VidNavigator Python SDK

The official Python client for the VidNavigator Developer API: transcribe, analyze, search, and extract structured data from video and audio at scale.

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Contents

• Installation · Authentication · Quick start · Which method should I use?
• Online videos: Transcripts · AI analysis · Structured extraction · Tweet claim analysis
• Long-running work: Background jobs · Recipes · Webhooks
• TikTok: Profile scraping · Keyword search
• Your own files: Semantic search · File uploads · Namespaces
• Reference: Per-call usage · Usage & billing · Error handling · Configuration · Upgrading from 1.x

---

What You Can Do

Capability	Online Videos	Uploaded Files
Transcripts (segments with timestamps or plain text)	Yes	Yes
AI analysis (summary, people, places, key subjects, Q&A)	Yes	Yes
Structured data extraction (your custom schema)	Yes	Yes
Semantic search across content	Yes (YouTube)	Yes
File upload, storage, and management	--	Yes
Namespace organization and scoped search	--	Yes
TikTok profile scraping and keyword search	Yes	--
Tweet claim analysis	Yes	--
Background jobs for long media, with optional signed webhooks	Yes	--

Supported Platforms

Platform	Transcript	Transcribe (speech-to-text)	Carousel
YouTube	Yes	-	-
Instagram Reels	-	Yes	Yes
Instagram Posts	-	Yes	Yes (all_videos)
TikTok	Yes	Yes	-
X / Twitter	Yes	Yes	-
Vimeo	Yes	Yes	-
Facebook	Yes	Yes	-
Dailymotion	Yes	Yes	-
Loom	Yes	Yes	-
Uploaded files	Yes	Yes	-

Transcript = fast extraction of the platform's own captions, via get_transcript. Transcribe = speech-to-text by AI models, via transcribe_video. Use it when captions are unavailable.

Supported upload formats: mp4, webm, mov, avi, wmv, flv, mkv, m4a, mp3, mpeg, mpga, wav.

Fully typed: every response is a Pydantic model with IDE autocompletion. Compatible with both Pydantic v1 and v2.

---

Installation

pip install vidnavigator

Requires Python 3.7+.

---

Authentication

from vidnavigator import VidNavigatorClient

# Pass your API key directly
client = VidNavigatorClient(api_key="YOUR_API_KEY")

# Or set the environment variable and the client picks it up automatically
# export VIDNAVIGATOR_API_KEY=your_key_here
client = VidNavigatorClient()

Get your API key at vidnavigator.com.

---

Quick Start

from vidnavigator import VidNavigatorClient

client = VidNavigatorClient()

# 1. Captions of a YouTube video
resp = client.get_transcript(video_url="https://www.youtube.com/watch?v=dQw4w9WgXcQ", transcript_text=True)
print(resp.data.transcript)

# 2. Speech-to-text for a video without captions (any length)
resp = client.transcribe_video(video_url="https://www.instagram.com/reel/C86ZvEaqRmo/", transcript_text=True)
print(resp.data.transcript)

# 3. Structured data from a video
resp = client.extract_video_data(
    video_url="https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    schema={"mood": {"type": "String", "description": "Overall mood in one word"}},
)
print(resp.data)  # {"mood": "upbeat"}

---

Which Method Should I Use?

I want to...	Use	Returns
Get the captions of a video (fast, cheap)	get_transcript	immediately
Transcribe audio when there are no captions (Instagram, TikTok, ...)	transcribe_video	when the job finishes
Summarize a video or ask a question about it	analyze_video	immediately
Pull specific fields out of a video into a dict	extract_video_data	when the job finishes
Analyze the claim made in a tweet	get_tweet_statement	when the job finishes
List a TikTok profile's videos	scrape_tiktok_profile	when the job finishes
Search TikTok by keyword	search_tiktok	when the job finishes
Find YouTube videos about a topic	search_youtube	immediately
Work with your own audio/video files	upload_file, then get_file, analyze_file, extract_file_data, search_files	immediately
Process many videos at once	the submit_* version of a job method	a job handle, right away

"When the job finishes" methods run as background jobs. They block until the result is ready, however long the media is. Each one also has a submit_* version that returns immediately.

---

Transcripts

Captions from any supported platform

A single get_transcript method handles every supported platform (YouTube, Vimeo, X/Twitter, TikTok, Facebook, Dailymotion, Loom, etc.). The API detects the platform from the URL. Note: Instagram has no captions, so use transcribe_video instead.

resp = client.get_transcript(
    video_url="https://www.youtube.com/watch?v=dQw4w9WgXcQ",
)

print(resp.data.video_info.title)
# "Rick Astley - Never Gonna Give You Up (Official Music Video)"

for segment in resp.data.transcript:
    print(f"[{segment.start:.1f}s] {segment.text}")
# [0.0s] We're no strangers to love
# [3.2s] You know the rules and so do I
# ...

