Skip to content

Readme update to fix duplication #11

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Oct 21, 2024
Merged

Readme update to fix duplication #11

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
990412b
Fixing up duplication in Readme
paulyuk Oct 15, 2024
d5e00b9
Merge branch 'Azure-Samples:main' into main
paulyuk Oct 15, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
28 changes: 4 additions & 24 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -98,25 +98,6 @@ code .

4) Test using same REST client steps above


## Source Code

The key code that makes this work is as follows in [function_app.py](./function_app.py). You can customize this or learn more snippets using the [LangChain Quickstart Guide](https://python.langchain.com/en/latest/getting_started/getting_started.html).

```python
llm = AzureOpenAI(deployment_name=AZURE_OPENAI_CHATGPT_DEPLOYMENT, temperature=0.3, openai_api_key=AZURE_OPENAI_KEY)

llm_prompt = PromptTemplate(
input_variables=["human_prompt"],
template="The following is a conversation with an AI assistant. The assistant is helpful.\n\nAI: I am an AI created by OpenAI. How can I help you today?\nHuman: {human_prompt}?",
)

from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=llm_prompt)

return chain.run(prompt) # prompt is human input from request body
```

## Deploy to Azure

The easiest way to deploy this app is using the [Azure Dev CLI](https://aka.ms/azd). If you open this repo in GitHub CodeSpaces the AZD tooling is already preinstalled.
Expand All @@ -128,21 +109,20 @@ azd up

## Source Code

The key code that makes the prompting and completion work is as follows in [function_app.py](function_app.py). The `/api/ask` function and route expects a prompt to come in the POST body using a standard HTTP Trigger in Python. Then once the environment variables are set to configure OpenAI and LangChain frameworks, we can leverage favorite aspects of LangChain. In this simple example we take a prompt, build a better prompt from a template, and then invoke the LLM. By default the LLM deployment is `gpt-35-turbo` as defined in [./infra/main.parameters.json](./infra/main.parameters.json) but you can experiment with other models.
The key code that makes the prompting and completion work is as follows in [function_app.py](function_app.py). The `/api/ask` function and route expects a prompt to come in the POST body using a standard HTTP Trigger in Python. Then once the environment variables are set to configure OpenAI and LangChain frameworks via `init()` function, we can leverage favorite aspects of LangChain in the `main()` (ask) function. In this simple example we take a prompt, build a better prompt from a template, and then invoke the LLM. By default the LLM deployment is `gpt-35-turbo` as defined in [./infra/main.parameters.json](./infra/main.parameters.json) but you can experiment with other models and other aspects of Langchain's breadth of features.

```python
llm = AzureChatOpenAI(
deployment_name=AZURE_OPENAI_CHATGPT_DEPLOYMENT,
temperature=0.3,
openai_api_key=AZURE_OPENAI_KEY
temperature=0.3
)
llm_prompt = PromptTemplate.from_template(
"The following is a conversation with an AI assistant. " +
"The assistant is helpful.\n\n" +
"A:How can I help you today?\nHuman: {human_prompt}?"
"A:How can I help you today?\n" +
"Human: {human_prompt}?"
)
formatted_prompt = llm_prompt.format(human_prompt=prompt)

response = llm.invoke(formatted_prompt)
logging.info(response.content)
```