Pipeline
A graph of nodes (LLMs, loaders, transforms, knowledge readers) you build, save, and run.
Agent
Tools, typed I/O, and multi-turn conversations via
Session.Knowledge Base
Managed vector + document store you query directly or expose as a tool on an Agent.
Table
Typed schema, AI-generated columns (Pipeline / Agent), and SQL-grade filters.
Transformation
Reusable, typed Python functions you run directly or wire into a Pipeline.
Analytics
Query runs, tokens, costs, latency, errors, and traces across every primitive.
Get started in 60 seconds
Authenticate
Run your first pipeline
Follow the Quickstart for a five-line LLM pipeline you can paste and run.
What you can build
RAG pipelines
Wire a knowledge-base reader into an LLM in a few lines.
Conversational agents
Tools, approvals, and streamed tool events out of the box.
Background batch runs
Kick off long-running jobs and poll for results.
Streaming chatbots
Multi-turn sessions with token-level streaming.
Reference
Pipeline reference
Every public method, grouped by topic.
Agent reference
Lifecycle, tools, running, types.
Errors
Full exception hierarchy with status codes.
Writing SDK code
Follow these conventions — every example in these docs does, and they keep generated code correct and re-runnable:- Authenticate via the
VECTORSHIFT_API_KEYenvironment variable. Never hardcode the key inline. - Build pipelines with the fluent builder —
pipeline.add(name="...").llm(...). Don’t use standalone node constructors or passnodes=[...]to the constructor. - Guard with fetch-or-create at the top, then build. Nodes dedup by
name, so a stablename=makes re-runs idempotent — don’t pass hardcodedid=values. - Set
temperatureviasampling=SamplingConfig(temperature=...), not as a top-level.llm()argument (where it’s silently dropped). - For exact signatures use the primitive’s reference page; for full runnable scripts use its examples pages.