Pass transcript_text=True to get one plain string instead of segments:

resp = client.get_transcript(video_url="https://www.youtube.com/watch?v=dQw4w9WgXcQ", transcript_text=True)
print(resp.data.transcript)
# "We're no strangers to love. You know the rules and so do I..."

get_transcript parameters:

Parameter	Type	Description
video_url	str	URL of the video
language	str	ISO 639-1 language code (e.g. "en", "fr")
transcript_text	bool	Return the transcript as a single string instead of segments
metadata_only	bool	Return only video metadata, no transcript
fallback_to_metadata	bool	Return metadata with an empty transcript instead of raising NotFoundError when no captions exist
include_usage	bool	Attach a per-call usage block (see Per-call usage)

get_youtube_transcript still works as a deprecated alias for get_transcript.

Speech-to-text

For videos without captions, transcribe_video runs speech-to-text. It runs as a background job: the call submits the video, waits until the transcript is ready, and returns it. Long videos work the same way as short ones.

resp = client.transcribe_video(video_url="https://www.instagram.com/reel/C86ZvEaqRmo/")

print(resp.data.video_info.title)
for segment in resp.data.transcript:
    print(f"[{segment.start:.1f}s] {segment.text}")

For Instagram carousel posts, pick one video with ?img_index=N in the URL, or transcribe all of them:

resp = client.transcribe_video(
    video_url="https://www.instagram.com/p/ABC123/",
    all_videos=True,
)

print(resp.data.carousel_info.video_count)
for video in resp.data.videos:
    print(f"Video {video.index} ({video.status}): {video.transcript}")

transcribe_video parameters:

Parameter	Type	Description
video_url	str	URL of the video (?img_index=N selects one video of an Instagram carousel)
transcript_text	bool	Return the transcript as a single string instead of segments
all_videos	bool	Carousel posts only: transcribe every video. Returns carousel_info and videos instead of video_info and transcript.
include_usage	bool	Attach a usage block to the result
webhook_url	str	Optional; see Webhooks
timeout	float	Seconds to wait for the job (default 3600; None waits indefinitely). See Timeouts.

Captions first, speech-to-text as a fallback

Captions are faster and cheaper than speech-to-text, so a common pattern is to try them first:

from vidnavigator import BadRequestError, NotFoundError

def best_transcript(url):
    try:
        return client.get_transcript(video_url=url, transcript_text=True).data.transcript
    except (NotFoundError, BadRequestError):  # no captions, or a platform without captions
        return client.transcribe_video(video_url=url, transcript_text=True).data.transcript

---

AI Analysis

Analyze any video or uploaded file. The API returns a summary, identifies people and places, extracts key subjects, and can answer natural-language questions about the content.

Analyze an online video

resp = client.analyze_video(
    video_url="https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    query="What is the main message of this song?",
)

analysis = resp.data.transcript_analysis

print(analysis.summary)
# "The song is a declaration of unwavering commitment and loyalty..."

print(analysis.query_answer.answer)
# "The main message is a promise to never give up on someone..."

for person in analysis.people or []:
    print(f"{person.name} -- {person.context}")
# "Rick Astley -- Singer and performer of the song"

for subject in analysis.key_subjects or []:
    print(f"{subject.name} -- {subject.description}")
# "commitment -- Central theme of never abandoning a loved one"

query is optional. Without it you get the summary, people, places and key subjects. Pass transcript_text=True to also receive the transcript as one string.

Analyze an uploaded file

resp = client.analyze_file(
    file_id="your_file_id",
    query="What are the key action items from this meeting?",
)
print(resp.data.transcript_analysis.summary)

---

Structured Extraction

Describe the fields you want and the API fills them in from the video's transcript. It's useful for building pipelines, populating databases, or feeding downstream systems.

From an online video

resp = client.extract_video_data(
    video_url="https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    schema={
        "mood": {
            "type": "String",
            "description": "Overall mood of the video in one word",
        },
        "main_topics": {
            "type": "Array",
            "description": "List of main topics discussed",
            "items": {"type": "String", "description": "A topic"},
        },
        "has_spoken_lyrics": {
            "type": "Boolean",
            "description": "Whether the video contains spoken or sung lyrics",
        },
    },
    what_to_extract="Focus on the tone and content of the lyrics.",
)

print(resp.data)
# {"mood": "upbeat", "main_topics": ["love", "commitment"], "has_spoken_lyrics": True}

extract_video_data runs as a background job. With transcribe=True (the default), a video without captions (Instagram, TikTok, Facebook, ...) is transcribed first, however long it is. YouTube videos rely on their captions. Set transcribe=False to never run speech-to-text.

