Brevia 0.2.1

Extensible API and framework to build your Retrieval Augmented Generation (RAG) and Information Extraction (IE) applications with LLMs

Brevia

Brevia is an extensible API and framework to build your Retrieval Augmented Generation (RAG) and Information Extraction (IE) applications with LLMs.

Ouf of the box Brevia provides:

• a complete API for RAG applications
• an API with Information extractions capabilities like summarization, with the possibility to create your own analysis logic with asycnhronous background operations support

Brevia uses:

• the popular LangChain framework that you can use to create your custom AI tools and logic
• the FastAPI framework to easily extend your application with new endpoints
• PostgreSQL with pg_vector extension as vector database

Documentation

Brevia documentation is available at docs.brevia.app.

Admin UI

An official UI is now available via Brevia App. It is a webapp with which you can:

• create and configure new RAG collections
• add files, questions or links to each collection
• test the collection with a chat UI
• analyze the chat history for each collection
• perform some Information Extraction actions like summarization, audio transcription or custom analysis

Requirements

A version of Python 3.10 or higher and Poetry is required. A PostgreSQL database version 14 or higher with pg_vector, but you can use the provided docker image for a quicker setup.

Quick try

The easiest and fastest way to try Brevia is through Docker. By launching docker compose with the following command you will have a working Brevia system without any setup or configuration:

docker compose --profile fullstack up

At this point you will have:

• Brevia API on http://localhost:8000
• Brevia App UI on http://localhost:3000

To use ports other than 8000 or 3000 just uncomment the variables BREVIA_API_PORT or BREVIA_APP_PORT in the .env file.

You can also use --profile api option to just start Brevia API and not Brevia App.

Create a Brevia Project

Quick start

The quickest way to create a new Brevia project is using the cookiecutter template project like this:

pip install cookiecutter
cookiecutter gh:brevia-ai/brevia-cookiecutter

Simply answer few simple questions and you're ready to go.

Manual setup

To manually create a project instead follow these steps:

• create a new project with poetry new {your-brevia-project}
• install brevia and its dependencies by running poetry add brevia, a virtualenv will automatically be created
• create a new main.py starting with a copy
• activate the virtualenv by running the poetry shell command
• copy the file .env.sample to .env and value the environment variables, especially OPENAI_API_KEY with the secret key of OpenAI and PGVECTOR_* see the Database section

Custom Model

In addition to using the default OpenAI models, you have the option to integrate other Large Language Models, such as the Cohere model or any other model of your choice. Follow the steps below to set up and use a custom model in your Brevia project.

Cohere Model Integration

Once you have the Cohere API key and model name, update your Brevia project to include these credentials.

0. Prerequisites: install cohere lib:

   poetry add cohere
   

1. Open the .env file in your project directory.

2. Set the COHERE_API_KEY variable with your Cohere API key:

   COHERE_API_KEY=your-cohere-api-key
   

3. Set the cohere variables with the desired Cohere parameter:

For QA/RAG application, set in QA_COMPLETION_LLM and QA_FOLLOWUP_LLM the json as follow:

```bash
QA_COMPLETION_LLM='{
    "_type": "cohere-chat",
    "model_name": "command",
    "temperature": 0,
    "max_tokens": 200,
    "verbose": true
}'

QA_FOLLOWUP_LLM='{
    "_type": "cohere-chat",
    "model_name": "command",
    "temperature": 0,
    "max_tokens": 200,
    "verbose": true
}'
```

With relatives embeddings:

```bash
EMBEDDINGS='{
    "_type": "cohere-embeddings"
}'
```

4. For Summarization module, set the SUMMARIZE_LLM var:

   SUMMARIZE_LLM='{
       "_type": "cohere-chat",
       "model_name": "command",
       "temperature": 0,
       "max_tokens": 2000
   }'
   

Database

If you have a PostgreSQL instance with pg_vector extension available you are ready to go, otherwise you can use the provided docker compose file.

In this case you can simply run docker compose to run a PostgreSQL database with pg_vector. You can also run the embedded pgAdmin admin tool running docker compose --profile admin up to run postgres+pgvector and pgadmin docker images at the same time. With your browser, open pgadmin at http://localhost:4000

The 4000 port is configurable with the PGADMIN_PORT environment var in the .env file.

