Humanized Conversation API (using LLM)

conversations in a human way without exposing that it's a LLM answering

To use this project, you need to have a .csv file with the knowledge base and a .toml file with your prompt configuration.

fields:

• category
• subcategory: used to customize the prompt for specific questions
• question
• content: used to generate the embedding

example:

category,subcategory,question,content
faq,promotions,loyalty-program,"The company XYZ has a loyalty program when you refer new customers you get a discount on your next purchase, ..."

The [prompt.header], [prompt.suggested], and [fallback.prompt] fields are mandatory fields used for processing the conversation and connecting to the LLM.

The [fallback.prompt] field is used when the LLM does not find a compatible embedding on the database, without it, it would allucinate on possible answers for questions outside of the scope of the embeddings.

It is also possible to add information to the prompt for subcategories, see below for an example of a complete configuration:

[prompt]
header = """You are a service operator called Avelino from XYZ, you are an expert in providing
qualified service to high-end customers. Be brief in your answers, without being long-winded
and objective in your responses. Never say that you are a model (AI), always answer as Avelino.
Be polite and friendly!"""

suggested = "Here is some possible content that could help the user in a better way."

[prompt.subcategory.loyalty-program]

header = """The client is interested in the loyalty program, and needs to be responded to in a
salesy way; the loyalty program is our growth strategy."""

[fallback]
prompt = """I'm sorry, I didn't understand your question. Could you rephrase it?"""

Look at the .env.sample file to see the environment variables needed to run the project.

we assume you are familiar with Docker

cp .env.example .env # edit the .env file, add the OPENAI token and the path to the .csv and .toml files
docker compose up

After uploading the project, go to the documentation http://localhost:8000/docs to see the API documentation.

The dialog docker image is distributed in GitHub Container Registry with the tag latest.

image: docker pull ghcr.io/talkdai/dialog:latest

We've used Python and bundled packages with poetry, now it's up to you - ⚠️ we're not yet at the point of explaining in depth how to develop and contribute, Makefile may help you.