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‎.gitattributes

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# Auto detect text files and perform LF normalization
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* text=auto

‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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.idea/

‎LICENSE

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MIT License
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Copyright (c) 2025 Robert DiBiano
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md

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# Conversational Programming / Agent Functions
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## Overview
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This repository introduces a new paradigm for conversational programming using **agent functions**, which leverage GPT-based reasoning to perform semi-autonomous tasks. These functions enable flexible and intuitive operations that surpass traditional functions, especially in scenarios requiring fuzzy logic, intuition, or handling of incomplete/bad input data.
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---
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## Key Concepts
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### **1. New Data Type: `conversation_fragment`**
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A `conversation_fragment` represents a segment of a GPT-powered conversation, either as input (query) or output (response).
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- Further categorization into **query** and **response** types.
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- Investigate inline tags governing conversation flow (noted by Supratik and Richard).
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### **2. New Class: `agent_function`**
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An **`agent_function`** is a GPT-powered function designed to perform a well-defined task autonomously, with minimal code and high flexibility.
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#### **Capabilities:**
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- Accepts `conversation_fragment` as input (alongside other data types).
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- Parses input types to construct the final query dynamically.
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- Produces output in the form of `conversation_fragment` or specific structured responses.
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- Supports **Chain of Thought** reasoning, breaking down complex problems into manageable subtasks.
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- Automatically logs reasoning processes for debugging and review.
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---
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## Features & Design Principles
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### **Agent Function Behavior**
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- **Flexible Input Handling**
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Other input types can be parsed together at the function's start to form a structured query.
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- **Structured Output Formatting**
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Queries can specify output formats, e.g.:
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```plaintext
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"The output should be an integer in brackets, e.g., '[2]', '[0]', etc."
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```
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The function then parses the structured response accordingly.
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### **Chain of Thought Reasoning**
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- Enables step-by-step problem-solving.
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- Automatically decomposes complex tasks into subtasks.
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- Summarizes reasoning and decisions for logging and debugging.
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- If parsing fails, retries with an extended query before throwing an exception.
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- Explicit handling of queries that are too long and get truncated:
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- Attempt to process truncated results.
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- Issue a follow-up query for a more concise response.
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- Modify the base query to request shorter responses if truncation happens frequently.
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- Special **failure assessment agent functions** can be created for critical tasks.
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- Cross-validation across multiple LLMs for comparison.
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### **Retrieval-Augmented Generation (RAG)**
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- Built-in support for both **web retrieval** and **user-defined databases**.
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### **Failure Rate Analysis & Statistics**
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- Failure rate tracking built into `agent_function` class.
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- Automatic failure categorization (potentially GPT-assisted).
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- GPT-based auto-correction of queries based on failure rate and type.
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- Versioned statistics—failure rates are tracked separately for different function versions.
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### **LLM-Agnostic Design**
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- **Agent functions should generalize across multiple LLMs** without requiring code duplication.
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### **Decision-Making Agent Functions**
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Agent functions can:
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1. **Process data and generate results**
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2. **Make branching decisions to determine program flow**
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---
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## Built-in Standard Workflows
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### **Basic Tasks**
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- **Pick an item from a small list** (single-step decision-making)
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- **Correct grammatical or formatting errors**
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### **Advanced Chain of Thought**
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1. **Reasoning step before decision-making** (standardized `conversation_fragment` structure)
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2. **Error correction with optional reasoning** (asks for clarification if ambiguous)
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3. **Simple True/False decision-making** (with reasoning)
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4. **Pick an item from a list with reasoning**
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5. **Process a large dataset item-by-item with reasoning**, including:
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- Categorization
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- Summarization
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- Other forms of processing
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### **Retrieval-Augmented Generation (RAG)**
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- Intelligent selection of retrieval sources based on short previews.
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- Summarization and compression of retrieved content for more efficient use.
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---
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## TODO List
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### **New Features to Implement**
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- **`BranchingConversation` class** for managing multi-step agent-driven dialogues.
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- **Versioning system** for conversation fragments and agent functions:
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- Every function using conversation fragments should be versioned.
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- Support calling specific function versions.
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- Ability to compare performance across multiple versions.
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- Reduce code duplication while integrating versioning across multiple functions.
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- **Integrate system messages into generated prompts**.
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- **Add notes/history fields** to conversations/fragments:
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- Track edits, transformations, agent interactions, and lineage.
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---
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## Existing Technologies & References
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- **OpenAI Function Calling & Assistants API**
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[Everything You Need to Know](https://medium.com/the-modern-scientist/everything-you-need-to-know-about-openai-function-calling-and-assistants-api-55c02570a21c)
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- **Agenta: Open-Source LLMOps Platform**
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[https://agenta.ai/](https://agenta.ai/)
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- **Pezzo: Open-Source AI Developer Platform**
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[https://github.com/pezzolabs/pezzo](https://github.com/pezzolabs/pezzo)

‎agent_functions/__init__.py

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# expose (a.k.a. re-export) your main classes so users can do something like
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# from agent_functions import ConversationFragment without needing to know the exact file structure.
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from .conversation_fragment import ConversationFragment, Conversation
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from .agent_function import AgentFunction
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__all__ = ["ConversationFragment", "Conversation", "AgentFunction"]

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