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Minor changes #120

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Minor changes #120

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4 changes: 2 additions & 2 deletions chat_with_pdf_query.py
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Original file line number Diff line number Diff line change
Expand Up @@ -223,7 +223,7 @@ def stream_string(s, chunk_size=10):
"https://emoji.slack-edge.com/T024FJS4M/couchbase/4a361e948b15ed91.png"
)

st.title("Chat with PDF (GSI Vector Search)")
st.title("Chat with PDF (Query Vector Store)")
st.markdown(
"Answers with [Couchbase logo](https://emoji.slack-edge.com/T024FJS4M/couchbase/4a361e948b15ed91.png) are generated using *RAG* while 🤖 are generated by pure *LLM (ChatGPT)*"
)
Expand Down Expand Up @@ -251,7 +251,7 @@ def stream_string(s, chunk_size=10):
)

st.markdown(
"For RAG, we are using [Langchain](https://langchain.com/), [Couchbase GSI Vector Search](https://couchbase.com/) & [OpenAI](https://openai.com/). We fetch parts of the PDF relevant to the question using N1QL-based Vector search & add it as the context to the LLM. The LLM is instructed to answer based on the context from the Vector Store."
"For RAG, we are using [Langchain](https://langchain.com/), [Couchbase GSI Vector Search](https://couchbase.com/) & [OpenAI](https://openai.com/). We fetch parts of the PDF relevant to the question using Query based Vector search & add it as the context to the LLM. The LLM is instructed to answer based on the context from the Vector Store."
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@gemini-code-assist gemini-code-assist bot Nov 21, 2025

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medium

There's a small inconsistency here. While you've updated N1QL-based Vector search to Query based Vector search in this line, and changed the page title from (GSI Vector Search) to (Query Vector Store) on line 226, the term Couchbase GSI Vector Search remains. For consistency with the other changes, you should update this as well.

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"For RAG, we are using [Langchain](https://langchain.com/), [Couchbase GSI Vector Search](https://couchbase.com/) & [OpenAI](https://openai.com/). We fetch parts of the PDF relevant to the question using Query based Vector search & add it as the context to the LLM. The LLM is instructed to answer based on the context from the Vector Store."
"For RAG, we are using [Langchain](https://langchain.com/), [Couchbase Query Vector Search](https://couchbase.com/) & [OpenAI](https://openai.com/). We fetch parts of the PDF relevant to the question using Query based Vector search & add it as the context to the LLM. The LLM is instructed to answer based on the context from the Vector Store."

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@nithishr nithishr Nov 25, 2025

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This is valid.

)

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