feat: implement create_context_item API for Local Context Store#3
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Hi Alain, |
| - **Public output:** [Post title](https://example.com/post) - Medium, X, LinkedIn, or another public platform | ||
| - **Topic:** Capture / Create Context | ||
| - **What I did:** I implemented the `create_context_item` API for the Local Context Store. It validates context type, title, content, source, tags, and importance before saving a new context item into SQLite. I verified it with the Week 2 storage test script and learned that useful local-first AI memory starts with clean, structured context capture. | ||
| - **Public output:** [Why Local-First AI Starts With Good Context Capture](https://dev.to/alaindevs/why-local-first-ai-starts-with-good-context-capture-3d9g) - Dev.to |
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| def create_context_item( |
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Comment on the implemeted function.
2 participants
Pull Request Checklist
Branch
fellows/<github-username>/month-XX-week-YY-<topic>.weeks/month-XX-week-YYbranch.Human Learning
Verification
scripts/verify-contribution.ps1.AI Use
Summary
Implemented the Fellow 1 create-context API for the Week 2 Local Context Store.
What changed?
Added create_context_item() in src/python/local_first_ai/storage/create_context.py. It validates the context type, title, content, source, tags, and importance, then saves the context item into SQLite with created_at and updated_at timestamps. I also updated the Week 2 report block for Fellow 1.
What did you learn?
I learned that local-first AI memory needs clean structure before it can be useful. Valid context types, source tracking, tags, importance, and timestamps make stored context easier to trust, search, and reuse later.
What should the next contributor know?
Fellow 1 create-context tests are passing. The next contributors can build read, search, update, delete, and persistence features on top of the saved context_items records.