The extracted fields are in resp.data. resp.video_info is not populated for online videos; call get_transcript(video_url=..., metadata_only=True) if you also need the metadata.

Schema format

Each field needs a type and a description. A schema can have at most 10 top-level fields and 3 levels of nesting.

type	Extra keys	Example value
String, Number, Integer, Boolean	--	"upbeat", 4.5, 3, True
Enum	enum: the allowed values	"positive"
Array	items: the schema of one element	["love", "commitment"]
Object	properties: the sub-fields	{"name": "Rick", "role": "singer"}

schema = {
    "sentiment": {
        "type": "Enum",
        "description": "Overall sentiment of the video",
        "enum": ["positive", "negative", "neutral"],
    },
    "speaker": {
        "type": "Object",
        "description": "The main speaker or singer",
        "properties": {
            "name": {"type": "String", "description": "Their name"},
            "role": {"type": "String", "description": "Their role in the video"},
        },
    },
    "key_takeaway": {"type": "String", "description": "The single most important takeaway"},
}

An invalid schema raises BadRequestError with error_code == "invalid_schema" before anything is transcribed or billed.

From a schema file

Keep schemas in JSON or YAML files and pass schema_file instead of schema:

# meeting-schema.yaml
action_items:
  type: Array
  description: Action items mentioned in the meeting
  items:
    type: String
    description: One action item
decisions_made:
  type: Array
  description: Key decisions that were agreed upon
  items:
    type: String
    description: One decision

resp = client.extract_video_data(video_url="...", schema_file="./meeting-schema.yaml")

From an uploaded file

resp = client.extract_file_data(
    file_id="your_file_id",
    schema_file="./meeting-schema.yaml",  # or schema={...}
    what_to_extract="Extract action items and deadlines.",
)
print(resp.data)

Token usage

resp = client.extract_video_data(
    video_url="...",
    schema={"summary": {"type": "String", "description": "One-line summary"}},
    include_usage=True,
)
tokens = resp.usage.analysis_tokens
print(f"LLM tokens: {tokens.total_tokens} (prompt {tokens.prompt_tokens}, completion {tokens.completion_tokens})")

---

Tweet Claim Analysis

resp = client.get_tweet_statement(tweet_id="1585841080431321088")

print(resp.data.final_statement)    # the core claim, in the author's voice
print(resp.data.detailed_analysis)  # claim, context, evidence and stance
print(resp.data.claim_type, resp.data.intent, resp.data.tone)
print(resp.data.topics, resp.data.entities)
print(resp.data.tweet_media_summary)  # summary of attached video/images, if any

Videos attached to the tweet (or to the tweet it quotes) are transcribed in full, so this runs as a background job too. claim_type, intent, tone, emotion and authority are based on the original tweet text only.

---

Background Jobs

Speech-to-text and TikTok operations run as background jobs on the API: submitting one returns a task_id immediately, and the result is read by polling. The work happens on VidNavigator's servers, not in your process. The SDK only uses these job endpoints, so media of any length works without special handling.

Each job operation comes in two shapes:

Operation	Blocking: wait and return the result	Non-blocking: return a job handle
Speech-to-text	transcribe_video(...)	submit_transcribe_video(...)
Structured extraction	extract_video_data(...)	submit_extract_video_data(...)
Tweet claim analysis	get_tweet_statement(...)	submit_tweet_statement(...)
TikTok profile scrape	scrape_tiktok_profile(...)	submit_tiktok_profile_scrape(...)
TikTok keyword search	search_tiktok(...)	submit_tiktok_search(...)

Both shapes take the same parameters, except that include_usage, timeout and (for TikTok) limit are passed to result() on a handle.

Blocking: one call, one result

resp = client.transcribe_video(video_url="https://www.instagram.com/reel/C86ZvEaqRmo/")
print(resp.data.transcript)

The call submits the job, polls it until it finishes, and returns the result, raising an exception if the job failed.

Non-blocking: submit now, collect later

submit_* methods return a Job handle as soon as the job is accepted:

job = client.submit_transcribe_video(video_url="https://www.instagram.com/reel/C86ZvEaqRmo/")
print(job.task_id)   # "550e8400-e29b-41d4-a716-446655440000", save it
print(job.status())  # "processing"

# ... do other work ...

resp = job.result()  # waits if needed, then returns the same TranscriptResponse as transcribe_video

Member	Description
task_id	The job's id. Keep it; see Timeouts.
job_type	"transcribe", "extract_video", "tweet_statement", "tiktok_profile" or "tiktok_search"
status()	Polls once and returns the task_status ("processing", "completed" or "failed")
done()	Polls once; True when the job is completed or failed
result(...)	Waits, then returns the same object as the blocking method; raises if the job failed
wait(...)	Waits, then returns the raw poll response: job metadata, the request you sent, webhook delivery status
refresh()	Polls once and returns the raw poll response
webhook_url, check_status_url, data	What the API returned when the job was submitted

result() and wait() accept:

