RunPod 聊天模型

开始使用 RunPod 聊天模型。

概述

本指南介绍如何使用 LangChainChatRunPod类与 RunPod Serverless 上托管的聊天模型进行交互。

设置

安装软件包：
```
pip install -qU langchain-runpod
```
部署聊天模型终端节点：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终端节点并获取其终端节点 ID。
设置环境变量：确保RUNPOD_API_KEY和RUNPOD_ENDPOINT_ID（或特定的RUNPOD_CHAT_ENDPOINT_ID）设置。

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

初始化ChatRunPod类。您可以通过以下方式传递特定于模型的参数model_kwargs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。还支持通过以下方式进行流式处理.stream()和.astream()（通过轮询 RunPod 进行模拟/stream端点）。

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 系统

链接

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 | 聊天提示模板

模型功能（取决于端点）

高级功能的可用性在很大程度上取决于 RunPod 端点处理程序的具体实现。这ChatRunPod集成提供了基本框架，但处理程序必须支持底层功能。

特征	集成支持	依赖于端点？	笔记
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：如果终端节点遵循基本的 RunPod API 约定，则标准聊天调用和模拟流式处理可以正常工作。高级功能需要特定的处理程序实现，并且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，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

聊天模型概念指南
聊天模型操作指南

概述

设置

实例

调用

链接

模型功能（取决于端点）

API 参考

相关