langsmith-sdk-christopher/python

LangSmith Client SDK

This package contains the Python client for interacting with the LangSmith platform.

To install:

pip install -U langsmith
export LANGCHAIN_API_KEY=ls_...

Then trace:

import openai
from langsmith.wrappers import wrap_openai

client = wrap_openai(openai.Client())

client.chat.completions.create(
    messages=[{"role": "user", "content": "Hello, world"}],
    model="gpt-3.5-turbo"
)

LangSmith helps you and your team develop and evaluate language models and intelligent agents. It is compatible with any LLM application.

Cookbook: For tutorials on how to get more value out of LangSmith, check out the Langsmith Cookbook repo.

A typical workflow looks like:

1. Set up an account with LangSmith.
2. Log traces while debugging and prototyping.
3. Run benchmark evaluations and continuously improve with the collected data.

We'll walk through these steps in more detail below.

1. Connect to LangSmith

Sign up for LangSmith using your GitHub, Discord accounts, or an email address and password. If you sign up with an email, make sure to verify your email address before logging in.

Then, create a unique API key on the Settings Page, which is found in the menu at the top right corner of the page.

Note: Save the API Key in a secure location. It will not be shown again.

2. Log Traces

You can log traces natively using the LangSmith SDK or within your LangChain application.

Logging Traces with LangChain

LangSmith seamlessly integrates with the Python LangChain library to record traces from your LLM applications.

1. Copy the environment variables from the Settings Page and add them to your application.

Tracing can be activated by setting the following environment variables or by manually specifying the LangChainTracer.

import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
os.environ["LANGCHAIN_API_KEY"] = "<YOUR-LANGSMITH-API-KEY>"
# os.environ["LANGCHAIN_PROJECT"] = "My Project Name" # Optional: "default" is used if not set

Tip: Projects are groups of traces. All runs are logged to a project. If not specified, the project is set to default.

2. Run an Agent, Chain, or Language Model in LangChain

If the environment variables are correctly set, your application will automatically connect to the LangSmith platform.

from langchain_core.runnables import chain

@chain
def add_val(x: dict) -> dict:
    return {"val": x["val"] + 1}

add_val({"val": 1})

Logging Traces Outside LangChain

You can still use the LangSmith development platform without depending on any LangChain code.

1. Copy the environment variables from the Settings Page and add them to your application.

import os
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
os.environ["LANGCHAIN_API_KEY"] = "<YOUR-LANGSMITH-API-KEY>"
# os.environ["LANGCHAIN_PROJECT"] = "My Project Name" # Optional: "default" is used if not set

2. Log traces

The easiest way to log traces using the SDK is via the @traceable decorator. Below is an example.

from datetime import datetime
from typing import List, Optional, Tuple

import openai
from langsmith import traceable
from langsmith.wrappers import wrap_openai

client = wrap_openai(openai.Client())

@traceable
def argument_generator(query: str, additional_description: str = "") -> str:
    return client.chat.completions.create(
        [
            {"role": "system", "content": "You are a debater making an argument on a topic."
             f"{additional_description}"
             f" The current time is {datetime.now()}"},
            {"role": "user", "content": f"The discussion topic is {query}"}
        ]
    ).choices[0].message.content



@traceable
def argument_chain(query: str, additional_description: str = "") -> str:
    argument = argument_generator(query, additional_description)
    # ... Do other processing or call other functions...
    return argument

argument_chain("Why is blue better than orange?")

Alternatively, you can manually log events using the Client directly or using a RunTree, which is what the traceable decorator is meant to manage for you!

