Bulk-export Podium conversation, review, and contact history into a vector-store-ready corpus with cursor-paginated full crawls, CDC via updated_at watermarks, attachment URL refresh on expiry, windowed semantic chunking, and PII redaction before embedding. Use when standing up a RAG pipeline on a Podium org, building nightly incremental syncs, or hardening export jobs against attachment expiry. Trigger with "podium history export", "podium rag corpus", "podium cdc sync", "podium incremental export", "podium chunk for embedding".
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
Bulk-export Podium conversation, review, and contact history into a vector-store-ready corpus with cursor-paginated full crawls, CDC via updated_at watermarks, attachment URL refresh on expiry, windowed semantic chunking, and PII redaction before embedding. Use when standing up a RAG pipeline on a Podium org, building nightly incremental syncs, or hardening export jobs against attachment expiry. Trigger with "podium history export", "podium rag corpus", "podium cdc sync", "podium incremental export", "podium chunk for embedding".
Bulk-export a Podium organization's historical conversations, reviews, and contacts into a corpus suitable for embedding into a vector store. This is the skill you run when the customer says "we have two years of knowledge in there" and the AI team wants every thread, every review, every contact note searchable by similarity. It is not a one-shot script — it is the full-export-plus-incremental-CDC pipeline that ingests the historical backlog once and then keeps the corpus current via nightly updated_at watermark passes.
The six production failures this skill prevents:
Cursor pagination drift — Podium's next_cursor is server-side state derived from a sort key plus a position. If a conversation is created, updated, or deleted mid-export, naive cursor walks duplicate records (an updated row reappears at the new position) or skip records (a row deleted between pages shifts the cursor's anchor). A correct walk pins the sort to a stable monotonic field and dedups on id.
Incremental CDC gaps via the updated_at watermark — naive updated_at > $watermark queries miss writes that happen at exactly the watermark second. Two writes within the same second on opposite sides of the boundary produce a permanent hole. Correct CDC uses >= with explicit overlap margin and dedups in the loader on (id, updated_at).
Attachment URL expiry mid-download — Podium attachment URLs are pre-signed S3-style URLs that expire on the order of 15 minutes. A bulk exporter that takes an hour will get 403 SignatureDoesNotMatch on every attachment whose URL was issued in the first quarter of the run. Correct downloaders detect the 403, fetch a fresh signed URL by attachment_id, and resume.
Oversized thread chunking failures — a 4000-message conversation thread (a long-running concierge thread for a high-touch RV dealer customer) blows the typical 8K-token embedding budget if naively concatenated. Chunking must be windowed with semantic boundaries (turn boundaries, day boundaries, idle gaps) and emit overlapping chunks for cross-window retrieval.
PII not redacted before embedding — vector stores are effectively eternal; once a customer's SSN, credit-card number, or address is embedded it cannot be unembedded without recomputing the index. Redact at chunk-emit time with the same PII pattern set used by podium-call-transcript-pipeline, before any vector is computed.
Export OOM on long threads — naively loading all messages of a 4000-message thread into memory before chunking blows the heap on the host running the export. Correct exports stream message-by-message into JSONL, then a separate pass streams JSONL into chunks. Memory cost stays O(window-size), not O(thread-size).
Prerequisites
Python 3.10+
podium-auth (this skill assumes a PodiumAuth instance is available; do not re-implement OAuth here)
podium-rate-limit-survival (this skill assumes the calling layer obeys per-endpoint quotas — bulk export is the most rate-limit-aggressive workload in the pack)
A persistent CDC watermark store — SQLite is the default; cdc_watermark.py ships with it
Local disk for streaming JSONL output, gzip-compressed (typical 2-year org: ~1–10 GB raw, 200 MB–2 GB gzipped)
An attachment-download target directory (S3 bucket, GCS bucket, or local path)
The PII redaction pattern set from podium-call-transcript-pipeline (reused; do not fork)
Authentication
This skill does NOT implement OAuth. All HTTP calls flow through a podium_get() injected dependency that holds a PodiumAuth instance from the podium-auth skill — that layer handles token caching, 80%-TTL refresh, single-flight locks, and scope validation. Bootstrap by Read-ing your refresh-token file, instantiate PodiumAuth(client_id, client_secret, refresh_token), then pass the instance to every export script via --refresh-token-file and the env-var credential flags. The five scripts in this skill never construct credentials in-process; they delegate.
If you need to harden the auth path itself (rotation, decay monitoring, multi-tenant routing), Readpodium-auth/SKILL.md and stack that skill on top — this skill is the bulk-data layer, not the auth layer.
Instructions
Step 1 → Step 6 below. Build in this order. Each step neutralizes one of the six production failure modes from the Overview, in the same order.
