Open
Conversation
- Updated Requested GPUs to '2' - Modified files: compose.yaml
- Added pip packages: pydantic==2.10.6 - Updated Base Environment to 'nvidia/ai-workbench/python-cuda122:1.0.8' - Modified files: compose.yaml, requirements.txt
- Added pip packages: pydantic==2.10.6 - Removed pip packages: pydantic==2.10.6 - Modified files: requirements.txt
- Updated Requested GPUs to '1'
- Updated Requested GPUs to '0' - Modified files: code/upload-pdfs.ipynb, compose.yaml
- Updated Requested GPUs to '1'
- Modified files: .project/spec.yaml
updated for local LLM & embedding
change to local LLM & embedding
change to local LLM and embedding
updo changes...
updo changes
new update add LLM & embedding
new update to use local LLM and embedding
more changes
update to use local LMM & embedding
update to use local LLM & embedding
remove key
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
update to use local LLM and embedding - all changes from Gemini -
Summary of Final Architecture
By merging these changes, your Agri-Culture intelligence system now follows a professional microservices pattern:
AI Workbench Container: Runs the Python/Streamlit frontend and orchestrates the RAG logic.
LLM Sidecar: Your local Llama 3.1 8B running in "Eager Mode" on the Blackwell GPU.
Embedding Sidecar: Your native ARM64 300M model providing sub-millisecond vectorization.