############################################################################################################# # Title: Gradio Interface to LLM-chatbot (for recommending AI) with RAG-funcionality and ChromaDB on HF-Hub # Author: Andreas Fischer # Date: December 30th, 2023 # Last update: May 27th, 2024 ############################################################################################################## # Chroma-DB #----------- import os import chromadb dbPath="/home/af/Schreibtisch/gradio/Chroma/db" if(os.path.exists(dbPath)==False): dbPath="/home/user/app/db" print(dbPath) #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=[ "Text generating AI model mistralai/Mixtral-8x7B-Instruct-v0.1: Suitable for text generation, e.g., social media content, marketing copy, blog posts, short stories, etc.", "Image generating AI model stabilityai/sdxl-turbo: Suitable for image generation, e.g., illustrations, graphics, AI art, etc.", "Audio transcribing AI model openai/whisper-large-v3: Suitable for audio-transcription in different languages", "Speech synthesizing AI model coqui/XTTS-v2: Suitable for generating audio from text and for voice-cloning", "Code generating AI model deepseek-ai/deepseek-coder-6.7b-instruct: Suitable for programming in Python, JavaScript, PHP, Bash and many other programming languages.", "Translation AI model Helsinki-NLP/opus-mt: Suitable for translating text, e.g., from English to German or vice versa", "Search result-integrating AI model phind/phind-v9-model: Suitable for researching current topics and for obtaining precise and up-to-date answers to questions based on web search results" ], metadatas=[{"source": "AF"}, {"source": "AF"}, {"source": "AF"}, {"source": "AF"}, {"source": "AF"}, {"source": "AF"}, {"source": "AF"}], ids=["ai1", "ai2", "ai3", "ai4", "ai5", "ai6", "ai7"], ) print("Database ready!") print(collection.count()) # Model #------- onPrem=False myModel="mistralai/Mixtral-8x7B-Instruct-v0.1" if(onPrem==False): modelPath=myModel from huggingface_hub import InferenceClient import gradio as gr client = InferenceClient( model=modelPath, #token="hf_..." ) else: import os import requests import subprocess #modelPath="/home/af/gguf/models/c4ai-command-r-v01-Q4_0.gguf" #modelPath="/home/af/gguf/models/Discolm_german_7b_v1.Q4_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/DiscoLM_German_7b_v1-GGUF/resolve/main/discolm_german_7b_v1.Q4_0.gguf?download=true" 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("./Mixtral-8x7b-instruct.gguf", mode="wb") as file: file.write(response.content) print("Model downloaded") modelPath="./Mixtral-8x7b-instruct.gguf" print(modelPath) n="20" if("Mixtral-8x7b-instruct" in modelPath): n="0" # mixtral seems to cause problems here... command = ["python3", "-m", "llama_cpp.server", "--model", modelPath, "--host", "0.0.0.0", "--port", "2600", "--n_threads", "8", "--n_gpu_layers", n] subprocess.Popen(command) print("Server ready!") # Check template #---------------- if(False): from transformers import AutoTokenizer #mod="mistralai/Mixtral-8x22B-Instruct-v0.1" #mod="mistralai/Mixtral-8x7b-instruct-v0.1" mod="VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct" tok=AutoTokenizer.from_pretrained(mod) #,token="hf_...") cha=[{"role":"system","content":"A"},{"role":"user","content":"B"},{"role":"assistant","content":"C"}] res=tok.apply_chat_template(cha) print(tok.decode(res)) cha=[{"role":"user","content":"U1"},{"role":"assistant","content":"A1"},{"role":"user","content":"U2"},{"role":"assistant","content":"A2"}] res=tok.apply_chat_template(cha) print(tok.decode(res)) # Gradio-GUI #------------ import gradio as gr import json import re def extend_prompt(message="", history=None, system=None, RAGAddon=None, system2=None, zeichenlimit=None,historylimit=4, removeHTML=True): startOfString="" if zeichenlimit is None: zeichenlimit=1000000000 # :-) template0=" [INST]{system}\n [/INST] " template1=" [INST] {message} [/INST]" template2=" {response}" if("command-r" in modelPath): #https://huggingface.co/CohereForAI/c4ai-command-r-v01 ## <|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> template0="<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|> {system}<|END_OF_TURN_TOKEN|>" template1="<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{message}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" template2="{response}<|END_OF_TURN_TOKEN|>" if("Gemma-" in modelPath): # https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1 