Argument	Default	Description
timeout	3600	Seconds to wait; None waits indefinitely
include_usage	False	Attach the job's usage block (available once completed)
poll_interval	3	Seconds between polls
fast_start	True	Poll every second for the first 10 seconds, so short clips return quickly
limit, cursor	--	TikTok jobs only: which page of results to return
raise_on_failure	True	wait() only: pass False to get a failed job back instead of an exception

How polling works

1. Submit. One POST; the API answers with a task_id. Errors such as insufficient credit are raised here, before any polling.
2. Poll. The SDK fetches the job right away, then every second for 10 seconds, then every 3 seconds. It keeps polling until the status is completed or failed.
3. Finish. A completed job is turned into the typed result; a failed job raises the matching exception.

Polling is free and is not rate-limited. A job's result stays readable for 1 hour after it finishes, and reading it does not consume it.

Timeouts: never lose the task_id

Blocking calls and result() wait up to timeout seconds (default one hour). When the timeout passes, they raise JobTimeoutError, but the job keeps running on the server. The error carries the task_id and a ready-to-use handle:

from vidnavigator import JobTimeoutError

try:
    resp = client.transcribe_video(video_url=url, timeout=600)
except JobTimeoutError as exc:
    print("Still running:", exc.task_id)
    resp = exc.job.result()  # keep waiting on the same job

Later, or from another process, rebuild the handle from a saved id with resume_job:

job = client.resume_job("transcribe", task_id)
if job.done():
    resp = job.result()

Failures

A failed job reports an error object with an error code, a message and an http_status. The SDK raises the exception matching that status, so the same except clauses work everywhere:

from vidnavigator import BadRequestError, PaymentRequiredError, VidNavigatorError

try:
    resp = client.transcribe_video(video_url=url)
except PaymentRequiredError:
    print("Out of transcription credit")
except BadRequestError as exc:
    print(exc.error_code, exc)  # e.g. "unsupported_platform"
except VidNavigatorError as exc:
    print("Job failed:", exc)

To inspect a failure without an exception, use job.wait(raise_on_failure=False) and read resp.data.error. A failed job has all its charges reverted.

Submit-time errors

Some errors happen before a job exists. No task is created, so there is nothing to poll:

Exception	Status	When
PaymentRequiredError	402	Not enough credit to start the job. It can also be raised by result() or a blocking call when the job runs out of credit while running.
TooManyActiveJobsError (subclass of RateLimitExceeded)	429	Too many jobs already running for your account; retry once some finish (recipe)
BadRequestError	400	Invalid parameters, an invalid extraction schema, or a non-public webhook_url

Result types

Operation	Returned by the blocking method and by result()
Transcribe	TranscriptResponse (data.video_info, data.transcript), or TranscribeAllVideosResponse with all_videos=True
Extract	ExtractionApiResponse (data is a dict matching your schema)
Tweet	TweetStatementResponse (data.final_statement, data.detailed_analysis, ...)
TikTok profile / search	TikTokProfileResponse / TikTokSearchResponse: the first page of results

---

Recipes

Process many videos at once

Submit every job first, then collect the results. Submitting takes a fraction of a second per job, and all the jobs then run at the same time on the server:

urls = [
    "https://www.tiktok.com/@a/video/1",
    "https://www.tiktok.com/@b/video/2",
    "https://www.tiktok.com/@c/video/3",
]

jobs = [client.submit_transcribe_video(video_url=url, transcript_text=True) for url in urls]
results = [job.result() for job in jobs]

for url, resp in zip(urls, results):
    print(url, resp.data.transcript[:80])

Collecting the results one after the other doesn't slow things down: while you wait on the first job, the others keep running. With jobs that take 60 s, 40 s and 90 s, the whole batch takes about 90 s, the slowest job. Calling the blocking transcribe_video in a loop would take 60 + 40 + 90 = 190 s, because each job would only start after the previous one finished.

