Fix Groq provider in CHANGELOG.md and update version in pyproject.toml

.envrc.template

@@ -1,5 +1,4 @@
 export OPENAI_API_KEY=""
 export ANTHROPIC_API_KEY=""
 export XAI_API_KEY=""
-export OLLAMA_HOST_URL=""
-export GROQ_API_KEY=""
+export GROQ_API_KEY=""

.gitignore

@@ -166,4 +166,3 @@ cython_debug/
 .env
 
 src/**
-requirements.txt

CHANGELOG.md

@@ -1,22 +1,6 @@
 Release History
 ===============
 
-## 0.1.4 (2024-10-30)
-
-- Introduce `Session` class to manage multiple conversations.
-- General improvements.
-
-## 0.1.3 (2024-10-30)
-
-- Make Conversation a context manager.
-- Add more robust conversation plugin hooks — replace `send_hook` with `pre_send_hook` and `post_send_hook`.
-- Change plugin hooks to try/except NotImplementedError.
-- Implement 'did you mean' with provider names. Can do this eventually with model names, as well.
-
-## 0.1.2 (2024-10-29)
-
-- Add ollama provider.
-
 ## 0.1.1 (2024-10-29)
 
 - Fix Groq provider.

Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.12.0
+
+RUN apt-get update -y && apt-get upgrade -y
+RUN pip install --upgrade pip
+
+COPY requirements.txt /src/requirements.txt
+
+WORKDIR /src
+
+RUN pip install -r requirements.txt
+
+ENTRYPOINT ["python", "build.py"]

README.md

@@ -2,16 +2,12 @@
 
 [![Auto Wiki](https://img.shields.io/badge/Auto_Wiki-Mutable.ai-blue)](https://mutable.ai/kennethreitz/simplemind)
 
-SimpleMind is an AI library designed to simplify your experience with AI APIs in Python. Inspired by a "for humans" philosophy, it abstracts away complexity, giving developers an intuitive and human-friendly way to interact with powerful AI capabilities.
-
-With SimpleMind, tapping into AI is as easy as a friendly conversation.
+SimpleMind is an AI library designed to simplify your experience with AI APIs in Python. Inspired by a "for humans" philosophy, it abstracts away complexity, giving developers an intuitive and human-friendly way to interact with powerful AI capabilities. With SimpleMind, tapping into AI is as easy as a friendly conversation.
 
 ```bash
 $ pip install simplemind
 ```
 
-**Note**: SimpleMind is currently in beta. We welcome feedback and contributions to help make it even better.
-
 ## Features
 - **Easy-to-use AI tools**: SimpleMind provides simple interfaces to popular AI services.
 - **Human-centered design**: The library prioritizes readability and usability—no need to be an expert to start experimenting.
@@ -25,7 +21,6 @@ To specify a specific provider or model, you can use the `llm_provider` and `llm
 - **[Anthropic's Claude](https://www.anthropic.com/claude)**
 - **[xAI's Grok](https://x.ai/)**
 - **[Groq's Groq](https://groq.com/)**
-- **[Ollama](https://ollama.com)**
 
 If you'd like to see SimpleMind support additional providers or models, please send a pull request!
 
@@ -45,7 +40,7 @@ First, authenticate your API keys by setting them in the environment variables:
 $ export OPENAI_API_KEY="sk-..."
 ```
 
-This pattern allows you to keep your API keys private and out of your codebase. Other supported environment variables: `ANTHROPIC_API_KEY`, `XAI_API_KEY`, and `GROQ_API_KEY`.
+This pattern allows you to keep your API keys private and out of your codebase. Other supported environment variables: `ANTHROPIC_API_KEY`, `GROK_API_KEY`, `XAI_API_KEY`, and `GROQ_API_KEY`.
 
 Next, import SimpleMind and start using it:
 
@@ -109,30 +104,6 @@ To continue the conversation, you can call `conversation.send()` again, which re
 <Message role=assistant text="The meaning of life is a profound philosophical question that has been explored by cultures, religions, and philosophers for centuries. Different people and belief systems offer varying interpretations:\n\n1. **Religious Perspectives:** Many religions propose that the meaning of life is to fulfill a divine purpose, serve God, or reach an afterlife. For example, Christianity often emphasizes love, faith, and service to God and others as central to life’s meaning.\n\n2. **Philosophical Views:** Philosophers offer diverse answers. Existentialists like Jean-Paul Sartre argue that life has no inherent meaning, and it is up to individuals to create their own purpose. Others, like Aristotle, suggest that achieving eudaimonia (flourishing or happiness) through virtuous living is the key to a meaningful life.\n\n3. **Scientific and Secular Approaches:** Some people find meaning through understanding the natural world, contributing to human knowledge, or through personal accomplishments and happiness. They may view life’s meaning as a product of connection, legacy, or the pursuit of knowledge and creativity.\n\n4. **Personal Perspective:** For many, the meaning of life is deeply personal, involving their relationships, passions, and goals. These individuals define life’s purpose through experiences, connections, and the impact they have on others and the world.\n\nUltimately, the meaning of life is a subjective question, with each person finding their own answers based on their beliefs, experiences, and reflections.">
 ```
 
