AI library

Model operations. Bound by AI Flow on top of the common library.

Every model call goes through the AI Gateway, so an atom here names an alias rather than a provider. Which model that resolves to, what it costs and whether it is cached are configuration, not flow text — which is what lets a flow outlive the model it was written against.

Model routing, vector-store connections and tenant context come from the pipeline environment that AI Flow sets up around a running flow. A plain kis script run has none of that, so the namespace functions that need it report so and tell you which task to use instead:

llm.chat requires a pipeline environment with ProxySelector for model routing.
Use the aiflow YAML interface (chat task with type=chat) instead.

The tables below mark each function pipeline-only or callable anywhere. Unlike the common library, where the two surfaces are interchangeable, here the flow task is the primary surface and the namespace is for use inside a flow — from a script: task or a plugin: stage, where the environment exists.

Generation

Operation	Flow task	Script functions
Chat completion	`chat:`	`llm.chat`, `llm.complete`	pipeline-only
Intent detection	`getintent:`	`intent.classify`	pipeline-only
Guardrails	`pre-guard:`, `post-guard:`	`guard.check`	pipeline-only

pre-guard: and post-guard: are the same atom in two positions — before the model sees input, and after it produces output. Both are worth having: an input guard stops what should never reach a model, an output guard stops what should never reach a user.

Embeddings and vectors

Operation	Flow task	Script functions
Embeddings	`embed:`, `embeddata:`	`embed.generate`	pipeline-only
Chunk text	—	`embed.split`	callable anywhere
Vector upsert	`vector-upsert:`	`vector.upsert`	callable anywhere
Vector search	`vector-search:`	`vector.search`	callable anywhere
Vector delete	`vector-delete:`	`vector.delete`	callable anywhere
Vector list	`vector-list:`	`vector.list`	callable anywhere
Collections	`topicsinsert:`, `topicsquery:`, `topicssearch:`, `topicslist:`, `topicsdelete:`	`milvus.insert`, `.query`, `.search`, `.list`, `.delete`	pipeline-only

embed.split chunks text before embedding it. Chunking is where retrieval quality is usually won or lost, so it is a separate call rather than a hidden step inside generate.

Retrieval

Operation	Flow task	Script functions
Ingest a corpus	`rag-ingest:`	`rag.ingest`	callable anywhere
Answer from a corpus	`rag-answer:`	`rag.answer`	callable anywhere
Rerank results	`rerank:`	`rag.rerank`	callable anywhere

Reranking is the cheapest quality improvement available to a retrieval pipeline: retrieve broadly, then rerank to precision. It is a separate atom because the two steps want different models.

Reasoning

Operation	Flow task	Script functions
Tree of thought	`tot:`	`reason.tot`	callable anywhere
Reflexion	`reflexion:`	`reason.reflexion`	callable anywhere

Both are multi-step patterns with a cost profile to match — they call a model repeatedly by design. Use them where a single call demonstrably is not enough, and put a budget on them. In a flow that means a harness rather than a plain flow.

Content

Operation	Flow task	Script functions
Scrape a source	`scrape:`, `scrapedata:`	`scrape.web`, `.pdf`, `.excel`, `.docx`, `.pptx`	pipeline-only
Split into sentences	`tosentence:`	`text.tosentence`	pipeline-only
Split on a delimiter	—	`text.split`	callable anywhere
Validate JSON	`validate-json:`	`validate.json`	callable anywhere
Validate YAML	`validate-yaml:`	`validate.yaml`	callable anywhere

validate-json: earns its place next to a model call. A model asked for JSON usually produces JSON; validating rather than assuming is the difference between a flow that fails at the validation node and one that fails three nodes later with something unhelpful. Both validators require a schema — validating against nothing is just a parse.

Pipelines

Operation	Flow task	Script functions
Pipeline chat	`mlpipelinechat:`, `mlpipechat:`	`pipeline.chat`	pipeline-only
Pipeline request	`mlpipelinerequest:`, `mlpiperequest:`	`pipeline.request`	pipeline-only
Pipeline response	`mlpipelineresponse:`, `mlpiperesponse:`	`pipeline.response`	pipeline-only

These are the innermost surface: they act on the pipe and the record currently in flight, so they only mean anything inside a running pipeline.

Streaming sources

AI Flow also binds the streaming forms of the pipeline operations, so a flow can read a source and feed it to a model without changing engine: readcsv:, readdb:, readexcel:, readparquet:, readrest:, readstdout:, streamrest:, streamplugin:, streamprint:, aggsum:, sum:, sumstr:, replicate:, writecsv:, writedb:, writeexcel:, writeparquet:, noop:.

For bulk movement with no model involved, use Datapipes instead — it is built for the record stream and does not carry the rest of this library.