Merge pull request #9 from debuggerone/do_documentation

Do documentation

docs/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/make.bat

@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

docs/source/conf.py

@@ -0,0 +1,28 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = 'agentm-py'
+copyright = '2024, Steven Ickman, Jochen Schultz'
+author = 'Steven Ickman, Jochen Schultz'
+release = '0.1'
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = []
+
+templates_path = ['_templates']
+exclude_patterns = []
+
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = 'alabaster'
+html_static_path = ['_static']

docs/source/index.rst

@@ -0,0 +1,17 @@
+.. agentm-py documentation master file, created by
+   sphinx-quickstart on Sat Sep 21 01:49:41 2024.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+agentm-py documentation
+=======================
+
+Add your content using ``reStructuredText`` syntax. See the
+`reStructuredText <https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html>`_
+documentation for details.
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+

examples/binary_classify_list_example.py

@@ -1,13 +1,18 @@
 import asyncio
-from core.binary_classify_list_agent import BinaryClassifyListAgent
+from core.binary_classify_list_agent import BinaryClassifyListAgent, BinaryClassifyListInput
 
 async def run_binary_classify_list_example():
-    items_to_classify = ['Apple', 'Chocolate', 'Carrot']
-    criteria = 'Classify each item as either healthy (true) or unhealthy (false)'
-    agent = BinaryClassifyListAgent(list_to_classify=items_to_classify, criteria=criteria)
+    input_data = BinaryClassifyListInput(
+        list_to_classify=['Apple', 'Chocolate', 'Carrot'],
+        criteria='Classify each item as either healthy (true) or unhealthy (false)',
+        max_tokens=1000,
+        temperature=0.0
+    )
+
+    agent = BinaryClassifyListAgent(input_data)
     classified_items = await agent.classify_list()
 
-    print("Original list:", items_to_classify)
+    print("Original list:", input_data.list_to_classify)
     print("Binary classified results:", classified_items)
 
 if __name__ == "__main__":

examples/chain_of_thought_example.py

@@ -1,12 +1,17 @@
 import asyncio
-from core.chain_of_thought_agent import ChainOfThoughtAgent
+from core.chain_of_thought_agent import ChainOfThoughtAgent, ChainOfThoughtInput
 
 async def run_chain_of_thought_example():
-    question = 'What is the square root of 144?'
-    agent = ChainOfThoughtAgent(question=question)
+    input_data = ChainOfThoughtInput(
+        question='What is the square root of 144?',
+        max_tokens=1000,
+        temperature=0.0
+    )
+
+    agent = ChainOfThoughtAgent(input_data)
     result = await agent.chain_of_thought()
 
-    print("Question:", question)
+    print("Question:", input_data.question)
     print("Chain of Thought Reasoning:", result)
 
 if __name__ == "__main__":

examples/classify_list_example.py

@@ -1,14 +1,18 @@
 import asyncio
-from core.classify_list_agent import ClassifyListAgent
+from core.classify_list_agent import ClassifyListAgent, ClassifyListInput
 
 async def run_classify_list_example():
-    items_to_classify = ['Apple', 'Chocolate', 'Carrot']
-    classification_criteria = 'Classify each item as healthy or unhealthy snack'
-    agent = ClassifyListAgent(list_to_classify=items_to_classify, classification_criteria=classification_criteria)
-    classified_items = await agent.classify_list()
+    input_data = ClassifyListInput(
+        list_to_classify=["Apple", "Banana", "Carrot"],
+        classification_criteria="Classify each item as a fruit or vegetable.",
+        max_tokens=1000
+    )
+
+    agent = ClassifyListAgent(input_data)
+    classifications = await agent.classify_list()
 
-    print("Original list:", items_to_classify)
-    print("Classified results:", classified_items)
+    print("Original list:", input_data.list_to_classify)
+    print("Classified results:", classifications)
 
 if __name__ == "__main__":
-    asyncio.run(run_classify_list_example())
+    asyncio.run(run_classify_list_example())

examples/filter_list_example.py

@@ -1,23 +1,27 @@
 import asyncio
-from core.filter_list_agent import FilterListAgent
+from core.filter_list_agent import FilterListAgent, FilterListInput
 
 async def run_filter_list_example():
-    goal = "Remove items that are unhealthy snacks."
-    items_to_filter = [
-        "Apple",
-        "Chocolate bar",
-        "Carrot",
-        "Chips",
-        "Orange"
-    ]
-
-    agent = FilterListAgent(goal=goal, items_to_filter=items_to_filter)
+    input_data = FilterListInput(
+        goal="Remove items that are unhealthy snacks.",
+        items_to_filter=[
+            "Apple",
+            "Chocolate bar",
+            "Carrot",
+            "Chips",
+            "Orange"
+        ],
+        max_tokens=500,
+        temperature=0.0
+    )
+
+    agent = FilterListAgent(input_data)
     filtered_results = await agent.filter()
 
-    print("Original list:", items_to_filter)
+    print("Original list:", input_data.items_to_filter)
     print("Filtered results:")
     for result in filtered_results:
         print(result)
 
 if __name__ == "__main__":
-    asyncio.run(run_filter_list_example())
+    asyncio.run(run_filter_list_example())

examples/generate_object_example.py

@@ -1,13 +1,17 @@
 import asyncio
-from core.generate_object_agent import GenerateObjectAgent
+from core.generate_object_agent import GenerateObjectAgent, ObjectGenerationInput
 
 async def run_generate_object_example():
-    description = "A machine that can sort fruits."
-    goal = "Generate a high-level design of the machine."
-    agent = GenerateObjectAgent(object_description=description, goal=goal)
+    input_data = ObjectGenerationInput(
+        object_description="A machine that can sort fruits.",
+        goal="Generate a high-level design of the machine.",
+        max_tokens=1000
+    )
+
+    agent = GenerateObjectAgent(input_data)
     generated_object = await agent.generate_object()
 