Migrations

Before using Brevia you need to launch the migrations script in order to create or update the initial schema. This is done with Alembic by using this command

db_upgrade

Launch

You are now ready to go, simply run

uvicorn --env-file .env main:app`

and your Brevia API project is ready to accept calls!

Test your API

While we are working on a simple reference frontend to test and use Brevia API the simplest way to test your new API is by using the provided OpenAPI Swagger UI and ReDoc UI or by using the official Postman files.

Simply point to /docs for your Swagger UI and to /redoc for ReDoc.

If you prefer to use Postman you can start by importing the collection file and an environment sample

Add documents via CLI

You can also quickly create collections and add documents via CLI using the import_file command.

Just run:

import_file --file-path /path/to/file --collection my-collection

Where

• /path/to/file is the path to a local PDF or txt file
• my-collection is the unique name of the collection that will be created if not exists

Import/export of collections

To import/export collections via CLI we take advantage of the PostgreSQL COPY command in the import_collection and export_collection scripts.

A psql client is required for these scripts, connection parameters will be read from environment variables (via .env file).

Two PostgreSQL CSV files will be created during export and imported in the import operation. One file, named {collection-name}-collection.csv, contains collection data and the other, named {collection-name}-embedding.csv, contains documents data and embeddings.

To export a collection use:

export_collection --folder-path /path/to/folder --collection collection-name

To import a collection use:

import_collection --folder-path /path/to/folder --collection collection-name

Where

• /path/to/folder is the path where the 2 CSV files will be created in export or searched in import
• collection-name is the name of the collection

LangSmith support

LangSmith is a platform to monitor, test and debug LLM apps built with LangChain. To use it in Brevia, if you have an account, you should export these environment variables when runnnin Brevia:

LANGCHAIN_TRACING_V2=True
LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
LANGCHAIN_API_KEY="########"
LANGCHAIN_PROJECT="My Project"

If you are using a .env file you should use BREVIA_ENV_SECRETS var like this:

BREVIA_ENV_SECRETS='{
  "LANGCHAIN_TRACING_V2": "True"
  "LANGCHAIN_ENDPOINT": "https://api.smith.langchain.com",
  "LANGCHAIN_API_KEY": "########",
  "LANGCHAIN_PROJECT": "My Project"
}'

This way Brevia will make sure that this variables will be available as environment variables.

Edit LANGCHAIN_API_KEY with your LangSmith API Key and set your project name in LANGCHAIN_PROJECT var.

Access Tokens

There is a built-in basic support for access tokens for API security.

Access tokens are actively checked via Authoritazion: Bearer <token> header if a TOKENS_SECRET env variable has been set. You may then generate a new access token using:

poetry run create_token --user {user} --duration {minutes}

If the env TOKENS_SECRET variable is set token verification is automatically performed on every endpoint using brevia.dependencies.get_dependencies in its dependencies.

The recommended way yo generate TOKENS_SECRET is by using openssl via cli like

openssl rand -hex 32

You can also define a list of valid users as a comma separated string in the TOKENS_USERS env variable.

Setting it like TOKENS_USERS="brevia,gustavo" means that only brevia and gustavo are considered valid users names. Remember to use double quotes in a .env file.

Brevia developer quick reference

Here some brief notes if you want to help develop Brevia.

Unit tests

To launch unit tests make sure to have dev dependencies installed. This is done with:

poetry install --with dev

A tests/.env file must be present where test environment variables are set, you can start with a copy of tests/.env.sample. Please make sure that PGVECTOR_* variables point to a unit test database that will be continuously dropped and recreated. Also make sure to set USE_TEST_MODELS=True in order to use fake LLM instances.

To launch unit tests, type from virtualenv:

pytest tests/

To create coverage in HTML format:

pytest --cov-report html --cov=brevia tests/

Covreage report is created using pytest-cov

Update documentation

Install mkdocs-material using pip without altering pyproject.toml

pip install mkdocs-material

When working on documentation files you can use a live preview server with:

mkdocs serve

Or you can build the documentation in site/ folder using

mkdocs build --clean