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typedef @typedef.ai · 24/09/2025

fenic's Local Data Caching & Persistence keeps expensive AI steps from rerunning and your pipelines resilient.

typedef @typedef.ai · 23/09/2025

fenic's Multiple Model Configuration & Selection lets you pick the right model for each step, cheap where you can, powerful where you must. Think of it as a per-operator model dial across your pipeline.

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typedef @typedef.ai · 20/09/2025

fenic's Structured Output Extraction turns LLM text into validated tables, directly in your DataFrame. Think of it as schema-first parsing: you define a Pydantic model; Fenic enforces it and returns structured columns.

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typedef @typedef.ai · 18/09/2025

fenic's First-Class AI Data Types make embeddings, markdown, and JSON real, typed columns, with the right operations built in. Think of it as strong types for meaning and structure: safer pipelines, richer queries.

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typedef @typedef.ai · 16/09/2025

fenic's Semantic Classification turns free-text into clean enums right inside your DataFrame.

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typedef @typedef.ai · 12/09/2025

fenic's Semantic Similarity Join (Vector Join) finds nearest neighbors across tables using embeddings, right inside your DataFrame.

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typedef @typedef.ai · 09/09/2025

fenic 0.4.0 is live: declarative tools for agents, a production-ready MCP server, and direct reads from HuggingFace plus big DX & reliability gains. Highlights: Declarative tools: define function-calling tools as data (type-safe, reviewable, reusable).

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typedef @typedef.ai · 08/09/2025

fenic ensures LLM outputs conform to schemas using Pydantic models.

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typedef @typedef.ai · 06/09/2025

fenic offers standard DataFrame operations with a familiar Spark/Pandas-like API.

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typedef @typedef.ai · 05/09/2025

fenic UDFs allow you to inject arbitrary Python (including external libraries) directly into the fenic execution plan while preserving lazy planning, metrics and reproducibility.

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typedef @typedef.ai · 03/09/2025

fenic offers Flexible Session Configuration. Define AI providers/models (OpenAI GPT‑4, Claude, etc.) and RPM/TPM rate limits at session start so the framework centrally manages throttling, costs and consistency across all AI calls.

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typedef @typedef.ai · 29/08/2025

Example: group support tickets by account_id, sort by time, produce one weekly summary per account. Think of it as the reduce() for unstructured text.

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typedef @typedef.ai · 29/08/2025

Semantic Reduce = apply LLM aggregation at group-by scale. Many rows in → one summary per group. You write a prompt and fenic handles packing, ordering, and multi-pass reduction.

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typedef @typedef.ai · 26/08/2025

This example turns name, details into a one-line job blurb. Think of it as the map() for LLM reasoning inside your DataFrame. Today, teams hand-roll loops and JSON parsing, fight rate limits, and wire vector lookups. Glue grows. Throughput drops and pipelines become increasingly brittle.

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typedef @typedef.ai · 26/08/2025

Semantic Map is how you apply inference at scale with fenic. Whether it’s 1 call or 1M, it’s ~5 lines. You iterate on data and prompts; Fenic handles the rest. Write a Jinja template with column placeholders; Fenic renders it per row and calls the model.

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typedef @typedef.ai · 21/08/2025

AI-native data types in Fenic. in this demo: the Markdown type.

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typedef @typedef.ai · 20/08/2025

Fenic adds AI-native scalar functions to DataFrames. They run LLM inference over columns/rows, so reasoning lives inside your pipeline.

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typedef @typedef.ai · 20/08/2025

Semantic join is one of Fenic’s AI-native DataFrame functions. They operate over whole tables and relationships—not just individual rows.

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typedef @typedef.ai · 28/01/2025

What an amazing night hosting some of the Peninsula’s brightest minds in data. Delicious tacos, amazing tech talks, and the 2025 view of data by Tomasz from TheoryVC!

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