If one job may fail without stopping the batch, catch errors per job:

from vidnavigator import VidNavigatorError

for url, job in zip(urls, jobs):
    try:
        print(url, job.result().data.transcript[:80])
    except VidNavigatorError as exc:
        print(url, "failed:", exc.error_code, exc)

Handle results as soon as they finish

[job.result() for job in jobs] returns results in submission order. To act on each job the moment it finishes, check them in turn:

import time

pending = list(jobs)
while pending:
    for job in list(pending):
        if job.done():            # one free poll
            handle(job.result())  # already finished: returns immediately
            pending.remove(job)
    time.sleep(3)

Stay under the running-job limit

Your account can only run a certain number of jobs at once. Past that limit, a submit raises TooManyActiveJobsError and no task is created. For large batches, keep a bounded number of jobs running and submit the next one when a slot frees up:

import time
from vidnavigator import TooManyActiveJobsError, VidNavigatorError

MAX_RUNNING = 5
queue = list(urls)
running = []  # (url, job) pairs
results, errors = {}, {}

while queue or running:
    # Fill free slots
    while queue and len(running) < MAX_RUNNING:
        try:
            running.append((queue[0], client.submit_transcribe_video(video_url=queue[0])))
            queue.pop(0)
        except TooManyActiveJobsError:
            break  # the account is at its limit; retry after some jobs finish

    # Collect finished jobs
    for url, job in list(running):
        if job.done():
            try:
                results[url] = job.result()
            except VidNavigatorError as exc:
                errors[url] = exc
            running.remove((url, job))

    time.sleep(3)

Survive a restart

A job keeps running if your process stops, and its result stays readable for 1 hour after it finishes. Save the task_id when you submit, and resume with resume_job:

import json

job = client.submit_extract_video_data(video_url=url, schema=schema)
with open("jobs.json", "w") as f:
    json.dump({"job_type": job.job_type, "task_id": job.task_id}, f)

# ... after a restart ...
with open("jobs.json") as f:
    saved = json.load(f)
resp = client.resume_job(saved["job_type"], saved["task_id"]).result()

Transcribe every video of a TikTok profile

profile = client.scrape_tiktok_profile(profile_url="https://www.tiktok.com/@tiktok", max_posts=20, limit=20)

jobs = [
    client.submit_transcribe_video(video_url=video.url, transcript_text=True)
    for video in profile.data.videos or []
    if video.url
]
for job in jobs:
    print(job.result().data.transcript[:80])

Get notified instead of polling

Pass a webhook_url and let VidNavigator call your server when each job finishes. See Webhooks.

---

Webhooks

A background job can call your server when it finishes. Every job method, blocking or submit_*, accepts a webhook_url and passes it through to the API unchanged. Webhooks are optional: polling always works, whether or not a webhook is configured.

job = client.submit_extract_video_data(
    video_url="https://www.tiktok.com/@user/video/1234567890",
    schema={"summary": {"type": "String", "description": "One-line summary"}},
    webhook_url="https://example.com/hooks/vidnavigator",
)

• The URL must be a publicly reachable https address. Private, loopback and link-local hosts are rejected with BadRequestError.
• You can also set an account-level default webhook in Studio → API. A per-request webhook_url overrides it, and webhook_url="" opts a single job out of it.
• job.webhook_url shows which webhook the job will call (None if none).
• For TikTok jobs, the event is only a notification: event.data.result holds stats and download_url_available, and you read the videos with the job handle (client.resume_job("tiktok_search", event.data.task_id).result()).
• If a result is larger than 256 KB, it is left out of the event (data.result_truncated is True).

Receive and verify deliveries

Deliveries are signed with HMAC-SHA256 using your signing secret. construct_webhook_event checks the signature and the timestamp, then returns a typed WebhookEvent. Always pass the raw request body, not re-serialized JSON:

from flask import Flask, request
from vidnavigator import VidNavigatorClient, WebhookSignatureError, construct_webhook_event

app = Flask(__name__)
client = VidNavigatorClient()
WEBHOOK_SECRET = "your_signing_secret"

@app.post("/hooks/vidnavigator")
def vidnavigator_webhook():
    try:
        event = construct_webhook_event(
            request.get_data(),
            request.headers.get("X-VidNavigator-Signature"),
            WEBHOOK_SECRET,
        )
    except WebhookSignatureError:
        return "invalid signature", 400

    delivery_id = request.headers.get("X-VidNavigator-Delivery")  # stable across retries
    if already_processed(delivery_id):
        return "", 200

    if event.type == "transcribe.completed":
        # The job handle gives the typed result, even when the event omits it
        resp = client.resume_job("transcribe", event.data.task_id).result()
        save_transcript(event.data.task_id, resp.data.transcript)
    elif event.type.endswith(".failed"):
        print(event.data.task_id, event.data.error.error, event.data.error.message)

    return "", 200

event.data.result also holds the result as a plain dict (unless it was truncated), if you'd rather skip the extra request.

