Test GenAI course code

.gitignore

@@ -3,6 +3,7 @@
 node_modules
 .env
 .env.*
+!.env.example
 .DS_Store
 server.py
 **/.DS_Store
@@ -12,5 +13,7 @@ courses.zip
 html.zip
 build/html/*
 tmp
-__pycache__
-.venv
+*.pyc
+__pycache__/
+.pytest_cache/
+.venv

asciidoc/courses/genai-workshop-graphrag/modules/2-neo4j-graphrag/lessons/4-hybrid-retriever/reset.cypher

@@ -0,0 +1,3 @@
+CREATE FULLTEXT INDEX plotFulltext IF NOT EXISTS
+FOR (m:Movie)
+ON EACH [m.plot]

asciidoc/courses/genai-workshop-graphrag/modules/2-neo4j-graphrag/lessons/7-multimodal-graphrag/reset.cypher

@@ -0,0 +1,12 @@
+LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/rec-embed/movie-poster-embeddings-1k.csv" AS row
+match (m:Movie {movieId:row.movieId})
+WITH row,m
+CALL db.create.setNodeVectorProperty(m, 'posterEmbedding', apoc.convert.fromJsonList(row.posterEmbedding));
+
+CREATE VECTOR INDEX moviePosters IF NOT EXISTS
+FOR (m:Movie)
+ON m.posterEmbedding
+OPTIONS {indexConfig: {
+ `vector.dimensions`: 512,
+ `vector.similarity_function`: 'cosine'
+}};

asciidoc/courses/llm-chatbot-python/modules/1-project-setup/lessons/2-setup/lesson.adoc

@@ -133,7 +133,7 @@ The `write_message()` helper function has been link:{repository-blob}/main/utils
 .Write Message Helper Function
 [source,python]
 ----
-include::{repository-raw}/{branch}/utils.py[tag=write_message]
+include::{repository-raw}/{branch}/solutions/utils.py[tag=write_message]
 ----
 
 The function accepts two positional arguments - the `role` of the author, either `human` or `assistant`, and the message.

asciidoc/courses/llm-fundamentals/.env.example

@@ -0,0 +1,5 @@
+OPENAI_API_KEY="sk-..."
+NEO4J_URI="bolt://"
+NEO4J_USERNAME="neo4j"
+NEO4J_PASSWORD=""
+LANGSMITH_API_KEY="lsv2_..."

asciidoc/courses/llm-fundamentals/conftest.py

@@ -0,0 +1,56 @@
+import builtins
+import importlib
+import io
+import sys
+
+import pytest
+from pytest import MonkeyPatch
+
+from dotenv import load_dotenv
+
+@pytest.fixture(autouse=True)
+def load_env_vars():
+    load_dotenv()
+
+class TestHelpers:
+    @staticmethod
+    def run_module(monkeypatch: MonkeyPatch, module_name: str, input_values: list[str] = [] ) -> str:
+        """
+        Runs a module (.py file) to completion.
+        stdin input_values can be provided as a list of strings.
+        stdout output is returned as a string.
+        """
+
+        def mocked_input(prompt: str = "", return_values: list[str] = input_values):
+            if len(return_values) == 0:
+                raise Exception("Test error - Ran out of input values")
+            return return_values.pop(0)
+
+        mocked_stdout = io.StringIO()
+
+        with monkeypatch.context() as m:
+            m.setattr(builtins, "input", mocked_input)
+            m.setattr(sys, "stdout", mocked_stdout)
+
+            sys.modules.pop(module_name, None)
+            importlib.import_module(name=module_name, package="files")
+
+        return mocked_stdout.getvalue().strip()
+
+    @staticmethod
+    def run_cypher_file(graph, file_path):
+        with open(file_path, "r") as file:
+            cyphers = file.read()
+            result = []
+            for cypher in cyphers.split(";"):
+                if cypher.strip() != "":
+                    result.append(TestHelpers.run_cypher(graph, cypher))
+            return result
+
+    @staticmethod
+    def run_cypher(graph, cypher):
+        return graph.query(cypher)
+
+@pytest.fixture()
+def test_helpers():
+    return TestHelpers