-### Stop Repeating Yourself
-
-You can use the `Session` class to set default parameters for all calls:
-
-```python
-import simplemind as sm
-
-# Create a session with defaults
-gpt_4o_mini = sm.Session(llm_provider="openai", llm_model="gpt-4o-mini")
-
-# Now all calls use these defaults
-response = gpt_4o_mini.generate_text("Hello!")
-conversation = gpt_4o_mini.create_conversation()
-```
-
-This maintains the simplicity of the original API while reducing repetition. The session object also supports overriding defaults on a per-call basis:
-
-```python
-response = gpt_4o_mini.generate_text(
-    "Complex task here",
-    llm_model="gpt-4"
-)
-```
-
 ### Basic Memory Plugin
 
 Harnessing the power of Python, you can easily create your own plugins to add additional functionality to your conversations:
@@ -151,7 +122,7 @@ class SimpleMemoryPlugin:
     def yield_memories(self):
         return (m for m in self.memories)
 
-    def pre_send_hook(self, conversation: sm.Conversation):
+    def send_hook(self, conversation: sm.Conversation):
         for m in self.yield_memories():
             conversation.add_message(role="system", text=m)
 
@@ -162,7 +133,7 @@ conversation.add_plugin(SimpleMemoryPlugin())
 
 conversation.add_message(
     role="user",
-    text="Please write a poem about the moon",
+    text="Write a poem about the moon",
 )
 ```
 ```pycon

docker-compose.yaml

@@ -0,0 +1,10 @@
+services:
+  simplemind:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    volumes:
+      - ./simplemind:/src/simplemind
+      - ./build.py:/src/build.py
+    env_file:
+      - .env

docs/Makefile

@@ -1,20 +0,0 @@
-# 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     = .
-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/conf.py

@@ -1,34 +0,0 @@
-# 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
-
-import os
-import sys
-
-sys.path.insert(0, os.path.abspath(".."))
-
-import simplemind
-
-project = "simplemind"
-copyright = "2024 Kenneth Reitz"
-author = "Kenneth Reitz"
-release = "v0.1.4"
-
-# -- General configuration ---------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
-
-extensions = ["sphinx.ext.autodoc"]
-
-templates_path = ["_templates"]
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
-
-
-# -- 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/index.rst

