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basdonax-ai-rag
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Stars: 102
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Basdonax AI RAG v1.0 is a repository that contains all the necessary resources to create your own AI-powered secretary using the RAG from Basdonax AI. It leverages open-source models from Meta and Microsoft, namely 'Llama3-7b' and 'Phi3-4b', allowing users to upload documents and make queries. This tool aims to simplify life for individuals by harnessing the power of AI. The installation process involves choosing between different data models based on GPU capabilities, setting up Docker, pulling the desired model, and customizing the assistant prompt file. Once installed, users can access the RAG through a local link and enjoy its functionalities.
README:
Este repositorio contiene todo lo necesario para poder crear tu propia secretaria hecha con Inteligencia Artificial, todo gracias al RAG de Basdonax AI, que utiliza los modelos open source de Meta y de Microsoft: Llama3-7b
y Phi3-4b
para de esta forma darte la posibilidad de subir tus documentos y hacer consultas a los mismos. Esto fue creado para poder facilitarle la vida a las personas con la IA.
- Docker o Docker desktop: https://www.docker.com/products/docker-desktop/
- (opcional) Tarjeta gráfica RTX
Antes de comenzar con la instalación, tenemos que analizar si tenemos o no una tarjeta gráfica capaz de utilizar Llama3-7b o no. Si tenemos una tarjeta gráfica capaz de utilizar este modelo de datos utilizaremos el archivo docker-compose.yml
, si no contamos con esa posibilidad vamos a eliminar el docker-compose.yml
y vamos a renombrar el archivo docker-compose_sin_gpu.yml
por docker-compose.yml
. La diferencia entre un archivo y otro es que el docker-compose_sin_gpu.yml
utiliza el LLM Phi3-4b
, que es mucho más ligero para correrlo en el procesador de tu PC, mientras que Llama3-7b
es mucho más pesado y si bien puede correr en CPU, es más recomendable una gráfica. En el video voy a estar utilizando una RTX 4060 8GB.
Tenemos que tener Docker o Docker Desktop instalado, te recomiendo ver este video para instalar todo: https://www.youtube.com/watch?v=ZyBBv1JmnWQ
Una vez instalado y prendido el Docker Desktop si lo estamos utilizando, vamos a ejecutar en esta misma carpeta:
docker-compose up
La primera vez vamos a tener que esperar a que todo se instale correctamente, va a tardar unos cuantos minutos en ese paso.
Ahora tenemos que instalarnos nuestro modelo LLM, si tenemos una GPU que pueda soportar vamos a ejecutar el comando para traernos Llama3, sino va a ser Phi3 (si queremos utilizar otro modelo, en esta pagina: https://ollama.com/library tenes la lista de todos los modelos open source posibles en esta página, recorda que seguramente vayas a tener que hacer cambios en la prompt si cambias el modelo), ejecutamos:
docker ps
Te va a aparecer algo como esto:
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
696d2e45ce7c ui "/bin/sh -c 'streaml…" About a minute ago Up About a minute 0.0.0.0:8080->8080/tcp ui-1
28cf32abee50 ollama/ollama:latest "/bin/ollama serve" About a minute ago Up About a minute 11434/tcp ollama-1
ec09714c3c86 chromadb/chroma:latest "/docker_entrypoint.…" About a minute ago Up About a minute 0.0.0.0:8000->8000/tcp chroma-1
En esta parte tenés que copiar el CONTAINER ID
de la imagen llamada ollama/ollama:latest
y utilizarla para este comando:
docker exec [CONTAINER ID] ollama pull [nombredelmodelo]
Un ejemplo con Llama3-7b
y mi CONTAINER ID
docker exec 28cf32abee50 ollama pull llama3
Un ejemplo con Phi3-4b
y mi CONTAINER ID
docker exec 28cf32abee50 ollama pull phi3
Ahora vamos a tener que esperar a que se descargue el modelo, una vez hecho esto solo nos queda modificar la prompt:
Esto se va a hacer a nuestro gusto en el archivo ./app/common/assistant_prompt.py
.
Una vez hecho todo lo anterior solo queda un paso: que entremos al siguiente link: http://localhost:8080 para poder utilizar el RAG.
Tenemos que dejarnos en el escritorio el archivo de open_rag.bat
si estamos en Windows y si estamos en Mac/Linux el open_rag.sh
Ahora tenemos que abrirlo y modificarlo, tenemos que agregar la ruta donde hicimos/tenemos el docker-compose.yml
, por ejemplo mi ruta es:
C:\Users\fcore\OneDrive\Desktop\Basdonax\basdonax-rag>
Entonces en mi caso va a ser así el open_rag.bat
(el .sh es lo mismo):
cd C:\Users\fcore\OneDrive\Desktop\Basdonax\basdonax-rag
docker-compose up -d
Ahora mientras que tengamos el Docker/Docker Desktop prendido y mientras que ejecutemos este archivo vamos a poder acceder al RAG en este link: http://localhost:8080
Próximo paso: disfrutar
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Basdonax AI RAG v1.0 is a repository that contains all the necessary resources to create your own AI-powered secretary using the RAG from Basdonax AI. It leverages open-source models from Meta and Microsoft, namely 'Llama3-7b' and 'Phi3-4b', allowing users to upload documents and make queries. This tool aims to simplify life for individuals by harnessing the power of AI. The installation process involves choosing between different data models based on GPU capabilities, setting up Docker, pulling the desired model, and customizing the assistant prompt file. Once installed, users can access the RAG through a local link and enjoy its functionalities.
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