Clarify external JSONL knowledge base and mobile project scope

README.md

@@ -18,7 +18,11 @@ tags:
 
 LAI V3 is a lightweight bilingual causal language model developed by the Pixxle / LAI team for local inference. The intended product target is an offline mobile assistant that can handle short French/English conversations, answer grounded factual questions from injected facts, and prefer "I don't know" / "Je ne sais pas" when relevant facts are not available.
 
-This Hugging Face repository contains project release artifacts produced by the team: PyTorch checkpoints and the SentencePiece tokenizer used by the model family.
+This Hugging Face repository contains the released model artifacts only: PyTorch checkpoints and the SentencePiece tokenizer used by the LAI V3 family.
+
+The complete product project is broader than this model release. The original target was to run LAI locally on mobile devices, especially iPhone, with a lightweight architecture adapted to local execution. The full application project, mobile integration, prompt pipeline, and product-level architecture are available on GitHub:
+
+- [pixxlefr/LAI on GitHub](https://github.com/pixxlefr/LAI/tree/main)
 
 ## Recommended checkpoint
 
@@ -38,6 +42,66 @@ For the most conservative grounded behavior:
 - User-context personalization when name, mood, or preferences are injected in the prompt
 - "I don't know" / "Je ne sais pas" style answers when a factual answer is requested without usable facts
 
+## Core Product Concept: Model + External Knowledge Base
+
+The main LAI idea is that the model should not be treated as the sole place where knowledge lives.
+
+Instead, the intended architecture separates:
+
+- the language model, which generates natural bilingual answers
+- the knowledge base, stored separately in local `JSONL` files
+- the retrieval layer, which searches that knowledge base before asking the model to answer
+
+In other words:
+
+- LAI V3 is the language and response layer
+- the factual knowledge is expected to live outside the model
+- the application should search the local knowledge base first, then inject the retrieved facts into the prompt
+
+This is important because the project goal was local mobile execution. Keeping a separate knowledge base makes it easier to:
+
+- update facts without retraining the model
+- keep the model lighter for mobile devices
+- control where factual answers come from
+- prefer grounded answers over hallucinated ones
+
+## Knowledge Base Format
+
+The intended product design uses local `JSONL` files as a simple knowledge store.
+
+Typical idea:
+
+- one JSON object per line
+- keywords for retrieval
+- language-specific fact strings to inject into the prompt
+
+Example:
+
+```json
+{"topic":"france","keywords":["france","paris"],"facts_fr":"La capitale de la France est Paris.","facts_en":"The capital of France is Paris."}
+```
+
+The application is expected to search those `JSONL` entries, retrieve the most relevant facts, and then build the prompt given to LAI.
+
+## Intended Retrieval Behavior
+
+For factual questions, the expected workflow is:
+
+1. the user asks a question
+2. the app searches the external knowledge base stored in `JSONL`
+3. the app selects matching facts
+4. the app injects those facts into `[FACTS]`
+5. LAI generates a short natural answer from those facts
+6. if nothing relevant is found, LAI should prefer an explicit unknown-answer style response
+
+So the intended product logic is not:
+
+- "ask the model and hope it knows"
+
+It is:
+
+- "search the local knowledge base first, then ask the model to formulate the answer"
+
 ## Important Format Note
 
 These files are raw PyTorch checkpoints for a custom LAI architecture. They are not drop-in `transformers` checkpoints.
@@ -82,6 +146,8 @@ LAI V3 is meant to be used as one part of a larger product pipeline, not as an a
 5. The model generates a short answer in French or English.
 6. The app cleans the answer, persists user knowledge updates, and displays the final reply.
 
+For product use, this means the model should usually answer from retrieved facts rather than act like a closed factual database by itself.
+
 Core prompt contract:
 
 ```text
@@ -179,6 +245,19 @@ In the app project, LAI V3 is paired with:
 
 The shipped mobile path uses a quantized runtime export of the final checkpoint for on-device inference. This Hub repo keeps the original released PyTorch checkpoints.
 
+This Hugging Face repository therefore publishes the model layer, while the GitHub repository contains the larger local-mobile project:
+
+- chat application
+- prompt builder
+- JSONL knowledge base handling
+- user memory
+- local storage
+- native mobile inference integration
+
+GitHub project:
+
+- [https://github.com/pixxlefr/LAI/tree/main](https://github.com/pixxlefr/LAI/tree/main)
+
 ## Example Loading Pattern
 
 Minimal loading pattern with the project code: