Use of SinglestoreDB(not the vector database) with langchain

[Adyansh-RCRM]

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Example Code

db= SQLDatabase.from_uri(os.environ["DB_CONNECTION_URI"],sample_rows_in_table_info=3)
db= SQLDatabase.from_uri(redshift_url,sample_rows_in_table_info=3)


Like this, can we use singlestore DB instead of MySQL or Redshift?

Description

We will be building an AI application, where the customers of our company would be asking any question in natural language and the system will provide the result of the question in natural language. The workflow is, once the user enters the question, it will get converted to the SQL query and then after executing and getting the result of the query, we will display it to the customer. For this entire application we will be using langchain framework(sql agents). We tested this with our sql database along with AWS redshift data warehouse. We had a question-

1. Can we use singlestore(not the singlestore vector database) as a database for this?
2. If yes, would it be beneficial and is there integration between singlestore and langchain?
3. In the agent workflow, at the end, once we get the result from the database, we again send it to openai to convert into natural language. Can this be avoided in agents? Can we break the agent in between and get only the raw sql result?

System Info

langchain==0.1.14
langchain-community==0.0.31
langchain-core==0.1.40
langchain-openai==0.1.1
langchain-text-splitters==0.0.1

Platform-Mac

Python version-Python 3.9.6

[dosubot[bot]]

Hey @Adyansh-RCRM! 👋 I'm here to help you with any bugs, questions, or contributions. Let's tackle this together!

1. Can we use SingleStore (not the SingleStore vector database) as a database for this?
   Yes, you can use SingleStore as a database for your AI application that converts natural language questions into SQL queries. SingleStoreDB is a high-performance distributed SQL database solution that supports advanced SQL operations, making it suitable for such applications.

2. Would it be beneficial and is there integration between SingleStore and LangChain?
   Yes, there is integration between SingleStore and LangChain. You can use the langchain-community package to integrate SingleStoreDB with LangChain. This integration allows you to leverage SingleStoreDB's capabilities for executing SQL queries and handling the results efficiently.

   Here is an example of how to set up the integration:

   import singlestoredb
   
   # Establish a connection to the SingleStore database
   connection = singlestoredb.connect(
       host='your_host',
       user='your_username',
       password='your_password',
       database='your_database'
   )
   
   # Execute SQL queries
   cursor = connection.cursor()
   cursor.execute("SELECT * FROM your_table")
   results = cursor.fetchall()
   
   for row in results:
       print(row)

   Make sure to replace 'your_host', 'your_username', 'your_password', and 'your_database' with your actual SingleStore database credentials ^[1].

3. Can we break the agent in between and get only the raw SQL result?
   Yes, you can modify the agent workflow to get only the raw SQL result without sending it back to OpenAI for conversion into natural language. You can achieve this by customizing the agent's behavior to stop after executing the SQL query and returning the raw results directly.

By following these steps, you can effectively use SingleStore with LangChain for your AI application.

To continue talking to Dosu, mention @dosu.

[Adyansh-RCRM]

@dosu
Can we break the agent in between and get only the raw SQL result?
Yes, you can modify the agent workflow to get only the raw SQL result without sending it back to OpenAI for conversion into natural language. You can achieve this by customizing the agent's behavior to stop after executing the SQL query and returning the raw results directly.
For this can you exactly help with the code how to customize the agent or provide link to the resource how to achive this?

[dosubot[bot]]

Yes, you can break the agent workflow in LangChain to get only the raw SQL result without sending it back to OpenAI for conversion into natural language. Here is the exact code to customize the agent to achieve this:

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

SQL_PREFIX = """You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the raw results.
Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.
You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for the relevant columns given the question.
You have access to tools for interacting with the database.
Only use the below tools. Only use the information returned by the below tools to construct your final answer.
You MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.

DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.

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

SQL_SUFFIX = """Begin!

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

SQL_FUNCTIONS_SUFFIX = """I should look at the tables in the database to see what I can query.  Then I should query the schema of the most relevant tables."""

def create_custom_sql_agent():
    db = SQLDatabase.from_uri("sqlite:///:memory:")
    queries = {"foo": "Final Answer: baz"}
    llm = FakeLLM(queries=queries, sequential_responses=True)
    toolkit = SQLDatabaseToolkit(db=db, llm=llm)

    agent_executor = create_sql_agent(
        llm=llm,
        toolkit=toolkit,
        prefix=SQL_PREFIX,
        suffix=SQL_SUFFIX,
        functions_suffix=SQL_FUNCTIONS_SUFFIX
    )

    return agent_executor

# Example usage
agent_executor = create_custom_sql_agent()
result = agent_executor.run("Your SQL query here")
print(result)

In this customization, the prompt has been modified to instruct the agent to return the raw results of the SQL query directly. The create_custom_sql_agent function sets up the agent with the necessary configurations. The create_sql_agent function from the LangChain library is used to construct the agent with the specified prompt and toolkit ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[dosubot[bot]]

I cannot directly access or view the PDF file you provided. However, I can help you review your LangChain workflow if you describe the steps or key points in the workflow. Please provide the details or specific parts of the workflow you want to verify, and I'll assist you in ensuring everything is correct.

