ragmail

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Query ragmail LanceDB workspaces for email search, counts, date/topic filters, and amount extraction. Use for answering questions about who/what/when from email data.

0xfe By 0xfe schedule Updated 2/8/2026
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name: ragmail description: Query ragmail LanceDB workspaces for email search, counts, date/topic filters, and amount extraction. Use for answering questions about who/what/when from email data. metadata: short-description: Query ragmail email databases

ragmail skill

Use this skill when the user asks questions about email content in ragmail workspaces (who said what, counts by sender/date, topic summaries, costs mentioned, etc.). Default to running small Python scripts that query LanceDB directly; use the bundled CLI script only as a convenience or for quick reference.

Quick start

  1. Identify the target workspace (e.g., 2026) or the full LanceDB path.
  2. Ensure Python runs from the repo .venv (created with either uv or python -m venv).
  3. Prefer a short ad‑hoc Python script that connects to LanceDB and runs the query.
  4. If the question uses relative dates ("last summer", "yesterday"), convert to absolute dates before querying.

Python environment (required)

If .venv already exists, reuse it:

source .venv/bin/activate
./.venv/bin/python -c "import lancedb; print('ok')"

If .venv is missing, set it up with either:

# Option A: uv-managed venv (recommended)
uv venv
source .venv/bin/activate
UV_PROJECT_ENVIRONMENT=$PWD/.venv uv sync --project python

# Option B: stdlib venv module
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e python

Locate the database

  • Workspace root: workspaces/<name>/
  • Database: workspaces/<name>/db/email_search.lancedb
  • If workspaces/<name>/workspace.json exists, prefer paths.db.

Direct query recipe (preferred)

Use Python + lancedb to query the tables directly.

import lancedb
from pathlib import Path

db_path = Path("workspaces/2026/db/email_search.lancedb")
db = lancedb.connect(str(db_path))
emails = db.open_table("emails")

# FTS search + filters
rows = (
    emails.search("Arkin teacher", query_type="fts")
    .where("year = 2026 AND month = 1", prefilter=True)
    .limit(50)
    .to_list()
)

For deeper body matches, use email_chunks and search chunk_text.

Direct Python Queries (recommended)

Use UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python - <<'PY' to run a short script inline. This enables more complex filtering and joins than the CLI wrapper.

Example: FTS + filter + custom projection

UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python - <<'PY'
import lancedb

db = lancedb.connect("workspaces/2026/db/email_search.lancedb")
emails = db.open_table("emails")

rows = (
    emails.search("school trip", query_type="fts")
    .where("year = 2025 AND month = 9", prefilter=True)
    .limit(20)
    .to_list()
)

for r in rows:
    print(r["date"], r["from_address"], r["subject"])
PY

Example: Pull full emails after chunk hits

UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python - <<'PY'
import lancedb

db = lancedb.connect("workspaces/2026/db/email_search.lancedb")
chunks = db.open_table("email_chunks")
emails = db.open_table("emails")

hits = (
    chunks.search("refund", query_type="fts")
    .where("year = 2024", prefilter=True)
    .limit(5)
    .to_list()
)

email_ids = {h["email_id"] for h in hits}
full = (
    emails.search()
    .where("email_id IN (" + ",".join([f\"'{i}'\" for i in email_ids]) + ")", prefilter=True)
    .to_list()
)

for r in full:
    print(r["subject"])
    print(r["body_plain"][:500])
    print("----")
PY

Example: Aggregate counts by sender

UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python - <<'PY'
import lancedb
from collections import Counter

db = lancedb.connect("workspaces/2026/db/email_search.lancedb")
emails = db.open_table("emails")

rows = (
    emails.search("invoice", query_type="fts")
    .where("year = 2024", prefilter=True)
    .limit(2000)
    .to_list()
)

counts = Counter(r["from_address"] for r in rows)
for addr, cnt in counts.most_common(10):
    print(cnt, addr)
PY

Optional CLI Script (reference / quick use)

Use this when you need fast counts, snippets, or amount extraction without writing a Python snippet.

UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python .agents/skills/ragmail/scripts/ragmail_query.py query --workspace 2026 --query "Arkin teacher"

Commands

Run from repo root:

UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python .agents/skills/ragmail/scripts/ragmail_query.py query --workspace 2026 --query "Arkin teacher" --limit 50

Query

UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python .agents/skills/ragmail/scripts/ragmail_query.py query \
  --workspace 2026 \
  --query "Arkin teacher" \
  --limit 50 \
  --fields date,from_name,from_address,subject,email_id,snippet

Count

UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python .agents/skills/ragmail/scripts/ragmail_query.py count \
  --workspace 2026 \
  --from-like "bob" \
  --year 2026 \
  --month 2

Sum amounts (costs)

UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python .agents/skills/ragmail/scripts/ragmail_query.py sum \
  --workspace 2026 \
  --query "house painting" \
  --start 2025-06-01 \
  --end 2025-08-31

Filters (all commands)

  • --year / --month for quick month scoping
  • --start / --end for date ranges (YYYY-MM-DD or ISO datetime)
  • --from-address for exact sender match
  • --from-like, --to-like, --subject-like, --labels-like for string filters
  • --table can be emails (default) or email_chunks

Output notes

  • query emits key fields and a snippet.
  • count returns count= and warns if it hits --max-scan.
  • sum extracts currency-like amounts from matching emails and reports totals plus examples.

Find And Display Full Email

Use a two-step flow: locate the matching chunk to get email_id, then fetch the full email body from the emails table.

  1. Find the matching chunk and capture email_id:
UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python .agents/skills/ragmail/scripts/ragmail_query.py query \
  --workspace 2026 \
  --table email_chunks \
  --query "Born 24 March 2010" \
  --limit 5 \
  --fields date,from_name,from_address,subject,email_id,chunk_text
  1. Fetch the full email by email_id (direct LanceDB query):
UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python - <<'PY'
import lancedb
from pathlib import Path

db = lancedb.connect("workspaces/2026/db/email_search.lancedb")
emails = db.open_table("emails")

email_id = "bf509cfe2e3ca574"
rows = (
    emails.search()
    .where(f"email_id = '{email_id}'", prefilter=True)
    .limit(1)
    .to_list()
)
print(rows[0]["body_plain"] if rows else "not found")
PY

Alternate: Use ragmail_query.py with --email-id to fetch the full body directly.

UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python .agents/skills/ragmail/scripts/ragmail_query.py query \
  --workspace 2026 \
  --email-id bf509cfe2e3ca574 \
  --limit 1 \
  --fields date,from_name,from_address,subject,email_id,body_plain

Tip: If the query uniquely identifies the email, you can also ask ragmail_query.py to emit body_plain directly by adding it to --fields.

Schema + indexing reference

See references/db.md for the current schema, FTS columns, workspace layout, and ingestion notes.

Raw data + pipeline locations

When you need deeper context or to verify a record, dig in:

  • Raw mbox: private/gmail-*.mbox
  • Workspace outputs: workspaces/<name>/clean, workspaces/<name>/spam, workspaces/<name>/split, workspaces/<name>/reports, workspaces/<name>/logs

When to go deeper

If search results are thin or you need more body context:

  • Try --table email_chunks with the same query.
  • Increase --limit or --max-scan.
  • Use narrower date ranges.

Attachments (opt-in, index-backed and usually fast)

Only fetch attachments if the user explicitly asks. Attachment extraction now uses split/mbox_index.jsonl (mbox_file + offset + length) to seek directly to one message, so it is usually fast and does not require full MBOX scans.

Preferred flow:

  1. Check metadata in LanceDB (has_attachment, attachment_names, attachment_types) to see if attachments exist.
  2. If the user explicitly asks, use the attachment extractor to pull from the indexed message bytes.
  3. Ensure workspaces/<name>/split/mbox_index.jsonl exists (created by preprocess).

Fast metadata check (no MBOX scan)

UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python - <<'PY'
import lancedb

db = lancedb.connect("workspaces/2026/db/email_search.lancedb")
emails = db.open_table("emails")

email_id = "bf509cfe2e3ca574"
row = (
    emails.search()
    .where(f"email_id = '{email_id}'", prefilter=True)
    .limit(1)
    .to_list()
)
print(row[0]["has_attachment"], row[0]["attachment_names"], row[0]["attachment_types"])
PY

Extract an attachment by Message-ID (optimized)

UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python .agents/skills/ragmail/scripts/ragmail_attachments.py \
  --workspace 2026 \
  --message-id "<abc123@example.com>" \
  --out-dir /tmp/attachments

Extract by email_id

UV_PROJECT_ENVIRONMENT=$PWD/.venv uv run --project python python .agents/skills/ragmail/scripts/ragmail_attachments.py \
  --workspace 2026 \
  --email-id bf509cfe2e3ca574 \
  --out-dir /tmp/attachments

Index location (required)

The pipeline creates workspaces/<name>/split/mbox_index.jsonl (during the preprocess stage). Attachment extraction requires this index. If it’s missing, ask the user to run ragmail pipeline --stages preprocess --workspace <name>.

Keep updated

If schemas, FTS columns, or workspace layouts change, update:

  • references/db.md
  • scripts/ragmail_query.py
Install via CLI
npx skills add https://github.com/0xfe/ragmail --skill ragmail
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