Using LangChain with Gemini and Chroma DB

[@sharathkumar8422]

Concise and to the point. Superbly explain! Thank you...

[@brenoav99]

Thank you for the useful video. I'm having a problem using the chroma and google embedding (new versions): >>> vectordb=Chroma.from_documents(pages,embeddings) ValueError: Expected each embedding in the embeddings to be a list, got ['Repeated'] Do you have any ideas? Thanks in advance!

[@fusionxfitness_]

you didn't shared the pdf

[@ammvr]

how can we deploy this to the web?

[@azadalmasov9039]

Thank you for the tutorial. Can you do the same chunk technique for dataframes? Should using create_pandas_dataframe_agent be enough for that. Also I integrated Landchain to postgresql data base. In that case, i am using SQLDatabaseSequentialChain. Is this module using chunk technique already? or do I have to take extra steps for utilizing chunking technique. Another question is that when I use SQLDatabaseSequentialChain, should I create prompt template and buffer memory to be able to track between chunks or SQLDatabaseSequentialChain module is already doing it?

[@Janakirammsv]

I have not worked on Pandas and SQL integration with LangChain. Sorry!

[@birolyildiz]

❤🙏