---
license: cc-by-4.0
language:
  - en
task_categories:
  - text-generation
tags:
  - persona
  - email
  - rag
  - synthetic
  - sft
  - instruction-tuning
size_categories:
  - 10K<n<100K
configs:
  - config_name: sft
    data_files:
      - split: train
        path: sft_train_priya.jsonl
      - split: eval
        path: sft_eval_priya.jsonl
  - config_name: rag
    data_files:
      - split: docs
        path: rag_docs_priya.jsonl
---

# Priya SFT + RAG dataset

Synthetic training and retrieval data for the **Priya** persona — a fictional Senior Customer Success Manager at a fictional B2B SaaS company. Used to train [`kader-xai/priya-qwen2.5-7b-lora`](https://huggingface.co/kader-xai/priya-qwen2.5-7b-lora) and [`kader-xai/priya-qwen2.5-7b-gguf`](https://huggingface.co/kader-xai/priya-qwen2.5-7b-gguf).

> **Fully synthetic.** No real person, customer, or company. Generated as the seed corpus for [Project Recall](https://github.com/kader-xai/EmployeeRecall), an experiment in employee-continuity AI.

- **📝 Blog post:** [Employee Recall — Capturing a Departing Employee's Writing Style and Memory](https://kader-xai.github.io/posts/employee-recall-lora-rag-persona-continuity/)
- **Source code:** [github.com/kader-xai/EmployeeRecall](https://github.com/kader-xai/EmployeeRecall)
- **How this was built:** [train-own-persona.md](https://github.com/kader-xai/EmployeeRecall/blob/main/train-own-persona.md)

---

## Files

| File | Lines | Purpose |
|---|---|---|
| `sft_train_priya.jsonl` | 1,223 | SFT training pairs (chat format) |
| `sft_eval_priya.jsonl` | 64 | SFT held-out eval pairs |
| `rag_docs_priya.jsonl` | 17,663 | Chunked corpus for RAG retrieval |
| `system_prompt_priya.txt` | — | Voice-fingerprint system prompt used at inference |

## SFT format

Each line is a chat-format conversation: a system prompt (Priya's voice fingerprint), a user turn (the prior email thread), and an assistant turn (Priya's actual reply).

```json
{
  "messages": [
    {"role": "system", "content": "You are an AI assistant that drafts email replies in the voice of Priya Sharma..."},
    {"role": "user",   "content": "From: customer@acme.com\nSubject: Re: Q3 renewal\n\nHi Priya — circling back on the pricing..."},
    {"role": "assistant", "content": "Hi Mike,\n\nThanks for the patience. To flag a couple of things..."}
  ]
}
```

Pairs are reconstructed from the synthetic corpus by grouping emails into threads, sorting by `thread_position` + `date`, and turning every Priya-authored reply (with prior context) into one `(thread → reply)` pair. 95/5 train/eval split.

## RAG format

Each line is a chunk of the corpus with provenance and metadata, ready to embed and load into FAISS / Qdrant / etc.

```json
{
  "doc_id": "...",
  "doc_type": "email | meeting_notes | transcript | rfc | adr | postmortem",
  "chunk_id": "...",
  "text": "...",
  "metadata": {
    "date": "...",
    "from": "...",
    "subject": "...",
    "boost": 1.0
  }
}
```

Chunking is type-aware:
- **Email**: one chunk per email (split by paragraph if > 1500 chars). Preamble carries from/to/subject/date.
- **Structured meeting notes**: one chunk per section (discussion / decisions / actions). Decisions and actions get a higher retrieval boost.
- **Transcripts**: window of 8 segments per chunk with timestamps + speakers.
- **RFC / ADR / design doc**: split on markdown headings.

Recommended embedder: `BAAI/bge-base-en-v1.5` (768-dim, normalized) → `IndexFlatIP` for ≤ 1M chunks.

## How to use

```python
from datasets import load_dataset

sft = load_dataset("kader-xai/priya-sft", "sft")
rag = load_dataset("kader-xai/priya-sft", "rag", split="docs")

print(sft["train"][0]["messages"])
print(rag[0]["text"])
```

End-to-end training recipe in the [persona-training article](https://github.com/kader-xai/EmployeeRecall/blob/main/train-own-persona.md).

## Provenance & ethics

- 100% synthetic. Generated by `scripts/generate_bulk_corpus.py` and v2 in the source repo. Deterministic — same seed produces the same corpus.
- The persona is fictional; no real mailbox was used. If you adapt this pipeline for a real person, you need consent, scope agreement, and a retention policy (see the methodology doc in the source repo).

## License

CC-BY-4.0. Use it freely for research, models, or examples — attribution appreciated.