@@ -1,236 +0,0 @@
-.. simplemind documentation master file, created by
-   sphinx-quickstart on Wed Oct 30 08:08:14 2024.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
-SimpleMind: AI for Humans™
-==========================
-
-**SimpleMind** is a versatile Python library designed to simplify interactions with various AI models. It provides a consistent and user-friendly interface to numerous AI providers, enabling developers to seamlessly integrate powerful AI capabilities into their applications without the overhead of managing multiple APIs and configurations.
-
-Features
---------
-
-- **Unified Interface**: Interact with multiple AI providers using a single, consistent API
-- **Plugin Architecture**: Extend functionality with custom plugins for tasks like memory management and sentiment analysis
-- **Structured Data Support**: Generate and manipulate structured data using Pydantic models
-- **Human-Centered Design**: Prioritizes readability and ease of use, making AI integration accessible to all developers
-- **Minimal Configuration**: Quickly get started without extensive setup or configuration
-
-Supported Providers
-------------------
-
-SimpleMind supports a variety of AI providers:
-
-- `OpenAI's GPT <https://openai.com/gpt>`_
-- `Anthropic's Claude <https://www.anthropic.com/claude>`_
-- `xAI's Grok <https://x.ai/>`_
-- `Groq's Groq <https://groq.com/>`_
-- `Ollama <https://ollama.com>`_
-
-Installation
------------
-
-Install SimpleMind using pip:
-
-.. code-block:: shell
-
-    $ pip install simplemind
-
-Quickstart
-----------
-
-1. Set your API keys as environment variables:
-
-   .. code-block:: bash
-
-       $ export OPENAI_API_KEY="sk-..."
-       $ export ANTHROPIC_API_KEY="..."
-       $ export XAI_API_KEY="..."
-       $ export GROQ_API_KEY="..."
-
-This is the only required configuration.
-
-2. Import and use SimpleMind:
-
-   .. code-block:: python
-
-       import simplemind as sm
-
-       # Generate text using the default provider (OpenAI)
-       response = sm.generate_text("Write a poem about the moon.", llm_model="gpt-4o-mini")
-       print(response)
-
-Things to know:
-
-- The primary function for generating text is ``generate_text()``, which is used in the example above.
-- To generate structured data, use ``generate_data()``, which most providers support. This is extremely useful.
-- The third function, ``create_conversation()``, is used to engage in conversations with AI models.
-
-All of these functions accept an ``llm_model`` and ``llm_provider`` parameter, which allows you to specify the AI model to use. If not provided, the default model for the given provider will be used.
-
-
-Usage Examples
---------------
-
-Here are some examples demonstrating SimpleMind's key features. From generating creative text and structured data to engaging in conversations and extending functionality with plugins, these examples showcase the library's versatility and ease of use.
-
-Feel free to adapt these examples to your specific use cases!
-
-
-Text Generation
-~~~~~~~~~~~~~~~
-
-This example generates a poem about the moon using the ``gpt-4o-mini`` model.
-
-.. code-block:: python
-
-    import simplemind as sm
-
-    poem = sm.generate_text("Write a poem about the moon.", llm_model="gpt-4o-mini")
-    print(poem)
-
-Structured Data Generation
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This example generates a poem about love using the ``gpt-4o-mini`` model.
-
-.. code-block:: python
-
-    from pydantic import BaseModel
-
-    class Poem(BaseModel):
-        title: str
-        content: str
-
-    poem = sm.generate_data(
-        prompt="Write a poem about love",
-        llm_model="gpt-4o-mini",
-        response_model=Poem,
-    )
-    print(poem)
-
-Conversational AI
-~~~~~~~~~~~~~~~~~
-
-This example engages in a conversation with the ``gpt-4o-mini`` model.
-
-
-.. code-block:: python
-
-    conversation = sm.create_conversation(llm_model="gpt-4o-mini")
-    conversation.add_message("user", "Hi there, how are you?")
-    response = conversation.send()
-    print(response.text)
-
-Plugins
-~~~~~~~
-
-This example adds a simple custom memory plugin to the conversation.
-
-.. code-block:: python
-
-    class SimpleMemoryPlugin:
-        def __init__(self):
-            self.memories = ["the moon is made of cheese."]
-
-        def send_hook(self, conversation):
-            for memory in self.memories:
-                conversation.add_message(role="system", text=memory)
-
-    conversation = sm.create_conversation()
-    conversation.add_plugin(SimpleMemoryPlugin())
-    conversation.add_message("user", "Write a poem about the moon")
-    print(conversation.send().text)
-
-Plugin Development
-~~~~~~~~~~~~~~~~~~
-
-Plugins in SimpleMind follow a simple hook-based architecture. The ``send_hook`` method shown above is just one of several hooks available. Here's a more detailed example showing the complete plugin interface:
-
-.. code-block:: python
-
-    from simplemind.plugins import BasePlugin
-
-    class CustomPlugin(BasePlugin):
-        def __init__(self):
-            self.conversation_history = []
-
-        def initialize_hook(self, conversation):
-            """Called when the plugin is first added to a conversation."""
-            print("Plugin initialized!")
-
-        def pre_send_hook(self, conversation):
-            """Called before the conversation is sent to the AI provider."""
-            # Add any system messages or modify the conversation
-            conversation.add_message("system", "Remember to be helpful.")
-
-        def send_hook(self, conversation):
-            """Called during the send process."""
-            # Add messages or modify the conversation
-            self.conversation_history.append(conversation.messages)
-
-        def post_send_hook(self, conversation, response):
-            """Called after receiving a response from the AI provider."""
-            # Process or modify the response
-            return response
-
-        def cleanup_hook(self):
-            """Called when the plugin is removed or the conversation ends."""
-            self.conversation_history.clear()
-
-All plugins should inherit from ``BasePlugin``, which provides default no-op implementations of these hooks. You only need to implement the hooks you want to use. Here's a simpler example:
-
-.. code-block:: python
-
-    from simplemind.plugins import BasePlugin
-
-    class LoggingPlugin(BasePlugin):
-        def pre_send_hook(self, conversation):
-            print(f"Sending conversation with {len(conversation.messages)} messages")
-
-        def post_send_hook(self, conversation, response):
-            print(f"Received response: {response.text[:50]}...")
-            return response
-
-    conversation = sm.create_conversation()
-    conversation.add_plugin(LoggingPlugin())
-    conversation.add_message("user", "Hello!")
-    response = conversation.send()
-
-Plugins can be used to implement features like:
-
-- Conversation logging
-- Memory management
-- Response filtering
-- Token counting
-- Custom prompt engineering
-- Analytics and monitoring
-
-Multiple plugins can be added to a single conversation, and they will be executed in the order they were added.
-
-
-Contributing
------------
-
-1. Fork the Repository
-2. Create a New Branch
-3. Make Your Changes
-4. Submit a Pull Request
-
-Please review our `Code of Conduct <LICENSE>`_ before contributing.
-
-License
--------
-
-SimpleMind is licensed under the `Apache 2.0 License <LICENSE>`_.
-
-.. toctree::
-   :maxdepth: 2
-   :caption: Contents:
-
-   installation
-   usage
-   api
-   contributing
-   changelog

docs/make.bat

@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=.
-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

examples/contextual_memory.py

@@ -9,12 +9,7 @@ import pickle
 
 
 class ContextualMemoryPlugin:
-    def __init__(
-        self,
-        api_key: str,
-        memory_file: str = "memories.pkl",
-        embedding_model: str = "text-embedding-ada-002",
-    ):
+    def __init__(self, api_key: str, memory_file: str = "memories.pkl", embedding_model: str = "text-embedding-ada-002"):
         openai.api_key = api_key
         self.memory_file = memory_file
         self.embedding_model = embedding_model
@@ -40,29 +35,29 @@ class ContextualMemoryPlugin:
     def build_faiss_index(self):
         if self.embeddings:
             self.index = faiss.IndexFlatL2(len(self.embeddings[0]))
-            self.index.add(np.array(self.embeddings).astype("float32"))
+            self.index.add(np.array(self.embeddings).astype('float32'))
         else:
             self.index = faiss.IndexFlatL2(1536)
 
