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SuperTutor
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Mario Gil 2024-10-09 09:44:26 -05:00
parent f37e295ad9
commit 415e798021
4 changed files with 113 additions and 30 deletions
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25
README.md
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@ -1,12 +1,17 @@
pip install llama-index
pip install llama-index-llms-groq
pip install llama-index-embeddings-huggingface
pip install llama-parse
pip install chromadb
pip install llama-index-vector-stores-chroma
pip install llama-index-embeddings-huggingface
pip install python-fasthtml
pip install grok
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
El sistema funciona en un docker
para generar:
docker build -t supertutor-app .
para ejecutar:
docker run -it -p 7884:7884 --rm fastapi-app
si tienes problemas con permisos:
sudo chmod 666 /var/run/docker.sock

96
main.py
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@ -12,7 +12,7 @@ from llama_index.core import Settings
#from llama_index.embeddings.huggingface_api import (
# HuggingFaceInferenceAPIEmbedding,
#)
import shutil
import chromadb
import os
import threading
@ -22,6 +22,9 @@ import json
#from llama_index.llms.ollama import Ollama
from llama_index.core.base.response.schema import Response
from groq import Groq
from fastapi import File as FileFast
from fastapi import UploadFile as UploadFileFast
from fastapi import Form as FormFast
#import shutil
pwd = os.getcwd()
def extractConfig(nameModel="SystemData",relPath=os.path.join(pwd,"conf/experiment_config.json"),dataOut="keyantrophics"):
@ -78,9 +81,9 @@ def checkInfoSources(user:str):
subdir = [Option(file.name,value="static/"+user+"/"+file.name) for file in files if (file.is_dir() and file.name!="chroma_db") ]
userdata=user
print("Cambio",userdata)
return Form(
H3("Grupos de archivos",cls="col-xs-3"),
H3("Grupo de archivos",cls="col-xs-3"),
Select(
*subdir,name="data",cls="col-xs-3"),
Input(id="name-db", name="collection", placeholder="Enter a collection name",cls="col-xs-4"),
@ -163,7 +166,7 @@ def loadCollection(data:str):
def queryPrompt(question:str):
#index=load_create_db(collection="my_collection")
query_engine = index.as_query_engine(similarity_top_k=15,vector_store_query_mode="default",response_mode="tree_summarize")
query_engine = index.as_query_engine(similarity_top_k=5,vector_store_query_mode="default",response_mode="tree_summarize")
summary_prompt = (
"Por favor, genera un resumen completo y detallado del material dado. "
"Incluye los principales temas, argumentos y conclusiones. "
@ -191,7 +194,7 @@ def queryPrompt(question:str):
response2 = query_engine.query(tematic_prompt)
response3 = query_engine.query(issues_prompt)
response4 = query_engine.query(Question_prompt)
Output="<H1>Summary</H1>"+str(response)+"<H1>Tematic</H1>"+str(response2)+"<H1>Issues</H1>"+str(response3)+"<H1>Questions</H1>"+str(response4)
Output="<H1>Resumen</H1>"+str(response)+"<H1>Tematica</H1>"+str(response2)+"<H1>Problemas</H1>"+str(response3)+"<H1>Preguntas</H1>"+str(response4)
return Output
@ -238,10 +241,6 @@ Evaluate the coherence and accuracy of previous response to respond %s in this e
Verificate if previous context is related to the previous response, if not, say that you do not have information about that issue
The format of output is a json with keys 'coherencia', 'exactitud', 'relacion_con_el_contexto' and 'comentario' .
'coherencia', 'exactitud', 'relacion_con_el_contexto' are numeric variables with max value is 10"""%(response,ContextNodes,message)
print(chat_engine.__dict__)
chat_completion = client.chat.completions.create(
#
@ -302,7 +301,7 @@ def SummarySources():
return Form("Este es muy caro para documentos grandes y tarda mucho",
Select(
*subdir,name="data"),
Input( name="query", placeholder="Enter a query"),
Input( name="query", placeholder="Dar una pregunta"),
Button("Submit",type="submit"), hx_post="/SummaryMake",hx_swap="innerHTML",hx_target="#summaryR" )
@app.post("/SummaryMake")
@ -374,18 +373,18 @@ def home():
