langchain-deepai 0.0.6
LangChain integration for DeepAI API with chat, images, and speech capabilities
License: MIT
Requires Python: >=3.8
Keywords: langchain, deepai, ai, chat, image-generation, text-to-speech, speech-to-text, machine-learning

# LangChain DeepAI Integration

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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A comprehensive LangChain integration for the DeepAI API, providing seamless access to chat completions, image generation, text-to-speech, and speech-to-text capabilities.

## Features

- **🗨️ Chat Completions**: Multiple specialized models for different use cases
- **🎨 Image Generation**: Text-to-image with various artistic styles
- **🔊 Text-to-Speech**: High-quality speech synthesis
- **🎤 Speech-to-Text**: Accurate audio transcription
- **⚡ LangChain Compatible**: Full integration with LangChain ecosystem
- **🔄 Async Support**: Both sync and async operations
- **📚 Well Documented**: Comprehensive docstrings and examples

## Installation

```bash
pip install langchain-deepai
```

For image processing capabilities:
```bash
pip install langchain-deepai[image]
```

For development:
```bash
pip install langchain-deepai[dev]
```

## Quick Start

### 1. Set up your API key

```bash
export DEEPAI_API_KEY="your-deepai-api-key"
```

Or pass it directly:

```python
from langchain_deepai import ChatDeepAI

chat = ChatDeepAI(api_key="your-deepai-api-key")
```

### 2. Basic Chat Example

```python
from langchain_deepai import ChatDeepAI
from langchain_core.messages import HumanMessage

# Initialize chat model
chat = ChatDeepAI(
    model="standard",
    chat_style="chatgpt-alternative"
)

# Send a message
messages = [HumanMessage(content="Hello! How are you today?")]
response = chat.invoke(messages)
print(response.content)
```

### 3. Specialized Models

```python
from langchain_deepai import DeepAIMath, DeepAICode

# Math-focused model
math_chat = DeepAIMath()
response = math_chat.invoke([HumanMessage(content="Solve: 2x + 5 = 15")])

# Code-focused model  
code_chat = DeepAICode()
response = code_chat.invoke([HumanMessage(content="Write a Python sorting function")])
```

### 4. Image Generation

```python
from langchain_deepai import ImageDeepAI, generate_image

# Using the class
image_gen = ImageDeepAI()
result = image_gen.generate(
    prompt="A beautiful sunset over mountains",
    model="text2img",
    width=512,
    height=512
)

# Save the image
with open("sunset.jpg", "wb") as f:
    f.write(result["image_data"])

# Using the convenience function
result = generate_image(
    prompt="A cyberpunk cityscape at night",
    model="cyberpunk-generator"
)
```

### 5. Text-to-Speech

```python
from langchain_deepai import TextToSpeechDeepAI

tts = TextToSpeechDeepAI()
result = tts.synthesize(
    text="Hello, this is a test of text-to-speech synthesis.",
    voice="default"
)

# Save audio file
with open("speech.wav", "wb") as f:
    f.write(result["audio_data"])
```

### 6. Speech-to-Text

```python
from langchain_deepai import SpeechToTextDeepAI

stt = SpeechToTextDeepAI()
result = stt.transcribe("audio_file.wav")
print(result["text"])
```

## Available Models and Styles

### Chat Models

- **standard**: General purpose conversational AI
- **math**: Mathematics and problem-solving specialized
- **online**: Web-aware model with current information
- **code**: Programming and development focused

### Chat Styles

- **chatgpt-alternative**: Default conversational style
- **ai-code**: Programming focused
- **mathematics**: Mathematical reasoning
- **professional**: Business communication
- **creative**: Creative and imaginative
- **casual**: Relaxed and informal
- **goku**: Enthusiastic character style
- **gojo_9**: Confident character style

### Image Models

- **text2img**: Standard text-to-image generation
- **fantasy-world-generator**: Fantasy themed
- **cyberpunk-generator**: Cyberpunk aesthetic
- **renaissance-painting-generator**: Renaissance art style
- **abstract-painting-generator**: Abstract art
- **impressionism-painting-generator**: Impressionist style