     def get_embedding(self, text: str) -> list:
         response = openai.Embedding.create(input=text, model=self.embedding_model)
-        return response["data"][0]["embedding"]
+        return response['data'][0]['embedding']
 
     def add_memory(self, memory: str):
         embedding = self.get_embedding(memory)
         self.memories.append(memory)
         self.embeddings.append(embedding)
-        self.index.add(np.array([embedding]).astype("float32"))
+        self.index.add(np.array([embedding]).astype('float32'))
         self.save_memories()
 
     def retrieve_memories(self, query: str, top_k: int = 3) -> list:
         if not self.index or len(self.embeddings) == 0:
             return []
         query_embedding = self.get_embedding(query)
-        D, I = self.index.search(np.array([query_embedding]).astype("float32"), top_k)
+        D, I = self.index.search(np.array([query_embedding]).astype('float32'), top_k)
         return [self.memories[i] for i in I[0] if i < len(self.memories)]
 
-    def pre_send_hook(self, conversation: sm.Conversation):
+    def send_hook(self, conversation: sm.Conversation):
         # Retrieve relevant memories based on the latest user message
         if conversation.messages:
             last_user_message = conversation.messages[-1].text
@@ -74,16 +69,13 @@ class ContextualMemoryPlugin:
         # Optionally, add the AI's response to memories
         self.add_memory(response)
 
-
 # Example Usage
 
-
 # Define a Pydantic model if needed
 class Story(BaseModel):
     title: str
     content: str
 
-
 # Initialize the conversation with the ContextualMemoryPlugin
 memory_plugin = ContextualMemoryPlugin(api_key=sm.settings.OPENAI_API_KEY)
 

examples/generate_data.py

@@ -1,4 +1,4 @@
-from typing import List, Iterator
+from typing import List
 
 from pydantic import BaseModel
 
@@ -9,36 +9,21 @@ class Movie(BaseModel):
     title: str
     year: int
 
-
 class MovieCharecter(BaseModel):
     name: str
     actor: str
 
-
 class MovieQuote(BaseModel):
     quote: str
     movie: Movie
     charecter: MovieCharecter
 
-
 class QuotesList(BaseModel):
     quotes: List[MovieQuote]
+    theme: str
 
 
-def gen_quotes(n=10) -> Iterator[MovieQuote]:
-    """Generate a list of quotes from famous movies."""
+quotes = sm.generate_data(llm_provider="openai", llm_model="gpt-4o-mini", prompt="Generate 20 quotes from famous movies", response_model=QuotesList)
 
-    for q in sm.generate_data(
-        llm_provider="openai",
-        llm_model="gpt-4o-mini",
-        prompt=f"Generate {n} quotes from famous movies",
-        response_model=QuotesList,
-    ).quotes:
-        yield q
-
-
-if __name__ == "__main__":
-    for quote in gen_quotes(n=20):
-        print(
-            f"{quote.charecter.name} from {quote.movie.title} ({quote.movie.year}): {quote.quote!r}"
-        )
+for quote in quotes.quotes:
+    print(f"{quote.charecter.name} from {quote.movie.title} ({quote.movie.year}): {quote.quote!r}")

examples/simple_logging_plugin.py

@@ -1,31 +0,0 @@
-import simplemind as sm
-
-
-class LoggingPlugin(sm.BasePlugin):
-    def pre_send_hook(self, conversation):
-        print(f"Sending conversation with {len(conversation.messages)} messages")
-
-    def add_message_hook(self, conversation, message):
-        print(f"Adding message to conversation: {message.text}")
-
-    def cleanup_hook(self, conversation):
-        print(f"Cleaning up conversation with {len(conversation.messages)} messages")
-
-    def initialize_hook(self, conversation):
-        print("Initializing conversation")
-
-    def post_send_hook(self, conversation, response):
-        print(f"Received response: {response.text}")
-
-
-with sm.create_conversation() as conversation:
-    # Add the logging plugin.
-    conversation.add_plugin(LoggingPlugin())
-
-    # Add a message to the conversation.
-    conversation.add_message("user", "Hello!", meta={})
-
-    # Send the conversation.
-    response = conversation.send()
-
-print(f"Response: {response.text}")

examples/simple_memory.py

@@ -1,7 +1,7 @@
 from _context import sm
 
 
-class SimpleMemoryPlugin(sm.BasePlugin):
+class SimpleMemoryPlugin:
     def __init__(self):
         self.memories = [
             "the earth has fictionally beeen destroyed.",
@@ -11,9 +11,9 @@ class SimpleMemoryPlugin(sm.BasePlugin):
     def yield_memories(self):
         return (m for m in self.memories)
 
