######################################################################################### # Title: German AI-Interface with advanced RAG # Author: Andreas Fischer # Date: January 31st, 2023 # Last update: May 27th, 2024 ########################################################################################## #https://github.com/abetlen/llama-cpp-python/issues/306 #sudo apt install libclblast-dev #CMAKE_ARGS="-DLLAMA_CLBLAST=on" FORCE_CMAKE=1 pip install llama-cpp-python --force-reinstall --upgrade --no-cache-dir -v # Prepare resources #------------------- import torch import gc torch.cuda.empty_cache() gc.collect() import os from datetime import datetime global filename filename=f"./{datetime.now().strftime('%Y%m%d')}_history.json" # where to store the history as json-file if(os.path.exists(filename)==True): os.remove(filename) # Chroma-DB #----------- import os import chromadb dbPath = "/home/af/Schreibtisch/Code/gradio/Chroma/db" onPrem = True if(os.path.exists(dbPath)) else False if(onPrem==False): dbPath="/home/user/app/db" #onPrem=True # uncomment to override automatic detection 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") embeddingModel = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="T-Systems-onsite/cross-en-de-roberta-sentence-transformer", device="cuda" if(onPrem) else "cpu") print(str(client.list_collections())) global collection dbName="myDB" if("name="+dbName in str(client.list_collections())): client.delete_collection(name=dbName) if("name="+dbName in str(client.list_collections())): print(dbName+" found!") collection = client.get_collection(name=dbName, embedding_function=embeddingModel ) else: print(dbName+" created!") collection = client.create_collection( dbName, embedding_function=embeddingModel, metadata={"hnsw:space": "cosine"}) # txts0: Intentions #------------------ txts0=[ "Ich suche ein KI-Programm mit bestimmten Fähigkeiten.", # 1a #"Ich suche kein KI-Programm mit bestimmten Fähigkeiten.", # !1a "Ich habe ein KI-Programm und habe Fragen zur Benutzung.", # !1a (besser, um 1a und 1b abzugrenzen) "Ich habe ein KI-Programm und habe Fragen zur Benutzung.", # 1b #"Ich habe kein KI-Programm und habe keine Fragen zur Benutzung.", # !1b "Ich habe eine allgemeine Frage ohne KI-Bezug." # !1b (greift besser bei Alltagsfragen) ] # txts1a: RAG-Infos for first intention: #--------------------------------------- txts1a=[ "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" ] # txts1b: RAG-Infos for second intention #---------------------------------------- txts1b=[ "Für Fragen zur Umsetzung von KI-Verfahren ist das KI-basierte Assistenzsystem nicht geeignet. Möglicherweise empfiehlt sich ein KI-Modell mit Internetzugriff, wie beispielsweise phind.com, oder das Kontaktieren eines Experten wie Dr. Andreas Fischer (andreasfischer1985@web.de)." ] #meta=[{"type":"0", "type2":"0","source":"AF"}]*len(txts0)+[{"type":"1a","type2":"0","source":"AF"}]*len(txts1a)+[{"type":"1b","type2":"0","source":"AF"}]*len(txts1b) meta = [] for _ in range(len(txts0)): meta.append({"type":"0", "type2":"0","source":"AF"}) for _ in range(len(txts1a)): meta.append({"type":"1a","type2":"0","source":"AF"}) for _ in range(len(txts1b)): meta.append({"type":"1b","type2":"0","source":"AF"}) #Change type2 for txt0-entries #----------------------------- meta[0]["type2"]="1a" # RAG mit txts1a meta[1]["type2"]="!1a" # else meta[2]["type2"]="1b" # RAG mit txts1b meta[3]["type2"]="!1b" # else txts=txts0+txts1a+txts1b collection.add( documents=txts, ids=[str(i) for i in list(range(len(txts)))], metadatas=meta ) # Add entry to episodic memory x=collection.get(include=[])["ids"] if(True): #len(x)==0): message="Ich bin der User." response="Hallo User, wie kann ich dienen?" x=collection.get(include=[])["ids"] collection.add( documents=[message,response], metadatas=[ {"source": "ICH", "dialog": f"ICH: {message}\nDU: {response}", "type":"episode"}, {"source": "DU", "dialog": f"ICH: {message}\nDU: {response}", "type":"episode"} ], ids=[str(len(x)+1),str(len(x)+2)] ) RAGResults=collection.query( query_texts=[message], n_results=1, #where={"source": "USER"} ) RAGResults["metadatas"][0][0]["dialog"] x=collection.get(include=[])["ids"] x collection.get() # Inspect db-entries print("Database ready!") print(collection.count()) rag0=collection.query( query_texts=[message], n_results=4, where={"type": "0"} ) x=rag0["metadatas"][0][0]["type2"] x=[x["type2"] for x in rag0["metadatas"][0]] x.index("1c") if "1c" in x else len(x)+1 # Model #------- #onPrem=False if(onPrem==False): modelPath="mistralai/Mixtral-8x7B-Instruct-v0.1" from huggingface_hub import InferenceClient import gradio as gr client = InferenceClient( modelPath #"mistralai/Mixtral-8x7B-Instruct-v0.1" #"mistralai/Mistral-7B-Instruct-v0.1" ) else: import os import requests import subprocess #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!") #import llama_cpp #llama_cpp.llama_backend_init(numa=False) #params=llama_cpp.llama_context_default_params() #params.n_ctx # Gradio-GUI #------------ 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.