Conversational AI Templates

$49

Chatbot frameworks with context management, multi-turn conversation handling, intent classification, and escalation workflows.

📁 39 files

MarkdownYAMLPythonFastAPIFlaskRedisLLMOpenAI

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📁 File Structure 39 files

conversational-ai-templates/ ├── LICENSE ├── README.md ├── free-sample.zip ├── guide/ │ ├── 01-building-a-chatbot.md │ └── 02-channel-integration.md ├── guides/ │ ├── building-a-chatbot.md │ └── channel-integration.md ├── index.html ├── requirements.txt ├── src/ │ ├── __init__.py │ ├── __pycache__/ │ │ ├── __init__.cpython-312.pyc │ │ ├── context_manager.cpython-312.pyc │ │ ├── dialogue.cpython-312.pyc │ │ ├── escalation.cpython-312.pyc │ │ ├── intent.cpython-312.pyc │ │ └── slots.cpython-312.pyc │ ├── channels/ │ │ ├── __init__.py │ │ ├── __pycache__/ │ │ │ ├── __init__.cpython-312.pyc │ │ │ ├── base.cpython-312.pyc │ │ │ ├── slack.cpython-312.pyc │ │ │ └── web.cpython-312.pyc │ │ ├── base.py │ │ ├── slack.py │ │ └── web.py │ ├── context_manager.py │ ├── dialogue.py │ ├── escalation.py │ ├── intent.py │ └── slots.py ├── templates/ │ ├── booking_bot.yaml │ ├── faq_bot.yaml │ └── support_bot.yaml └── tests/ ├── __pycache__/ │ ├── test_context.cpython-312.pyc │ ├── test_dialogue.cpython-312.pyc │ └── test_intent.cpython-312.pyc ├── test_context.py ├── test_dialogue.py └── test_intent.py

📖 Documentation Preview README excerpt

Conversational AI Templates

A production-ready framework for building chatbots with multi-turn dialogue management, intent classification, slot filling, escalation workflows, and channel adapters. Includes three ready-to-deploy bot configurations: support bot, FAQ bot, and booking bot.

What's Inside


conversational-ai-templates/
├── src/                          # Core framework
│   ├── __init__.py
│   ├── context_manager.py        # Conversation state & history tracking
│   ├── dialogue.py               # State-machine dialogue manager
│   ├── intent.py                 # Intent classification (keyword + LLM)
│   ├── slots.py                  # Slot extraction (email, phone, date, etc.)
│   ├── escalation.py             # Human handoff workflows
│   └── channels/                 # Platform adapters
│       ├── __init__.py
│       ├── base.py               # Abstract adapter interface
│       ├── web.py                # Web chat widget adapter
│       └── slack.py              # Slack Events API adapter
├── templates/                    # Ready-to-deploy bot configs (YAML)
│   ├── support_bot.yaml          # Customer support bot
│   ├── faq_bot.yaml              # FAQ knowledge-base bot
│   └── booking_bot.yaml          # Appointment scheduling bot
├── tests/                        # Test suite
│   ├── test_context.py           # ConversationContext tests
│   ├── test_intent.py            # Intent classifier tests
│   └── test_dialogue.py          # DialogueManager tests
├── guides/                       # How-to documentation
│   ├── building-a-chatbot.md     # Step-by-step build guide
│   └── channel-integration.md    # Platform integration guide
├── requirements.txt
├── LICENSE
└── .gitignore

Features

Conversation Management

Sliding-window history with configurable depth (default 20 turns)
Slot tracking with typed extraction (email, phone, date, time, number, choice)
Session serialization via to_dict()/from_dict() for Redis, DynamoDB, or any store
Idle detection and session timeout handling

Dialogue Engine

State-machine flows defined declaratively in YAML or Python
Slot-gated transitions — states block until required data is collected
Global intents — cancel, help, and escalate work from any state
Entry/exit callbacks for running custom logic on state transitions
Config-driven — change bot behavior without changing code

Intent Classification

KeywordIntentClassifier — fast, zero-dependency, no API key needed
Keyword matching (exact and partial) with configurable confidence scores
Regex pattern matching for structured utterances
Priority-based tie-breaking between similar intents
LLMIntentClassifier — production-grade accuracy via OpenAI/Anthropic
Automatic prompt construction from intent catalog
Graceful fallback to keyword classifier when LLM is unavailable
JSON response parsing with error recovery

... continues with setup instructions, usage examples, and more.

📄 Code Sample .py preview

src/context_manager.py """Conversation context manager — tracks state across multi-turn dialogues. Maintains a sliding window of conversation history, extracted slot values, the current intent, and metadata like user identity and session timing. The context object is the central data structure that every other module reads from and writes to during a conversation turn. """ from __future__ import annotations import logging import time import uuid from dataclasses import dataclass, field from typing import Any logger = logging.getLogger(__name__) # How many past turns to keep in the sliding window. Older turns are # summarized (if a summarizer is configured) or simply dropped. DEFAULT_HISTORY_WINDOW = 20 # Maximum number of slots a single context can hold before we warn # about potential memory bloat in long-running sessions. SLOT_LIMIT_WARNING = 50 @dataclass class Message: """A single message in the conversation history. Attributes: role: Who sent the message — "user", "assistant", or "system". content: The text content of the message. timestamp: Unix timestamp when the message was created. metadata: Arbitrary key-value pairs (channel info, message ID, etc.). """ role: str content: str timestamp: float = field(default_factory=time.time) metadata: dict[str, Any] = field(default_factory=dict) def to_dict(self) -> dict[str, Any]: return { "role": self.role, "content": self.content, "timestamp": self.timestamp, "metadata": self.metadata, } # ... 240 more lines ...

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