-    def initialize_hook(self, conversation: sm.Conversation):
+    def send_hook(self, conversation: sm.Conversation):
         for m in self.yield_memories():
-            conversation.prepend_system_message(role="system", text=m)
+            conversation.add_message(role="system", text=m)
 
 
 conversation = sm.create_conversation(llm_model="grok-beta", llm_provider="xai")
@@ -21,7 +21,7 @@ conversation.add_plugin(SimpleMemoryPlugin())
 
 conversation.add_message(
     role="user",
-    text="Please write a poem about the moon",
+    text="Write a poem about the moon",
 )
 
 r = conversation.send()

examples/translate_text.py

@@ -1,13 +1,9 @@
 from _context import sm
 
+conversation = sm.create_conversation(llm_model="gpt-4o", llm_provider="openai")
 
-def translate_to_french(text: str) -> str:
-    conversation = sm.create_conversation(llm_model="gpt-4o", llm_provider="openai")
+conversation.add_message(
+    "user", "Translate the following text to French: 'Hello, world!'"
+)
 
-    conversation.add_message(
-        "user", f"Translate the following text to French: {text!r}"
-    )
-    return conversation.send().text
-
-
-print(translate_to_french("an omlette with cheese"))
+print(conversation.send().text)

examples/two_llms_talking.py

@@ -1,58 +0,0 @@
-import simplemind as sm
-import time
-
-
-class ConversationPlugin(sm.BasePlugin):
-    def post_send_hook(self, conversation, response):
-        # Print the LLM model and the response text.
-        print(f"{conversation.llm_model}:\n{response.text.strip()}\n\n------------\n")
-
-
-def have_conversation(rounds=3):
-    # Create two conversations - one for each AI
-    with (
-        sm.create_conversation(
-            llm_model="claude-3-5-sonnet-20241022", llm_provider="anthropic"
-        ) as claude_conv,
-        sm.create_conversation(
-            llm_model="llama3.2", llm_provider="ollama"
-        ) as llama_conv,
-    ):
-
-        # Add our plugin to both
-        plugin = ConversationPlugin()
-        claude_conv.add_plugin(plugin)
-        llama_conv.add_plugin(plugin)
-
-        # Start the conversation
-        prompt = "What do you think about the future of artificial intelligence? Please keep your response brief."
-        claude_conv.add_message("user", prompt, meta={})
-        claude_response = claude_conv.send()
-
-        # Have them discuss back and forth
-        for _ in range(rounds):
-            # Llama responds to Claude
-            llama_conv.add_message(
-                "user",
-                f"Respond to this statement from another AI: {claude_response.text}",
-                meta={},
-            )
-            llama_response = llama_conv.send()
-
-            time.sleep(1)  # Add a small delay between responses
-
-            # Claude responds to Llama
-            claude_conv.add_message(
-                "user",
-                f"Respond to this statement from another AI: {llama_response.text}",
-                meta={},
-            )
-            claude_response = claude_conv.send()
-
-            time.sleep(1)
-
-
-if __name__ == "__main__":
-    print("Starting AI conversation...\n")
-    have_conversation()
-    print("\nConversation ended.")

pyproject.toml

@@ -1,10 +1,10 @@
 [project]
 name = "simplemind"
-version = "0.1.4"
+version = "0.1.1"
 description = "An experimental client for AI providers that intends to replace LangChain and LangGraph for most common use cases."
 readme = "README.md"
 requires-python = ">=3.11"
-dependencies = ["pydantic", "pydantic-settings", "instructor", "openai", "anthropic", "ollama", "groq"]
+dependencies = ["pydantic", "pydantic-settings", "instructor", "openai", "anthropic", "groq"]
 
 [build-system]
 requires = ["hatchling"]

simplemind/__init__.py

@@ -1,83 +1,21 @@
-from typing import List, Optional, Type
-
-from .models import Conversation, BasePlugin, BaseModel
+from .models import Conversation
 from .utils import find_provider
 from .settings import settings
 
 
-class Session:
-    """A session object that maintains configuration across multiple API calls.
-
-    Similar to `requests.Session`, this allows you to specify default settings
-    that will be used for all operations within the session.
-    """
-
-    def __init__(
-        self,
-        *,
-        llm_provider: str = settings.DEFAULT_LLM_PROVIDER,
-        llm_model: str = settings.DEFAULT_LLM_MODEL,
-        **kwargs,
-    ):
-        self.llm_provider = llm_provider
-        self.llm_model = llm_model
-        self.default_kwargs = kwargs
-
-    def generate_text(self, prompt: str, **kwargs) -> str:
-        """Generate text using the session's default provider and model."""
-        merged_kwargs = {**self.default_kwargs, **kwargs}
-        return generate_text(
-            prompt=prompt,
-            llm_provider=self.llm_provider,
-            llm_model=self.llm_model,
-            **merged_kwargs,
-        )
-
-    def generate_data(
-        self, prompt: str, response_model: Type[BaseModel], **kwargs
-    ) -> BaseModel:
-        """Generate structured data using the session's default provider and model."""
-        merged_kwargs = {**self.default_kwargs, **kwargs}
-        return generate_data(
-            prompt=prompt,
-            response_model=response_model,
-            llm_provider=self.llm_provider,
-            llm_model=self.llm_model,
-            **merged_kwargs,
-        )
-
-    def create_conversation(self, **kwargs) -> "Conversation":
-        """Create a conversation using the session's default provider and model."""
-        merged_kwargs = {**self.default_kwargs, **kwargs}
-        return create_conversation(
-            llm_provider=self.llm_provider, llm_model=self.llm_model, **merged_kwargs
-        )
-
-
-def create_conversation(
-    llm_model=None, llm_provider=None, *, plugins: Optional[List[BasePlugin]] = None
-):
+def create_conversation(llm_model=None, llm_provider=None):
     """Create a new conversation."""
 
