File size: 3,892 Bytes
c02b4bf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
import chromadb
import os
import gradio as gr
import json
from huggingface_hub import InferenceClient
dbPath='/Users/thiloid/Desktop/LSKI/ole_nest/Chatbot/LLM/chromaTS'
if(os.path.exists(dbPath)==False): dbPath="/home/user/app/chromaTS'"
print(dbPath)
#path='chromaTS'
#settings = Settings(persist_directory=storage_path)
#client = chromadb.Client(settings=settings)
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")#"VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct")
#instructor_ef = embedding_functions.InstructorEmbeddingFunction(model_name="hkunlp/instructor-large", device="cuda")
#print(str(client.list_collections()))
collection = client.get_collection(name="chromaTS", embedding_function=sentence_transformer_ef)
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
def format_prompt(message):
prompt = "" #"<s>"
#for user_prompt, bot_response in history:
# prompt += f"[INST] {user_prompt} [/INST]"
# prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def response(
prompt, history,temperature=0.9, max_new_tokens=500, top_p=0.95, repetition_penalty=1.0,
):
temperature = float(temperature)
if temperature < 1e-2: temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
addon=""
results=collection.query(
query_texts=[prompt],
n_results=10,
#where={"source": "google-docs"}
#where_document={"$contains":"search_string"}
)
#print("REsults")
#print(results)
#print("_____")
dists=["<br><small>(relevance: "+str(round((1-d)*100)/100)+";" for d in results['distances'][0]]
#sources=["source: "+s["source"]+")</small>" for s in results['metadatas'][0]]
results=results['documents'][0]
combination = zip(results,dists)
combination = [' '.join(triplets) for triplets in combination]
#print(str(prompt)+"\n\n"+str(combination))
if(len(results)>1):
addon=" Bitte berücksichtige bei deiner Antwort ausschießlich folgende Auszüge aus unserer Datenbank, sofern sie für die Antwort erforderlich sind. Beantworte die Frage knapp und präzise. Ignoriere unpassende Datenbank-Auszüge OHNE sie zu kommentieren, zu erwähnen oder aufzulisten:\n"+"\n".join(results)
system="Du bist ein deutschsprachiges KI-basiertes Studienberater Assistenzsystem, das zu jedem Anliegen möglichst geeignete Studieninformationen empfiehlt."+addon+"\n\nUser-Anliegen:"
formatted_prompt = format_prompt(system+"\n"+prompt,history)
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
yield output
#output=output+"\n\n<br><details open><summary><strong>Sources</strong></summary><br><ul>"+ "".join(["<li>" + s + "</li>" for s in combination])+"</ul></details>"
yield output
gr.ChatInterface(response, chatbot=gr.Chatbot(value=[[None,"Herzlich willkommen! Ich bin Chätti ein KI-basiertes Studienassistenzsystem, das für jede Anfrage die am besten Studieninformationen empfiehlt.<br>Erzähle mir, was du gerne tust!"]],render_markdown=True),title="German BERUFENET-RAG-Interface to the Hugging Face Hub").queue().launch(share=True) #False, server_name="0.0.0.0", server_port=7864)
print("Interface up and running!")
|