A RunTree tracks your application. Each RunTree object is required to have a name and run_type. These and other important attributes are as follows:

• name: str - used to identify the component's purpose
• run_type: str - Currently one of "llm", "chain" or "tool"; more options will be added in the future
• inputs: dict - the inputs to the component
• outputs: Optional[dict] - the (optional) returned values from the component
• error: Optional[str] - Any error messages that may have arisen during the call

from langsmith.run_trees import RunTree

parent_run = RunTree(
    name="My Chat Bot",
    run_type="chain",
    inputs={"text": "Summarize this morning's meetings."},
    serialized={},  # Serialized representation of this chain
    # project_name= "Defaults to the LANGCHAIN_PROJECT env var"
    # api_url= "Defaults to the LANGCHAIN_ENDPOINT env var"
    # api_key= "Defaults to the LANGCHAIN_API_KEY env var"
)
parent_run.post()
# .. My Chat Bot calls an LLM
child_llm_run = parent_run.create_child(
    name="My Proprietary LLM",
    run_type="llm",
    inputs={
        "prompts": [
            "You are an AI Assistant. The time is XYZ."
            " Summarize this morning's meetings."
        ]
    },
)
child_llm_run.post()
child_llm_run.end(
    outputs={
        "generations": [
            "I should use the transcript_loader tool"
            " to fetch meeting_transcripts from XYZ"
        ]
    }
)
child_llm_run.patch()
# ..  My Chat Bot takes the LLM output and calls
# a tool / function for fetching transcripts ..
child_tool_run = parent_run.create_child(
    name="transcript_loader",
    run_type="tool",
    inputs={"date": "XYZ", "content_type": "meeting_transcripts"},
)
child_tool_run.post()
# The tool returns meeting notes to the chat bot
child_tool_run.end(outputs={"meetings": ["Meeting1 notes.."]})
child_tool_run.patch()

child_chain_run = parent_run.create_child(
    name="Unreliable Component",
    run_type="tool",
    inputs={"input": "Summarize these notes..."},
)
child_chain_run.post()

try:
    # .... the component does work
    raise ValueError("Something went wrong")
    child_chain_run.end(outputs={"output": "foo"}
    child_chain_run.patch()
except Exception as e:
    child_chain_run.end(error=f"I errored again {e}")
    child_chain_run.patch()
    pass
# .. The chat agent recovers

parent_run.end(outputs={"output": ["The meeting notes are as follows:..."]})
res = parent_run.patch()
res.result()

Create a Dataset from Existing Runs

Once your runs are stored in LangSmith, you can convert them into a dataset. For this example, we will do so using the Client, but you can also do this using the web interface, as explained in the LangSmith docs.

from langsmith import Client

client = Client()
dataset_name = "Example Dataset"
# We will only use examples from the top level AgentExecutor run here,
# and exclude runs that errored.
runs = client.list_runs(
    project_name="my_project",
    execution_order=1,
    error=False,
)

dataset = client.create_dataset(dataset_name, description="An example dataset")
for run in runs:
    client.create_example(
        inputs=run.inputs,
        outputs=run.outputs,
        dataset_id=dataset.id,
    )

Evaluating Runs

Check out the LangSmith Testing & Evaluation dos for up-to-date workflows.

For generating automated feedback on individual runs, you can run evaluations directly using the LangSmith client.

from typing import Optional
from langsmith.evaluation import StringEvaluator


def jaccard_chars(output: str, answer: str) -> float:
    """Naive Jaccard similarity between two strings."""
    prediction_chars = set(output.strip().lower())
    answer_chars = set(answer.strip().lower())
    intersection = prediction_chars.intersection(answer_chars)
    union = prediction_chars.union(answer_chars)
    return len(intersection) / len(union)


def grader(run_input: str, run_output: str, answer: Optional[str]) -> dict:
    """Compute the score and/or label for this run."""
    if answer is None:
        value = "AMBIGUOUS"
        score = 0.5
    else:
        score = jaccard_chars(run_output, answer)
        value = "CORRECT" if score > 0.9 else "INCORRECT"
    return dict(score=score, value=value)

evaluator = StringEvaluator(evaluation_name="Jaccard", grading_function=grader)

runs = client.list_runs(
    project_name="my_project",
    execution_order=1,
    error=False,
)
for run in runs:
    client.evaluate_run(run, evaluator)

Additional Documentation

To learn more about the LangSmith platform, check out the docs.