initial commit

requirements.txt

@@ -0,0 +1,3 @@
+llama-cpp-python[server]
+chromadb
+sentence_transformers

run.py

@@ -0,0 +1,345 @@
+#########################################################################################
+# Title:  Gradio Writing Assistant
+# Author: Andreas Fischer
+# Date:   May 23th, 2024
+# Last update: May 23th, 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()
+
+
+# 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="writingStyleDB1"
+
+if("name="+dbName in str(client.list_collections())): client.delete_collection(name=dbName) # deletes collection
+
+if("name="+dbName in str(client.list_collections())):
+  print(dbName+" found!")
+  collection = client.get_collection(name=dbName, embedding_function=embeddingModel) #sentence_transformer_ef)
+else:
+  #client.delete_collection(name=dbName)
+  print(dbName+" created!")
+  collection = client.create_collection(
+    dbName,
+    embedding_function=embeddingModel,
+    metadata={"hnsw:space": "cosine"})
+
+print("Database ready!")
+print(collection.count()) 
+
+x=collection.get(include=[])["ids"]
+if(len(x)==0):
+  x=collection.get(include=[])["ids"]
+  collection.add(
+    documents=["Ich möchte einen Blogbeitrag","Ich möchte einen Gliederungsvorschlag","Ich möchte einen Social Media Beitrag"], 
+    metadatas=[
+      {"prompt": "Bitte schreibe einen Blogbeitrag zur Anfrage des Users!"},
+      {"prompt": "Bitte entwerfe einen Gliederungsvorschlag zur Anfrage des Users!"},
+      {"prompt": "Bitte verfasse einen Beitrag für die professionelle social media Plattform LinkedIn zur Anfrage des Users!"}], 
+    ids=[str(len(x)+1),str(len(x)+2),str(len(x)+3)] 
+  )
+
+RAGResults=collection.query(
+    query_texts=["Dies ist ein Test"],
+    n_results=1,
+      #where={"source": "USER"}
+)
+RAGResults["metadatas"][0][0]["prompt"]
+x=collection.get(where_document={"$contains":"Blogbeitrag"},include=["metadatas"])['metadatas'][0]['prompt']
+
+
+# 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 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] </s>" 
+  template1=" [INST] {message} [/INST]"
+  template2=" {response}</s>"
+  if("command-r" in modelPath): #https://huggingface.co/CohereForAI/c4ai-command-r-v01
+    ## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
+    template0="<BOS_TOKEN><|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="<start_of_turn>user{system}</end_of_turn>" 
+    template1="<start_of_turn>user{message}</end_of_turn><start_of_turn>model"
+    template2="{response}</end_of_turn>"      
+  if("Mixtral-8x22B-Instruct" in modelPath): # AutoTokenizer: <s>[INST] U1[/INST] A1</s>[INST] U2[/INST] A2</s>
+    startOfString="<s>"
+    template0="[INST]{system}\n  [/INST] </s>"  
+    template1="[INST] {message}[/INST]"
+    template2=" {response}</s>"
+  if("Mixtral-8x7b-instruct" in modelPath): # https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
+    startOfString="<s>"                     # AutoTokenzizer: <s> [INST] U1 [/INST]A1</s> [INST] U2 [/INST]A2</s>
+    template0=" [INST]{system}\n  [/INST] </s>"  
+    template1=" [INST] {message} [/INST]"
+    template2=" {response}</s>"
+  if("Mistral-7B-Instruct" in modelPath): #https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
+    startOfString="<s>"
+    template0="[INST]{system}\n [/INST]</s>"
+    template1="[INST] {message} [/INST]"
+    template2=" {response}</s>"
+  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} " #<s>
+    template1="USER: {message} ASSISTANT: "
+    template2="{response}</s>"
+  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) #"<s>"
+  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<details>((.|\n)*?)</details>","", 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
+
+
+import gradio as gr
+import requests
+import json
+from datetime import datetime
+import os
+import re
+
+def response(message, history,customSysPrompt, genre, hfToken):
+  if((onPrem==False) & (hfToken.startswith("hf_"))): # use HF-hub with custom token if token is provided
+    from huggingface_hub import InferenceClient
+    import gradio as gr
+    client = InferenceClient(
+      model=myModel,
+      token=hfToken
+    )
+  removeHTML=True
+  system=customSysPrompt # system-prompt can be changed in the UI (usually defaults to something like the following system-prompt)
+  if(system==""): system="Du bist wissenschaftlicher Mitarbeiter an einem Forschungsinstitut und zuständig für die Wissenschaftskommunikation."
+  message=message.replace("[INST]","")
+  message=message.replace("[/INST]","")
+  message=message.replace("</s>","")
+  message=re.sub("<[|](im_start|im_end|end_of_turn)[|]>", '', message)
+  x=collection.get(include=[])["ids"]  
+  rag=None # RAG is turned off until history gets too long
+  historylimit=2
+  if(genre==""): # use RAG to define genre if there is none
+    RAGResults=collection.query(query_texts=[message], n_results=1)
+    genre=str(RAGResults['documents'][0][0]) # determine genre based on best-matching db-entry 
+  
+  rag="\n\n"+collection.get(where_document={"$contains":genre},include=["metadatas"])['metadatas'][0]['prompt'] # genre-specific addendum to system prompt (rag)
+  if(len(history)>0):
+    rag=rag+"\nFalls der User Rückfragen oder Änderungsvorschläge zu deinem Entwurf hat, gehe darauf ein." # add dialog-specific addendum to rag
+  
+  system2=None # system2 can be used as fictive first words of the AI, which are not displayed or stored
+  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)
+    )
+  if(True):
+    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=1000 
+    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(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|>","</end_of_turn>"]})   # 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
+    history.append((message, response)) # add current dialog to history
+
+val=None
+gr.ChatInterface(
+  response, 
+  chatbot=gr.Chatbot(value=val, render_markdown=True),
+  title="KI Schreibassistenz (on prem)" if onPrem else "KI Schreibassistenz (HFHub)",
+  additional_inputs=[
+    gr.Textbox(
+      value="Du bist wissenschaftlicher Mitarbeiter an einem Forschungsinstitut und zuständig für die Wissenschaftskommunikation.",
+      label="System Prompt"),
+    gr.Dropdown(
+      ["Blogbeitrag","Gliederungsvorschlag","Social Media Beitrag",""],
+      value="Blogbeitrag",
+      label="Genre"),
+     gr.Textbox(
+      value="",
+      label="HF_token"),     
+  ]
+  ).queue().launch(share=True) #False, server_name="0.0.0.0", server_port=7864)
+print("Interface up and running!")
+
+
+
+