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#########################################################################################
# Title: Gradio Interface to LLM-chatbot with RAG-funcionality and ChromaDB on premises
# Author: Andreas Fischer
# Date: October 15th, 2023
# Last update: December 21th, 2023
##########################################################################################
# Get model
#-----------
import os
import requests
dbPath="/home/af/Schreibtisch/gradio/Chroma/db"
if(os.path.exists(dbPath)==False):
dbPath="/home/user/app/db"
#modelPath="/home/af/gguf/models/SauerkrautLM-7b-HerO-q8_0.gguf"
modelPath="/home/af/gguf/models/mixtral-8x7b-instruct-v0.1.Q4_0.gguf"
if(os.path.exists(modelPath)==False):
url="https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/resolve/main/mixtral-8x7b-instruct-v0.1.Q4_0.gguf?download=true"
response = requests.get(url)
with open("./model.gguf", mode="wb") as file:
file.write(response.content)
print("Model downloaded")
modelPath="./model.gguf"
# Llama-cpp-Server
#------------------
command = ["python3", "-m", "llama_cpp.server", "--model", modelPath, "--host", "0.0.0.0", "--port", "2600"]
subprocess.Popen(command)
print("Model ready!")
# Chroma-DB
#-----------
import chromadb
#client = chromadb.Client()
path=dbPath
client = chromadb.PersistentClient(path=path)
print(client.heartbeat())
print(client.get_version())
print(client.list_collections())
from chromadb.utils import embedding_functions
default_ef = embedding_functions.DefaultEmbeddingFunction()
sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="T-Systems-onsite/cross-en-de-roberta-sentence-transformer")
#instructor_ef = embedding_functions.InstructorEmbeddingFunction(model_name="hkunlp/instructor-large", device="cuda")
print(str(client.list_collections()))
global collection
if("name=ChromaDB1" in str(client.list_collections())):
print("ChromaDB1 found!")
collection = client.get_collection(name="ChromaDB1", embedding_function=sentence_transformer_ef)
else:
print("ChromaDB1 created!")
collection = client.create_collection(
"ChromaDB1",
embedding_function=sentence_transformer_ef,
metadata={"hnsw:space": "cosine"})
collection.add(
documents=["The meaning of life is to love.", "This is a sentence", "This is a sentence too"],
metadatas=[{"source": "notion"}, {"source": "google-docs"}, {"source": "google-docs"}],
ids=["doc1", "doc2", "doc3"],
)
print(collection.count())
# Gradio-GUI
#------------
import gradio as gr
import requests
import random
import json
def response(message, history):
addon=""
results=collection.query(
query_texts=[message],
n_results=2,
#where={"source": "google-docs"}
#where_document={"$contains":"search_string"}
)
results=results['documents'][0]
print(results)
if(len(results)>1):
addon=" Bitte berücksichtige bei deiner Antwort ggf. folgende Auszüge aus unserer Datenbank, sofern sie für die Antwort erforderlich sind. Ingoriere unpassende Auszüge unkommentiert:\n"+"\n".join(results)+"\n\n"
#url="https://afischer1985-wizardlm-13b-v1-2-q4-0-gguf.hf.space/v1/completions"
url="http://localhost:2600/v1/completions"
system="Du bist ein KI-basiertes Assistenzsystem."+addon+"\n\n"
#body={"prompt":system+"### Instruktion:\n"+message+"\n\n### Antwort:","max_tokens":500, "echo":"False","stream":"True"} #e.g. SauerkrautLM
body={"prompt":"<s>[INST]"+system+"\n"+message+"[/INST]### Antwort:","max_tokens":500, "echo":"False","stream":"True"} #e.g. Mixtral-Instruct
response=""
buffer=""
print("URL: "+url)
print(str(body))
print("User: "+message+"\nAI: ")
for text in requests.post(url, json=body, stream=True): #-H 'accept: application/json' -H 'Content-Type: application/json'
print("*** Raw String: "+str(text)+"\n***\n")
text=text.decode('utf-8')
if(text.startswith(": ping -")==False):buffer=str(buffer)+str(text)
print("\n*** Buffer: "+str(buffer)+"\n***\n")
buffer=buffer.split('"finish_reason": null}]}')
if(len(buffer)==1):
buffer="".join(buffer)
pass
if(len(buffer)==2):
part=buffer[0]+'"finish_reason": null}]}'
if(part.lstrip('\n\r').startswith("data: ")): part=part.lstrip('\n\r').replace("data: ", "")
try:
part = str(json.loads(part)["choices"][0]["text"])
print(part, end="", flush=True)
response=response+part
buffer="" # reset buffer
except:
pass
yield response
gr.ChatInterface(response).queue().launch(share=True) #False, server_name="0.0.0.0", server_port=7864)
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