Version 0.8.0

New Features

The main changes in this release are:

• AutoPDL: automatic optimization of PDL programs
• Calling PDL functions from Jinja and Python
• Map/reduce block

AutoPDL: automatic optimization of PDL programs

Given a PDL program with some free variables, a search space for these free variable, a loss function, and a training set, you can optimize the PDL program using the pdl-optimize command. This allows to optimize any part of a PDL program such as the textual prompts, the few-shots examples, or the prompting patterns. This work is based on the paper AutoPDL: Automatic Prompt Optimization for LLM Agents. A new section of the manual is describing how to use the optimizer.

Calling PDL functions from Jinja and Python

It is now possible to call a function defined in PDL inside a Jinja expression or a Python code block. Here is an example where the function translate is called in the Jinja expression ${ translate("Hello", language="French") }:

description: Calling a PDL function from Jinja
defs:
  translate:
    function:
      sentence: string
      language: string
    return:
      lastOf:
      - |
        Translate the sentence '${ sentence }' to ${ language }.
        Only give the result of the translation.
      - model: ollama_chat/granite3.2:2b
text: |
  The way to say hello in French is ${ translate("Hello", language="French") }.

Map/reduce block

Similar to the repeat loops, PDL now offers a map block. The difference with the repeat block is that the context is not accumulated between iterations, each iteration is executed with the same context. Here is an example of map block:

lastOf:
- "Hello, "
- for:
    name: [Alice, Bob, Charlie]
  map:
    lastOf:
    - my name is ${ name }
    - model: ollama/granite3.2:2b
  join:
    as: array

What's Changed

• chore: Configure Renovate by @renovate[bot] in #944
• Dataset preprocessing scripts for AutoPDL by @claudiosv in #994
• fix: avoid side effects in PdlContext by @mandel in #1054
• tests: update react example by @mandel in #1001
• tests: update calc example to use .signature by @mandel in #1067
• fix: output streaming in case of fallback by @mandel in #1071
• feat: make PDL functions callable as Python and jinja functions by @mandel in #1070
• feat: allow unnamed arguments when calling a PDL function in Jinja by @mandel in #1076
• feat: map/reduce by @mandel in #1010
• refactor: refactor AST to generate simpler JSON Schema by @mandel in #1079
• Fix for exception in optimizer by @vazirim in #1084
• docs: update tutorial by @mandel in #1080
• Requirements implementation by @vazirim in #1097
• fix: update AST in the UI by @mandel in #1100
• fix: ensure that PazyApply.result returns a non-lazy value by @mandel in #1101
• feat: parallel map reduce by @mandel in #1102
• Fix for llm message post-processing by @vazirim in #1106
• AutoPDL Simplification by @claudiosv in #1088
• tests: map-based gsm8k by @mandel in #1107
• optimizer CLI and doc by @vazirim in #1151

Full Changelog: v0.7.1...v0.8.0