{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "2022hpsk-speakermemr1--speaker-attributed-evidence-quickstart",
 "repo": "2022hpsk/SpeakerMemR1",
 "repo_url": "https://github.com/2022hpsk/SpeakerMemR1",
 "repo_slug": "2022hpsk-speakermemr1",
 "title": "Retrieve speaker-labeled evidence from a group chat locally",
 "page_title": "How to retrieve speaker-labeled evidence from a group chat locally with Speaker Mem R1",
 "summary": "Store a multi-person conversation and pull back who said what for a question, with no API key.",
 "who": "A developer building a group chat or meeting assistant who needs to check that retrieved evidence keeps speaker attribution before wiring in a model.",
 "problem": "General memory tools can lose track of which person made a statement, so answers about a specific person or group decision can be wrong.",
 "outcome": "A local run prints memory statistics and retrieved entries, each tagged with its speaker.",
 "steps": [
  "Run `python3 -m venv .venv` and then `source .venv/bin/activate` at the repository root.",
  "Run `python -m pip install --upgrade pip`.",
  "Run `python -m pip install -r code/requirements.txt`.",
  "Run `python -m pip install -e code/speakermem_pkg`.",
  "Run `python code/speakermem_pkg/examples/quickstart.py` and read the stats and the speaker-tagged entries printed for the two sample questions."
 ],
 "agent_prompt": "Clone the SpeakerMem-R1 repo (2022hpsk/SpeakerMemR1), set up the virtual environment and install the package as its README describes, then run code/speakermem_pkg/examples/quickstart.py. Report the memory stats and the retrieved entries for the questions 'Who handles evaluation?' and 'What does Bob handle?'. Verify that each retrieved entry shows the speaker name.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "30 minutes",
 "basis": "readme",
 "caveat": "The quickstart only returns stored evidence; it does not generate answers, and the first run may download the all-MiniLM-L6-v2 embedding model.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "Python with the ability to create a virtual environment",
  "A CPU environment is enough for the quickstart and offline checks",
  "Model service credentials (kept outside version control) for full benchmark runs",
  "Benchmark payloads placed as described in DATA_SOURCES.md for full evaluations"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/2022hpsk-speakermemr1/speaker-attributed-evidence-quickstart/",
 "md": "https://dedupedai.com/use-cases/2022hpsk-speakermemr1/speaker-attributed-evidence-quickstart/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/2022hpsk-speakermemr1--speaker-attributed-evidence-quickstart.json",
 "api_version": "v1"
}