Update run.py

run.py

@@ -1,52 +1,126 @@
-#############################################################################
-# Title:  Gradio Interface to AI hosted by Huggingface
+#########################################################################################
+# Title:  Gradio Interface to LLM-chatbot with RAG-funcionality and ChromaDB on premises 
 # Author: Andreas Fischer
-# Date:   October 7th, 2023
-# Last update: December 19th, 2023
-#############################################################################
+# Date:   October 15th, 2023
+# Last update: December 21th, 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"
+
+#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/Mixtral-8x7B-Instruct-v0.1-GGUF/resolve/main/mixtral-8x7b-instruct-v0.1.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"
+
+
+# Llama-cpp-Server
+#------------------
+
+command = ["python3", "-m", "llama_cpp.server", "--model", modelPath, "--host", "0.0.0.0", "--port", "2600"]
+subprocess.Popen(command)
+print("Model 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(collection.count()) 
+
+
+# Gradio-GUI
+#------------
 
 import gradio as gr
 import requests
-import time
+import random
 import json
-
-def response(message, history, model):
-  if(model=="Default"): model = "mistralai/Mixtral-8x7B-Instruct-v0.1" 
-  model_id = model
-  params={"max_new_tokens":600, "return_full_text":False} #, "max_length":500, "stream":True
-  url = f"https://api-inference.huggingface.co/models/{model_id}"
-  correction=1
-  prompt=f"[INST] {message} [/INST]" # skipped <s>
+def response(message, history):
+  addon=""
+  results=collection.query(
+    query_texts=[message],
+    n_results=2,
+    #where={"source": "google-docs"}
+    #where_document={"$contains":"search_string"}
+  )
+  results=results['documents'][0]
+  print(results)
+  if(len(results)>1):
+    addon=" Bitte berücksichtige bei deiner Antwort ggf. folgende Auszüge aus unserer Datenbank, sofern sie für die Antwort erforderlich sind. Ingoriere unpassende Auszüge unkommentiert:\n"+"\n".join(results)+"\n\n"
+  #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\n"  
+  #body={"prompt":system+"### Instruktion:\n"+message+"\n\n### Antwort:","max_tokens":500, "echo":"False","stream":"True"} #e.g. SauerkrautLM
+  body={"prompt":"<s>[INST]"+system+"\n"+message+"[/INST]### Antwort:","max_tokens":500, "echo":"False","stream":"True"} #e.g. Mixtral-Instruct
+  response=""
+  buffer=""
   print("URL: "+url)
-  print(params)
+  print(str(body))
   print("User: "+message+"\nAI: ")
-  response=""
-  for text in requests.post(url, json={"inputs":prompt, "parameters":params}, stream=True):
-    text=text.decode('UTF-8')
-    print(text)
-    if(correction==3):
-      text='"}]'+text
-      correction=2
-    if(correction==1):
-      text=text.lstrip('[{"generated_text":"')
-      correction=2
-    if(text.endswith('"}]')):
-      text=text.rstrip('"}]')
-      correction=3 
-    response=response+text
-    print(text)
-    time.sleep(0.2)
-    yield response
-
-x=requests.get(f"https://api-inference.huggingface.co/framework/text-generation-inference")
-x=[i["model_id"] for i in x.json()]
-print(x)
-x=[s for s in x if s.startswith("mistral")]
-print(x)
-x.insert(0,"Default")
-
-gr.ChatInterface(
-  response,
-  additional_inputs=[gr.Dropdown(x,value="Default",label="Model")]).queue().launch(share=True) #False, server_name="0.0.0.0", server_port=7864) 
+  for text in requests.post(url, json=body, stream=True):  #-H 'accept: application/json' -H 'Content-Type: application/json'
+    print("*** Raw String: "+str(text)+"\n***\n")
+    text=text.decode('utf-8')
+    if(text.startswith(": ping -")==False):buffer=str(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:
+        pass
+    yield response 
+
+gr.ChatInterface(response).queue().launch(share=True) #False, server_name="0.0.0.0", server_port=7864)