*?","", bot_response, flags=re.DOTALL) # 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 import gradio as gr import requests import json from datetime import datetime import os import re def response(message, history): settings="Memory Off" removeHTML=True # Preprocessing to revent simple forms of prompt injection: #---------------------------------------------------------- message=message.replace("[INST]","") message=message.replace("[/INST]","") message=re.sub("<[|](im_start|im_end|end_of_turn)[|]>", '', message) # Load Memory if memory is turned on #------------------------------------- if (settings=="Memory On"): if((len(history)==0)&(os.path.isfile(filename))): history=json.load(open(filename,'r',encoding="utf-8")) # retrieve history (if available) system="Du bist ein deutschsprachiges wortkarges KI-basiertes Assistenzsystem. Antworte kurz, in deutsche Sprache und verzichte auf HTML und Code jeder Art." #RAG-layer 0: Intention-RAG #--------------------------- typeResults=collection.query( query_texts=[message], n_results=4, where={"type": "0"} ) myType=typeResults["metadatas"][0][0]["type2"] # einfachste Variante x=[x["type2"] for x in typeResults["metadatas"][0]] # liste die type2-Einträge auf myType="1a" if ((x.index("1a") if "1a" in x else len(x)+1) < (x.index("!1a") if "!1a" in x else len(x)+1)) else "else" # setze 1a wenn es besser passt als !1a if ((x.index("1b") if "1b" in x else len(x)+1) < (x.index("1a") if "1a" in x else len(x)+1)): # prüfe 1b wenn 1b besser passt als 1a if ((x.index("1b") if "1b" in x else len(x)+1) < (x.index("!1b") if "!1b" in x else len(x)+1)): myType="1b" # setze 1b wenn besser als !1b (sonst lass 1a/else) print("Message:"+message+"\n\nIntention-Type: "+myType+"\n\n"+str(typeResults)) #RAG-layer 1: Respond with CustomDB-RAG (1a, 1b) or Memory-RAG #-------------------------------------------------------------- rag=None historylimit=4 combination=None ## RAG 1a: Respond with CustomDB-RAG #----------------------------------- if(myType=="1a"): RAGResults=collection.query( query_texts=[message], n_results=2, where={"type": myType} #where_document={"$contains":"search_string"} ) dists=["
(relevance: "+str(round((1-d)*100)/100)+";" for d in RAGResults['distances'][0]] sources=["source: "+s["source"]+")" for s in RAGResults['metadatas'][0]] texts=RAGResults['documents'][0] combination = zip(texts,dists,sources) combination = [' '.join(triplets) for triplets in combination] #print(combination) rag="\n\n" rag += "Mit Blick auf die aktuelle Äußerung des Users empfiehlst du AUSSCHLIEßLICH folgende KI-Verfahren aus unserer Datenbank:\n" rag += str(texts) rag += "\n\nIm Folgenden siehst du den jüngsten Dialog-Verlauf:" else: ## RAG 1a: Respond with CustomDB-RAG #----------------------------------- if(myType=="1b"): RAGResults=collection.query( query_texts=[message], n_results=2, where={"type": myType} #where_document={"$contains":"search_string"} ) dists=["
(relevance: "+str(round((1-d)*100)/100)+";" for d in RAGResults['distances'][0]] sources=["source: "+s["source"]+")" for s in RAGResults['metadatas'][0]] texts=RAGResults['documents'][0] combination = zip(texts,dists,sources) combination = [' '.join(triplets) for triplets in combination] #print(combination) rag="\n\n" rag += "Beziehe dich in deiner Fortsetzung des Dialogs AUSSCHLIEßLICH auf die folgenden Informationen und gebe keine weiteren Informationen heraus:\n" rag += str(texts) rag += "\n\nIm Folgenden siehst du den jüngsten Dialog-Verlauf:" ## Else: Respond with Memory-RAG #-------------------------------- else: x=collection.get(include=[])["ids"] if(len(x)>(historylimit*2)): # turn on RAG when the database contains entries that are not shown within historylimit RAGResults=collection.query( query_texts=[message], n_results=1, where={"type": "episode"} ) texts=RAGResults["metadatas"][0][0]["dialog"] #str() #print("Message: "+message+"\n\nBest Match: "+texts) rag="\n\n" rag += "Mit Blick auf die aktuelle Äußerung des Users erinnerst du dich insb. an folgende Episode aus eurem Dialog:\n" rag += str(texts) rag += "\n\nIm Folgenden siehst du den jüngsten Dialog-Verlauf:" # Request Response from LLM: system2=None # system2 can be used as fictive first words of the AI, which are not displayed or stored #print("RAG: "+rag) #print("System: "+system+"\n\nMessage: "+message) prompt=extend_prompt( message, # current message of the user history, # complete history system, # system prompt rag, # RAG-component added to the system prompt system2, # fictive first words of the AI (neither displayed nor stored) historylimit=historylimit,# number of past messages to consider for response to current message removeHTML=removeHTML # remove HTML-components from History (to prevent bugs with Markdown) ) #print("\n\nMESSAGE:"+str(message)) #print("\n\nHISTORY:"+str(history)) #print("\n\nSYSTEM:"+str(system)) #print("\n\nRAG:"+str(rag)) #print("\n\nSYSTEM2:"+str(system2)) print("\n\n*** Prompt:\n"+prompt+"\n***\n\n") ## Request response from model #------------------------------ print("AI running on prem!" if(onPrem) else "AI running HFHub!") if(onPrem==False): temperature=float(0.9) max_new_tokens=500 top_p=0.95 repetition_penalty=1.0 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, ) stream = client.text_generation(prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) response = "" #print("User: "+message+"\nAI: ") for text in stream: part=text.token.text #print(part, end="", flush=True) response += part if removeHTML==True: response = re.sub("<(.*?)>","\n", response) # remove HTML-components in general (may cause bugs with markdown-rendering) yield response if((myType=="1a")): #add RAG-results to chat-output if appropriate response=response+"\n\n
Sources
    "+ "".join(["
  • " + s + "
    • " for s in combination])+"
" yield response history.append((message, response)) # add current dialog to history # Store current state in DB if memory is turned on if (settings=="Memory On"): x=collection.get(include=[])["ids"] # add current dialog to db collection.add( documents=[message,response], metadatas=[ { "source": "ICH", "dialog": f"ICH: {message.strip()}\n DU: {response.strip()}", "type":"episode"}, { "source": "DU", "dialog": f"ICH: {message.strip()}\n DU: {response.strip()}", "type":"episode"} ], ids=[str(len(x)+1),str(len(x)+2)] ) json.dump(history,open(filename,'w',encoding="utf-8"),ensure_ascii=False) if(onPrem==True): # url="https://afischer1985-wizardlm-13b-v1-2-q4-0-gguf.hf.space/v1/completions" url="http://0.0.0.0:2600/v1/completions" body={"prompt":prompt,"max_tokens":None, "echo":"False","stream":"True"} # e.g. Mixtral-Instruct if("Discolm_german_7b" in modelPath): body.update({"stop": ["<|im_end|>"]}) # fix stop-token of DiscoLM if("Gemma-" in modelPath): body.update({"stop": ["<|im_end|>",""]}) # fix stop-token of Gemma response="" #+"("+myType+")\n" buffer="" #print("URL: "+url) #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 if removeHTML==True: response = re.sub("<(.*?)>","\n", response) # remove HTML-components in general (may cause bugs with markdown-rendering) yield response if((myType=="1a")): #add RAG-results to chat-output if appropriate response=response+"\n\n
Sources
    "+ "".join(["
  • " + s + "
    • " for s in combination])+"
" yield response # Store current state in DB if memory is turned on if (settings=="Memory On"): x=collection.get(include=[])["ids"] # add current dialog to db collection.add( documents=[message,response], metadatas=[ { "source": "ICH", "dialog": f"ICH: {message.strip()}\n DU: {response.strip()}", "type":"episode"}, { "source": "DU", "dialog": f"ICH: {message.strip()}\n DU: {response.strip()}", "type":"episode"} ], ids=[str(len(x)+1),str(len(x)+2)] ) json.dump(history,open(filename,'w',encoding="utf-8"),ensure_ascii=False) 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.\nAktuell bin ich wenig mehr als eine Tech-Demo und kenne nur 7 KI-Modelle - also sei bitte nicht zu streng mit mir.
  • Wenn du ein KI-Modell suchst, antworte ich auf Basis der Liste
  • Wenn du Fragen zur Benutzung eines KI-Modells hast, verweise ich an andere Stellen
  • Wenn du andre Fragen hast, antworte ich frei und berücksichtige dabei Relevantes aus dem gesamten bisherigen Dialog.
\nWas ist dein Anliegen?"]],render_markdown=True), title="German AI-Interface with advanced RAG (on prem)" if onPrem else "German AI-Interface with advanced RAG (HFHub)", #additional_inputs=[gr.Dropdown(["Memory On","Memory Off"],value="Memory Off",label="Memory")] ).queue().launch(share=True) #False, server_name="0.0.0.0", server_port=7864) print("Interface up and running!")