the errors list in this function, you can add them to this list to enable the retry mechanism for those errors.

errors = [
    openai.error.Timeout,
    openai.error.APIError,
    openai.error.APIConnectionError,
    openai.error.RateLimitError,
    openai.error.ServiceUnavailableError,
    # Add any other specific errors you want to handle here
]

Remember to import the necessary modules and replace llm with your instance of the ChatOpenAI class.

Additionally, you can refer to these similar solved issues in the LangChain repository for more context and potential solutions:

• Issue #2333
• Issue #3670

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

Best, Dosu

Sources

• openai.error.InvalidRequestError: This model's maximum context length is 4097 tokens, however you requested 11836 tokens (11580 in your prompt; 256 for the completion). Please reduce your prompt; or completion length.
• Feedback error while running langchain
• libs/langchain/langchain/chat_models/openai.py
• libs/langchain/langchain/llms/openai.py

---

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

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

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

[jyoti194]

Hi @dosu-bot , ImportError: cannot import name 'SQLDatabaseSequentialChain' from 'langchain.chains' (c:\Users\rndbcpsoft\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains__init__.py)

[dosubot[bot]]

🤖

Hello,

Based on the information you've provided and the context from the LangChain repository, it appears that the 'SQLDatabaseSequentialChain' class does not exist in the 'langchain.chains' module in LangChain version 0.0.306. This is likely the cause of the ImportError you're encountering.

There are a few similar issues that have been resolved in the past, and the solutions generally involve updating the LangChain version or importing from a different module. However, none of these solutions specifically mention the 'SQLDatabaseSequentialChain' class.

Given that the latest version of LangChain at the time of your issue is v0.0.312, I would recommend updating to this version to see if it resolves your issue. You can do this by running the following command in your terminal:

pip install langchain==0.0.312

If the 'SQLDatabaseSequentialChain' class is still not available after updating, it's possible that it may have been moved to a different module or renamed. If you could provide more information about where you found reference to this class, it would be helpful in further diagnosing the issue.

I hope this helps! Let me know if you have any other questions or need further clarification.

Sources

• cannot import name 'create_citation_fuzzy_match_chain' from 'langchain.chains'
• Issue: ImportError: cannot import name 'SQLDatabaseChain' from 'langchain'
• Cannot import RetrievalQA from langchain.chains
• libs/langchain/langchain/chains/init.py

---

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

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

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

[dosubot[bot]]

Hi, @jyoti194,

I'm helping the LangChain team manage their backlog and am marking this issue as stale. It seems like you received comprehensive guidance on troubleshooting the connection issue, including checking the server name, network access, SQL Server configuration, ODBC driver, and connection string format. Additionally, you were advised on using a DSN (Data Source Name) and modifying the SQLRecordManager class to accept a DSN for creating the SQLAlchemy engine. Furthermore, you received assistance with connecting to a SQL Server using SQLAlchemy with a DSN and working with SQL Views in the LangChain framework, including guidance on using the SQLDatabaseSequentialChain class to handle large databases and SQL Views.

Could you please confirm if this issue is still relevant to the latest version of the LangChain repository? If it is, please let the LangChain team know by commenting on the issue. Otherwise, feel free to close the issue yourself, or the issue will be automatically closed in 7 days. Thank you!