RunPod 对话模型

开始使用RunPod聊天模型。

概览

本指南介绍了如何使用 LangChain 的 ChatRunPod 类与托管在 RunPod 无服务器 上的聊天模型进行交互。

设置

1. 安装包：

   pip install -qU langchain-runpod
2. 部署聊天模型端点： 按照RunPod 提供商指南中的设置步骤，在 RunPod Serverless 上部署兼容的聊天模型端点并获取其端点 ID。
3. 设置环境变量： 确保已设置 RUNPOD_API_KEY 和 RUNPOD_ENDPOINT_ID（或特定的 RUNPOD_CHAT_ENDPOINT_ID）。

import getpass
import os

# Make sure environment variables are set (or pass them directly to ChatRunPod)
if "RUNPOD_API_KEY" not in os.environ:
    os.environ["RUNPOD_API_KEY"] = getpass.getpass("Enter your RunPod API Key: ")

if "RUNPOD_ENDPOINT_ID" not in os.environ:
    os.environ["RUNPOD_ENDPOINT_ID"] = input(
        "Enter your RunPod Endpoint ID (used if RUNPOD_CHAT_ENDPOINT_ID is not set): "
    )

# Optionally use a different endpoint ID specifically for chat models
# if "RUNPOD_CHAT_ENDPOINT_ID" not in os.environ:
#     os.environ["RUNPOD_CHAT_ENDPOINT_ID"] = input("Enter your RunPod Chat Endpoint ID (Optional): ")

chat_endpoint_id = os.environ.get(
    "RUNPOD_CHAT_ENDPOINT_ID", os.environ.get("RUNPOD_ENDPOINT_ID")
)
if not chat_endpoint_id:
    raise ValueError(
        "No RunPod Endpoint ID found. Please set RUNPOD_ENDPOINT_ID or RUNPOD_CHAT_ENDPOINT_ID."
    )

实例化

初始化 ChatRunPod 类。你可以通过 model_kwargs 传递模型特定的参数，并配置轮询行为。

from langchain_runpod import ChatRunPod

chat = ChatRunPod(
    runpod_endpoint_id=chat_endpoint_id,  # Specify the correct endpoint ID
    model_kwargs={
        "max_new_tokens": 512,
        "temperature": 0.7,
        "top_p": 0.9,
        # Add other parameters supported by your endpoint handler
    },
    # Optional: Adjust polling
    # poll_interval=0.2,
    # max_polling_attempts=150
)

调用

使用标准的 LangChain .invoke() 和 .ainvoke() 方法来调用模型。还支持通过 .stream() 和 .astream() 进行流式传输（通过轮询 RunPod /stream 端点模拟）。

from langchain_core.messages import HumanMessage, SystemMessage

messages = [
    SystemMessage(content="You are a helpful AI assistant."),
    HumanMessage(content="What is the RunPod Serverless API flow?"),
]

# Invoke (Sync)
try:
    response = chat.invoke(messages)
    print("--- Sync Invoke Response ---")
    print(response.content)
except Exception as e:
    print(
        f"Error invoking Chat Model: {e}. Ensure endpoint ID/API key are correct and endpoint is active/compatible."
    )

# Stream (Sync, simulated via polling /stream)
print("\n--- Sync Stream Response ---")
try:
    for chunk in chat.stream(messages):
        print(chunk.content, end="", flush=True)
    print()  # Newline
except Exception as e:
    print(
        f"\nError streaming Chat Model: {e}. Ensure endpoint handler supports streaming output format."
    )

### Async Usage

# AInvoke (Async)
try:
    async_response = await chat.ainvoke(messages)
    print("--- Async Invoke Response ---")
    print(async_response.content)
except Exception as e:
    print(f"Error invoking Chat Model asynchronously: {e}.")

# AStream (Async)
print("\n--- Async Stream Response ---")
try:
    async for chunk in chat.astream(messages):
        print(chunk.content, end="", flush=True)
    print()  # Newline
except Exception as e:
    print(
        f"\nError streaming Chat Model asynchronously: {e}. Ensure endpoint handler supports streaming output format.\n"
    )

API 参考：HumanMessage | SystemMessage

链式调用

聊天模型与 LangChain 表达式语言 (LCEL) 链无缝集成。

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant."),
        ("human", "{input}"),
    ]
)

parser = StrOutputParser()

chain = prompt | chat | parser

try:
    chain_response = chain.invoke(
        {"input": "Explain the concept of serverless computing in simple terms."}
    )
    print("--- Chain Response ---")
    print(chain_response)
except Exception as e:
    print(f"Error running chain: {e}")


# Async chain
try:
    async_chain_response = await chain.ainvoke(
        {"input": "What are the benefits of using RunPod for AI/ML workloads?"}
    )
    print("--- Async Chain Response ---")
    print(async_chain_response)
except Exception as e:
    print(f"Error running async chain: {e}")

API 参考：StrOutputParser | ChatPromptTemplate

模型特性（取决于端点）

高级功能的可用性很大程度上取决于您的RunPod端点处理器的具体实现。ChatRunPod集成提供了基本框架，但处理器必须支持底层功能。

特性	集成支持	端点依赖？	备注
Tool calling	❌	✅	Requires handler to process tool definitions and return tool calls (e.g., OpenAI format). Integration needs parsing logic.
Structured output	❌	✅	Requires handler support for forcing structured output (JSON mode, function calling). Integration needs parsing logic.
JSON mode	❌	✅	Requires handler to accept a json_mode parameter (or similar) and guarantee JSON output.
Image input	❌	✅	Requires multimodal handler accepting image data (e.g., base64). Integration does not support multimodal messages.
Audio input	❌	✅	Requires handler accepting audio data. Integration does not support audio messages.
Video input	❌	✅	Requires handler accepting video data. Integration does not support video messages.
Token-level streaming	✅ (Simulated)	✅	Polls /stream. Requires handler to populate stream list in status response with token chunks (e.g., [{"output": "token"}]). True low-latency streaming not built-in.
Native async	✅	✅	Core ainvoke/astream implemented. Relies on endpoint handler performance.
Token usage	❌	✅	Requires handler to return prompt_tokens, completion_tokens in the final response. Integration currently does not parse this.
Logprobs	❌	✅	Requires handler to return log probabilities. Integration currently does not parse this.

关键要点：如果端点遵循基本的RunPod API约定，标准聊天调用和模拟流式传输即可正常工作。高级功能需要特定的处理器实现，并可能需要扩展或自定义此集成包。

API 参考

有关 ChatRunPod 类、参数和方法的详细文档，请参阅源代码或生成的API参考（如果可用）。

源代码链接: https://github.com/runpod/langchain-runpod/blob/main/langchain_runpod/chat_models.py

相关

• 聊天模型 概念指南
• 聊天模型 操操作指南