For programmers, who are interested in AI who are deploying RAGs without knowledge on server maintenance, Dialog is an App to simplify LLM deploys, letting you spend less time coding and more time training your model.
This repository serves an API focused on letting you deploy any LLM you want, based on the structure provided by dialog-lib.
We started as a way to humanize RAGs, but we are expanding for broader approaches on better RAG deployment and maintenance.
For more information, check our documentation!
We assume you are familiar with Docker, if you are not, this amazing tutorial will help you. Follow the Quick Start for setup and then run
docker-compose upit will start two services:
db: where the PostgresSQL database runs to support chat history and document retrieval for RAG;dialog: the service with the api.
To use this project, you need to have a .csv file with the knowledge base and a .toml file with your prompt configuration.
We recommend that you create a folder inside this project called data and put CSVs and TOMLs files over there.
The knowledge base has needed columns:
- 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, ..."
When the dialog service starts, it loads the knowledge base into the database, so make sure the database is up and paths are correct (see environment variables section). Alternatively, inside src folder, run make load-data path="<path-to-your-knowledge-base>.csv".
See our documentation for more options about the the knowledge base, including embedding more coluns together.
The [prompt.header], [prompt.suggested], and [fallback.prompt] fields are mandatory fields used for processing the conversation and connecting to the LLM.
The [prompt.fallback] field is used when the LLM does not find a compatible embedding in the database; that is, the [prompt.header] is ignored and the [prompt.fallback] is used. Without it, there could be hallucinations about possible answers to questions outside the scope of the embeddings.
In
[prompt.fallback]the response will be processed by LLM. If you need to return a default message when there is no recommended question in the knowledge base, use the[prompt.fallback_not_found_relevant_contents]configuration in the.toml(project configuration).
It is also possible to add information to the prompt for subcategories and choose some optional llm parameters like temperature (defaults to 0.2) or model_name, see below for an example of a complete configuration:
[model]
temperature = 0.2
model_name = "gpt-3.5-turbo"
[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."
fallback = "I'm sorry, I couldn't find a relevant answer for your question."
fallback_not_found_relevant_contents = "I'm sorry, I couldn't find a relevant answer for your question."
[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."""Look at the .env.sample file to see the environment variables needed to run the project. While the .csv contains only the knowledge base, the .toml contains some llm parameters and prompts, and finally the .env contains the OpenAI token, paths and some project parameters. We recommend you to read our documentation that discusses configuration.
We are thankful for all of the contributions we receive, mostly are reviewed by this awesome maintaining team we have:
made with 💜 by talkd.ai