## Advanced Usage

### Custom Chat Styles

```python
from langchain_deepai import ChatDeepAI

# Professional business communication
business_chat = ChatDeepAI(
    model="standard",
    chat_style="professional"
)

# Creative writing assistant
creative_chat = ChatDeepAI(
    model="standard", 
    chat_style="creative"
)
```

### Session Management

```python
chat = ChatDeepAI()
chat.set_session_id("user-123")

# All messages in this session will maintain context
response1 = chat.invoke([HumanMessage(content="My name is Alice")])
response2 = chat.invoke([HumanMessage(content="What's my name?")])
```

### Error Handling

```python
from langchain_deepai import ChatDeepAI
import logging

# Enable logging
logging.basicConfig(level=logging.INFO)

try:
    chat = ChatDeepAI(api_key="invalid-key")
    response = chat.invoke([HumanMessage(content="Hello")])
except Exception as e:
    print(f"Error: {e}")
```

### Batch Processing

```python
from langchain_deepai import ImageDeepAI

image_gen = ImageDeepAI()

prompts = [
    "A serene lake at sunrise",
    "A bustling city street at night", 
    "A peaceful forest in autumn"
]

results = image_gen.generate_multiple(prompts, model="text2img")
```

## Integration with LangChain

### Chains

```python
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_deepai import ChatDeepAI

# Create a chain
prompt = PromptTemplate(
    input_variables=["topic"],
    template="Write a short story about {topic}"
)

chain = LLMChain(
    llm=ChatDeepAI(chat_style="creative"),
    prompt=prompt
)

result = chain.run(topic="time travel")
```

### Agents

```python
from langchain.agents import initialize_agent, Tool
from langchain_deepai import ChatDeepAI, ImageDeepAI

def generate_image_tool(prompt: str) -> str:
    image_gen = ImageDeepAI()
    result = image_gen.generate(prompt)
    return f"Image generated and saved. URL: {result['image_url']}"

tools = [
    Tool(
        name="ImageGenerator",
        func=generate_image_tool,
        description="Generate images from text descriptions"
    )
]

agent = initialize_agent(
    tools=tools,
    llm=ChatDeepAI(),
    agent="zero-shot-react-description"
)
```

## API Reference

### ChatDeepAI

Main chat model class with full LangChain compatibility.

**Parameters:**
- `model_name`: Model to use ("standard", "math", "online", "code")
- `chat_style`: Conversation style 
- `api_key`: DeepAI API key
- `max_tokens`: Maximum tokens to generate
- `temperature`: Response randomness
- `session_id`: Session identifier for context

### Specialized Models

- **DeepAIMath**: Mathematics-focused chat model
- **DeepAICode**: Programming-focused chat model  
- **DeepAIOnline**: Web-aware chat model
- **DeepAIProfessional**: Business communication model
- **DeepAICreative**: Creative writing model
- **DeepAICasual**: Casual conversation model

### ImageDeepAI

Image generation with multiple artistic styles.

**Methods:**
- `generate()`: Generate single image
- `generate_multiple()`: Generate multiple images
- `generate_and_save()`: Generate and save to file

### TextToSpeechDeepAI

High-quality text-to-speech synthesis.

**Methods:**
- `synthesize()`: Convert text to speech
- `synthesize_and_save()`: Convert and save audio
- `synthesize_multiple()`: Batch TTS processing

### SpeechToTextDeepAI

Accurate speech-to-text transcription.

**Methods:**
- `transcribe()`: Transcribe audio to text
- `transcribe_multiple()`: Batch STT processing

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Support

For support and questions:
- Check the [documentation](https://langchain-deepai.readthedocs.io)
- Open an [issue](https://github.com/your-org/langchain-deepai/issues)
- DeepAI API documentation: [https://deepai.org/docs](https://deepai.org/docs)