Div(Div(id="options",hx_target="this",hx_swap="outerHTML",hx_get="/listmodelactives",hx_trigger="click from:#buttonMenuuser delay:3s"),cls="col-xs-12"),
Div(Div(id="Infomodel"),cls="col-xs-12"),
#Div("Resumen",Div(id="summary",hx_target="this",hx_swap="outerHTML",hx_get="/SummarySources",hx_trigger="click from:#buttonMenuuser"),Div(id="summaryR")),
Div(
Div(H3("Chat para preguntarle al material de estudios "),
Div(
Form(
Input(id="question", name="message", placeholder="Enter a message"),
Input(id="question", name="message", placeholder="Dar una pregunta"),
Button("Submit",type="submit"), hx_post="/chatData",hx_swap="afterend",hx_target="#questionR" ),
Div(id="questionR")
,id="questions"),
cls="col-xs-6"),
Div(
Div(H3("Este genera informacion general del material, pero es intensivo en unso del api. No USAR."),
Div(
Form(
Input(id="query", name="question", placeholder="Enter a query"),
Input(id="query", name="question", placeholder="Dar una pregunta"),
Button("Submit",type="submit"), hx_post="/queryprompt",hx_swap="innerHTML",hx_target="#queryR" ),
Div(id="queryR"),
id="query"),
@ -394,14 +393,71 @@ def home():
))
return page
# @app.post("/upload")
# def upload(data: UploadFile = File(...),user : str = Form(...), dir: str = Form(...)):
# filename="static/"+user+dir+data.filename
@app.get("/fileup")
def fileup():
return Div(
P('Upload data Super tutor'),
Div(H2("Subir Archivos"),Form(
Input(type='file', name='file',cls="col-xs-3"),
Input( name='dir',placeholder="Enter a directory",cls="col-xs-2"),
Input( name='user',placeholder="Enter a user",cls="col-xs-2"),
Button('Upload', type='submit',cls="col-xs-4"),
hx_post="/upload",
hx_target="#info",
hx_swap="innerHTML",
enctype="multipart/form-data",
),cls="col-xs-12"),
Div(H2("Crear Usuario"),Form(
Input( name='user',placeholder="Enter a user",cls="col-xs-7"),
Button('Upload', type='submit',cls="col-xs-5"),
hx_post="/createuser",
hx_target="#info",
hx_swap="innerHTML",
enctype="multipart/form-data",
),cls="col-xs-12"),
Div(H2("Crear grupo de Archivos"),Form(
Input( name='dir',placeholder="Enter a directory",cls="col-xs-4"),
Input( name='user',placeholder="Enter a user",cls="col-xs-3"),
Button('Upload', type='submit',cls="col-xs-5"),
hx_post="/creategroup",
hx_target="#info",
hx_swap="innerHTML",
enctype="multipart/form-data",
),cls="col-xs-12"),
Div(id="info")
# with open(f"{filename}", "wb") as buffer:
# shutil.copyfileobj(data.file, buffer)
# app.mount("/static", StaticFiles(directory="static"), name="static")
)
@app.post("/upload")
def upload(file: UploadFile,dir : str = Form(...),user : str = Form(...)):
filenameB="static/"+user+"/"+dir+"/"+file.filename
pathB="static/"+user+"/"+dir
if not os.path.exists(pathB):
os.makedirs(pathB)
if not os.path.exists(filenameB):
with open(f"{filenameB}", "wb") as buffer:
shutil.copyfileobj(file.file, buffer)
return P("Se ha subido %s"%(filenameB))
@app.post("/createuser")
def createuser(user : str = Form(...)):
pathB="static/"+user
if not os.path.exists(pathB):
os.makedirs(pathB)
return P("Se ha creado %s"%(pathB))
@app.post("/creategroup")
def createuser(user : str = Form(...),dir : str = Form(...)):
pathB="static/"+user+"/"+dir
if not os.path.exists(pathB):
os.makedirs(pathB)
return P("Se ha creado %s"%(pathB))
app.mount("/static", StaticFiles(directory="static"), name="static")

1
run.sh Normal file
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@ -0,0 +1 @@
docker run -it -p 7884:7884 --rm fastapi-app

21
static/Ricardo/PCL/dockerfile Normal file
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@ -0,0 +1,21 @@
# Usar una imagen base de Python
FROM python:3.11.10-bookworm
# Establecer el directorio de trabajo en el contenedor
WORKDIR /app
# Copiar el archivo requirements.txt y otros archivos necesarios
COPY requirements.txt ./
# Instalar las dependencias de Python
RUN pip install --no-cache-dir -r requirements.txt
# Copiar todo el código de la aplicación al contenedor
COPY . .
# Expone el puerto que usará Uvicorn
EXPOSE 7884
# Especificar el comando
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7884", "--reload"]