CogSol Framework

Version: 0.2.0 (Alpha)

CogSol is a lightweight, agent-first Python framework for building, managing, and deploying AI assistants. It provides scaffolding, agent abstractions, and file-based migration utilities for CogSol projects without requiring an external database.

Table of Contents

• Overview
• Key Features
• Installation
• Quick Start
• Project Structure
• CLI Commands
• Core Concepts
  • Agents
  • Data & Topics
• Configuration
• API Reference
• Contributing

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Overview

CogSol is designed to provide a Django-like development experience for building AI agents. It uses a code-first approach where you define your agents, tools, and configurations in Python, then use migrations to sync with a remote CogSol API.

Design Philosophy

• Code-First: Define agents and tools as Python classes
• Migration-Based Deployments: Track changes via migrations (similar to Django)
• No Database Required: Uses JSON files for state tracking
• API-Synchronized: Push local definitions to remote CogSol APIs
• Lightweight: Minimal dependencies, uses only Python standard library

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Key Features

Feature	Description
Agent Definitions	Define AI agents as Python classes with configurable attributes
Tool System	Create reusable tools with typed parameters and decorators
Topics & Documents	Organize knowledge bases with hierarchical topics and document ingestion
Retrievals	Configure semantic search across your document collections
Migrations	Track and version agent/tool/topic changes
Remote Sync	Push definitions to CogSol Cognitive and Content APIs
Interactive Chat	Built-in CLI for testing agents
Import/Export	Import existing assistants from the API

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Installation

From Source

git clone <repository-url>
cd framework
pip install -e .

Requirements

• Python 3.9+
• No external dependencies (uses only Python standard library)

After installation, the cogsol-admin command becomes available globally.

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Quick Start

1. Create a New Project

cogsol-admin startproject myproject
cd myproject

This creates:

myproject/
├── manage.py           # Project CLI entry point
├── settings.py         # Project configuration
├── .env.example        # Environment template
├── README.md
├── agents/
│   ├── __init__.py
│   ├── tools.py        # Global tool definitions
│   ├── searches.py     # Retrieval tool definitions
│   └── migrations/
│       └── __init__.py
└── data/
    ├── __init__.py
    ├── formatters.py   # Reference formatters
    ├── ingestion.py    # Ingestion configurations
    ├── retrievals.py   # Retrieval configurations
    └── migrations/
        └── __init__.py

2. Create an Agent

python manage.py startagent SalesAgent

This creates a complete agent package:

agents/salesagent/
├── __init__.py
├── agent.py            # Main agent definition
├── faqs.py             # Frequently asked questions
├── fixed.py            # Fixed responses
├── lessons.py          # Contextual lessons
└── prompts/
    └── salesagent.md   # System prompt

3. Configure Environment

Copy .env.example to .env and set your API credentials:

COGSOL_ENV=local
COGSOL_API_BASE=https://api.cogsol.ai/cognitive/
COGSOL_CONTENT_API_BASE=https://api.cogsol.ai/content/
COGSOL_
COGSOL_API_KEY=your-api-key
# Optional: Azure AD B2C client credentials for JWT
# If not provided, the Auth will be skipped
COGSOL_AUTH_CLIENT_ID=your-client-id
COGSOL_AUTH_SECRET=your-client-secret

4. Create Migrations

python manage.py makemigrations

5. Apply Migrations

python manage.py migrate

6. Chat with Your Agent

python manage.py chat --agent SalesAgent

7. Add Document Topics (Optional)

# Create a topic for documents
python manage.py starttopic documentation

# Create nested topics
python manage.py starttopic tutorials --path documentation

# Create migrations for data
python manage.py makemigrations data
python manage.py migrate data

# Ingest documents into a topic
python manage.py ingest documentation ./docs/*.pdf

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Project Structure

A typical CogSol project has the following structure:

myproject/
├── manage.py                    # CLI entry point
├── settings.py                  # Project settings
├── .env                         # Environment variables
├── agents/                      # Agents application
│   ├── __init__.py
│   ├── tools.py                 # Shared tool definitions
│   ├── searches.py              # Retrieval tool definitions
│   ├── migrations/              # Migration files
│   │   ├── __init__.py
│   │   ├── 0001_initial.py
│   │   ├── .applied.json        # Applied migrations tracker
│   │   └── .state.json          # Current state and remote IDs
│   └── <agent-slug>/            # Per-agent package
│       ├── __init__.py
│       ├── agent.py             # Agent class definition
│       ├── faqs.py              # FAQ definitions
│       ├── fixed.py             # Fixed response definitions
│       ├── lessons.py           # Lesson definitions
│       └── prompts/
│           └── <slug>.md        # System prompt
└── data/                        # Data application
    ├── __init__.py
    ├── formatters.py            # Reference formatter definitions
    ├── ingestion.py             # Ingestion configuration definitions
    ├── retrievals.py            # Retrieval configuration definitions
    ├── migrations/              # Migration files
    │   ├── __init__.py
    │   ├── 0001_initial.py
    │   ├── .applied.json
    │   └── .state.json
    └── <topic-path>/            # Topic folder (can be nested)
        ├── __init__.py          # Topic class definition
        └── metadata.py          # Metadata configurations

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CLI Commands

CogSol provides the following management commands:

startproject

Create a new CogSol project.

cogsol-admin startproject <project-name> [directory]

Arguments:

• project-name: Name of the project
• directory: (Optional) Target directory

startagent

Create a new agent package with all required files.

python manage.py startagent <agent-name> [app]

Arguments:

• agent-name: Agent class name (e.g., SalesAgent)
• app: (Optional) App name, defaults to agents

makemigrations

Generate migration files based on agent/tool/topic changes.

python manage.py makemigrations [app] [--name <suffix>]

Arguments:

• app: (Optional) App to migrate, agents or data (when omitted, runs both)
• --name: (Optional) Custom migration name suffix

migrate

Apply pending migrations and sync with the CogSol API.

python manage.py migrate [app]

Arguments:

• app: (Optional) App to migrate, agents or data (when omitted, runs both)

starttopic

Create a new topic folder under data/.

python manage.py starttopic <topic-name> [--path <parent-path>]

Arguments:

• topic-name: Name of the topic (used as folder name)
• --path: (Optional) Parent path for nested topics (e.g., parent/child)

Examples:

# Create a root topic
python manage.py starttopic documentation

# Create a nested topic
python manage.py starttopic tutorials --path documentation
# Creates: data/documentation/tutorials/

ingest

Ingest documents into a topic.

python manage.py ingest <topic> <files...> [options]

Arguments:

• topic: Topic path (e.g., documentation or parent/child/topic)
• files: Files, directories, or glob patterns to ingest

Options:

• --doc-type: Document type (defaults to Text Document)
• --ingestion-config: Name of an ingestion config from data/ingestion.py
• --pdf-mode: PDF parsing mode (manual, OpenAI, both, ocr, ocr_openai)
• --chunking: Chunking mode (langchain, ingestor)
• --max-size-block: Maximum characters per block (default: 1500)
• --chunk-overlap: Overlap between blocks (default: 0)
• --separators: Comma-separated chunk separators
• --ocr: Enable OCR parsing
• --additional-prompt-instructions: Extra parsing instructions
• --assign-paths-as-metadata: Assign file paths as metadata
• --dry-run: Show what would be ingested without uploading

Examples:

# Ingest PDF files
python manage.py ingest documentation ./docs/*.pdf

# Ingest with custom config
python manage.py ingest documentation ./docs/ --ingestion-config HighQuality

# Dry run to preview
python manage.py ingest documentation ./data/ --dry-run

topics

List topics from the API or local definitions.

python manage.py topics [options]

Options:

• --local: Show topics from local definitions instead of API
• --sync-status: Show synchronization status (local vs API)

importagent

Import an existing assistant from the CogSol API.

python manage.py importagent <assistant-id> [app]

Arguments:

• assistant-id: Remote assistant ID to import
• app: (Optional) App name, defaults to agents

chat

Start an interactive chat session with an agent.

python manage.py chat --agent <agent-name-or-id> [app]

Arguments:

• --agent: Agent name or remote ID (required)
• app: (Optional) App name, defaults to agents

Chat Commands:

