Ingest documents & media

Pull files, web pages, and media into the knowledge store so the agent can recall them. Ingestion extracts text, chunks it, embeds it, and indexes it — one call handles plain text, Markdown, HTML, PDF, audio, video, and web/YouTube URLs.

This is the ingest (write) side. For how recall works and the tuning knobs, see Tune the knowledge store (RAG); for the design, see Memory & the knowledge store.

From the console

Knowledge → Store → Add source. Drop a file, or paste a web/YouTube URL, pick a domain (defaults to general), and import. The form accepts txt, md, html, pdf and audio/video (mp3, wav, m4a, flac, ogg, opus, aac, mp4, mov, mkv, webm, avi, m4v). You'll see Added N chunks from "<title>".

From the agent

The agent can ingest on its own with the knowledge_ingest(source, domain, title?) tool — source is an http(s) URL (including YouTube) or a local file path. Hand it a link or a file in chat ("read this and remember it", "ingest this PDF") and it runs the same pipeline below, rather than trying to web_search/fetch_url a media link (which can't get a transcript). It's distinct from memory_ingest, which only stores text the agent already has — see the tool table.

Audio/video/image paths need the same setup as the console (knowledge.transcribe_model / knowledge.image_describe_model, plus ffmpeg on PATH for video); if one isn't configured the tool says so instead of failing silently.

Slow sources run in the background so they never block the chat. Anything that fetches over the network (any URL, including YouTube) or transcribes media (audio/video) is detached as a background job — the tool returns a job id immediately, and the result is delivered back into the conversation (and the console's background jobs surface) when indexing finishes, so a 20-minute video never freezes the turn. Only a small local text/Markdown file (≤64 KB) ingests inline. It's the same durable background machinery as task(run_in_background=true): concurrency-capped, cancellable, and it survives past the turn. (With the background subsystem disabled via BACKGROUND_DISABLED, the tool falls back to inline/blocking ingestion.)

From the API

# a file
curl -F file=@notes.pdf -F domain=research \
  http://localhost:7870/api/knowledge/ingest

# a web page or YouTube URL
curl -F url=https://example.com/post -F title="That post" \
  http://localhost:7870/api/knowledge/ingest

# raw text
curl -F text="the thing to remember" -F title=Note \
  http://localhost:7870/api/knowledge/ingest

POST /api/knowledge/ingest (multipart) takes one of file / url / text, plus optional title and domain. It responds with the chunk ids, the chunks count, the detected source_type, and chars.

What's supported

Source	How it's handled	Needs
Text / Markdown	decoded directly	—
HTML	readable text extracted (script/nav/footer stripped)	—
PDF	text extracted per page	pypdf
Web URL	fetched, then dispatched by content-type	—
YouTube URL	transcript via the captions API, else gateway STT	youtube-transcript-api
Audio	transcribed via the gateway's /audio/transcriptions (Whisper)	knowledge.transcribe_model set
Video	ffmpeg extracts the audio track → gateway STT	ffmpeg on PATH + transcribe_model

Format is detected from the file extension, then the content-type, then a UTF-8 heuristic. The pypdf / youtube-transcript-api deps are lazy-imported — a missing one fails that source with a clear message, never the server. Audio/video always transcribe through the gateway (no local ASR); leave transcribe_model blank to disable media ingestion.

Chunking & enrichment (config)

Large documents are split before embedding. Tune under knowledge: in langgraph-config.yaml:

knowledge:
  transcribe_model: whisper-1     # gateway STT model; blank disables audio/video
  chunk_max_chars: 1200           # target ceiling per chunk
  chunk_overlap_chars: 150        # shared tail between adjacent chunks
  chunk_min_chars: 200            # trailing fragments fold into the prior chunk
  contextual_enrichment: false    # prepend a 1-sentence doc context per chunk before
                                  # embedding (Anthropic Contextual Retrieval) — one aux
                                  # LLM call per chunk at INGEST time; off by default
  context_max_doc_chars: 12000    # doc cap fed to the enrichment prompt

Chat attachments take the same path above attach_inline_budget (default 8000 chars): small files are read inline for the turn; larger ones are ingested + indexed and a lede is surfaced, so a big paste never dumps into context. See chat file upload.