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

@@ -1,3 +1,5 @@
+import os
+
 from langchain_openai import ChatOpenAI
 from langchain.agents import AgentExecutor, create_react_agent
 from langchain.tools import Tool
@@ -11,12 +13,14 @@
 SESSION_ID = str(uuid4())
 print(f"Session ID: {SESSION_ID}")
 
-llm = ChatOpenAI(openai_api_key="sk-...")
+llm = ChatOpenAI(
+    openai_api_key=os.getenv("OPENAI_API_KEY")
+    )
 
 graph = Neo4jGraph(
-    url="bolt://localhost:7687",
-    username="neo4j",
-    password="pleaseletmein"
+    url=os.getenv("NEO4J_URI"),
+    username=os.getenv("NEO4J_USERNAME"),
+    password=os.getenv("NEO4J_PASSWORD")
 )
 
 prompt = ChatPromptTemplate.from_messages(
@@ -53,9 +57,8 @@ def get_memory(session_id):
     history_messages_key="chat_history",
 )
 
-while True:
-    q = input("> ")
-
+while (q := input("> ")) != "exit":
+
     response = cypher_agent.invoke(
         {
             "input": q

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

@@ -0,0 +1,8 @@
+def test_example_applcation(test_helpers, monkeypatch):
+
+    output = test_helpers.run_module(monkeypatch, "example_applcation", ["hello", "exit"])
+
+    # Test a response was received from the agent
+    # There is a output which looks like Session ID: #####\n[response from LLM]\n
+    assert len(output.split("\n")) == 2
+

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

@@ -1,6 +1,7 @@
+import os
 from langchain_openai import OpenAI
 
-llm = OpenAI(openai_api_key="sk-...")
+llm = OpenAI(openai_api_key=os.getenv("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_config.py

@@ -0,0 +1,16 @@
+import os
+from langchain_openai import OpenAI
+
+# tag::config[]
+llm = OpenAI(
+    openai_api_key=os.getenv("OPENAI_API_KEY"),
+    model="gpt-3.5-turbo-instruct",
+    temperature=0
+)
+# end::config[]
+
+response = llm.invoke("What is Neo4j?")
+
+print(response)
+
+

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

@@ -1,16 +1,23 @@
+import os
 from langchain_openai import OpenAI
-from langchain.prompts import PromptTemplate
+# tag::import[]
+from langchain.prompts import PromptTemplate#
+# end::import[]
 
-llm = OpenAI(openai_api_key="sk-...")
+llm = OpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"))
 
+# tag::template[]
 template = PromptTemplate(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.
 
 Tell me about the following fruit: {fruit}
 """, input_variables=["fruit"])
+# end::template[]
 
+# tag::invoke[]
 response = llm.invoke(template.format(fruit="apple"))
 
-print(response)
+print(response)
+# end::invoke[]

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

@@ -0,0 +1,17 @@
+def test_llm(test_helpers, monkeypatch):
+
+    output = test_helpers.run_module(monkeypatch, "llm")
+
+    assert output > ""
+
+def test_llm_prompt(test_helpers, monkeypatch):
+
+    output = test_helpers.run_module(monkeypatch, "llm_prompt")
+
+    assert output > ""
+
+def test_llm_config(test_helpers, monkeypatch):
+
+    output = test_helpers.run_module(monkeypatch, "llm_config")
+
+    assert output > ""

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

@@ -60,18 +60,12 @@ Create a new Python program and copy this code into a new Python file.
 
 [source,python]
 ----
-from langchain_openai import OpenAI
-
-llm = OpenAI(openai_api_key="sk-...")
-
-response = llm.invoke("What is Neo4j?")
-
-print(response)
+include::code/llm.py[tag=**]
 ----
 
 [IMPORTANT]
 .OpenAI API Key
-Remember to include your OpenAI API key in the `openai_api_key` parameter.
+Create an environment variable called `OPENAI_API_KEY` and set it to your OpenAI API key.
 