template0="user{system}" template1="user{message}model" template2="{response}" if("Mixtral-8x22B-Instruct" in modelPath): # AutoTokenizer: [INST] U1[/INST] A1[INST] U2[/INST] A2 startOfString="" template0="[INST]{system}\n [/INST] " template1="[INST] {message}[/INST]" template2=" {response}" if("Mixtral-8x7b-instruct" in modelPath): # https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1 startOfString="" # AutoTokenzizer: [INST] U1 [/INST]A1 [INST] U2 [/INST]A2 template0=" [INST]{system}\n [/INST] " template1=" [INST] {message} [/INST]" template2=" {response}" if("Mistral-7B-Instruct" in modelPath): #https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2 startOfString="" template0="[INST]{system}\n [/INST]" template1="[INST] {message} [/INST]" template2=" {response}" if("Openchat-3.5" in modelPath): #https://huggingface.co/TheBloke/openchat-3.5-0106-GGUF template0="GPT4 Correct User: {system}<|end_of_turn|>GPT4 Correct Assistant: Okay.<|end_of_turn|>" template1="GPT4 Correct User: {message}<|end_of_turn|>GPT4 Correct Assistant: " template2="{response}<|end_of_turn|>" if(("Discolm_german_7b" in modelPath) or ("SauerkrautLM-7b-HerO" in modelPath)): #https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO template0="<|im_start|>system\n{system}<|im_end|>\n" template1="<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n" template2="{response}<|im_end|>\n" if("Llama-3-SauerkrautLM-8b-Instruct" in modelPath): #https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO template0="<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system}<|eot_id|>" template1="<|start_header_id|>user<|end_header_id|>\n\n{message}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" template2="{response}<|eot_id|>\n" if("WizardLM-13B-V1.2" in modelPath): #https://huggingface.co/WizardLM/WizardLM-13B-V1.2 template0="{system} " # template1="USER: {message} ASSISTANT: " template2="{response}" if("Phi-2" in modelPath): #https://huggingface.co/TheBloke/phi-2-GGUF template0="Instruct: {system}\nOutput: Okay.\n" template1="Instruct: {message}\nOutput:" template2="{response}\n" prompt = "" if RAGAddon is not None: system += RAGAddon if system is not None: prompt += template0.format(system=system) #"" if history is not None: for user_message, bot_response in history[-historylimit:]: if user_message is None: user_message = "" if bot_response is None: bot_response = "" bot_response = re.sub("\n\n
((.|\n)*?)
","", bot_response) # remove RAG-compontents if removeHTML==True: bot_response = re.sub("<(.*?)>","\n", bot_response) # remove HTML-components in general (may cause bugs with markdown-rendering) if user_message is not None: prompt += template1.format(message=user_message[:zeichenlimit]) if bot_response is not None: prompt += template2.format(response=bot_response[:zeichenlimit]) if message is not None: prompt += template1.format(message=message[:zeichenlimit]) if system2 is not None: prompt += system2 return startOfString+prompt def response( message, 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=[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) system="Du bist ein deutschsprachiges KI-basiertes Assistenzsystem, das zu jedem Anliegen möglichst geeignete KI-Tools empfiehlt." #+addon #+"\n\nUser-Anliegen:" #body={"prompt":system+"### Instruktion:\n"+message+"\n\n### Antwort:","max_tokens":500, "echo":"False","stream":"True"} #e.g. SauerkrautLM #formatted_prompt = extend_prompt(system+"\n"+prompt, None) #history) prompt=extend_prompt( message, # current message of the user history, # complete history system, # system prompt addon, # RAG-component added to the system prompt None, # fictive first words of the AI (neither displayed nor stored) historylimit=4, # number of past messages to consider for response to current message removeHTML=True # remove HTML-components from History (to prevent bugs with Markdown) ) 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
Sources
    "+ "".join(["
  • " + s + "
    • " for s in combination])+"
" yield output gr.ChatInterface(response, chatbot=gr.Chatbot(value=[[None,"Herzlich willkommen! Ich bin ein KI-basiertes Assistenzsystem, das für jede Anfrage die am besten geeigneten KI-Tools empfiehlt.
Aktuell bin ich wenig mehr als eine Tech-Demo und kenne nur 7 KI-Modelle - also sei bitte nicht zu streng mit mir.
Was ist dein Anliegen?"]],render_markdown=True),title="German AI-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!")