Cookbook: Langchain Integration

%pip install langfuse langchain langchain_openai --upgrade

import os
 
# get keys for your project from https://cloud.langfuse.com
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-***"
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-***"
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" # for EU data region
# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # for US data region
 
# your openai key
os.environ["OPENAI_API_KEY"] = "***"

from langfuse.callback import CallbackHandler
 
langfuse_handler = CallbackHandler()

# Tests the SDK connection with the server
langfuse_handler.auth_check()

from operator import itemgetter
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
 
langfuse_handler = CallbackHandler()
 
prompt1 = ChatPromptTemplate.from_template("what is the city {person} is from?")
prompt2 = ChatPromptTemplate.from_template(
    "what country is the city {city} in? respond in {language}"
)
model = ChatOpenAI()
chain1 = prompt1 | model | StrOutputParser()
chain2 = (
    {"city": chain1, "language": itemgetter("language")}
    | prompt2
    | model
    | StrOutputParser()
)
 
chain2.invoke({"person": "obama", "language": "spanish"}, config={"callbacks":[langfuse_handler]})

# Async Invoke
await chain2.ainvoke({"person": "biden", "language": "german"}, config={"callbacks":[langfuse_handler]})
 
# Batch
chain2.batch([{"person": "elon musk", "language": "english"}, {"person": "mark zuckerberg", "language": "english"}], config={"callbacks":[langfuse_handler]})
 
# Async Batch
await chain2.abatch([{"person": "jeff bezos", "language": "english"}, {"person": "tim cook", "language": "english"}], config={"callbacks":[langfuse_handler]})
 
# Stream
for chunk in chain2.stream({"person": "steve jobs", "language": "english"}, config={"callbacks":[langfuse_handler]}):
    print("Streaming chunk:", chunk)
 
# Async Stream
async for chunk in chain2.astream({"person": "bill gates", "language": "english"}, config={"callbacks":[langfuse_handler]}):
    print("Async Streaming chunk:", chunk)
 

from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_openai import OpenAI
 
llm = OpenAI(temperature=0)
 
conversation = ConversationChain(
    llm=llm, memory=ConversationBufferMemory()
)

# Create a callback handler with a session
langfuse_handler = CallbackHandler(session_id="conversation_chain")

conversation.predict(input="Hi there!", callbacks=[langfuse_handler])

conversation.predict(input="How to build great developer tools?", callbacks=[langfuse_handler])

conversation.predict(input="Summarize your last response", callbacks=[langfuse_handler])

import os
os.environ["SERPAPI_API_KEY"] = ""

%pip install unstructured selenium langchain-chroma --upgrade

from langchain_community.document_loaders import SeleniumURLLoader
from langchain_chroma import Chroma
from langchain_text_splitters import CharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain.chains import RetrievalQA
 
langfuse_handler = CallbackHandler()
 
urls = [
    "https://raw.githubusercontent.com/langfuse/langfuse-docs/main/public/state_of_the_union.txt",
]
loader = SeleniumURLLoader(urls=urls)
llm = OpenAI()
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
chain = RetrievalQA.from_chain_type(
    llm,
    retriever=docsearch.as_retriever(search_kwargs={"k": 1}),
)
 
chain.invoke(query, config={"callbacks":[langfuse_handler]})

from langchain.agents import AgentExecutor, load_tools, create_openai_functions_agent
from langchain_openai import ChatOpenAI
from langchain import hub
 
langfuse_handler = CallbackHandler()
 
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
tools = load_tools(["serpapi"])
prompt = hub.pull("hwchase17/openai-functions-agent")
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
 
agent_executor.invoke({"input": "What is Langfuse?"}, config={"callbacks":[langfuse_handler]})

os.environ["AZURE_OPENAI_ENDPOINT"] = "<Azure OpenAI endpoint>"
os.environ["AZURE_OPENAI_API_KEY"] = "<Azure OpenAI API key>"
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_VERSION"] = "2023-09-01-preview"

from langchain_openai import AzureChatOpenAI
from langchain.prompts import ChatPromptTemplate
 
langfuse_handler = CallbackHandler()
 
prompt = ChatPromptTemplate.from_template("what is the city {person} is from?")
model = AzureChatOpenAI(
    deployment_name="gpt-35-turbo",
    model_name="gpt-3.5-turbo",
)
chain = prompt | model
 
chain.invoke({"person": "Satya Nadella"}, config={"callbacks":[langfuse_handler]})

