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Overview

DeepSeekLLMService provides access to DeepSeek’s language models through an OpenAI-compatible interface. It inherits from OpenAILLMService and supports streaming responses, function calling, and context management.

API Reference

Complete API documentation and method details

DeepSeek Docs

Official DeepSeek API documentation and features

Example Code

Working example with function calling

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Installation

To use DeepSeek services, install the required dependency: 
pip install "pipecat-ai[deepseek]"
You’ll also need to set up your DeepSeek API key as an environment variable: DEEPSEEK_API_KEY.
Get your API key from DeepSeek Platform.

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Frames

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Input

  • OpenAILLMContextFrame - Conversation context and history
  • LLMMessagesFrame - Direct message list
  • VisionImageRawFrame - Images for vision processing
  • LLMUpdateSettingsFrame - Runtime parameter updates

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Output

  • LLMFullResponseStartFrame / LLMFullResponseEndFrame - Response boundaries
  • LLMTextFrame - Streamed completion chunks
  • FunctionCallInProgressFrame / FunctionCallResultFrame - Function call lifecycle
  • ErrorFrame - API or processing errors

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Function Calling

Function Calling Guide

Learn how to implement function calling with standardized schemas, register handlers, manage context properly, and control execution flow in your conversational AI applications.

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Context Management

Context Management Guide

Learn how to manage conversation context, handle message history, and integrate context aggregators for consistent conversational experiences.

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Usage Example

import os
from pipecat.services.deepseek.llm import DeepSeekLLMService
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema

# Configure DeepSeek service
llm = DeepSeekLLMService(
    api_key=os.getenv("DEEPSEEK_API_KEY"),
    model="deepseek-chat",
    params=DeepSeekLLMService.InputParams(
        temperature=0.7,
        max_tokens=1000
    )
)

# Define function for tool calling
weather_function = FunctionSchema(
    name="get_current_weather",
    description="Get current weather information",
    properties={
        "location": {
            "type": "string",
            "description": "City and state, e.g. San Francisco, CA"
        },
        "format": {
            "type": "string",
            "enum": ["celsius", "fahrenheit"],
            "description": "Temperature unit to use"
        }
    },
    required=["location", "format"]
)

tools = ToolsSchema(standard_tools=[weather_function])

# Create context with reasoning-focused system message
context = OpenAILLMContext(
    messages=[
        {
            "role": "system",
            "content": """You are a helpful assistant with strong reasoning capabilities.
            Infer temperature units based on location unless specified.
            Provide logical, step-by-step responses."""
        }
    ],
    tools=tools
)

# Create context aggregators
context_aggregator = llm.create_context_aggregator(context)

# Register function handler with feedback
async def fetch_weather(params):
    location = params.arguments["location"]
    await params.result_callback({"conditions": "sunny", "temperature": "75°F"})

llm.register_function("get_current_weather", fetch_weather)

# Optional: Add function call feedback
@llm.event_handler("on_function_calls_started")
async def on_function_calls_started(service, function_calls):
    await tts.queue_frame(TTSSpeakFrame("Let me check on that."))

# Use in pipeline
pipeline = Pipeline([
    transport.input(),
    stt,
    context_aggregator.user(),
    llm,
    tts,
    transport.output(),
    context_aggregator.assistant()
])

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Metrics

Inherits all OpenAI metrics capabilities:
  • Time to First Byte (TTFB) - Response latency measurement
  • Processing Duration - Total request processing time
  • Token Usage - Prompt tokens, completion tokens, and totals
Learn how to enable Metrics in your Pipeline.

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Additional Notes

  • OpenAI Compatibility: Full compatibility with OpenAI API features and parameters
  • Cost Efficiency: Competitive pricing compared to other high-capability models
  • Streaming Support: Real-time response streaming for low-latency applications