-    # Create the conversation.
-    conversation = Conversation(
+    return Conversation(
         llm_model=llm_model, llm_provider=llm_provider or settings.DEFAULT_LLM_PROVIDER
     )
 
-    # Add plugins to the conversation.
-    for plugin in plugins or []:
-        conversation.add_plugin(plugin)
-
-    return conversation
-
 
 def generate_data(prompt, *, llm_model=None, llm_provider=None, response_model=None):
     """Generate structured data from a given prompt."""
 
-    # Find the provider.
     provider = find_provider(llm_provider or settings.DEFAULT_LLM_PROVIDER)
 
-    # Generate the data.
     return provider.structured_response(
         prompt=prompt,
         llm_model=llm_model,
@@ -87,20 +25,16 @@ def generate_data(prompt, *, llm_model=None, llm_provider=None, response_model=N
 
 def generate_text(prompt, *, llm_model=None, llm_provider=None, **kwargs):
     """Generate text from a given prompt."""
-
-    # Find the provider.
     provider = find_provider(llm_provider or settings.DEFAULT_LLM_PROVIDER)
 
-    # Generate the text.
     return provider.generate_text(prompt=prompt, llm_model=llm_model, **kwargs)
 
 
 __all__ = [
+    "Conversation",
     "create_conversation",
     "find_provider",
     "generate_data",
     "generate_text",
     "settings",
-    "BasePlugin",
-    "Session",
 ]

simplemind/models.py

@@ -22,30 +22,12 @@ class SMBaseModel(BaseModel):
         return str(self)
 
 
-class BasePlugin:
+class BasePlugin(ABC):
     """The base conversation plugin class."""
 
-    # Plugin metadata.
-    meta: Dict[str, Any] = {}
-
-    def initialize_hook(self, conversation: "Conversation"):
-        """Initialize a hook for the plugin."""
-        raise NotImplementedError
-
-    def cleanup_hook(self, conversation: "Conversation"):
-        """Cleanup a hook for the plugin."""
-        raise NotImplementedError
-
-    def add_message_hook(self, conversation: "Conversation", message: "Message"):
-        """Add a message hook for the plugin."""
-        raise NotImplementedError
-
-    def pre_send_hook(self, conversation: "Conversation"):
-        """Pre-send hook for the plugin."""
-        raise NotImplementedError
-
-    def post_send_hook(self, conversation: "Conversation", response: "Message"):
-        """Post-send hook for the plugin."""
+    @abstractmethod
+    def send_hook(self, conversation: "Conversation"):
+        """Send a hook to the plugin."""
         raise NotImplementedError
 
 
@@ -78,82 +60,25 @@ class Conversation(SMBaseModel):
     def __str__(self):
         return f"<Conversation id={self.id!r}>"
 
-    def __enter__(self):
-        # Execute all initialize hooks.
-        for plugin in self.plugins:
-            if hasattr(plugin, "initialize_hook"):
-                try:
-                    plugin.initialize_hook(self)
-                except NotImplementedError:
-                    pass
-
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        # Execute all cleanup hooks.
-        for plugin in self.plugins:
-            if hasattr(plugin, "cleanup_hook"):
-                try:
-                    plugin.cleanup_hook(self)
-                except NotImplementedError:
-                    pass
-
-    def prepend_system_message(
-        self, role: str, text: str, meta: Optional[Dict[str, Any]] = None
-    ):
-        """Prepend a system message to the conversation."""
-        self.messages = [Message(role=role, text=text, meta=meta or {})] + self.messages
-
     def add_message(
         self, role: MESSAGE_ROLE, text: str, meta: Optional[Dict[str, Any]] = None
     ):
         """Add a new message to the conversation."""
-
-        # Ensure meta is a dict.
         if meta is None:
             meta = {}
-
-        # Execute all add-message hooks.
-        for plugin in self.plugins:
-            if hasattr(plugin, "add_message_hook"):
-                try:
-                    plugin.add_message_hook(
-                        self, Message(role=role, text=text, meta=meta)
-                    )
-                except NotImplementedError:
-                    pass
-
-        # Add the message to the conversation.
         self.messages.append(Message(role=role, text=text, meta=meta))
 
     def send(
         self, llm_model: Optional[str] = None, llm_provider: Optional[str] = None
     ) -> Message:
         """Send the conversation to the LLM."""
-
-        # Execute all pre send hooks.
         for plugin in self.plugins:
-            if hasattr(plugin, "pre_send_hook"):
-                try:
-                    plugin.pre_send_hook(self)
-                except NotImplementedError:
-                    pass
+            plugin.send_hook(self)
 
