追踪 LangChain 应用（Python 和 JS/TS）

pip install langchain_openai

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

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant. Please respond to the user's request only based on the given context."),
    ("user", "Question: {question}\nContext: {context}")
])

model = ChatOpenAI(model="gpt-5.4-mini")
output_parser = StrOutputParser()
chain = prompt | model | output_parser

question = "Can you summarize this morning's meetings?"
context = "During this morning's meeting, we solved all world conflict."

chain.invoke({"question": question, "context": context})

# 你可以选择特定的调用进行追踪...
import langsmith as ls

with ls.tracing_context(enabled=True):
    chain.invoke({"question": "Am I using a callback?", "context": "I'm using a callback"})

# 这将不会被追踪（假设未设置 LANGSMITH_TRACING）
chain.invoke({"question": "Am I being traced?", "context": "I'm not being traced"})

# 即使 LANGSMITH_TRACING=true，这也不会被追踪
with ls.tracing_context(enabled=False):
    chain.invoke({"question": "Am I being traced?", "context": "I'm not being traced"})

# 你可以使用 project_name 参数设置项目名称。
import langsmith as ls

with ls.tracing_context(project_name="My Project", enabled=True):
    chain.invoke({"question": "Am I using a context manager?", "context": "I'm using a context manager"})

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

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

# 标签 "model-tag" 和元数据 {"model-key": "model-value"} 将仅附加到 ChatOpenAI 运行
chat_model = ChatOpenAI().with_config({"tags": ["model-tag"], "metadata": {"model-key": "model-value"}})
output_parser = StrOutputParser()

# 标签和元数据可以通过 RunnableConfig 配置
chain = (prompt | chat_model | output_parser).with_config({"tags": ["config-tag"], "metadata": {"config-key": "config-value"}})

# 标签和元数据也可以在运行时传递
chain.invoke({"input": "What is the meaning of life?"}, {"tags": ["invoke-tag"], "metadata": {"invoke-key": "invoke-value"}})

# 在 LangChain 中进行追踪时，运行名称默认为被追踪对象的类名（例如 'ChatOpenAI'）。
configured_chain = chain.with_config({"run_name": "MyCustomChain"})
configured_chain.invoke({"input": "What is the meaning of life?"})

# 你也可以在调用时配置运行名称，如下所示
chain.invoke({"input": "What is the meaning of life?"}, {"run_name": "MyCustomChain"})

from langchain_openai import ChatOpenAI
from langchain_ollama import ChatOllama

# 为本地模型覆盖模型名称
llm = ChatOllama(
    model="llama2:13b-chat",  # 实际模型 ID
    metadata={"ls_model_name": "llama2-13b-production"}  # 在 LangSmith 中显示的名称
)

# 或者使用 OpenAI 来区分配置
llm_creative = ChatOpenAI(
    model="gpt-5.4",
    temperature=0.9,
    metadata={"ls_model_name": "gpt-5.4-creative"}
)

llm_factual = ChatOpenAI(
    model="gpt-5.4",
    temperature=0.1,
    metadata={"ls_model_name": "gpt-5.4-factual"}
)

# 当模型在链中使用时，元数据会被继承
result = llm.invoke("What is the meaning of life?")

import uuid

my_uuid = uuid.uuid4()

# 你可以在调用时配置运行 ID：
chain.invoke({"input": "What is the meaning of life?"}, {"run_id": my_uuid})

import uuid

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

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant. Please respond to the user's request only based on the given context."),
    ("user", "Question: {question}\n\nContext: {context}")
])
model = ChatOpenAI(model="gpt-5.4-mini")
output_parser = StrOutputParser()

chain = prompt | model | output_parser

question = "Can you summarize this morning's meetings?"
context = "During this morning's meeting, we solved all world conflict."
my_uuid = uuid.uuid4()
result = chain.invoke({"question": question, "context": context}, {"run_id": my_uuid})
print(my_uuid)

from langchain_openai import ChatOpenAI
from langchain_core.tracers.langchain import wait_for_all_tracers

llm = ChatOpenAI()

try:
  llm.invoke("Hello, World!")
finally:
  wait_for_all_tracers()

import langsmith as ls

# 你可以使用 API 密钥和 API URL 创建客户端实例
client = ls.Client(
    api_key="YOUR_API_KEY",  # 可以从密钥管理器中检索
    api_url="https://api.smith.langchain.com",  # 根据需要使用自托管、欧盟 (GCP) (`eu.api...`) 或美国 (AWS) (`aws.api...`)
)

# 你可以将客户端和 project_name 传递给 tracing_context
with ls.tracing_context(client=client, project_name="test-no-env", enabled=True):
    chain.invoke({"question": "Am I using a callback?", "context": "I'm using a callback"})

import { traceable } from "langsmith/traceable";
import { RunnableLambda } from "@langchain/core/runnables";
import { RunnableConfig } from "@langchain/core/runnables";

const tracedChild = traceable((input: string) => `Child Run: ${input}`, {
  name: "Child Run",
});

const parrot = new RunnableLambda({
  func: async (input: { text: string }, config?: RunnableConfig) => {
    // 将 config 传递给现有的 traceable 函数
    await tracedChild(config, input.text);
    return input.text;
  },
});