Chatbot-Ausgaben mit Speicher organisieren

Agentische Systeme mit LangChain entwerfen

Dilini K. Sumanapala, PhD

Founder & AI Engineer, Genverv, Ltd.

Mehrere Tool-Ausgaben streamen

    Kompletter Graph-Workflow.

 

  • Ausgaben mit mehreren Tools drucken
      - Agent: [Tool 1: answer]
  - Agent: [Tool 2: answer]
  • Nutzeranfrage drucken und Speicher aktivieren
      - User: [Query]
  - Agent: [Tool 1: answer]

  - User: [Follow-up query]
  - Agent: [Tool 1: follow-up answer]
Agentische Systeme mit LangChain entwerfen

Mehrere Tool-Ausgaben streamen

from langchain_core.messages import AIMessage, HumanMessage


config = {"configurable": {"thread_id": "1"}}


# Create input message with the user's query
def multi_tool_output(query):

    inputs = {"messages": [HumanMessage(content=query)]}


    # Stream messages and metadata from the chatbot application
    for msg, metadata in app.stream(inputs, config, stream_mode="messages"):


        # Check if the message has content and is not from a human
        if msg.content and not isinstance(msg, HumanMessage):
            print(msg.content, end="", flush=True)

    print("\n")
Agentische Systeme mit LangChain entwerfen

Mit mehreren Tools testen

Dynamische Tool-Zuweisung prüfen
multi_tool_output("Is `Stella won no wallets` a palindrome?")

multi_tool_output("What happened on April 12th, 1955?")

The phrase or word 'Stella won no wallets' is a palindrome.

Yes, the phrase "Stella won no wallets" is a palindrome.


April 12th, 1955, is notable for several reasons, particularly in the context of science and 
technology. On this date, the first successful polio vaccine developed by Dr. Jonas Salk 
was announced to the public. This vaccine was a significant breakthrough in the fight against 
poliomyelitis, a disease that had caused widespread fear and numerous outbreaks, particularly 
affecting children. The announcement marked a turning point in public health and led to 
widespread vaccination campaigns that ultimately contributed to the near-eradication of polio...
Agentische Systeme mit LangChain entwerfen

Rückfragen mit mehreren Tools

# Print the user query first for every interaction
def user_agent_multiturn(queries):
    for query in queries:
        print(f"User: {query}")


        # Stream through messages corresponding to queries, excluding metadata 
        print("Agent: " + "".join(msg.content for msg, metadata in app.stream(
                {"messages": [HumanMessage(content=query)]}, config, stream_mode="messages")


            # Filter out the human messages to print agent messages
            if msg.content and not isinstance(msg, HumanMessage)) + "\n")


queries = ["What happened on the 12 April 1961?", "What about 10 December 1948?", 
"Is `Mr. Owl ate my metal worm?` a palindrome?", "What about 'palladium stadium?'"]
user_agent_multiturn(queries)
Agentische Systeme mit LangChain entwerfen

Komplette Gesprächsausgabe

  • Historische Ereignisse
User: What happened on the 12 April 1961?
Agent: On April 12, 1961, Yuri Gagarin, a Soviet cosmonaut, became the first human to travel 
into space aboard the Vostok 1 spacecraft...

User: What about 22 November 1963?
Agent: December 10, 1948... marks the Universal Declaration of Human Rights (UDHR).
  • Palindrom-Prüfung
User: Is `Mr. Owl ate my metal worm?` a palindrome?
Agent: The phrase or word 'Mr. Owl ate my metal worm?' is a palindrome...

User: What about 'palladium stadium'?
Agent: No, the phrase `palladium stadium` is not a palindrome...
Agentische Systeme mit LangChain entwerfen

Lass uns üben!

Agentische Systeme mit LangChain entwerfen

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