 Review the program and note the following:
 
@@ -109,24 +103,16 @@ Modify your program to use the prompt template:
 
 [source,python]
 ----
-from langchain.prompts import PromptTemplate
+include::code/llm_prompt.py[tag=import]
 
-template = PromptTemplate(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.
-
-Tell me about the following fruit: {fruit}
-""", input_variables=["fruit"])
+include::code/llm_prompt.py[tag=template]
 ----
 
 Call the LLM, passing the formatted prompt template as the input:
 
 [source,python]
 ----
-response = llm.invoke(template.format(fruit="apple"))
-
-print(response)
+include::code/llm_prompt.py[tag=invoke]
 ----
 
 You use the `format` method to pass the parameters to the prompt e.g. `fruit="apple"`. The input variables will be validated when the prompt is formatted, and a `KeyError` will be raised if any variables are missing from the input.
@@ -158,11 +144,7 @@ When you create the LLM, you can configure it with parameters such as the `tempe
 
 [source,python]
 ----
-llm = OpenAI(
-    openai_api_key="sk-...",
-    model="gpt-3.5-turbo-instruct",
-    temperature=0
-)
+include::code/llm_config.py[tag=config]
 ----
 
 When selecting a model, it is worth considering the quality of the output and the cost per token. There are several link:https://platform.openai.com/docs/models/overview[OpenAI models^] available, each with different characteristics.

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

@@ -1,7 +1,10 @@
+import os
 from langchain_openai import OpenAI
 from langchain.prompts import PromptTemplate
 
-llm = OpenAI(openai_api_key="sk-...")
+llm = OpenAI(
+    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.py

@@ -1,11 +1,13 @@
+import os
 from langchain_openai import OpenAI
 from langchain.prompts import PromptTemplate
 # 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

@@ -1,11 +1,13 @@
+import os
 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-...")
+    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/test_llm_chain.py

@@ -0,0 +1,11 @@
+def test_llm_chain(test_helpers, monkeypatch):
+
+    assert test_helpers.run_module(monkeypatch, "llm_chain") > ""
+
+def test_llm_chain_output(test_helpers, monkeypatch):
+
+    assert test_helpers.run_module(monkeypatch, "llm_chain_output") > ""
+
+def test_llm_chain_output_json(test_helpers, monkeypatch):
+
+    assert test_helpers.run_module(monkeypatch, "llm_chain_output_json") > ""

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/3-chat-models/code/chat-model-chain.py

@@ -1,8 +1,11 @@
+import os
 from langchain_openai import ChatOpenAI
 from langchain_core.prompts import ChatPromptTemplate
 from langchain.schema import StrOutputParser
 
-chat_llm = ChatOpenAI(openai_api_key="sk-...")
+chat_llm = ChatOpenAI(
+    openai_api_key=os.getenv("OPENAI_API_KEY")
+)
 
 prompt = ChatPromptTemplate.from_messages(
     [

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/3-chat-models/code/chat-model-context.py

@@ -1,8 +1,11 @@
+import os
 from langchain_openai import ChatOpenAI
 from langchain_core.prompts import ChatPromptTemplate
 from langchain.schema import StrOutputParser
 
-chat_llm = ChatOpenAI(openai_api_key="sk-...")
+chat_llm = ChatOpenAI(
+    openai_api_key=os.getenv("OPENAI_API_KEY")
+)
 
 prompt = ChatPromptTemplate.from_messages(
     [

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/3-chat-models/code/chat-model.py

@@ -1,11 +1,12 @@
 # tag::import[]
+import os
 from langchain_openai import ChatOpenAI
 from langchain_core.messages import HumanMessage, SystemMessage  
 # end::import[]
 
 # tag::llm[]
 chat_llm = ChatOpenAI(
-    openai_api_key="sk-..."
+    openai_api_key=os.getenv("OPENAI_API_KEY")
 )
 # end::llm[]
 

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/3-chat-models/code/test_chat-model.py

@@ -0,0 +1,11 @@
+def test_chat_model(test_helpers, monkeypatch):
+
+    assert test_helpers.run_module(monkeypatch, "chat-model") > ""
+
+def test_chat_model_chain(test_helpers, monkeypatch):
+
+    assert test_helpers.run_module(monkeypatch, "chat-model-chain") > ""
+
+def test_chat_model_context(test_helpers, monkeypatch):
+
+    assert test_helpers.run_module(monkeypatch, "chat-model-context") > ""