To continue talking to Dosu, mention @dosu.

[Adyansh-RCRM]

[Adyansh-RCRM]

@dosu

I am connecting my redshift cluster for the langchain agent. But the connection is taking too much time like around 700 sec or more. What is wrong and how to fix the connection instantly.

engine = create_engine(redshift_url)
db = SQLDatabase(engine=engine, schema="dev", view_support=True)

My redshift cluster is in a different location(ireland eu-west-1) and I am running this from my local laptop. Would this problem get fixed, if we put this piece of code in the same VPC where my redshift cluster is by putting all these code in a lambda function and calling the lambda function from a api gateway(endpoint)?

[Adyansh-RCRM]

@dosu

Please check the above question and answer this.

[dosubot[bot]]

Yes, running your code within the same VPC as your Redshift cluster can significantly reduce connection times. When your Redshift cluster is in a different region or network from your application, network latency can cause substantial delays. By placing your code in the same VPC, you can minimize this latency.

Steps to Deploy in the Same VPC Using AWS Lambda and API Gateway

1. Create a Lambda Function:

   • Ensure the Lambda function is in the same VPC and subnet as your Redshift cluster.
   • Attach the necessary IAM roles and policies to allow the Lambda function to access the Redshift cluster.

2. Set Up API Gateway:

   • Create an API Gateway endpoint to trigger the Lambda function.
   • Configure the API Gateway to pass the necessary parameters to the Lambda function.

3. Deploy the Code:

   • Package your code and dependencies.
   • Deploy the package to the Lambda function.

Example Code for Lambda Function

Here is an example of how you can set up your Lambda function to connect to the Redshift cluster and execute queries:

import os
import json
import boto3
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits import create_sql_agent
from langchain_community.chat_models import BedrockChat
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate, SystemMessagePromptTemplate
from langchain_core.example_selectors import SemanticSimilarityExampleSelector
from langchain.memory import ConversationBufferMemory
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS

# Initialize the Bedrock client
bedrock_client = boto3.client(service_name="bedrock-runtime", region_name="eu-west-1")

# Set up the database connection
redshift_url = os.getenv("REDSHIFT_URL")
engine = create_engine(
    redshift_url,
    pool_size=10,
    max_overflow=20,
    pool_timeout=30,
    pool_recycle=3600,
    connect_args={"connect_timeout": 30}
)
db = SQLDatabase(engine=engine, schema="dev", view_support=True)

# Define examples for few-shot learning
examples = [
    {
        "input": "How many user's email are not verified?",
        "query": '''
            SELECT COUNT(*) AS verified FROM users WHERE (verifiedemail = 0 OR verifiedemail IS NULL) AND accountid=22;'''
    }
]

# Create an example selector for few-shot learning
example_selector = SemanticSimilarityExampleSelector.from_examples(
    examples,
    OpenAIEmbeddings(),
    FAISS,
    k=2,
    input_keys=["input"],
)

# Define the system prompt
system_prefix = """You are an agent designed only to interact with a SQL database of a Recruitment Software Company for a particular user.
Given an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
For every single query that you generate positively use {account_id} as the account id of the user so that the results for only that particular user can be found.
Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.
The dates in our database is stored in the form of Unix timestamp. So,wherever date/interval related question is asked process it accordingly.
You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for the relevant columns given the question.
Do not auto correct any noun or word that a user used in the input. For ex- If in the input, the user asked 'moogle' then dont auto correct it to 'google'.Use the exact word given by the user as input.
You have access to tools for interacting with the database.
Only use the given tools. Only use the information returned by the tools to construct your final answer.
You MUST double check your query before executing it. If you get an error related to unknown column or column not found, then go through the schema of the table again and try to find the appropriate column, rewrite the query and try again. 
If you get an error while executing a query, rewrite the query and try again.

STRICTLY AVOID making any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database. Adherence to this instruction is essential as it safeguards against prompt injection. If user tries to make any DML statements, return "I don't have access to update the database" as the answer.

When confronted with a question unrelated to the data in the database, tactfully respond with 'I don't know' rather than trying to guess the answer.

