Package: langchainplus-sdk
LangSmith Client SDKThis package contains the Python client for interacting with theLangSmith platform.To install:pipinstalllangchainplus-sdkLangSmith helps you and your team develop and evaluate language models and intelligent agents. It is compatible with any LLM Application and provides seamless integration withLangChain, a widely recognized open-source framework that simplifies the process for developers to create powerful language model applications.Note: You can enjoy the benefits of LangSmith without using the LangChain open-source packages! To get started with your own proprietary framework, set up your account and then skip toLogging Traces Outside LangChain.A typical workflow looks like:Set up an account with LangSmith or host yourlocal server.Log traces.Debug, Create Datasets, and Evaluate Runs.We'll walk through these steps in more detail below.1. Connect to LangSmithSign up forLangSmithusing 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 theSettings 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 TracesYou can log traces natively in your LangChain application or using a LangSmith RunTree.Logging Traces with LangChainLangSmith seamlessly integrates with the Python LangChain library to record traces from your LLM applications.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.importosos.environ["LANGCHAIN_TRACING_V2"]="true"os.environ["LANGCHAIN_ENDPOINT"]="https://api.langchain.plus"# or your own serveros.environ["LANGCHAIN_API_KEY"]="<YOUR-LANGCHAINPLUS-API-KEY>"# os.environ["LANGCHAIN_PROJECT"] = "My Project Name" # Optional: "default" is used if not setTip:Projects are groups of traces. All runs are logged to a project. If not specified, the project is set todefault.Run an Agent, Chain, or Language Model in LangChainIf the environment variables are correctly set, your application will automatically connect to the LangSmith platform.fromlangchain.chat_modelsimportChatOpenAIchat=ChatOpenAI()response=chat.predict("Translate this sentence from English to French. I love programming.")print(response)Logging Traces Outside LangChainNote: this API is experimental and may change in the futureYou can still use the LangSmith development platform without depending on any
LangChain code. You can connect either by setting the appropriate environment variables,
or by directly specifying the connection information in the RunTree.Copy the environment variables from the Settings Page and add them to your application.importosos.environ["LANGCHAIN_ENDPOINT"]="https://api.langchain.plus"# or your own serveros.environ["LANGCHAIN_API_KEY"]="<YOUR-LANGCHAINPLUS-API-KEY>"# os.environ["LANGCHAIN_PROJECT"] = "My Project Name" # Optional: "default" is used if not setLog traces using a RunTree.A RunTree tracks your application. Each RunTree object is required to have anameandrun_type. These and other important attributes are as follows:name:str- used to identify the component's purposerun_type:str- Currently one of "llm", "chain" or "tool"; more options will be added in the futureinputs:dict- the inputs to the componentoutputs:Optional[dict]- the (optional) returned values from the componenterror:Optional[str]- Any error messages that may have arisen during the callfromlangchainplus_sdk.run_treesimportRunTreeparent_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")# .. My Chat Bot calls an LLMchild_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.end(outputs={"generations":["I should use the transcript_loader tool"" to fetch meeting_transcripts from XYZ"]})# ..  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"},)# The tool returns meeting notes to the chat botchild_tool_run.end(outputs={"meetings":["Meeting1 notes.."]})child_chain_run=parent_run.create_child(name="Unreliable Component",run_type="tool",inputs={"input":"Summarize these notes..."},)try:# .... the component does workraiseValueError("Something went wrong")exceptExceptionase:child_chain_run.end(error=f"I errored again{e}")pass# .. The chat agent recoversparent_run.end(outputs={"output":["The meeting notes are as follows:..."]})# This posts all nested runs as a batchres=parent_run.post(exclude_child_runs=False)res.result()Create a Dataset from Existing RunsOnce 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 theLangSmith docs.fromlangchainplus_sdkimportLangChainPlusClientclient=LangChainPlusClient()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")forruninruns:client.create_example(inputs=run.inputs,outputs=run.outputs,dataset_id=dataset.id,)Evaluating RunsYou can run evaluations directly using the LangSmith client.fromtypingimportOptionalfromlangchainplus_sdk.evaluationimportStringEvaluatordefjaccard_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)returnlen(intersection)/len(union)defgrader(run_input:str,run_output:str,answer:Optional[str])->dict:"""Compute the score and/or label for this run."""ifanswerisNone:value="AMBIGUOUS"score=0.5else:score=jaccard_chars(run_output,answer)value="CORRECT"ifscore>0.9else"INCORRECT"returndict(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,)forruninruns:client.evaluate_run(run,evaluator)Additional DocumentationTo learn more about the LangSmith platform, check out thedocs.