######################################################################################### # Title: Gradio Interface to LLM-chatbot with RAG-funcionality and ChromaDB on premises # Author: Andreas Fischer # Date: October 15th, 2023 # Last update: December 31th, 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" print(dbPath) #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/WizardLM-13B-V1.2-GGUF/resolve/main/wizardlm-13b-v1.2.Q4_0.gguf" #url="https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/resolve/main/mixtral-8x7b-instruct-v0.1.Q4_0.gguf?download=true" url="https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.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" print(modelPath) # Llama-cpp-Server #------------------ import subprocess command = ["python3", "-m", "llama_cpp.server", "--model", modelPath, "--host", "0.0.0.0", "--port", "2600", "--n_threads", "2"] subprocess.Popen(command) print("Server 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("Database ready!") print(collection.count()) # Gradio-GUI #------------ import gradio as gr import requests 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"} ) dists=["(relevance: "+str(round((1-d)*100)/100)+";" for d in results['distances'][0]] sources=["source: "+s["source"]+")" for s in results['metadatas'][0]] results=results['documents'][0] combination = zip(results,dists,sources) combination = [' '.join(triplets) for triplets in combination] print(combination) if(len(results)>1): addon=" Bitte berücksichtige bei deiner Antwort ggf. 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) #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\nUser-Anliegen:" #body={"prompt":system+"### Instruktion:\n"+message+"\n\n### Antwort:","max_tokens":500, "echo":"False","stream":"True"} #e.g. SauerkrautLM body={"prompt":"[INST]"+system+"\n"+message+"[/INST]","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' if buffer is None: buffer="" buffer=str("".join(buffer)) #print("*** Raw String: "+str(text)+"\n***\n") text=text.decode('utf-8') if((text.startswith(": ping -")==False) & (len(text.strip("\n\r"))>0)): buffer=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 Exception as e: print("Exception:"+str(e)) pass yield response yield response+"\n\n
Sources
" gr.ChatInterface(response, chatbot=gr.Chatbot(render_markdown=True),title="German RAG-Interface to Mistral-7B-Instruct-v0.2-GGUF").queue().launch(share=True) #False, server_name="0.0.0.0", server_port=7864) print("Interface up and running!")