-        # Find the provider and send the conversation.
         provider = find_provider(llm_provider or self.llm_provider)
         response = provider.send_conversation(self)
 
-        # Execute all post-send hooks.
-        for plugin in self.plugins:
-            if hasattr(plugin, "post_send_hook"):
-                try:
-                    plugin.post_send_hook(self, response)
-                except NotImplementedError:
-                    pass
-
-        # Add the response to the conversation.
         self.add_message(role="assistant", text=response.text, meta=response.meta)
-
         return response
 
     def get_last_message(self, role: MESSAGE_ROLE) -> Optional[Message]:

simplemind/providers/__init__.py

@@ -1,10 +1,9 @@
 from typing import List, Type
 
-from ._base import BaseProvider
-from .anthropic import Anthropic
-from .groq import Groq
-from .openai import OpenAI
-from .ollama import Ollama
-from .xai import XAI
+from simplemind.providers._base import BaseProvider
+from simplemind.providers.anthropic import Anthropic
+from simplemind.providers.groq import Groq
+from simplemind.providers.openai import OpenAI
+from simplemind.providers.xai import XAI
 
-providers: List[Type[BaseProvider]] = [Anthropic, Groq, OpenAI, Ollama, XAI]
+providers: List[Type[BaseProvider]] = [Anthropic, Groq, OpenAI, XAI]

simplemind/providers/anthropic.py

@@ -39,7 +39,7 @@ class Anthropic(BaseProvider):
         ]
 
         response = self.client.messages.create(
-            model=conversation.llm_model or self.DEFAULT_MODEL,
+            model=conversation.llm_model or DEFAULT_MODEL,
             messages=messages,
             max_tokens=DEFAULT_MAX_TOKENS,
             **kwargs,
@@ -53,13 +53,13 @@ class Anthropic(BaseProvider):
             role="assistant",
             text=assistant_message,
             raw=response,
-            llm_model=conversation.llm_model or self.DEFAULT_MODEL,
+            llm_model=conversation.llm_model or DEFAULT_MODEL,
             llm_provider=PROVIDER_NAME,
         )
 
     def structured_response(self, model, response_model, **kwargs):
         response = self.structured_client.messages.create(
-            model=model, response_model=response_model or self.DEFAULT_MODEL, **kwargs
+            model=model, response_model=response_model, **kwargs
         )
         return response
 
@@ -69,7 +69,7 @@ class Anthropic(BaseProvider):
         ]
 
         response = self.client.messages.create(
-            model=llm_model or self.DEFAULT_MODEL,
+            model=llm_model,
             messages=messages,
             max_tokens=DEFAULT_MAX_TOKENS,
             **kwargs,

simplemind/providers/groq.py

@@ -42,7 +42,7 @@ class Groq(BaseProvider):
         ]
 
         response = self.client.chat.completions.create(
-            model=conversation.llm_model or self.DEFAULT_MODEL,
+            model=conversation.llm_model or DEFAULT_MODEL,
             messages=messages,
             **kwargs,
         )
@@ -55,7 +55,7 @@ class Groq(BaseProvider):
             role="assistant",
             text=assistant_message.content or "",
             raw=response,
-            llm_model=conversation.llm_model or self.DEFAULT_MODEL,
+            llm_model=conversation.llm_model or DEFAULT_MODEL,
             llm_provider=PROVIDER_NAME,
         )
 
@@ -85,7 +85,7 @@ class Groq(BaseProvider):
 
         response = self.client.chat.completions.create(
             messages=messages,
-            model=llm_model or self.DEFAULT_MODEL,
+            model=llm_model,
             **kwargs,
         )
 

simplemind/providers/ollama.py

@@ -1,82 +0,0 @@
-import ollama as ol
-import instructor
-from openai import OpenAI
-
-from ._base import BaseProvider
-from ..settings import settings
-
-PROVIDER_NAME = "ollama"
-DEFAULT_MODEL = "llama3.2"
-DEFAULT_TIMEOUT = 60
-
-
-class Ollama(BaseProvider):
-    NAME = PROVIDER_NAME
-    DEFAULT_MODEL = DEFAULT_MODEL
-    TIMEOUT = DEFAULT_TIMEOUT
-
-    def __init__(self, host_url: str = None):
-        self.host_url = host_url or settings.OLLAMA_HOST_URL
-
-    @property
-    def client(self):
-        """The raw Ollama client."""
-        if not self.host_url:
-            raise ValueError("No ollama host url provided")
-        return ol.Client(timeout=self.TIMEOUT, host=self.host_url)
-
-    @property
-    def structured_client(self):
-        """A client patched with Instructor."""
-        return instructor.from_openai(
-            OpenAI(
-                base_url=f"{self.host_url}/v1",
-                api_key="ollama",
-            ),
-            mode=instructor.Mode.JSON,
-        )
-
-    def send_conversation(self, conversation: "Conversation"):
-        """Send a conversation to the Ollama API."""
-        from ..models import Message
-
-        messages = [
-            {"role": msg.role, "content": msg.text} for msg in conversation.messages
-        ]
-        response = self.client.chat(
-            model=conversation.llm_model or DEFAULT_MODEL, messages=messages
-        )
-        assistant_message = response.get("message")
-
-        # Create and return a properly formatted Message instance
-        return Message(
-            role="assistant",
-            text=assistant_message.get("content"),
-            raw=response,
-            llm_model=conversation.llm_model or self.DEFAULT_MODEL,
-            llm_provider=PROVIDER_NAME,
-        )
-
-    def structured_response(self, prompt, response_model, *, llm_model: str, **kwargs):
-        messages = [
-            {"role": "user", "content": prompt},
-        ]
-
-        response = self.structured_client.chat.completions.create(
-            messages=messages,
-            model=llm_model or self.DEFAULT_MODEL,
-            response_model=response_model,
-            **kwargs,
-        )
-        return response
-
-    def generate_text(self, prompt, *, llm_model):
-        messages = [
-            {"role": "user", "content": prompt},
-        ]
-
-        response = self.client.chat(
-            messages=messages, model=llm_model or self.DEFAULT_MODEL
-        )
-
-        return response.get("message").get("content")

