i am trying to optimize the cost of SQLDatabaseToolkit calls

System Info

aws

Who can help?

No response

Information

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

Related Components

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

Reproduction

here's my PROMPT and code:

from langchain.prompts.chat import ChatPromptTemplate

updated_prompt = ChatPromptTemplate.from_messages( [ ("system", """ You are a knowledgeable AI assistant specializing in extracting information from the 'inquiry' table in the MySQL Database. Your primary task is to perform a single query on the schema of the 'inquiry' table and table and retrieve the data using SQL.

 When formulating SQL queries, keep the following context in mind:

 - Filter records based on exact column value matches.
 - If the user inquires about the Status of the inquiry fetch all these columns: status, name, and time values, and inform the user about these specific values.
 - Limit query results to a maximum of 3 unless the user specifies otherwise.
 - Only query necessary columns.
 - Avoid querying for non-existent columns.
 - Place the 'ORDER BY' clause after 'WHERE.'
 - Do not add a semicolon at the end of the SQL.

 If the query results in an empty set, respond with "information not found"

 Use this format:

 Question: The user's query
 Thought: Your thought process
 Action: SQL Query
 Action Input: SQL query
 Observation: Query results
 ... (repeat for multiple queries)
 Thought: Summarize what you've learned
 Final Answer: Provide the final answer

 Begin!
 """),
("user", "{question}\n ai: "),

] )

llm = ChatOpenAI(model=os.getenv("OPENAI_CHAT_MODEL"), temperature=0) # best result sql_toolkit = SQLDatabaseToolkit(db=db, llm=llm) sql_toolkit.get_tools()

sqldb_agent = create_sql_agent( llm=llm, toolkit=sql_toolkit, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, handle_parsing_errors=True, )

sqldb_agent.run(updated_prompt.format( question="What is the status of inquiry 123?" ))

Expected behavior

This is my current cost

response: The inquiry 123 is Completed on August 21, 2020, at 12 PM. Total Tokens: 18566 Prompt Tokens: 18349 Completion Tokens: 217 Total Cost (USD): $0.055915

I want to reduce the cost to less than > $0.01

any suggestions will help.

🤖

Hello @akashkumar398, it's good to see you again!

I understand you're looking to optimize the cost of SQLDatabaseToolkit calls in the LangChain framework. The cost you're referring to seems to be related to the number of tokens processed by the language model, which is directly proportional to the cost of using the model.

Here are a few suggestions that might help you reduce the cost:

Optimize the Prompt: The prompt you're using is quite large (18349 tokens). You could try to make it more concise, removing any unnecessary information. Remember, the cost is directly proportional to the number of tokens processed, so reducing the size of the prompt can significantly reduce the cost.
Limit the Completion Length: You can limit the length of the model's output by setting a maximum token limit. This can be done by setting the max_tokens parameter when calling the model. However, be careful with this approach as setting a limit too low might result in incomplete or nonsensical responses.
Use a Smaller Model: If you're not already doing so, you could consider using a smaller language model. Smaller models usually generate fewer tokens, which could help reduce the cost.
Batch Requests: If you're making multiple requests, you could try to batch them together. This can sometimes be more cost-effective, as the model can process multiple prompts at once.

Remember, these are just suggestions and the effectiveness of each will depend on your specific use case. It's always a good idea to experiment with different approaches and measure their impact on cost and performance.

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

Sources

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On Thu, Oct 26, 2023 at 8:38 PM dosubot[bot] @.***> wrote:

🤖

Hello @akashkumar398 https://github.com/akashkumar398, it's good to see you again!

I understand you're looking to optimize the cost of SQLDatabaseToolkit calls in the LangChain framework. The cost you're referring to seems to be related to the number of tokens processed by the language model, which is directly proportional to the cost of using the model.

Here are a few suggestions that might help you reduce the cost:

1.

Optimize the Prompt: The prompt you're using is quite large (18349 tokens). You could try to make it more concise, removing any unnecessary information. Remember, the cost is directly proportional to the number of tokens processed, so reducing the size of the prompt can significantly reduce the cost. 2.

