Chat SDK

from gaia.chat.sdk import quick_chat

# Single query
response = quick_chat("What is machine learning?")
print(response.text)

# With options
response = quick_chat(
    "Explain quantum computing",
    model="Qwen3-Coder-30B-A3B-Instruct-GGUF",
    max_tokens=500,
    show_stats=True
)
print(response.text)
if response.stats:
    print(f"Tokens: {response.stats['total_tokens']}")

from gaia.chat.sdk import ChatSDK, ChatConfig

# Configure chat
config = ChatConfig(
    model="Qwen2.5-0.5B-Instruct-CPU",
    max_tokens=512,
    max_history_length=4,  # Keep 4 conversation pairs
    system_prompt="You are a helpful coding assistant.",
    assistant_name="CodeBot"
)

# Create chat instance
chat = ChatSDK(config)

# Multi-turn conversation
response1 = chat.send("What is Python?")
print(response1.text)

response2 = chat.send("Show me an example")
print(response2.text)
# LLM remembers context from first question!

# Check history
history = chat.get_history()
print(f"Conversation turns: {len(history)}")

# Clear history
chat.clear_history()

from gaia.chat.sdk import ChatSDK, ChatConfig

config = ChatConfig(streaming=True)
chat = ChatSDK(config)

# Stream response token by token
for chunk in chat.send_stream("Tell me a story"):
    print(chunk, end="", flush=True)

from gaia.chat.sdk import ChatSDK, ChatConfig
from gaia.rag.sdk import RAGSDK, RAGConfig

# Set up RAG
rag_config = RAGConfig()
rag = RAGSDK(rag_config)
rag.index_document("manual.pdf")
rag.index_document("documentation.pdf")

# Create chat with RAG enabled
chat_config = ChatConfig()
chat = ChatSDK(chat_config)
chat.enable_rag(rag)

# Now chat uses document context automatically
response = chat.send("What does the manual say about installation?")
print(response.text)
# LLM has access to indexed documents!

# Disable RAG
chat.disable_rag()

from gaia.chat.sdk import ChatSDK

chat = ChatSDK()

# Send custom message array
messages = [
    {"role": "system", "content": "You are a math tutor."},
    {"role": "user", "content": "Explain calculus"},
    {"role": "assistant", "content": "Calculus is..."},
    {"role": "user", "content": "Give me an example"}
]

response = chat.send_messages(
    messages=messages,
    system_prompt="Be concise and clear"
)
print(response.text)