Support OpenAI Proxy

asciidoc/courses/genai-workshop/course.adoc

@@ -5,12 +5,14 @@
 :usecase: recommendations
 :key-points: A comma, separated, list of learnings
 :repository: neo4j-graphacademy/genai-workshop
+:allow-llm-calls: true
+:llm-call-limit: 0
 
 == Course Description
 
-In this GenAI and Neo4j workshop, you will learn how Neo4j can support your GenAI projects. 
+In this GenAI and Neo4j workshop, you will learn how Neo4j can support your GenAI projects.
 
-You will: 
+You will:
 
 * Use Vector indexes and embeddings in Neo4j to perform similarity and keyword search
 * Use Python and LangChain to integrate with Neo4j and OpenAI
@@ -34,7 +36,7 @@ While not essential, we completing the GraphAcademy link:/courses/neo4j-fundamen
 
 == What you need
 
-To complete the practical tasks within this workshop, you will need: 
+To complete the practical tasks within this workshop, you will need:
 
 * Access to gitpod.io (you will need a github, gitpod, or bitbucket account) or a local Python environment
 * An OpenAI billing account and API key

asciidoc/courses/genai-workshop/modules/1-knowledge-graphs-vectors/lessons/1-getting-started/lesson.adoc

@@ -5,7 +5,7 @@
 :disable-cache: true
 
 We have created a link:https://github.com/neo4j-graphacademy/genai-workshop[repository^] for this workshop.
-It contains the starter code and resources you need. 
+It contains the starter code and resources you need.
 
 A blank Neo4j Sandbox instance has also been created for you to use during this course.
 
@@ -48,7 +48,7 @@ pip install -r requirements.txt
 
 == Setup the environment
 
-Create a copy of the `.env.example` file and name it `.env`. 
+Create a copy of the `.env.example` file and name it `.env`.
 Fill in the required values.
 
 [source]
@@ -57,7 +57,7 @@ Fill in the required values.
 include::{repository-raw}/main/.env.example[]
 ----
 
-Add your Open AI API key (`OPENAI_API_KEY`), which you can get from link:https://platform.openai.com[platformn.openai.com].
+Add your Open AI API key (`OPENAI_API_KEY`), which you can get from link:https://platform.openai.com[platform.openai.com].
 
 Update the Neo4j sandbox connection details:
 

asciidoc/courses/llm-chatbot-python/course.adoc

@@ -7,10 +7,14 @@
 // :video: https://www.youtube.com/embed/vVCHJFa01gA
 :key-points: Building a Neo4j-backed Chatbot, Neo4j & Langchain, Neo4j & Streamlit
 :repository: neo4j-graphacademy/llm-chatbot-python
+:allow-llm-calls: true
+:llm-call-limit: 50
+:llm-call-limit-period: PT2M
+
 
 == Course Description
 
-In this hands-on course, you will use the knowledge obtained from the link:/courses/llm-fundamentals[Neo4j & LLM Fundamentals course^] to create a Movie Recommendation Chatbot backed by a Neo4j database.
+In this hands-on course, you will use the knowledge from the link:/courses/llm-fundamentals[Neo4j & LLM Fundamentals course^] to create a Movie Recommendation Chatbot backed by a Neo4j database.
 
 You will take a simple chat interface that repeats the user's input and modify it to answer questions about movies via the Neo4j Recommendations Dataset using _GPT 3.5 Turbo_, complete with conversational history.
 

asciidoc/courses/llm-chatbot-typescript/course.adoc

@@ -12,9 +12,12 @@
 :reward-form: https://graphacademy.neo4j.com/account/rewards/llm-chatbot-typescript/
 :reward-provider: printful
 :reward-product-id: @65f874e831d488,@65f875094279d1
+:allow-llm-calls: true
+:llm-call-limit: 20
+:llm-call-limit-period: PT2M
 
 == Course Description
-at
+
 In this hands-on course, you will use the knowledge obtained from the link:/courses/llm-fundamentals[Neo4j & LLM Fundamentals course^] to create a Movie Recommendation Chatbot backed by a Neo4j database.
 
 You will take a simple chat interface that repeats the user's input and modify it to answer questions about movies via the Neo4j Recommendations Dataset using GPT-4, complete with conversation history.

