Great work. Excellent topic. Llama index opens up so much more possibility for RAG. Im very much interested in building a knowledge base. That gets added to on a daily basis. What do think of knowledge graph in this context
Great I understood most of the explanation in video but Where is the RAG implementation in it ? I have also created a vector_store, storage_context, index etc when I was implementing chatBot with my data, but I am confused on how to implement RAG as an added functionality ?
I always seem to run into the problem of exclusions when using vector similarity search for RAG. I.e. when you run a query for "Tell me everything you know about dogs other then Labradors." guess which documents will be returned as first 10 (assuming you have a lot of chunks)? Yes, about Labradors. Has anyone figured a way around that yet? I've been attempting to filter out results if queries include exclusions with additional LLM passes, but only GPT4 seems to have enough brains to do it correctly. PaLM 2 gets it right in 50% of cases.
Is there a way to bypass the rate limit error for openai api? Additionally, why is the openai being used even after specifically mentioning the service context?
I keep receiving this error : cannot import name 'Doc' from 'typing_extensions' I am trying to run your codes on jupyter notebook environment. Can you please help and let me know how to create a vector db?
setting up the vector store as persistent should help like he says in the video. Once you have your data stored you just need to load the vector store to communicate with the data if I understand it correctly
Would like to have a video on local download model ( llama2 ggml/gguf ) using llamaindex to build rag pipeline with chormadb. Thank you for videos its helps a lot.
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This tutorial series is great ! Best one I found so far. Thank you for sharing this.
Outstanding video content
Great work. Excellent topic. Llama index opens up so much more possibility for RAG. Im very much interested in building a knowledge base. That gets added to on a daily basis. What do think of knowledge graph in this context
Super interesting, looking forward to the video
Thank you 🙏
This was great, love this kind of content! ❤❤❤
Thank you 🙏
great video. Thanks. waiting for addition of Local LLM in the same code
Awesome Work, Like Always.
Can you refer to documentation or video "on how to update the chromadb in this context"
Why is OpenAI API Key needed when it does not use OpenAI? Thanks!
Great I understood most of the explanation in video but Where is the RAG implementation in it ? I have also created a vector_store, storage_context, index etc when I was implementing chatBot with my data, but I am confused on how to implement RAG as an added functionality ?
I always seem to run into the problem of exclusions when using vector similarity search for RAG. I.e. when you run a query for "Tell me everything you know about dogs other then Labradors." guess which documents will be returned as first 10 (assuming you have a lot of chunks)? Yes, about Labradors. Has anyone figured a way around that yet?
I've been attempting to filter out results if queries include exclusions with additional LLM passes, but only GPT4 seems to have enough brains to do it correctly. PaLM 2 gets it right in 50% of cases.
Self hosting? Seems interesting
Is there a way to bypass the rate limit error for openai api?
Additionally, why is the openai being used even after specifically mentioning the service context?
please make the video comparing different embedding models
I keep receiving this error :
cannot import name 'Doc' from 'typing_extensions'
I am trying to run your codes on jupyter notebook environment. Can you please help and let me know how to create a vector db?
I have 2 million data chunks of text, i was used db chroma but it didn't work. Can you help me?
Can you share the full architecture diagram
hey i have question as we have injested our data to the vector db how do retrive answer without runnin the injestion code all the time
setting up the vector store as persistent should help like he says in the video. Once you have your data stored you just need to load the vector store to communicate with the data if I understand it correctly
is this a LangChain competitor library?
Yes
1:30 onw
Would like to have a video on local download model ( llama2 ggml/gguf ) using llamaindex to build rag pipeline with chormadb. Thank you for videos its helps a lot.