Test GenAI Courses

.gitignore

@@ -12,3 +12,5 @@ courses.zip
 html.zip
 build/html/*
 tmp
+.venv/
+__pycache__/

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/1-langchain/code/example_application.py

@@ -0,0 +1,80 @@
+import os
+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+from langchain.agents import AgentExecutor, create_react_agent
+from langchain.tools import Tool
+from langchain import hub
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain.schema import StrOutputParser
+from langchain_neo4j.chat_message_histories.neo4j import Neo4jChatMessageHistory
+from langchain_neo4j import Neo4jGraph
+from uuid import uuid4
+
+# Load environment variables
+load_dotenv()
+
+def create_cypher_agent(session_id=None):
+    if session_id is None:
+        session_id = str(uuid4())
+
+    llm = ChatOpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+    graph = Neo4jGraph(
+        url=os.getenv("NEO4J_URI", "bolt://localhost:7687"),
+        username=os.getenv("NEO4J_USERNAME", "neo4j"),
+        password=os.getenv("NEO4J_PASSWORD")
+    )
+
+    prompt = ChatPromptTemplate.from_messages([
+        (
+            "system",
+            "You are a Neo4j expert having a conversation about how to create Cypher queries",
+        ),
+        ("human", "{input}"),
+    ])
+
+    cypher_chat = prompt | llm | StrOutputParser()
+
+    def get_memory(session_id):
+        return Neo4jChatMessageHistory(session_id=session_id, graph=graph)
+
+    tools = [
+        Tool.from_function(
+            name="Cypher Support",
+            description="For when you need to talk about Cypher queries.",
+            func=cypher_chat.invoke,
+        )
+    ]
+
+    agent_prompt = hub.pull("hwchase17/react-chat")
+    agent = create_react_agent(llm, tools, agent_prompt)
+    agent_executor = AgentExecutor(agent=agent, tools=tools)
+
+    return RunnableWithMessageHistory(
+        agent_executor,
+        get_memory,
+        input_messages_key="input",
+        history_messages_key="chat_history",
+    )
+
+def main():
+    session_id = str(uuid4())
+    print(f"Session ID: {session_id}")
+
+    cypher_agent = create_cypher_agent(session_id)
+
+    while True:
+        try:
+            q = input("> ")
+            response = cypher_agent.invoke(
+                {"input": q},
+                {"configurable": {"session_id": session_id}},
+            )
+            print(response["output"])
+        except KeyboardInterrupt:
+            print("\nGoodbye!")
+            break
+
+if __name__ == "__main__":
+    main()

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/1-langchain/code/test_example_application.py

@@ -0,0 +1,25 @@
+import pytest
+from example_application import create_cypher_agent
+import os
+
+@pytest.fixture(autouse=True)
+def setup():
+    # Verify required environment variables are set
+    required_vars = ["OPENAI_API_KEY", "NEO4J_PASSWORD"]
+    missing_vars = [var for var in required_vars if not os.getenv(var)]
+    if missing_vars:
+        pytest.skip(f"Missing required environment variables: {', '.join(missing_vars)}")
+
+def test_cypher_agent_creation():
+    agent = create_cypher_agent("test-session")
+    assert agent is not None
+
+def test_cypher_agent_basic_query():
+    agent = create_cypher_agent("test-session")
+    response = agent.invoke(
+        {"input": "How do I create a simple node in Neo4j?"},
+        {"configurable": {"session_id": "test-session"}},
+    )
+    assert response is not None
+    assert "output" in response
+    assert isinstance(response["output"], str)

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/1-langchain/lesson.adoc

@@ -47,7 +47,7 @@ After completing this module, you will understand what this program does and how
 
 [source, python]
 ----
-include::code/example_applcation.py[][]
+include::code/example_application.py[][]
 ----
 ====
 

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/2-initialising-the-llm/code/llm.py

@@ -1,6 +1,10 @@
+# Unused
 from langchain_openai import OpenAI
 
-llm = OpenAI(openai_api_key="sk-...")
+import os 
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+llm = OpenAI(openai_api_key=OPENAI_API_KEY)
 
 response = llm.invoke("What is Neo4j?")
 

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/2-initialising-the-llm/code/llm_prompt.py

@@ -1,7 +1,11 @@
+# Unused
 from langchain_openai import OpenAI
 from langchain.prompts import PromptTemplate
 
-llm = OpenAI(openai_api_key="sk-...")
+import os 
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+llm = OpenAI(openai_api_key=OPENAI_API_KEY)
 
 template = PromptTemplate(template="""
 You are a cockney fruit and vegetable seller.

