What is the difference between Pandas Data frame agent, CSV agent and SQL Agent?

Kuramdasu-ujwala-devi commented 10 months ago

Issue you'd like to raise.

What is the difference between Pandas Data frame agent, CSV agent and SQL Agent? Can you brief each and when to use ?

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dosubot[bot] commented 10 months ago

🤖

Hello,

In the LangChain codebase, we have two types of agents you mentioned: the Pandas Dataframe agent and the CSV agent. However, there is no SQL Agent in the current version of LangChain.

The Pandas Dataframe agent is designed to facilitate the interaction between language models and pandas dataframes. It provides a set of functions to generate prompts for language models based on the content of a pandas dataframe. The main function create_pandas_dataframe_agent constructs a pandas agent from a language model and a dataframe. It supports two types of agents: ZERO_SHOT_REACT_DESCRIPTION and OPENAI_FUNCTIONS. Depending on the type, it generates a prompt and tools for the language model, which include the dataframe content and the functions that can be applied to it. You can find more details in the Pandas Agent Toolkit.

The CSV agent is created by loading a CSV file to a dataframe and using the pandas agent. The function create_csv_agent is responsible for this. It reads a CSV file (or multiple files) into a pandas dataframe and then uses the create_pandas_dataframe_agent function to create a pandas dataframe agent. You can find more details in the CSV Agent Toolkit.

As for when to use each, you would use the Pandas Dataframe agent when you have data in a pandas dataframe that you want to interact with using a language model. On the other hand, you would use the CSV agent when you have data in a CSV file that you want to load into a pandas dataframe and then interact with using a language model.

Unfortunately, as mentioned earlier, there is no SQL Agent in the current version of LangChain. If you need to interact with data in an SQL database, you would need to first load the data into a pandas dataframe and then use the Pandas Dataframe agent.

I hope this answers your question. If you have any more questions or need further clarification, feel free to ask.

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Kuramdasu-ujwala-devi commented 10 months ago

@dosu-bot. Then earlier version of langchain has SQLDatabaseChain in chains , now I couldn't fine the SQLChain is it now not supported . So in order to interact with the sql data base and get the sql query from the natural language passed by the user. What approach I have to follow here .

dosubot[bot] commented 10 months ago

🤖

Hello,

Yes, the SQLDatabaseChain feature is still supported in the latest version of LangChain for converting natural language input into SQL queries. You can use it to interact with an SQL database. Here is an example of how to use it:

from langchain_experimental.sql import SQLDatabaseChain
from langchain import OpenAI, SQLDatabase

db = SQLDatabase(...)  # Fill in your database connection details
db_chain = SQLDatabaseChain.from_llm(OpenAI(), db)

Please note the security warning in the code comments. Make sure that the database connection uses credentials that are narrowly-scoped to only include the permissions this chain needs. Failure to do so may result in data corruption or loss, since this chain may attempt commands like DROP TABLE or INSERT if appropriately prompted.

In addition to SQLDatabaseChain, there's another class available, SQLDatabaseSequentialChain, which you might find useful depending on your specific needs. The difference between SQLDatabaseChain and SQLDatabaseSequentialChain lies in the way they handle and return the results of SQL queries.

SQLDatabaseChain executes SQL queries and returns the final result. It does not provide any information about the intermediate steps involved in the execution of the SQL query unless the return_intermediate_steps parameter is set to True.

On the other hand, SQLDatabaseSequentialChain is designed to handle cases where the number of tables in the database is large. It first determines which tables to use based on the query, and then calls the normal SQL database chain (SQLDatabaseChain). It can also return the intermediate steps involved in the execution of the SQL query, along with the final result, if return_intermediate_steps is set to True.

Here is the relevant code:

For SQLDatabaseChain:

db_chain = SQLDatabaseChain.from_llm(OpenAI(temperature=0), db)
output = db_chain.run("What company does Harrison work at?")

The output is the final result: " Harrison works at Foo."

For SQLDatabaseSequentialChain with return_intermediate_steps=True:

db_chain = SQLDatabaseSequentialChain.from_llm(
    OpenAI(temperature=0), db, return_intermediate_steps=True
)
output = db_chain("What company does Harrison work at?")

The output is a dictionary containing the final result and the intermediate steps:

{
    "result": " Harrison works at Foo.",
    "intermediate_steps": [
        " SELECT user_company FROM user WHERE user_name = 'Harrison' LIMIT 1;",
        "[('Foo',)]"
    ]
}

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

Best, Dosu

Sources

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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] commented 7 months ago

Hi, @Kuramdasu-ujwala-devi,

I'm helping the LangChain team manage their backlog and am marking this issue as stale. From what I understand, the issue requested an explanation of the differences between Pandas Data frame agent, CSV agent, and SQL Agent, and when to use each one. Dosubot provided a detailed response, explaining the differences and also clarified the availability of SQLChain in the latest version of LangChain. Your follow-up question was also addressed with further clarification and examples.

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!

tianyinhuang commented 4 months ago

Base on my not-so-much testing experience, Pandas agents fits better on situations that needs data (pre-)processing, like a data analyst; SQL agent like a database engineer. Saw applications using SQL agent -> Pandas agent. I'm trying not to complicated the whole flow, otherwise takes long time to get the final output.

langchain-ai / langchain