• /exit or Ctrl+C: Exit the chat
• /new: Start a new chat session

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Core Concepts

Agents

Agents are the central concept in CogSol. An agent is defined as a Python class that inherits from BaseAgent:

from cogsol.agents import BaseAgent, genconfigs
from cogsol.prompts import Prompts

class CustomerSupportAgent(BaseAgent):
    # Core configuration
    system_prompt = Prompts.load("support.md")
    generation_config = genconfigs.QA()
    
    # Tools
    tools = [MyTool()]
    pretools = []
    
    # Limits
    max_interactions = 10
    max_msg_length = 2048
    max_consecutive_tool_calls = 5
    temperature = 0.3
    
    # Behaviors
    initial_message = "Hello! How can I help you today?"
    forced_termination_message = "Thank you for chatting!"
    no_information_message = "I don't have information on that topic."
    
    # Features
    streaming = False
    realtime = False
    
    class Meta:
        name = "CustomerSupportAgent"
        chat_name = "Customer Support"
        logo_url = "https://example.com/logo.png"
        primary_color = "#007bff"

Agent Attributes

Attribute	Type	Description
system_prompt	Prompt	The system prompt loaded from a file
generation_config	genconfigs.*	LLM generation configuration
pregeneration_config	genconfigs.*	Pre-tool generation configuration
tools	list[BaseTool]	Tools available to the agent
pretools	list[BaseTool]	Pre-processing tools
temperature	float	LLM temperature (0.0 - 1.0)
max_interactions	int	Maximum conversation turns
user_message_length	int	Maximum user message length
consecutive_tool_calls_limit	int	Max consecutive tool calls
streaming	bool	Enable response streaming
realtime	bool	Enable real-time mode

Tools

Tools extend agent capabilities. Define tools in agents/tools.py:

from cogsol.tools import BaseTool, tool_params

# class SearchTool(BaseTool):
#     description = "Search for information in the knowledge base."
#     
#     @tool_params(
#         query={"description": "Search query", "type": "string", "required": True},
#         limit={"description": "Max results", "type": "integer", "required": False},
#     )
#     def run(self, chat=None, data=None, secrets=None, log=None, 
#             query: str = "", limit: int = 10):
#         """
#         query: The search query.
#         limit: Maximum number of results.
#         """
#         # Implementation here
#         results = perform_search(query, limit)
#         response = format_results(results)
#         return response

Retrieval tools connect agents to Content API retrievals. Define them in agents/searches.py:

from cogsol.tools import BaseRetrievalTool
# from data.retrievals import ProductDocsRetrieval
#
# class ProductDocsSearch(BaseRetrievalTool):
#     name = "product_docs_search"
#     description = "Search the product documentation."
#     retrieval = ProductDocsRetrieval
#     parameters = [
#         {"name": "question", "description": "Search query", "type": "string", "required": True}
#     ]

Tool Parameters

The @tool_params decorator defines parameter metadata:

@tool_params(
    param_name={
        "description": "Parameter description",
        "type": "string",      # string, integer, boolean, etc.
        "required": True       # Required or optional
    }
)

FAQs, Fixed Responses, and Lessons

These provide additional context to agents:

FAQs (faqs.py)

from cogsol.tools import BaseFAQ
#
# class PricingFAQ(BaseFAQ):
#     question = "What are your pricing plans?"
#     answer = "We offer three tiers: Basic ($10/mo), Pro ($25/mo), Enterprise (custom)."

Fixed Responses (fixed.py)

from cogsol.tools import BaseFixedResponse
#
# class ClosingFixed(BaseFixedResponse):
#     key = "goodbye"
#     response = "Thank you for contacting us. Have a great day!"

Lessons (lessons.py)

from cogsol.tools import BaseLesson
#
# class ToneLesson(BaseLesson):
#     name = "Communication Tone"
#     content = "Always maintain a professional yet friendly tone."
#     context_of_application = "general"

Prompts

Prompts are loaded from markdown files:

from cogsol.prompts import Prompts

# In agent definition
system_prompt = Prompts.load("agent.md")

The prompt file is resolved relative to the agent's prompts/ directory.

Generation Configurations

from cogsol.agents import genconfigs

# Question-Answering mode
generation_config = genconfigs.QA()

# Fast retrieval mode
generation_config = genconfigs.FastRetrieval()

Data & Topics

CogSol provides a complete system for managing document collections and semantic search through the Content API.

