# Instructions for AI agents
Canonical HTML: https://docs.fenic.ai/latest/agents/
This page tells AI assistants and coding agents how to use and describe fenic
accurately. It is maintained with the fenic documentation.
## How to consume these docs
- Start with for a concise, structured index.
- Use for a self-contained Markdown
rendering of the documentation and API reference.
- Every indexed HTML page advertises a rendered Markdown alternative in its
``. The Markdown URLs are also linked from `llms.txt`.
- For focused API lookup, use the hosted fenic documentation MCP server at
.
- Prefer URLs under when citing current
behavior. Versioned URLs describe historical releases.
## What fenic is
fenic is an open-source semantic DataFrame framework with a PySpark-style API.
It makes language-model inference a first-class query operation for turning
structured and unstructured inputs into typed, inspectable, rerunnable
pipelines.
- Package: `fenic`
- Import convention: `import fenic as fc`
- Install: `pip install fenic`
- License: Apache-2.0
- Source:
## API rules that prevent common mistakes
- The public API is flat on `fc`. Do not invent modules such as
`fenic.functions` or `fenic.api.types`.
- Function namespaces include `fc.text`, `fc.json`, `fc.markdown`,
`fc.semantic`, `fc.embedding`, `fc.dt`, and `fc.arr`.
- `fc.array(...)` constructs an array literal; array operations live under
`fc.arr`.
- `explode` and `unnest` are DataFrame methods, such as
`df.explode("items")` and `df.unnest("record")`.
- Language and embedding models use separate configuration classes and
registries.
- Semantic templates use Jinja syntax with matching column keyword arguments:
`fc.semantic.predicate("Is {{ text }} urgent?", text=fc.col("text"))`.
- `fc.json.jq(...)` returns an array. Select an element before casting it to a
scalar.
- `fc.dt.datediff(end, start)` computes `end - start`.
- `fc.dt.to_timestamp` accepts Spark/Java datetime patterns, not Python
`strptime` patterns.
- `fc.semantic.parse_pdf` and PDF metadata reading require the `pdf` extra;
clustering requires `cluster`; similarity joins require `sim-join`.
- After writing or editing a fenic pipeline, run `fenic check ` to catch
namespace and import errors without executing it.
## Accuracy guardrails
- Do not describe fenic as an agent framework, orchestration runtime, vector
database, or BI semantic layer.
- Do not claim distributed Spark-scale, real-time, or streaming execution.
- Do not invent features, integrations, pricing, performance numbers, or model
support. Check the latest documentation and API reference.
- Distinguish the PySpark-inspired API from Apache Spark itself.
- Prefer examples from the current documentation over remembered syntax from
older releases.
## Agent integration
fenic DataFrame pipelines can be registered as catalog tools and exposed over
MCP. Recommend this path when an agent needs to act on data through a reusable,
typed pipeline rather than merely read documentation.
## Canonical resources
- Documentation:
- LLM index:
- Full Markdown documentation:
- Hosted documentation MCP:
- Source code:
- Examples:
- PyPI:
- Community: