aicontext 0.0.1

Simple context management for AI conversations

AIContext

Simple context management for AI chat applications. Handles message history and formatting across different LLM providers.

Example

from aicontext import AIChatContext, ai_chat_context
from openai import OpenAI
from anthropic import Anthropic

# Initialize
context = AIChatContext(system_prompt="You are a helpful assistant.")
openai, anthropic = OpenAI(), Anthropic()

# Create chat functions
@ai_chat_context(context)
def ask_gpt(prompt: str):
    return openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "system", "content": context.system_prompt}] + context.get_messages()
    )

@ai_chat_context(context)
def ask_claude(prompt: str):
    return anthropic.messages.create(
        model="claude-3-5-sonnet-20240620",
        system=context.system_prompt,
        messages=context.get_messages(),
        max_tokens=1024
    )

# Chat with different models
ask_gpt("What's the capital of France?")
ask_claude("What's interesting about it?")
print(context.format())  # Print conversation

More Examples

Named Assistants

@ai_chat_context(context, assistant_name="Researcher")
def researcher(prompt: str):
    return anthropic.messages.create(
        model="claude-3-5-sonnet-20240620",
        system=context.system_prompt,
        messages=context.get_messages(),
        max_tokens=1024
    )

@ai_chat_context(context, assistant_name="Coder")
def coder(prompt: str):
    return openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "system", "content": context.system_prompt}] + context.get_messages()
    )

# Use different assistants
researcher("Explain quantum computing")
coder("Show me a quantum random number generator")

# Get messages by assistant
print(context.format(assistant_name="Researcher"))
print(context.latest(assistant_name="Coder"))

Message Filtering

# Last 5 messages
print(context.format(limit=5))

# Filter by assistant and role
print(context.format(
    assistant_name="Researcher",
    role="assistant",
    limit=2
))

# Get raw messages for API
messages = context.get_messages()

Save & Load

# Save context
saved = context.to_json()

# Load in new context
new_context = AIChatContext(messages=saved)

Installation

pip install aicontext

API Reference

AIChatContext

AIChatContext(
    system_prompt: Optional[str] = None,    # System instructions
    max_messages: Optional[int] = None,     # Message limit
    messages: Optional[Union[List[Dict], str]] = None  # Load existing
)

Methods

Method	Description
get_messages()	Get messages for API calls
filter(assistant_name=None, role=None, limit=None)	Filter messages
format(**kwargs)	Get formatted history
latest(assistant_name=None)	Get latest message
to_json()	Export to JSON
load_messages(messages)	Load messages
clear(assistant_name=None)	Clear history

Decorator

@ai_chat_context(
    context: AIChatContext,         # Context instance
    assistant_name: str = "default" # Optional name
)

Filtering Options

context.filter(
    assistant_name="Researcher",  # Filter by assistant
    role="assistant",            # Filter by role
    limit=5,                     # Limit results
    offset=2                     # Skip messages
)

License

MIT