Event types	Sent when
transcribe.completed / transcribe.failed	A speech-to-text job finishes
extract_video.completed / extract_video.failed	An extraction job finishes
tweet_statement.completed / tweet_statement.failed	A tweet analysis finishes
tiktok_profile.completed / tiktok_profile.failed	A profile scrape finishes
tiktok_search.completed / tiktok_search.failed	A keyword search finishes

construct_webhook_event rejects deliveries whose timestamp is more than 5 minutes from the current time. Change the window with tolerance_seconds= (or pass None to skip the check). To verify without parsing, use verify_webhook_signature(body, header, secret), which raises the same WebhookSignatureError.

Delivery is at-least-once and best effort: up to 5 attempts over about 13 minutes. Return a 2xx response quickly, deduplicate on X-VidNavigator-Delivery, and treat polling as the source of truth. See the webhooks guide for the full contract.

---

TikTok Profile Scraping

scrape_tiktok_profile runs a background job and returns the first page of videos:

result = client.scrape_tiktok_profile(
    profile_url="https://www.tiktok.com/@tiktok",
    max_posts=100,
    after_datetime="2024-01-01",
    limit=50,
)
task_id = result.data.task_id

for video in result.data.videos or []:
    print(video.title, video.published_at, video.likes, video.url)

To start the scrape without waiting, use submit_tiktok_profile_scrape(...), then call job.result(limit=50) when you want the videos.

Parameter	Type	Description
profile_url	str	Public TikTok profile URL
max_posts	int	Stop once this many matching videos are collected
after_datetime / before_datetime	str / date / datetime	Only include videos published in this window
min_likes / max_likes	int	Like-count filters
webhook_url	str	Optional; see Webhooks
limit	int	Page size of the returned first page (default 50)
include_usage, timeout		As for other job methods

after_datetime and before_datetime accept YYYY-MM-DD strings, ISO datetimes with a timezone, or Python date / datetime objects. In responses, video.published_at is a Python datetime, and views, likes, reposts and comments are integers.

Read the remaining pages

pagination = result.data.pagination

while pagination and pagination.has_next:
    page = client.get_tiktok_profile_scrape(task_id, cursor=pagination.next_cursor, limit=50)
    for video in page.data.videos or []:
        print(video.title, video.url)
    pagination = page.data.pagination

Download the full result

Completed scrapes usually include a download_url: a short-lived (about 1 hour) link to one JSON file with every video. Calling get_tiktok_profile_scrape again creates a fresh link.

import json
from urllib.request import urlopen

if result.data.download_url:
    with urlopen(result.data.download_url) as response:
        full_profile = json.load(response)

    for video in full_profile.get("videos", []):
        print(video.get("title"), video.get("url"))  # plain dicts, not typed models

---

TikTok Keyword Search

search_tiktok runs a background job and returns the first page of results. submit_tiktok_search returns a handle instead.

result = client.search_tiktok(
    query="ai tools",
    max_results=100,
    sort_by="most_liked",
    published_within="this_month",
    min_views=1000,
    limit=50,
)

for item in result.data.results or []:
    print(item.description, item.published_at, item.stats.views if item.stats else None, item.url)

print(result.data.stats.sort_by, result.data.stats.published_within)  # what the search actually ran with

Parameter	Type	Description
query	str	Keyword phrase (at least 2 characters)
max_results	int	Cap on merged results. 0 or omitted means no cap.
parallel_search_slices	int	1-4 concurrent search chains, deduplicated. More slices return more unique videos but bill up to N times the residential pages.
sort_by	str	"relevance", "most_liked" or "newest", applied by TikTok
published_within	str	"all", "past_24_hours", "this_week", "this_month", "last_3_months" or "last_6_months" (rolling windows)
after_datetime / before_datetime	str / date / datetime	Exact publish-date bounds
min_likes / max_likes / min_views / max_views	int	Engagement filters
webhook_url	str	Optional; see Webhooks
limit	int	Page size of the returned first page (default 50)
include_usage, timeout		As for other job methods

Without sort_by, results come back newest first. If you set after_datetime but not published_within, the smallest window covering after_datetime is picked automatically. parallel_search_slices is not a time filter; use published_within or the datetime bounds for that.

Read further pages with get_tiktok_search(task_id, cursor=...), exactly as for profiles. Completed searches may include a download_url with the full JSON result.

---

Semantic Search

Search YouTube

Search across all of YouTube. This runs a standard YouTube search, then ranks the results with AI analysis of their transcripts.

results = client.search_youtube(
    query="how to train a neural network from scratch",
    start_year=2023,
    max_results=5,
)

print(f"Found {results.data.total_found} results")
print(results.data.explanation)

for r in results.data.results:
    print(f"{r.title} (score: {r.relevance_score})")
    print(f"  {r.transcript_summary}")

Parameter	Type	Description
query	str	Natural-language search query
use_enhanced_search	bool	AI-enhanced query (default: True)
start_year / end_year	int	Filter by publish date
focus	str	"relevance" (default), "popularity", or "brevity"
duration	int	Filter by video duration in seconds
max_results	int	Max candidate videos to analyze and return; each one is billed, so this caps cost (defaults to your plan ceiling)
include_usage	bool	Attach a per-call usage block to the response

search_videos still works as a deprecated alias for search_youtube.