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/2-initialising-the-llm/lesson.adoc

@@ -34,11 +34,11 @@ pip install langchain
 You may find it helpful to create a Python virtual environment using a tool like link:https://virtualenv.pypa.io/en/latest/[virtualenv^]. Using a virtual environment allows you to install packages without affecting your system Python installation.
 ====
 
-During the course, you will also be using components from the `neo4j`, `langchain-community` and `langchainhub` packages:
+During the course, you will also be using components from the  `langchain-neo4j`, `langchain-community` and `langchainhub` packages:
 
 [source,sh]
 .Install Langchain
-pip install langchain-community langchainhub neo4j
+pip install langchain-neo4j langchain-community langchainhub neo4j
 
 === Installing OpenAI
 
@@ -54,7 +54,7 @@ You can install the `openai` and `langchain-openai` Python packages using `pip`:
 .Install OpenAI SDK
 pip install openai langchain-openai
 
-== Create a Langchain application
+== Create a LangChain application
 
 Create a new Python program and copy this code into a new Python file.
 

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/2.5-chains/code/llm_chain.py

@@ -1,22 +1,36 @@
 from langchain_openai import OpenAI
 from langchain.prompts import PromptTemplate
+import os
 
-llm = OpenAI(openai_api_key="sk-...")
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 
-template = PromptTemplate.from_template("""
-You are a cockney fruit and vegetable seller.
-Your role is to assist your customer with their fruit and vegetable needs.
-Respond using cockney rhyming slang.
+# tag::llm[]
+llm = OpenAI(openai_api_key=OPENAI_API_KEY)
+# end::llm[]
 
-Tell me about the following fruit: {fruit}
-""")
+def create_fruit_chain():
+    # tag::code[]
+    template = PromptTemplate.from_template("""
+    You are a cockney fruit and vegetable seller.
+    Your role is to assist your customer with their fruit and vegetable needs.
+    Respond using cockney rhyming slang.
 
-# tag::llm_chain[]
-llm_chain = template | llm
-# end::llm_chain[]
+    Tell me about the following fruit: {fruit}
+    """)
+
+    # tag::llm_chain[]
+    llm_chain = template | llm
+    # end::llm_chain[]
+
+    return llm_chain
+
+
+llm_chain = create_fruit_chain()
 
 # tag::invoke[]
 response = llm_chain.invoke({"fruit": "apple"})
 # end::invoke[]
 
-print(response)
+print(response)
+
+# end::code[]

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/2.5-chains/code/llm_chain_output.py

@@ -1,11 +1,19 @@
 from langchain_openai import OpenAI
 from langchain.prompts import PromptTemplate
+
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 # tag::import[]
 from langchain.schema import StrOutputParser
 # end::import[]
 
+
 llm = OpenAI(
-    openai_api_key="sk-...")
+    openai_api_key=os.getenv("OPENAI_API_KEY"))
+
 
 template = PromptTemplate.from_template("""
 You are a cockney fruit and vegetable seller.

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/2.5-chains/code/llm_chain_output_json.py

@@ -1,26 +1,30 @@
+import os
+from dotenv import load_dotenv
 from langchain_openai import OpenAI
 from langchain.prompts import PromptTemplate
-# tag::import[]
 from langchain.output_parsers.json import SimpleJsonOutputParser
-# end::import[]
 
-llm = OpenAI(
-    openai_api_key="sk-...")
+def create_fruit_chain():
+    load_dotenv()
+
+    llm = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 
-template = PromptTemplate.from_template("""
-You are a cockney fruit and vegetable seller.
-Your role is to assist your customer with their fruit and vegetable needs.
-Respond using cockney rhyming slang.
+    template = PromptTemplate.from_template("""
+    You are a cockney fruit and vegetable seller.
+    Your role is to assist your customer with their fruit and vegetable needs.
+    Respond using cockney rhyming slang.
 
-Output JSON as {{"description": "your response here"}}
+    Output JSON as {{"description": "your response here"}}
 
-Tell me about the following fruit: {fruit}
-""")
+    Tell me about the following fruit: {fruit}
+    """)
 
-# tag::llm_chain[]
-llm_chain = template | llm | SimpleJsonOutputParser()
-# end::llm_chain[]
+    return template | llm | SimpleJsonOutputParser()
 
-response = llm_chain.invoke({"fruit": "apple"})
+def main():
+    llm_chain = create_fruit_chain()
+    response = llm_chain.invoke({"fruit": "apple"})
+    print(response)
 
-print(response)
+if __name__ == "__main__":
+    main()