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

@@ -84,17 +84,30 @@ NEO4J_USERNAME="{sandbox_username}"
 NEO4J_PASSWORD="{sandbox_password}"
 ----
 
-You must also set your OpenAI API key.
-You can find link:https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key[instructions on generating an API key on openai.com^].
+You will also need an API Key to generate LLM responses using OpenAI.
+
+We have generated an OpenAI API key for you to use through our OpenAI Proxy for the duration of this course using a proxy.
+The API key will be limited to 5 requests every two minutes.
+
 
 Replace the `OPENAI_API_KEY` value with your OpenAI API key.
 
 .env.local
-[source,env]
+[source,env,subs="attributes+"]
 ----
-OPENAI_API_KEY="sk-..."
+OPENAI_API_KEY="{llm-api-key}"
+OPENAI_API_BASE="{llm-api-base}"
 ----
 
+If you find the rate limits restricting, you can always use an OpenAI API Key.
+In this case, you would omit the Base URL.
+You can find link:https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key[instructions on generating an API key on openai.com^].
+
+This course's challenges do not require you use OpenAI LLMs, so feel free to use an alternative LLM provider.
+We also like link:https://ollama.com/[Ollama^].
+
+
+
 [WARNING]
 .Keep your secrets safe
 ====
@@ -151,6 +164,6 @@ include::questions/1-server.adoc[leveloffset=+1]
 [.summary]
 == Summary
 
-In this lesson, you obtained a copy of the course code, installed the dependency and used the `npm run dev` command to start the app.
+In this lesson, you obtained a copy of the course code, installed the dependency, and used the `npm run dev` command to start the app.
 
 In the next lesson, you will set the scope for the project.

asciidoc/courses/llm-chatbot-typescript/modules/2-chains/lessons/1-introduction-to-lcel/lesson.adoc

@@ -26,6 +26,8 @@ You can link:https://js.langchain.com/docs/expression_language/[read more about
 In the link:https://graphacademy.neo4j.com/courses/llm-fundamentals/3-intro-to-langchain/2.5-chains/[Chains lesson in Neo4j & LLM Fundamentals^], you learned about the `LLMChain`.
 The link:https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm.html#LLMChain[`LLMChain`^] is an example of a simple chain that, when invoked, takes a user input, replaces the value inside the prompt and passes the prompt to an LLM and specifies the result.
 
+image::./images/chain.png[Prompt → LLM → Response]
+
 // [source]
 // ----
 // // TODO: Diagram
@@ -60,6 +62,16 @@ The prompt will be passed to an LLM, in this case, the `ChatOpenAI` model.
 include::{repository-raw}/main/examples/chain.mjs[tag=llm]
 ----
 
+=== The Response Parser
+
+You will then pass the LLM's response to an output parser.
+The most straightforward parser is a `StringOutputParser`.
+
+[source,typescript]
+----
+include::{repository-raw}/main/examples/chain.mjs[tag=parser]
+----
+
 
 === Creating the Chain
 

asciidoc/courses/llm-chatbot-typescript/modules/6-agent/lessons/5-using-the-agent/lesson.adoc

@@ -43,6 +43,12 @@ The retrieval tool requires an embedding model.
 include::{repository-raw}/main/{lab-solution}[tag="embeddings", indent=0]
 ----
 
+[IMPORTANT]
+.Defining a `baseURL`
+The `baseURL` configuration option uses the `OPENAI_API_BASE` key to use the GraphAcademy OpenAI Proxy as mentioned in link:../../1-project-setup/2-setup/[the Project Setup lesson^].
+If you are using an OpenAI API key (starting with `sk-`), you can omit the `configuration` options.
+
+
 To interact with the graph, the agent should use link:/courses/llm-chatbot-typescript/3-conversation-history/2-neo4j-graph/[the singleton instance created in the Initializing Neo4j lesson^].
 
 .Embeddings

asciidoc/courses/llm-chatbot-typescript/modules/6-agent/lessons/6-limiting-scope/lesson.adoc

@@ -19,7 +19,7 @@ The agent currently uses a pre-written prompt from LangChain Hub.
 You can link:https://smith.langchain.com/hub/hwchase17/openai-functions-agent[view this prompt in LangChain Hub^].
 
 The prompt is relatively basic.
-It consists of an array of messages consisting of a role definition, human input, and placeholders.
+It consists of an array of messages, including a role definition, human input, and placeholders.
 