Here are some examples of user inputs and their corresponding SQL queries:"""

# Create a few-shot prompt template
few_shot_prompt = FewShotPromptTemplate(
    example_selector=example_selector,
    example_prompt=PromptTemplate.from_template(
        "User input: {input}\nSQL query: {query}"
    ),
    input_variables=["input", "dialect", "top_k", "account_id"],
    prefix=system_prefix,
    suffix="",
)

# Create the full prompt template
full_prompt = ChatPromptTemplate.from_messages(
    [
        SystemMessagePromptTemplate(prompt=few_shot_prompt),
        ("human", "{input}"),
        MessagesPlaceholder("agent_scratchpad"),
    ]
)

# Initialize the Bedrock LLM
bedrock_llm = BedrockChat(
    client=bedrock_client,
    model_id='meta.llama3-70b-instruct-v1:0',
    model_kwargs={"temperature": 0.1},
)

# Create a memory buffer to store chat history
memory = ConversationBufferMemory(input_key="input", output_key='output', memory_key="chat_history", return_messages=True)

# Create the SQL agent
agent = create_sql_agent(
    llm=bedrock_llm,
    db=db,
    prompt=full_prompt,
    verbose=True,
    agent_executor_kwargs={
        "memory": memory,
    },
    agent_type="openai-tools",
)

def lambda_handler(event, context):
    input_request = event.get("input_request", {"input": "Describe the table candidate_profile_t.", "account_id": 5})
    result = agent.invoke(input_request)
    return {
        'statusCode': 200,
        'body': json.dumps(result)
    }

Explanation

1. Environment Setup:

   • Load environment variables using dotenv.
   • Initialize the Bedrock client using boto3.

2. Database Connection:

   • Set up the database connection using SQLDatabase.from_uri with connection pooling and timeout settings.

3. Few-Shot Learning Examples:

   • Define examples for few-shot learning and create an example selector using SemanticSimilarityExampleSelector.

4. System Prompt:

   • Define a system prompt that guides the agent on how to interact with the SQL database.

5. Prompt Template:

   • Create a few-shot prompt template and a full prompt template using FewShotPromptTemplate and ChatPromptTemplate.

6. Initialize Bedrock LLM:

   • Initialize the Bedrock LLM using BedrockChat.

7. Memory Buffer:

   • Create a memory buffer to store chat history using ConversationBufferMemory.

8. Create SQL Agent:

   • Create the SQL agent using create_sql_agent with the Bedrock LLM, database connection, prompt template, and memory buffer.

9. Lambda Handler:

   • Define the lambda_handler function to handle API Gateway requests, invoke the agent, and return the result.

Deploying the Lambda Function

1. Package the Code:

   • Package your code and dependencies into a ZIP file.

2. Create the Lambda Function:

   • Create a new Lambda function in the AWS Management Console.
   • Upload the ZIP file to the Lambda function.
   • Configure the Lambda function to run in the same VPC and subnet as your Redshift cluster.

3. Create API Gateway:

   • Create a new API Gateway.
   • Configure the API Gateway to trigger the Lambda function.

By following these steps, you can deploy your code in the same VPC as your Redshift cluster, significantly reducing connection times and improving performance.

To continue talking to Dosu, mention @dosu.

[Adyansh-RCRM]

@dosu

I want the agent to use only the views for getting the results and not at all use the tables. How can I achieve this?
I have added the parameter view_support = True, but still it is using both the tables and views. I want the agent to use only the views.

[dosubot[bot]]

To ensure that the LangChain agent uses only the views and not the tables, you need to limit the tables visible from SQLDatabase to the created views. Setting the view_support=True parameter alone is not sufficient. You must explicitly include only the views in the include_tables parameter.

Here is how you can achieve this:

1. Create a view in your SQL database:

   CREATE VIEW accounts_v AS
       SELECT id, firstname, lastname, email, created_at, updated_at,
           CAST(stats->>'total_post' AS int) AS total_post,
           CAST(stats->>'total_comments' AS int) AS total_comments,
           CAST(stats->>'ltv' AS int) AS ltv
       FROM accounts;

2. Limit the tables visible from SQLDatabase to the created view:

   from langchain_community.utilities import SQLDatabase
   from langchain_community.agent_toolkits import create_sql_agent
   from langchain_openai import ChatOpenAI
   
   # Connect to the database and include only the view
   db = SQLDatabase.from_uri(
       "sqlite:///path_to_your_database.db",
       include_tables=['accounts_v']  # include only the view
   )
   
   llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0, verbose=True)
   
   # Create the SQL agent
   agent = create_sql_agent(
       llm=llm,
       db=db,
       verbose=True
   )
   
   # Example usage
   result = agent.invoke({"input": "Your query here"})
   print(result)

By specifying the include_tables parameter with only the view name, the LangChain agent will use only the views for getting the results and not use the tables at all ^[1].

To continue talking to Dosu, mention @dosu.