simplemind/providers/openai.py

@@ -60,10 +60,7 @@ class OpenAI(BaseProvider):
         ]
 
         response = self.structured_client.chat.completions.create(
-            messages=messages,
-            model=llm_model or self.DEFAULT_MODEL,
-            response_model=response_model,
-            **kwargs,
+            messages=messages, model=llm_model, response_model=response_model, **kwargs
         )
         return response
 
@@ -73,7 +70,7 @@ class OpenAI(BaseProvider):
         ]
 
         response = self.client.chat.completions.create(
-            messages=messages, model=llm_model or self.DEFAULT_MODEL, **kwargs
+            messages=messages, model=llm_model, **kwargs
         )
 
         return response.choices[0].message.content

simplemind/providers/xai.py

@@ -43,7 +43,7 @@ class XAI(BaseProvider):
         ]
 
         response = self.client.chat.completions.create(
-            model=conversation.llm_model or self.DEFAULT_MODEL,
+            model=conversation.llm_model or DEFAULT_MODEL,
             messages=messages,
             **kwargs,
         )
@@ -56,7 +56,7 @@ class XAI(BaseProvider):
             role="assistant",
             text=assistant_message.content,
             raw=response,
-            llm_model=conversation.llm_model or self.DEFAULT_MODEL,
+            llm_model=conversation.llm_model or DEFAULT_MODEL,
             llm_provider=PROVIDER_NAME,
         )
 
@@ -70,7 +70,7 @@ class XAI(BaseProvider):
 
         response = self.client.chat.completions.create(
             messages=messages,
-            model=llm_model or self.DEFAULT_MODEL,
+            model=llm_model,
             **kwargs,
         )
 

simplemind/settings.py

@@ -12,12 +12,8 @@ class Settings(BaseSettings):
     )
     GROQ_API_KEY: Optional[SecretStr] = Field(None, description="API key for Groq")
     OPENAI_API_KEY: Optional[SecretStr] = Field(None, description="API key for OpenAI")
-    OLLAMA_HOST_URL: Optional[str] = Field(
-        "http://127.0.0.1:11434", description="Fully qualified host URL for Ollama"
-    )
     XAI_API_KEY: Optional[SecretStr] = Field(None, description="API key for xAI")
     DEFAULT_LLM_PROVIDER: str = Field("openai", description="The default LLM provider")
-    DEFAULT_LLM_MODEL: str = Field("gpt-4o-mini", description="The default LLM model")
 
     model_config = SettingsConfigDict(
         env_file=".env", env_file_encoding="utf-8", case_sensitive=True, extra="ignore"

simplemind/utils.py

@@ -1,38 +1,13 @@
-import difflib
-from typing import Optional, Type
+from typing import Union
 
-from .providers import providers, BaseProvider
-
-_PROVIDER_NAMES = [provider.NAME.lower() for provider in providers]
+from .providers import providers
 
 
-def find_provider(provider_name: str) -> BaseProvider:
-    """
-    Find and instantiate a provider by name.
-
-    Parameters:
-    provider_name (Union[str, None]): The name of the provider to find.
-
-    Returns:
-    An instance of the provider class if found.
-
-    Raises:
-    ValueError: If the provider is not found, with a suggestion for the closest match.
-    """
-    # Find the provider by name.
-    for provider_class in providers:
-        if provider_class.NAME.lower() == provider_name.lower():
-            # Instantiate the provider
-            return provider_class()
-
-    # Find the closest match
-    provider_found = difflib.get_close_matches(
-        provider_name.lower(), _PROVIDER_NAMES, n=1
-    )
-
-    if provider_found:
-        raise ValueError(
-            f"Provider {provider_name!r} not found. Did you mean {provider_found[0]!r}?"
-        )
-    else:
-        raise ValueError(f"Provider {provider_name} not found.")
+def find_provider(provider_name: Union[str, None]):
+    """Find a provider by name."""
+    if provider_name:
+        for provider_class in providers:
+            if provider_class.NAME.lower() == provider_name.lower():
+                # Instantiate the provider
+                return provider_class()
+    raise ValueError(f"Provider {provider_name} not found")