LangSmith：赋能灵活高效的LLM开发

# Step 1: Install required packages# Run this command in your terminal:# pip install -U langchain langchain-openai
# Step 2: Set up environment variables for LangSmith configuration# In a real scenario, you would set these as actual environment variables# Here, we're setting them as Python variables for demonstrationimport os
os.environ['LANGCHAIN_TRACING_V2'] = 'true'# Enable the new version of tracingos.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'# LangSmith API endpointos.environ['LANGCHAIN_API_KEY'] = '<your-api-key>'# Replace with your actual API keyos.environ['LANGCHAIN_PROJECT'] = 'pr-sunny-kill-63'# Project name in LangSmith
# Step 3: Import necessary modulefrom langchain_openai import ChatOpenAI
# Step 4: Create an instance of ChatOpenAI# This uses the default model (usually the latest GPT model)llm = ChatOpenAI()
# Step 5: Invoke the LLM with a simple prompt# This will automatically send trace information to LangSmithresponse = llm.invoke("Hello, world!")
# Step 6: Print the responseprint(response)
# Explanation of what happens:# 1. The environment variables configure the connection to LangSmith.# 2. When we create the ChatOpenAI instance, it uses these environment variables.# 3. The `invoke` method sends the prompt to the LLM and returns the response.# 4. Behind the scenes, LangChain sends trace information to LangSmith, including:#- The prompt sent to the model#- The response received from the model#- Metadata about the request (timestamp, model used, etc.)# 5. You can then view and analyze this information in the LangSmith interface.
# Note: In a production environment, you would typically set the environment# variables outside of your Python script for better security and flexibility.

# Step 1: Install the necessary packages# pip install langchain langsmith
# Step 2: Import required modulesfrom langchain.chat_models import ChatOpenAIfrom langchain.prompts import ChatPromptTemplatefrom langchain.schema import HumanMessagefrom langsmith import Client
# Step 3: Set up your LangSmith clientclient = Client()
# Step 4: Create a simple chainprompt = ChatPromptTemplate.from_messages([("system", "You are a helpful AI assistant."),("human", "{input}")])model = ChatOpenAI()chain = prompt | model
# Step 5: Run the chain with tracingfrom langchain.callbacks.tracers import LangChainTracertracer = LangChainTracer(project_name="My LangSmith Project")
input_text = "What is the capital of France?"result = chain.invoke({"input": input_text}, config={"callbacks": [tracer]})
# Step 6: Print the resultprint(result.content)
# Step 7: Query LangSmith for run informationruns = client.list_runs(project_name="My LangSmith Project",execution_order=1,error=False)
for run in runs:print(f"Run ID: {run.id}")print(f"Run Name: {run.name}")print(f"Input: {run.inputs}")print(f"Output: {run.outputs}")print("---")