Search your uploaded files

Search the content of your uploaded files in natural language. Results are ranked by relevance, with the timestamps where the matching content appears:

results = client.search_files(
    query="customer feedback about pricing",
    namespace_ids=["ns_client_calls"],  # optional: scope to specific namespaces
)

for r in results.data.results:
    print(f"{r.name} (score: {r.relevance_score})")
    print(f"  Timestamps: {r.timestamps}")
    print(f"  {r.relevant_text}")

---

File Uploads

Upload audio or video files for persistent storage. Once processed, files can be transcribed, analyzed, searched, and used for extraction, just like online videos.

Upload and process

result = client.upload_file("./meeting.mp4", wait_for_completion=True)
file_id = result["file_id"]
print(result["file_status"])  # "completed"

With wait_for_completion=False (the default), the upload returns immediately and processing continues in the background. Check get_file(file_id).data.file_info.status until it is "completed".

Retrieve a file and its transcript

resp = client.get_file(file_id)  # pass transcript_text=True for a single string
info = resp.data.file_info

print(f"{info.name} ({info.duration}s, {info.status})")
print(f"Namespaces: {[ns.name for ns in info.namespaces or []]}")

for segment in resp.data.transcript or []:
    print(f"[{segment.start:.1f}s] {segment.text}")

List and filter files

resp = client.get_files(limit=20, status="completed")
for f in resp.data.files:
    print(f"{f.name} -- {f.status}")

# Filter by namespace
resp = client.get_files(namespace_id="ns_client_calls")

# Paginate
resp = client.get_files(limit=20, offset=20)
print(resp.data.has_more)

File management

client.get_file_url(file_id)            # signed download URL
client.retry_file_processing(file_id)   # retry after failure
client.cancel_file_upload(file_id)      # cancel in-progress processing
client.delete_file(file_id)             # permanently delete

---

Namespaces

Organize uploaded files into namespaces. Use them to group content by project, client, topic, or any other category, then scope searches and listings to specific namespaces.

# Create
ns = client.create_namespace(name="Client Calls")
print(ns.data.id)  # "ns_abc123"

# List all
for ns in client.get_namespaces().data:
    print(f"{ns.id}: {ns.name}")

# Rename
client.update_namespace("ns_abc123", name="Sales Calls")

# Upload a file into a namespace
client.upload_file("./call.mp3", namespace_ids=["ns_abc123"])

# Reassign a file's namespaces
resp = client.update_file_namespaces(file_id, namespace_ids=["ns_abc123", "ns_def456"])
print(resp.data.namespaces)  # updated namespace list

# Delete (files are unlinked, not deleted)
client.delete_namespace("ns_abc123")

---

Per-call usage

Pass include_usage=True to see exactly which meters a call charged, how many credits were deducted, and (for AI features) how many LLM tokens were used.

It is supported by get_transcript, transcribe_video, analyze_video, analyze_file, extract_video_data, extract_file_data, search_youtube, search_files, get_tweet_statement, scrape_tiktok_profile, search_tiktok, get_tiktok_profile_scrape, get_tiktok_search, and job.result(include_usage=True). For background jobs, usage appears once the job is completed; failed jobs have all their charges reverted.

resp = client.search_youtube(query="react best practices", include_usage=True)

usage = resp.usage
if usage:
    print(f"Credits this call: {usage.total_credits}")

    for charge in usage.charges or []:
        print(f"  {charge.service_type}: {charge.quantity} unit(s), {charge.credits} credits")

    # LLM token tally (analyze / extract / YouTube search / tweet analysis)
    tokens = usage.analysis_tokens
    if tokens:
        print(f"  LLM tokens: {tokens.total_tokens}")

Field	Description
charges	List of UsageCharge (one per meter): service_type, quantity, credits, waived, credits_saved, and (analysis only) nested tokens
total_credits	Net credits deducted by the call
waived	Present when a cache-hit sponsorship waived charges (waived.credits_saved)
analysis_tokens	The LLM token counts (prompt_tokens, completion_tokens, total_tokens)
charge_for(service_type)	The charge entry for one meter, e.g. usage.charge_for("transcription_hour")

Meters: standard_request, residential_request, transcription_hour, analysis_request, search_request and scene_analysis_hour.