Topics

Topics are hierarchical containers for organizing documents. Define them in data/<topic>/:

# data/documentation/__init__.py
from cogsol.content import BaseTopic
#
# class DocumentationTopic(BaseTopic):
#     name = "documentation"
#
#     class Meta:
#         description = "Product documentation and guides."

Topics can be nested by creating subdirectories:

data/
├── documentation/
│   ├── __init__.py
│   ├── metadata.py
│   └── tutorials/
│       ├── __init__.py
│       └── metadata.py

Metadata Configurations

Define custom metadata fields for documents within a topic:

# data/documentation/metadata.py
from cogsol.content import BaseMetadataConfig, MetadataType
#
# class CategoryMetadata(BaseMetadataConfig):
#     name = "category"
#     type = MetadataType.STRING
#     possible_values = ["Guide", "Tutorial", "Reference", "FAQ"]
#     filtrable = True
#     required = False
#
# class VersionMetadata(BaseMetadataConfig):
#     name = "version"
#     type = MetadataType.STRING
#     required = True
#     default_value = "1.0"

Ingestion Configurations

Define reusable ingestion settings in data/ingestion.py:

from cogsol.content import BaseIngestionConfig, PDFParsingMode, ChunkingMode
#
# class HighQualityConfig(BaseIngestionConfig):
#     name = "high_quality"
#     pdf_parsing_mode = PDFParsingMode.OCR
#     chunking_mode = ChunkingMode.AGENTIC_SPLITTER
#     max_size_block = 2000
#     chunk_overlap = 100
#
# class FastConfig(BaseIngestionConfig):
#     name = "fast"
#     pdf_parsing_mode = PDFParsingMode.MANUAL
#     chunking_mode = ChunkingMode.LANGCHAIN
#     max_size_block = 1500

Use with the ingest command:

python manage.py ingest documentation ./docs/ --ingestion-config high_quality

Reference Formatters

Define how document blocks are formatted when referenced:

# data/formatters.py
from cogsol.content import BaseReferenceFormatter
#
# class DetailedFormatter(BaseReferenceFormatter):
#     name = "detailed"
#     description = "Include page and category."
#     expression = "[{name}, p.{page_num}] ({metadata.category})"
#
# class SimpleFormatter(BaseReferenceFormatter):
#     name = "simple"
#     expression = "{name}"

Retrievals

Configure semantic search across topics:

# data/retrievals.py
from cogsol.content import BaseRetrieval, ReorderingStrategy
# from data.formatters import DetailedFormatter
# from data.documentation.metadata import VersionMetadata
#
# class DocumentationRetrieval(BaseRetrieval):
#     name = "documentation_search"
#     topic = "documentation"
#     num_refs = 10
#     reordering = False
#     strategy_reordering = ReorderingStrategy.NONE
#     formatters = {"Text Document": DetailedFormatter}
#     filters = []
#
# class FilteredRetrieval(BaseRetrieval):
#     name = "v2_docs"
#     topic = "documentation"
#     num_refs = 5
#     filters = [VersionMetadata]

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Configuration

Environment Variables

Variable	Required	Description
COGSOL_API_BASE	Yes	Base URL for the CogSol Cognitive API
COGSOL_CONTENT_API_BASE	No	Base URL for the CogSol Content API (defaults to COGSOL_API_BASE)
COGSOL_API_KEY	Yes	API Key authentication
COGSOL_ENV	No	Environment name (e.g., local, production)
COGSOL_AUTH_CLIENT_ID	No	Client Id provided for adminitrators
COGSOL_AUTH_SECRET	No	Auth Secret provided for adminitrators

Optional: Azure AD B2C client credentials for JWT\

If not provided, the Auth will be skipped

COGSOL_AUTH_CLIENT_ID=your-client-id COGSOL_AUTH_SECRET=your-client-secret

Project Settings (settings.py)

from pathlib import Path

BASE_DIR = Path(__file__).resolve().parent
PROJECT_NAME = "myproject"
AGENTS_APP = "agents"

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API Reference

For detailed API documentation, see:

• Architecture Documentation
• CLI Commands Reference
• API Client Reference
• Agents & Tools Reference

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License

Copyright © Cognitive Solutions

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This is an alpha release. APIs and features may change.