Limit the Completion Length: You can limit the length of the model's output by setting a maximum token limit. This can be done by setting the max_tokens parameter when calling the model. However, be careful with this approach as setting a limit too low might result in incomplete or nonsensical responses. 3.

Use a Smaller Model: If you're not already doing so, you could consider using a smaller language model. Smaller models usually generate fewer tokens, which could help reduce the cost. 4.

Batch Requests: If you're making multiple requests, you could try to batch them together. This can sometimes be more cost-effective, as the model can process multiple prompts at once.

Remember, these are just suggestions and the effectiveness of each will depend on your specific use case. It's always a good idea to experiment with different approaches and measure their impact on cost and performance.

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

libs/langchain/langchain/agents/agent_toolkits/sql/toolkit.py https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/agents/agent_toolkits/sql/toolkit.py

libs/langchain/langchain/utilities/sql_database.py https://github.com/langchain-ai/langchain/blob/master/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 https://github.com/dosu-bot.

— Reply to this email directly, view it on GitHub https://github.com/langchain-ai/langchain/issues/12341#issuecomment-1781318436, or unsubscribe https://github.com/notifications/unsubscribe-auth/AF55W6BZ6TOXLJCPFV7UMKTYBJ4F3AVCNFSM6AAAAAA6RLFCWWVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTOOBRGMYTQNBTGY . You are receiving this because you were mentioned.Message ID: @.***>

🤖

To optimize the cost of your SQLDatabaseToolkit calls in the LangChain framework, you can implement the following suggestions:

Optimizing the prompt: The prompt you're using is quite large (18349 tokens), which contributes significantly to the cost. You can reduce the size of the prompt by removing unnecessary information or by using a more concise format. For example, you could replace the detailed instructions with a shorter version like this:

("system",
"""
You are an AI assistant. Extract information from the 'inquiry' table in the MySQL Database. Perform a single query, filter records, fetch status, name, and time values, limit results to 3, only query necessary columns, avoid non-existent columns, place 'ORDER BY' after 'WHERE', and do not add a semicolon. If empty set, respond with "information not found". Begin!
"""),

Limiting the completion length: You can limit the length of the completion by setting the max_tokens attribute when creating an instance of the OpenAI class or by modifying it directly on an existing instance. Here's how you can do it:

llm = ChatOpenAI(model=os.getenv("OPENAI_CHAT_MODEL"), temperature=0, max_tokens=100) # best result

Using a smaller model: If you're using a large model like gpt-3.5-turbo, you could switch to a smaller model to reduce the cost. However, please note that this might also affect the quality of the completions. Here's how you can switch to a smaller model:

llm = ChatOpenAI(model="text-davinci-002", temperature=0, max_tokens=100) # smaller model

Batching requests: If you're making multiple requests, you can batch them together to reduce the cost. The LangChain framework currently handles batching of requests through the apply method, which processes each request sequentially. You can optimize this by processing the requests in parallel. Here's how you can modify the apply method to process requests in parallel using concurrent.futures:

from concurrent.futures import ThreadPoolExecutor

def apply(
    self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
) -> List[Dict[str, str]]:
    """Call the chain on all inputs in the list."""
    with ThreadPoolExecutor() as executor:
        return list(executor.map(lambda inputs: self(inputs, callbacks=callbacks), input_list))

Please note that these are just suggestions and the actual cost reduction might vary depending on various factors like the size of the prompt, the length of the completion, the model used, and the number of requests. Also, please be aware that optimizing the cost might also affect the quality of the completions, so you might need to find a balance between cost and quality that works best for your specific use case.

Sources

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.

Hi, @akashkumar398,

I'm helping the LangChain team manage their backlog and am marking this issue as stale. From what I understand, you opened this issue seeking suggestions to optimize the cost of SQLDatabaseToolkit calls in the LangChain framework. There was a discussion between you and me where suggestions were provided to optimize the cost, including optimizing the prompt, limiting the completion length, using a smaller model, and batching requests. It seems that the suggestions were provided and the issue was resolved.

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 it will be automatically closed in 7 days.

Thank you!

langchain-ai / langchain