---

Usage & Billing

usage = client.get_usage().data

print(f"Credits: {usage.credits.monthly_remaining} / {usage.credits.monthly_total}")
print(f"Storage: {usage.storage.used_formatted} / {usage.storage.limit_formatted}")

if usage.usage:
    print(f"Standard requests: {usage.usage.standard_request.used}")
    print(f"Residential requests: {usage.usage.residential_request.used}")
    print(f"Search requests: {usage.usage.search_request.used}")
    print(f"Analysis requests: {usage.usage.analysis_request.used}")
    print(f"Transcription hours: {usage.usage.transcription_hour.used}")

---

Error Handling

Every API error maps to a specific exception, and all of them inherit from VidNavigatorError:

from vidnavigator import NotFoundError, RateLimitExceeded, VidNavigatorError

try:
    client.get_file("nonexistent-id")
except NotFoundError:
    print("File not found")
except RateLimitExceeded:
    print("Rate limit hit -- slow down")
except VidNavigatorError as exc:
    print("Something else went wrong:", exc)

Exception	Status	When
BadRequestError	400	Invalid parameters or schema
AuthenticationError	401	Missing or invalid API key
PaymentRequiredError	402	Not enough credits; upgrade or top up
AccessDeniedError	403	Insufficient permissions, or content that requires login or age verification
NotFoundError	404	Resource not found, including an unknown or expired job task_id
StorageQuotaExceededError	413	Storage quota exceeded
RateLimitExceeded	429	Too many requests
TooManyActiveJobsError	429	Too many background jobs already running. Subclass of RateLimitExceeded.
GeoRestrictedError	451	Content unavailable in your region
SystemOverloadError	503	Temporary overload (check .retry_after_seconds)
ServerError	5xx	Unexpected server error
JobTimeoutError	--	A blocking call or job.result() hit its timeout. The job keeps running; resume with .job or .task_id.
WebhookSignatureError	--	A webhook delivery failed signature or timestamp verification
VidNavigatorError	--	Base class for all errors, also raised for network failures

API exceptions carry the details of the error response:

from vidnavigator import BadRequestError

try:
    client.extract_video_data(video_url=url, schema={"x": {"type": "Nope", "description": "?"}})
except BadRequestError as exc:
    print(exc.status_code)  # 400
    print(exc.error_code)   # "invalid_schema" -- machine-readable, safe to branch on
    print(exc.docs_url)     # link to the relevant docs, when provided
    print(exc.payload)      # the full JSON error body

The same exceptions, with the same fields, are raised when a background job fails.

---

Configuration

client = VidNavigatorClient(
    api_key="YOUR_API_KEY",       # or set VIDNAVIGATOR_API_KEY env var
    base_url="https://...",       # override for staging / self-hosted
    timeout=60,                   # per HTTP request, in seconds (default: 30)
    session=my_requests_session,  # bring your own requests.Session
)

The client's timeout applies to each HTTP request. It is separate from the timeout argument of job methods such as transcribe_video(timeout=...), which limits how long to wait for a whole job.

Use the client as a context manager to close its HTTP session automatically:

with VidNavigatorClient() as client:
    resp = client.get_transcript(video_url="...")
    print(resp.data.transcript)

One client can be reused for any number of calls. If you use threads, create one client per thread.

---

Upgrading from 1.x

Version 2.0 runs speech-to-text and TikTok operations as background jobs. Most code keeps working unchanged. Check these points:

In 1.x	In 2.0
transcribe_video, extract_video_data and get_tweet_statement made one long HTTP request	They submit a job and poll it. Same arguments and return types, and no media duration limit. They can raise JobTimeoutError (default wait: one hour).
extract_video_data(...).video_info held the video metadata	video_info is None for online videos; use get_transcript(video_url=..., metadata_only=True)
extract_video_data(..., include_usage=True).usage.total_tokens	Use usage.analysis_tokens.total_tokens
submit_tiktok_profile_scrape / submit_tiktok_search returned the submit response	They return a Job. job.data.task_id still works; job.status is now a method, so replace task.status == "success" checks.
Manual polling loops on get_tiktok_profile_scrape	scrape_tiktok_profile(...), or job.result() on a submitted job
task.data.error_message on failed TikTok tasks	Deprecated; read task.data.error (error, message, http_status), or let job.result() raise
A 401 raised the generic VidNavigatorError	It raises AuthenticationError

New in 2.0: submit_transcribe_video, submit_extract_video_data, submit_tweet_statement, scrape_tiktok_profile, search_tiktok, resume_job, webhook_url on every job method, sort_by and published_within for TikTok search, the webhook helpers, and status_code / error_code / docs_url / payload on every API exception.

---

Requirements

• Python 3.7+
• requests >= 2.31
• pydantic >= 1.10 (v1 and v2 both supported)

---

Documentation

Full API reference and guides: docs.vidnavigator.com

---

License

Apache License 2.0 -- see LICENSE for details.