https://github.com/okahu-demos/okahu-demo-lc-openai
Okahu demo chatboth with Langchain and OpenAI
https://github.com/okahu-demos/okahu-demo-lc-openai
demo-app genai genai-chatbot langchain observability openai
Last synced: 3 months ago
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Okahu demo chatboth with Langchain and OpenAI
- Host: GitHub
- URL: https://github.com/okahu-demos/okahu-demo-lc-openai
- Owner: okahu-demos
- License: mit-0
- Created: 2024-09-27T23:12:40.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-20T10:02:56.000Z (10 months ago)
- Last Synced: 2025-04-09T00:46:28.351Z (6 months ago)
- Topics: demo-app, genai, genai-chatbot, langchain, observability, openai
- Language: Python
- Homepage:
- Size: 29.3 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Okahu demo with OpenAI + Langchain
This repo includes a demo chat application built using OpenAI & Langchain that is pre-instrumented for observation with Okahu AI Observability cloud.
You can fork this repo and run the app in Github Codespaces to get started quickly.
## Try Okahu with this OpenAI app
To try this chatbot
- Fork this repo and run in the Github Codespace
- To run this on Mac
- Install python dependencies: ```pip install -r requirement.txt```
- Remove libmagic: ```pip uninstall python-magic```
- To run this on Windows
- Install python dependencies: ```pip install -r requirement.txt```
You'll need
- An OpenAI subscription and an API key to [OpenAI developer platform](https://platform.openai.com/overview)
- An Okahu tenant and API key to [Okahu AI Observability Cloud](https://www.okahu.ai)
## Configure the demo environment
- Copy the file botenv.sh.template to botenv.sh
- Edit the botenv.sh file to add the OpenAI API Key and Okahu API key and save
## Run the interactive chatbot
This application is an interactive chatbot that answers questions about coffee and built with a RAG design pattern.
Workflow is a python program using Langchain LLM orchestration framework.
The vector dataset is built using text-embedding-3-large embedding model from OpenAI from a [local dataset about coffee](data/coffee.txt). The vector data is stored in a local filebased Chroma vectorDB.
The app uses OpenAI gpt-4o-mini model for inference.
To try Okahu from the Github Codespace
1. Run the pre-instrumented chatbot app with following command from top level directory
```python lc-openai-with-okahu.py```
2. View the workflow discovered by Okahu AI Observability Cloud with following commands with your Okahu API key
- Discover all components
```curl --location --request PUT 'https://api.okahu.ai/api/v1/discovery' --header 'x-api-key: ;' ```
- Get discovered components
```curl --location 'https://api.okahu.ai/api/v1/components' --header 'x-api-key: ;' ```
Check out Okahu AI Observability Cloud API docs [here](https://apidocs.okahu.ai)
### Example output
```
$ python lc-openai-with-okahu.py
Ask a coffee question [Press return to exit]: What is an americano?
An americano is a type of coffee drink that is made by diluting an espresso shot with hot water at a 1:3 to 1:4 ratio, resulting in a drink that retains the complex flavors of espresso, but in a lighter way.
```
### Okahu instrumentation
To run the chatbot app without Okahu instrumentation, use the command ```python lc-openai.py```
To understand how Okahu instrumentation works, compare the [lc-openai.py](lc-openai.py) and [lc-openai-with-okahu.py](lc-openai-with-okahu.py)