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**Solution:** I first implemented the second option because it is simple and works fine, but the summary generation needs to be done separately for each agent, which I don't think is ideal. Therefore, I have decided to try the first option. I have already started working on it. I need to fix some bugs, and it will be completed by tomorrow (2025-06-16).
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-**Challenge:** Retrieved context is irrelevant when the project code is passed. The retriever component returns three chunks arranged in priority. However, a project code can contain many keywords, making the retriever not particularly useful.
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**Solution:** I am considering scanning all the keywords (like block names) first and passing their information to the LLM. This data will be stored in a dictionary. Example:
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```python
@@ -73,7 +74,7 @@ This way the LLM can understand block structure and their meaning as well.
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## Key Learnings
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- The prototype for this project is developing in Streamlit and I am learning Streamlit a lot.
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- The prototype for this project is developing in Streamlit, and I am learning Streamlit a lot.
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- Also gainig experience in LangChain as this is the primary tool of development.
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