svector-sdk 1.0.3

Official Python SDK for SVECTOR AI Models - Advanced conversational AI and language models

SVECTOR Python SDK

The official Python SDK for SVECTOR AI Models. Build powerful AI applications with advanced conversational AI and language models.

🚀 Installation

pip install svector

🎯 Quick Start

Basic Usage

from svector import SVECTOR

# Initialize the client
client = SVECTOR(api_key="your-api-key-here")

# Simple chat completion
response = client.chat.create(
    model="spec-3-turbo:latest",
    messages=[
        {"role": "user", "content": "What is artificial intelligence?"}
    ]
)

print(response["choices"][0]["message"]["content"])

Streaming Responses

# Stream responses in real-time
stream = client.chat.create(
    model="spec-3-turbo:latest",
    messages=[
        {"role": "user", "content": "Write a poem about technology"}
    ],
    stream=True
)

for event in stream:
    if event.get("choices") and event["choices"][0].get("delta", {}).get("content"):
        print(event["choices"][0]["delta"]["content"], end="", flush=True)

File Upload and RAG

# Upload a file for RAG
file_response = client.files.create("document.pdf", purpose="rag")
file_id = file_response["file_id"]

# Ask questions about the uploaded file
response = client.chat.create(
    model="spec-3-turbo:latest",
    messages=[
        {"role": "user", "content": "Summarize this document"}
    ],
    files=[{"type": "file", "id": file_id}]
)

print(response["choices"][0]["message"]["content"])

🖥️ Command Line Interface

SVECTOR also provides a powerful CLI:

# Set up your API key
svector config set-key your-api-key-here

# Start chatting
svector chat "Hello, SVECTOR!"

# Stream responses
svector stream "Write a poem about AI"

# List available models
svector models

# Upload files for RAG
svector file upload document.pdf

# Ask questions about files
svector ask "Summarize this document" --file file-123

📚 API Reference

SVECTOR Client

client = SVECTOR(
    api_key="your-api-key",           # Required: Your SVECTOR API key
    base_url="https://api.svector.co.in",  # Optional: Custom API base URL
    timeout=30,                       # Optional: Request timeout in seconds
    max_retries=3                     # Optional: Max retry attempts
)

Chat Completions

response = client.chat.create(
    model="spec-3-turbo:latest",      # Required: Model name
    messages=[                        # Required: List of messages
        {"role": "user", "content": "Hello"}
    ],
    temperature=0.7,                  # Optional: 0.0 to 2.0
    max_tokens=150,                   # Optional: Max tokens to generate
    files=[                           # Optional: Files for RAG
        {"type": "file", "id": "file-123"}
    ],
    stream=False                      # Optional: Enable streaming
)

Models API

# List available models
models = client.models.list()
print(models["models"])

Files API

# Upload from file path
response = client.files.create("path/to/file.pdf", purpose="rag")

# Upload from bytes
with open("file.pdf", "rb") as f:
    response = client.files.create(f.read(), purpose="rag", filename="file.pdf")

# Upload from file object
with open("file.pdf", "rb") as f:
    response = client.files.create(f, purpose="rag", filename="file.pdf")

🔧 Advanced Examples

Multi-turn Conversation

conversation = [
    {"role": "system", "content": "You are a helpful programming assistant."},
    {"role": "user", "content": "How do I create a function in Python?"},
]

response = client.chat.create(
    model="spec-3-turbo:latest",
    messages=conversation,
    temperature=0.3
)

# Add the AI response to conversation history
conversation.append({
    "role": "assistant", 
    "content": response["choices"][0]["message"]["content"]
})

# Continue the conversation
conversation.append({
    "role": "user", 
    "content": "Can you show me an example?"
})

response = client.chat.create(
    model="spec-3-turbo:latest",
    messages=conversation
)

Multi-file RAG

# Upload multiple files
file1 = client.files.create("technical_specs.pdf")
file2 = client.files.create("user_manual.pdf")

# Query across multiple documents
response = client.chat.create(
    model="spec-3-turbo:latest",
    messages=[
        {"role": "user", "content": "Compare the technical specifications with the user manual"}
    ],
    files=[
        {"type": "file", "id": file1["file_id"]},
        {"type": "file", "id": file2["file_id"]}
    ]
)

Error Handling

from svector import SVECTOR, AuthenticationError, RateLimitError, APIError

try:
    client = SVECTOR(api_key="invalid-key")
    response = client.chat.create(
        model="spec-3-turbo:latest",
        messages=[{"role": "user", "content": "Hello"}]
    )
except AuthenticationError:
    print("Invalid API key")
except RateLimitError:
    print("Rate limit exceeded")
except APIError as e:
    print(f"API error: {e}")

🌟 Features

• ✅ Complete API Coverage: Chat completions, streaming, file upload, RAG
• ✅ Type Safety: Full type hints for better development experience
• ✅ Error Handling: Comprehensive error types and retry logic
• ✅ Streaming Support: Real-time response streaming
• ✅ File Upload: Support for various file formats and RAG
• ✅ CLI Interface: Command-line tool for quick interactions
• ✅ Production Ready: Robust error handling and retry mechanisms

🔑 Authentication

Get your API key from the SVECTOR Dashboard.

Set it as an environment variable:

export SVECTOR_API_KEY="your-api-key-here"

Or pass it directly to the client:

client = SVECTOR(api_key="your-api-key-here")

🌍 Supported Models

• spec-3-turbo:latest - General purpose model with excellent performance
• spec-3-pro:latest - Advanced reasoning model for complex tasks

Get the latest list:

models = client.models.list()

📋 Requirements

• Python 3.8+
• requests library (automatically installed)

🤝 Support

• Documentation: https://www.svector.co.in/docs
• Email: [e-mail]
• Issues: GitHub Issues

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

🚀 Getting Started

1. Install the package:

   pip install svector
   

2. Get your API key: Visit https://www.svector.co.in

3. Start building:

   from svector import SVECTOR
   
   client = SVECTOR(api_key="your-key")
   response = client.chat.create(
       model="spec-3-turbo:latest",
       messages=[{"role": "user", "content": "Hello, SVECTOR!"}]
   )
   

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Built with ❤️ by the SVECTOR Team