Great post! I have just started following along but getting a "NameError: name 'partition_pdf' is not defined" at the pdf loader step: docs = loader.load()
Using default Google Colab notebook. After some online search, downgraded "unstructured" package to 0.7.12, but still get the error. Would be nice if you can update the post with the Python version/env details.
Excellent post !! Easy to follow along !! What is the python version you are using? I am getting error when trying to import langchain.experimental .. "ModuleNotFoundError: No module named 'langchain.experimental" . Have seen there are some python version dependency? I am using default colab version of 3.10.12.
Amazing, beautifully written and a great example. Looking forward to learn more about LLM's from you. Probably if you have some time could you write a how LLM works, best practices of prompt engineering to have a holistic view of the LLM's landscape.
Great post! I have just started following along but getting a "NameError: name 'partition_pdf' is not defined" at the pdf loader step: docs = loader.load()
Using default Google Colab notebook. After some online search, downgraded "unstructured" package to 0.7.12, but still get the error. Would be nice if you can update the post with the Python version/env details.
Excellent post !! Easy to follow along !! What is the python version you are using? I am getting error when trying to import langchain.experimental .. "ModuleNotFoundError: No module named 'langchain.experimental" . Have seen there are some python version dependency? I am using default colab version of 3.10.12.
I think Langchain is updated its versions very fast and experimental doesn't exist anymore I think
Great content! I just managed to get the whole thing up and running and started wondering if we can use a chat model instead of llm as retrieval?
Yes you can
Amazing, beautifully written and a great example. Looking forward to learn more about LLM's from you. Probably if you have some time could you write a how LLM works, best practices of prompt engineering to have a holistic view of the LLM's landscape.
The tutorial uses PineCone as vector database. What if I wanted to use a different vector db? Pinecone at $70 per month is a tad pricey! Any ideas?
Look at the documentation. It integrates with 30 different databases
Yes it does... just used FAISS instead.
Nicely written! Please note the code where you are setting up "docs_db" does not work.
doc_db = Pinecone.from_documents(
docs_split,
embeddings,
index_name='langchain-demo'
)
Why? You need to create the index first and connect to pinecone with the API key. Then you can run the above code.
Correct, I have the index. The following import needs to be added as well.
from langchain.vectorstores import Pinecone
Thank you!
Oh yeah I forgot to add that. I'll correct that.