 [source]
 .The prompt
@@ -47,7 +47,7 @@ For example, the following prompt instructs the LLM to refuse to answer question
 .Modified Prompt
 [source]
 ----
-include::{repository-raw}/main/src/{lab-solution}[tag="scoped", indent=0]
+include::{repository-raw}/main/{lab-solution}[tag="scoped", indent=0]
 ----
 
 [NOTE]
@@ -65,7 +65,7 @@ The link:{repository-blob}/main/{test-file}[unit test^] will verify whether the
 == Experiment
 
 You can extend the prompt to instruct the agent to act in specific ways.
-Experiment with different prompts to see how the agent responds to various types of questions.
+Experiment with different prompts to see how the agent responds to various questions.
 
 When you are ready, click Continue.
 

asciidoc/courses/llm-fundamentals/course.adoc

@@ -7,6 +7,7 @@
 :usecase: recommendations
 // :video: https://www.youtube.com/embed/vVCHJFa01gA
 :key-points: Neo4j and Generative AI, Grounding LLMs with Neo4j, Using Neo4j with Langchain
+:allow-llm-calls: true
 
 == Course Description
 
@@ -38,7 +39,7 @@ To complete the practical tasks within this course, you will need an link:https:
 === What you will learn
 
 * Retrieval Augmented Generation (RAG) and its role in grounding LLM generated content
-* Using Vector and full text indexes in Neo4j to perform similarity and keyword search
+* Using Vector and full-text indexes in Neo4j to perform similarity and keyword search
 * Coordinating your LLM interactions with Langchain chains, agents and tools
 
 [.includes]

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

@@ -54,7 +54,7 @@ pip install openai langchain-openai
 
 == Create a Langchain application
 
-Create a new Python program and copy this code into a new Python file.
+Create a new Python program and copy this code into it.
 
 [source,python]
 ----

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

@@ -2,7 +2,9 @@
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
 
-llm = OpenAI(openai_api_key="sk-...")
+llm = OpenAI(
+  openai_api_key="sk-..."
+)
 
 template = PromptTemplate.from_template("""
 You are a cockney fruit and vegetable seller.
@@ -13,8 +15,8 @@
 """)
 
 llm_chain = LLMChain(
-    llm=llm,
-    prompt=template
+  llm=llm,
+  prompt=template
 )
 
 response = llm_chain.invoke({"fruit": "apple"})

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

@@ -4,7 +4,8 @@
 from langchain.schema import StrOutputParser
 
 llm = OpenAI(
-    openai_api_key="sk-...")
+  openai_api_key="sk-..."
+)
 
 template = PromptTemplate.from_template("""
 You are a cockney fruit and vegetable seller.
@@ -15,9 +16,9 @@
 """)
 
 llm_chain = LLMChain(
-    llm=llm,
-    prompt=template,
-    output_parser=StrOutputParser()
+  llm=llm,
+  prompt=template,
+  output_parser=StrOutputParser()
 )
 
 response = llm_chain.invoke({"fruit": "apple"})

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

@@ -4,7 +4,8 @@
 from langchain.output_parsers.json import SimpleJsonOutputParser
 
 llm = OpenAI(
-    openai_api_key="sk-...")
+  openai_api_key="sk-..."
+)
 
 template = PromptTemplate.from_template("""
 You are a cockney fruit and vegetable seller.
@@ -17,9 +18,9 @@
 """)
 
 llm_chain = LLMChain(
-    llm=llm,
-    prompt=template,
-    output_parser=SimpleJsonOutputParser()
+  llm=llm,
+  prompt=template,
+  output_parser=SimpleJsonOutputParser()
 )
 
 response = llm_chain.invoke({"fruit": "apple"})

asciidoc/courses/llm-fundamentals/modules/3-intro-to-langchain/lessons/2.5-chains/lesson.adoc

@@ -8,22 +8,23 @@ Chains allows you to combine language models with different data sources and thi
 
 == Using LLMChain
 
-The simplest chain is an `LLMChain`. An `LLMChain` combines a prompt template with an LLM and returns a response.
+The most straightforward chain is an `LLMChain`. An `LLMChain` combines a prompt template with an LLM and returns a response.
 
 Previously, you created a program that used a prompt template and an LLM to generate a response about fruit.
 
+
 [%collapsible]
 .Click to reveal the code for the program.
 ====
-[source,python]
+[source,python,subs="attributes+"]
 ----
 include::../2-initialising-the-llm/code/llm_prompt.py[]
 ----
 ====
 
 You can combine this program into a chain and create a reusable component.
 
-[source,python]
+[source,python,subs="attributes+"]
 ----
 include::code/llm_chain.py[]
 ----