Step 1. Cursor-paginated full crawl (neutralizes cursor drift)
Pin the sort to a stable monotonic field (created_at ascending) and dedup on id in the loader. Persist the cursor after every successful page so a crash mid-walk resumes at the last successful page boundary, not at the start.
import asyncio, json, time
from pathlib import Path
from typing import AsyncIterator
CURSOR_PATH = Path("./.cursor.conversations.json")
PAGE_SIZE = 100# Podium documents 100 as the max; do not exceedasyncdefcrawl_conversations(podium_get, location_uid: str) -> AsyncIterator[dict]:
"""Yield conversations in created_at-ascending order. Resumable across crashes."""
state = json.loads(CURSOR_PATH.read_text()) if CURSOR_PATH.exists() else {}
cursor = state.get("cursor")
seen_ids = set(state.get("seen_ids", [])) # bounded; trim periodicallywhileTrue:
params = {
"location_uid": location_uid,
"sort": "created_at:asc",
"limit": PAGE_SIZE,
}
if cursor:
params["cursor"] = cursor
resp = await podium_get("/v4/conversations", params=params)
body = resp.json()
page = body.get("data", [])
for row in page:
if row["id"] in seen_ids:
continue# dedup against mid-walk updates
seen_ids.add(row["id"])
yield row
cursor = body.get("next_cursor")
# Persist after EVERY page — crash resume must land on the last good cursor
CURSOR_PATH.write_text(json.dumps({
"cursor": cursor,
"seen_ids": list(seen_ids)[-50_000:], # keep last 50k ids only"updated_at": time.time(),
}))
ifnot cursor:
return
The seen_ids set is bounded at 50k to prevent unbounded memory growth on a multi-million-row export. Tune the cap to roughly 5× PAGE_SIZE × pages_in_one_hour — large enough to dedup any reasonable update churn within the page-write window, small enough to fit in memory.
Step 2. Incremental CDC via overlap-margin watermark (neutralizes boundary gaps)
A naive updated_at > $watermark query misses any row whose updated_at is exactly the watermark second. Use >= and dedup in the loader; advance the watermark only after the page is fully persisted.
import sqlite3, time, json
WATERMARK_DB = "./watermarks.sqlite"defget_watermark(resource: str) -> float:
con = sqlite3.connect(WATERMARK_DB)
cur = con.execute("SELECT watermark FROM cdc WHERE resource = ?", (resource,))
row = cur.fetchone()
con.close()
return row[0] if row else0.0defadvance_watermark(resource: str, new_watermark: float) -> None:
con = sqlite3.connect(WATERMARK_DB)
con.execute("""
INSERT INTO cdc(resource, watermark, updated_at) VALUES(?, ?, ?)
ON CONFLICT(resource) DO UPDATE SET watermark = excluded.watermark, updated_at = excluded.updated_at
""", (resource, new_watermark, time.time()))
con.commit()
con.close()
asyncdefincremental_pull(podium_get, resource: str, overlap_margin_s: int = 60):
"""Pull rows with updated_at >= (watermark - overlap_margin) and dedup."""
watermark = get_watermark(resource)
since = max(0, watermark - overlap_margin_s) # explicit overlap
cursor = None
max_seen = watermark
seen_keys = set()
whileTrue:
params = {"updated_since": since, "sort": "updated_at:asc", "limit": 100}
if cursor:
params["cursor"] = cursor
body = (await podium_get(f"/v4/{resource}", params=params)).json()
for row in body.get("data", []):
key = (row["id"], row["updated_at"])
if key in seen_keys:
continue
seen_keys.add(key)
yield row
max_seen = max(max_seen, row["updated_at"])
cursor = body.get("next_cursor")
ifnot cursor:
break# Advance watermark only after the full pass succeeds, not per-page —# a partial pass must re-pull from the previous watermark on retry.
advance_watermark(resource, max_seen)
The overlap_margin_s = 60 is intentional. Podium's updated_at granularity is one second; a 60s overlap absorbs clock-skew and same-second writes without producing useful duplicate volume.
Step 3. Attachment download with refresh-on-403 (neutralizes pre-signed URL expiry)
Podium attachment URLs are pre-signed and expire ~15 minutes after they appear in the conversation payload. A multi-hour bulk export must re-fetch the signed URL by attachment ID when a download returns 403.
import asyncio, httpx
from pathlib import Path
asyncdefdownload_attachment(podium_get, attachment_id: str, signed_url: str, dest: Path) -> None:
asyncwith httpx.AsyncClient(timeout=60) as c:
r = await c.get(signed_url)
if r.status_code == 403:
# URL expired — fetch a fresh one
fresh = (await podium_get(f"/v4/attachments/{attachment_id}")).json()
signed_url = fresh["url"]
r = await c.get(signed_url)
r.raise_for_status()
dest.write_bytes(r.content)
asyncdefparallel_download(podium_get, attachments: list[dict], out_dir: Path, concurrency: int = 8) -> None:
sem = asyncio.Semaphore(concurrency)
asyncdef_one(att):
asyncwith sem:
dest = out_dir / f"{att['id']}{att.get('ext', '')}"await download_attachment(podium_get, att["id"], att["url"], dest)
await asyncio.gather(*[_one(a) for a in attachments])
concurrency=8 is the safe default — Podium documents per-org rate limits, and the attachments are served from a CDN that tolerates moderate parallelism. Raise only after observing the integration's headroom against the rate-limit-survival skill's metrics.