-    print("Object description:", description)
+    print("Object description:", input_data.object_description)
     print("Generated object:", generated_object)
 
 if __name__ == "__main__":

examples/grounded_answer_example.py

@@ -1,15 +1,19 @@
 import asyncio
-from core.grounded_answer_agent import GroundedAnswerAgent
+from core.grounded_answer_agent import GroundedAnswerAgent, GroundedAnswerInput
 
 async def run_grounded_answer_example():
-    question = "What is the capital of France?"
-    context = "France is a country in Western Europe. Paris is its capital and largest city."
-    instructions = "Ensure the answer is grounded only in the provided context."
-    agent = GroundedAnswerAgent(question=question, context=context, instructions=instructions)
-    result = await agent.answer()
+    input_data = GroundedAnswerInput(
+        question="What is the capital of France?",
+        context="France is a country in Western Europe known for its wine and cuisine. The capital is a major global center for art, fashion, and culture.",
+        instructions="",
+        max_tokens=1000
+    )
+
+    agent = GroundedAnswerAgent(input_data)
+    answer = await agent.answer()
 
-    print("Question:", question)
-    print("Result:", result)
+    print("Question:", input_data.question)
+    print("Answer:", answer)
 
 if __name__ == "__main__":
-    asyncio.run(run_grounded_answer_example())
+    asyncio.run(run_grounded_answer_example())

examples/map_list_example.py

@@ -1,13 +1,17 @@
 import asyncio
-from core.map_list_agent import MapListAgent
+from core.map_list_agent import MapListAgent, MapListInput
 
 async def run_map_list_example():
-    items_to_map = ['Apple', 'Banana', 'Carrot']
-    transformation = 'Convert all items to uppercase'
-    agent = MapListAgent(list_to_map=items_to_map, transformation=transformation)
+    input_data = MapListInput(
+        list_to_map=['Apple', 'Banana', 'Carrot'],
+        transformation='Convert all items to uppercase',
+        max_tokens=1000
+    )
+
+    agent = MapListAgent(input_data)
     transformed_items = await agent.map_list()
 
-    print("Original list:", items_to_map)
+    print("Original list:", input_data.list_to_map)
     print("Transformed list:", transformed_items)
 
 if __name__ == "__main__":

examples/project_list_example.py

@@ -1,13 +1,17 @@
 import asyncio
-from core.project_list_agent import ProjectListAgent
+from core.project_list_agent import ProjectListAgent, ProjectListInput
 
 async def run_project_list_example():
-    items_to_project = ['Apple', 'Banana', 'Carrot']
-    projection_rule = 'Project these items as their vitamin content'
-    agent = ProjectListAgent(list_to_project=items_to_project, projection_rule=projection_rule)
+    input_data = ProjectListInput(
+        list_to_project=['Apple', 'Banana', 'Carrot'],
+        projection_rule='Project these items as their vitamin content',
+        max_tokens=1000
+    )
+
+    agent = ProjectListAgent(input_data)
     projected_items = await agent.project_list()
 
-    print("Original list:", items_to_project)
+    print("Original list:", input_data.list_to_project)
     print("Projected results:", projected_items)
 
 if __name__ == "__main__":

examples/reduce_list_example.py

@@ -1,14 +1,18 @@
 import asyncio
-from core.reduce_list_agent import ReduceListAgent
+from core.reduce_list_agent import ReduceListAgent, ReduceListInput
 
 async def run_reduce_list_example():
-    items_to_reduce = ['Banana', 'Apple', 'Carrot']
-    reduction_goal = 'Reduce these items to a single word representing their nutritional value'
-    agent = ReduceListAgent(list_to_reduce=items_to_reduce, reduction_goal=reduction_goal)
+    input_data = ReduceListInput(
+        list_to_reduce=["Apple", "Banana", "Carrot"],
+        reduction_goal="Reduce each item to its first letter.",
+        max_tokens=1000
+    )
+
+    agent = ReduceListAgent(input_data)
     reduced_items = await agent.reduce_list()
 
-    print("Original list:", items_to_reduce)
+    print("Original list:", input_data.list_to_reduce)
     print("Reduced results:", reduced_items)
 
 if __name__ == "__main__":
-    asyncio.run(run_reduce_list_example())
+    asyncio.run(run_reduce_list_example())

examples/summarize_list_example.py

@@ -1,12 +1,19 @@
 import asyncio
-from core.summarize_list_agent import SummarizeListAgent
+from core.summarize_list_agent import SummarizeListAgent, SummarizeListInput
 
 async def run_summarize_list_example():
-    items_to_summarize = ['The quick brown fox jumps over the lazy dog.', 'Python is a popular programming language.']
-    agent = SummarizeListAgent(list_to_summarize=items_to_summarize)
+    input_data = SummarizeListInput(
+        list_to_summarize=[
+            'The quick brown fox jumps over the lazy dog.',
+            'Python is a popular programming language.'
+        ],
+        max_tokens=1000
+    )
+
+    agent = SummarizeListAgent(input_data)
     summaries = await agent.summarize_list()
 
-    print("Original list:", items_to_summarize)
+    print("Original list:", input_data.list_to_summarize)
     print("Summarized results:", summaries)
 
 if __name__ == "__main__":

requirements.txt

@@ -19,3 +19,7 @@ anyio
 trio
 openai
 jsonschema
+sphinx
+sphinx-rtd-theme
+myst-parser
+pydantic