Mahdi Bazrafshan
e693b9bbfc
Grounded Ask owns the product chat UI: persistent thread_id across a Chat Session, New chat / Continue conversation, and a sidebar that lists Conversations from the Conversation Record with formatted run metadata (timing, tokens, evidence chunks). Co-authored-by: Cursor <cursoragent@cursor.com>
37 lines
1.3 KiB
Markdown
37 lines
1.3 KiB
Markdown
# Grounded Ask
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Upload docs, ask one question, read the answer plus the passages it used.
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Domain language: [`CONTEXT.md`](CONTEXT.md) · product ADR: [`docs/adr/0001-separate-app-tenant-profiles.md`](docs/adr/0001-separate-app-tenant-profiles.md) · API Sources: `tamasino-ai-api` ADR-0023
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## Prerequisites
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1. `tamasino-ai-api` running
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2. A tenant API key with upload scopes **and** `threads:run`:
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```bash
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cd ../tamasino-ai-api
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uv run python -m src.cli.provision_tenant --slug acme --domain fire \
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--scopes files:write,domains:read,domains:write,points:read,points:write,retrieval:read,threads:run
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```
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## How to use (local)
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1. Open the app (usually `http://localhost:8502`).
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2. **Connect** — paste an API key that includes `files:write`, `domains:read`, `domains:write`, and `threads:run` (Hybrid Console’s key is fine if it has those).
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3. **Add documents** — create or pick a topic (domain), upload CSV / Excel / Word, wait for “chunks indexed”.
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4. **Ask** — type a question → read the answer and **Passages used**.
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Each ask is single-turn (no chat history). Questions search the whole tenant corpus.
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## Setup
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```bash
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cd tamasino-ai-grounded-ask
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cp .env.example .env
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uv sync
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uv run streamlit run app.py
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```
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Connections are stored locally in `data/tenant_profiles.json` (gitignored).
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