A long thread must chunk on natural boundaries — turn boundaries inside a conversation, idle gaps > 24h, and a hard cap on tokens per chunk. The chunker streams the JSONL export, never holding more than window_size messages in memory.
defchunk_thread(messages: list[dict], target_tokens: int = 1500, overlap_tokens: int = 200):
"""Yield chunks of ~target_tokens with overlap, breaking on idle gaps > 24h."""
chunks = []
current: list[dict] = []
current_tokens = 0for i, msg inenumerate(messages):
msg_tokens = approx_tokens(msg["body"])
idle_gap = 0if i > 0:
idle_gap = msg["created_at"] - messages[i-1]["created_at"]
# Force-break on >24h idle OR token capif current_tokens + msg_tokens > target_tokens or idle_gap > 86400:
if current:
chunks.append(_emit_chunk(current))
# Overlap: carry last ~overlap_tokens of messages into next chunk
carry = []
carry_tokens = 0for m inreversed(current):
t = approx_tokens(m["body"])
if carry_tokens + t > overlap_tokens:
break
carry.insert(0, m)
carry_tokens += t
current = carry
current_tokens = carry_tokens
current.append(msg)
current_tokens += msg_tokens
if current:
chunks.append(_emit_chunk(current))
return chunks
defapprox_tokens(text: str) -> int:
returnmax(1, len(text) // 4) # OpenAI rule of thumb; replace with tiktoken in prod
The 24-hour idle-gap break is critical for support-style threads where a single thread spans months — without it, the semantic context of a chunk is incoherent (a Q1 issue and a Q4 follow-up share an embedding).
Step 5. PII redaction before embedding (neutralizes eternal-PII compliance failure)
Run the same redaction pattern set as podium-call-transcript-pipeline at chunk-emit time, before any text leaves the export process toward the embedding API. Redactions are non-recoverable — that is the point.
import re
PII_PATTERNS = [
(re.compile(r"\b\d{3}-\d{2}-\d{4}\b"), "[REDACTED_SSN]"),
(re.compile(r"\b(?:\d[ -]*?){13,16}\b"), "[REDACTED_CARD]"),
(re.compile(r"\b[A-Z]{1,2}\d{6,9}\b"), "[REDACTED_LICENSE]"),
(re.compile(r"\b\d{1,5} [\w ]{1,40}(?:Street|St|Ave|Avenue|Rd|Road|Blvd|Drive|Dr|Lane|Ln|Way|Court|Ct)\b", re.I), "[REDACTED_ADDR]"),
(re.compile(r"[\w\.-]+@[\w\.-]+\.\w+"), "[REDACTED_EMAIL]"),
(re.compile(r"\+?\d{1,3}[ -.]?\(?\d{3}\)?[ -.]?\d{3}[ -.]?\d{4}"), "[REDACTED_PHONE]"),
]
defredact(text: str) -> str:
for pat, repl in PII_PATTERNS:
text = pat.sub(repl, text)
return text
Run redaction on the chunk body, the customer-facing display name in any quoted message, and the attachment filename if it is reproduced in the chunk. Do not redact the message id or created_at — those are non-PII and necessary for retrieval traceability.
Stream one record per line, gzip-compressed, with no in-memory aggregation. The chunker reads JSONL line-by-line and emits chunks; the embedder reads chunks line-by-line and emits vectors. Memory cost is O(one record) at every stage.
import gzip, json
from pathlib import Path
asyncdefstream_export(rows_iter, out_path: Path) -> int:
"""Write rows to a gzip-compressed JSONL file, one record per line."""
count = 0with gzip.open(out_path, "wt", encoding="utf-8") as f:
asyncfor row in rows_iter:
f.write(json.dumps(row, separators=(",", ":")))
f.write("\n")
count += 1if count % 1000 == 0:
f.flush() # bound data loss on crashreturn count
separators=(",", ":") saves ~5% on disk; f.flush() every 1000 rows bounds the data-loss window on a crash to one flush interval.
Error Handling
HTTP Status
Podium Error
Root Cause
Action
403 Forbidden
SignatureDoesNotMatch (attachment)
Pre-signed URL expired
Re-fetch via GET /v4/attachments/:attachment_id, retry once
404 Not Found
conversation_not_found
Conversation deleted mid-export
Skip; log the id; do not retry
409 Conflict
cursor_invalid
Cursor from a previous export run is no longer valid
Drop cursor; restart from current watermark
429 Too Many Requests
rate_limited
Page rate exceeded org quota
Honor Retry-After; delegate to podium-rate-limit-survival
500/502/503
server_error
Podium-side transient
Exponential backoff with jitter, max 4 attempts
(local)
watermark_drift
CDC watermark advanced past max_seen on a partial run
Reset to last good watermark via cdc_watermark.py --reset
Output is JSONL where each line is a chunk: {chunk_id, source_id, source_type, created_at_window, body, token_estimate, pii_redacted: true}. Pipe directly into your vector-store loader.