# further imports
from langchain_openai import OpenAI
from langchain.chains import LLMChain, SimpleSequentialChain
from langchain.prompts import PromptTemplate
 
llm = OpenAI()
template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
    Title: {title}
    Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)
template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.
    Play Synopsis:
    {synopsis}
    Review from a New York Times play critic of the above play:"""
prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)
review_chain = LLMChain(llm=llm, prompt=prompt_template)
overall_chain = SimpleSequentialChain(
    chains=[synopsis_chain, review_chain],
)
 
# invoke
review = overall_chain.invoke("Tragedy at sunset on the beach", {"callbacks":[langfuse_handler]}) # add the handler to the run method
# run [LEGACY]
review = overall_chain.run("Tragedy at sunset on the beach", callbacks=[langfuse_handler])# add the handler to the run method

from langfuse.decorators import langfuse_context, observe
 
@observe(as_type="generation")
def deeply_nested_llm_call():
    # Enrich the current observation with a custom name, input, and output
    langfuse_context.update_current_observation(
        name="Deeply nested LLM call", input="Ping?", output="Pong!"
    )
    # Set the parent trace's name from within a nested observation
    langfuse_context.update_current_trace(
        name="Trace name set from deeply_nested_llm_call",
        session_id="1234",
        user_id="5678",
        tags=["tag1", "tag2"],
        public=True
    )
 
@observe()
def nested_span():
    # Update the current span with a custom name and level
    langfuse_context.update_current_observation(name="Nested Span", level="WARNING")
    deeply_nested_llm_call()
 
@observe()
def main():
    nested_span()
 
# Execute the main function to generate the enriched trace
main()

from langfuse.decorators import langfuse_context, observe
 
# Create a trace via Langfuse decorators and get a Langchain Callback handler for it
@observe() # automtically log function as a trace to Langfuse
def main():
    # update trace attributes (e.g, name, session_id, user_id)
    langfuse_context.update_current_trace(
        name="custom-trace",
        session_id="user-1234",
        user_id="session-1234",
    )
    # get the langchain handler for the current trace
    langfuse_context.get_current_langchain_handler()
 
    # use the handler to trace langchain runs ...
 
main()

TRACE: person-locator
|
|-- SPAN: Chain (Alan Turing)
|
|-- SPAN: Physics
|   |
|   |-- SPAN: Chain (Albert Einstein)
|   |
|   |-- SPAN: Chain (Isaac Newton)
|   |
|   |-- SPAN: Favorites
|   |   |
|   |   |-- SPAN: Chain (Richard Feynman)

from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
 
prompt = ChatPromptTemplate.from_template("what is the city {person} is from?")
model = ChatOpenAI()
 
chain = prompt | model

from langfuse.decorators import langfuse_context, observe
 
# On span "Physics"."Favorites"
@observe()  # decorator to automatically log function as sub-span to Langfuse
def favorites():
    # get the langchain handler for the current sub-span
    langfuse_handler = langfuse_context.get_current_langchain_handler()
    # invoke chain with langfuse handler
    chain.invoke({"person": "Richard Feynman"},
                 config={"callbacks": [langfuse_handler]})
 
# On span "Physics"
@observe()  # decorator to automatically log function as span to Langfuse
def physics():
    # get the langchain handler for the current span
    langfuse_handler = langfuse_context.get_current_langchain_handler()
    # invoke chains with langfuse handler
    chain.invoke({"person": "Albert Einstein"},
                 config={"callbacks": [langfuse_handler]})
    chain.invoke({"person": "Isaac Newton"},
                 config={"callbacks": [langfuse_handler]})
    favorites()
 
# On trace
@observe()  # decorator to automatically log function as trace to Langfuse
def main():
    # get the langchain handler for the current trace
    langfuse_handler = langfuse_context.get_current_langchain_handler()
    # invoke chain with langfuse handler
    chain.invoke({"person": "Alan Turing"},
                 config={"callbacks": [langfuse_handler]})
    physics()
 
main()