Hi, thanks for the video. I have 2 questions: Question 1: what is the difference between "SQLDatabaseChain" and "SQLDatabaseToolkit"? Question 2: I get an error when i try using the create_sql_agent in the below code. toolkit = SQLDatabaseToolkit(db=db) agent_executor = create_sql_agent( llm=OpenAI(temperature=0), toolkit=toolkit, verbose=True ) ValidationError: 1 validation error for SQLDatabaseToolkit llm field required (type=value_error.missing) Can you please help here? Appretiate your help
Unfortunately, in reality this functionality is very early days and doesn't work very well, generating joins across non-existent columns and SQL that is engine specific. It's only good for playing around with at the moment.
@@datasciencebasics I've persisted with this library and the more information about my database that I include in the prompt, the better the results. Less, denormalised tables help too. You can see the potential though and I've gotten some 'wow factor' from demoing it to management.
nice video! i try this with ggml-gpt4all-l13b-snoozy.bin and i have this problem : SQLQuery:The prompt size exceeds the context window size and cannot be processed. The prompt size exceeds the context window size and cannot be processed. any ideas?
Many Thanks
Tapai ko video haru Sarai ramro cha bro! Keep up the good work.
dhanyabaad :)
Awesome, Thanks bro
Hi, thanks for the video.
I have 2 questions:
Question 1: what is the difference between "SQLDatabaseChain" and "SQLDatabaseToolkit"?
Question 2: I get an error when i try using the create_sql_agent in the below code.
toolkit = SQLDatabaseToolkit(db=db)
agent_executor = create_sql_agent(
llm=OpenAI(temperature=0),
toolkit=toolkit,
verbose=True
)
ValidationError: 1 validation error for SQLDatabaseToolkit
llm
field required (type=value_error.missing)
Can you please help here? Appretiate your help
how we could get answer in json format please advice
I made a function to compare between LLMs results and humans results. is sql Query already exist in the database or is it you who makes them ?
SQL queries are created by LLMs when we ask the natural langauge.
Excellent info ,,, pls do one video in querying JSON Documents . No one has done any video
Hello, Created one, hope you find it helpful :)
Thank you, would querying a large database with say 1million row use up a lot of tokens / is there any for of caching that can be done with ChatGPT ?
Hello, haven’t tested it out but you could try it out and have a look at OpenAI usage to see how it behaves.
Unfortunately, in reality this functionality is very early days and doesn't work very well, generating joins across non-existent columns and SQL that is engine specific. It's only good for playing around with at the moment.
I agree, hopefully more functionality in the future as llms and usecases around it are quite in early stage.
@@datasciencebasics I've persisted with this library and the more information about my database that I include in the prompt, the better the results. Less, denormalised tables help too. You can see the potential though and I've gotten some 'wow factor' from demoing it to management.
@@colofthedead6101 Great findings. Eventually, everything in LLM ends in good prompt :)
Can we plot a graph of thr output data
this is quite old video and I haven’t tried myself yet. Give a try with latest models.
Thank you. FUBAR! LOL
nice video! i try this with ggml-gpt4all-l13b-snoozy.bin and i have this problem : SQLQuery:The prompt size exceeds the context window size and cannot be processed.
The prompt size exceeds the context window size and cannot be processed. any ideas?