Upload 2 files

requirements.txt

@@ -0,0 +1,3 @@
+chromadb
+huggingface_hub
+streamlit

run.py

@@ -0,0 +1,82 @@
+import chromadb
+import os
+import gradio as gr
+import json
+from huggingface_hub import InferenceClient
+
+dbPath='/Users/thiloid/Desktop/LSKI/ole_nest/Chatbot/LLM/chromaTS'
+if(os.path.exists(dbPath)==False): dbPath="/home/user/app/chromaTS'"
+
+print(dbPath)
+#path='chromaTS'
+#settings = Settings(persist_directory=storage_path)
+#client = chromadb.Client(settings=settings)
+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")#"VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct")
+#instructor_ef = embedding_functions.InstructorEmbeddingFunction(model_name="hkunlp/instructor-large", device="cuda")
+#print(str(client.list_collections()))
+collection = client.get_collection(name="chromaTS", embedding_function=sentence_transformer_ef)
+
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+
+def format_prompt(message):
+  prompt = "" #"<s>"
+  #for user_prompt, bot_response in history:
+  #  prompt += f"[INST] {user_prompt} [/INST]"
+  #  prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
+
+def response(
+    prompt, 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=[prompt],
+      n_results=10,
+      #where={"source": "google-docs"}
+      #where_document={"$contains":"search_string"}
+    )
+    #print("REsults")
+    #print(results)
+    #print("_____")
+    dists=["<br><small>(relevance: "+str(round((1-d)*100)/100)+";" for d in results['distances'][0]]
+  
+    #sources=["source: "+s["source"]+")</small>" for s in results['metadatas'][0]]
+    results=results['documents'][0]
+    combination = zip(results,dists)
+    combination = [' '.join(triplets) for triplets in combination]
+    #print(str(prompt)+"\n\n"+str(combination))
+    if(len(results)>1):
+      addon=" Bitte berücksichtige bei deiner Antwort ausschießlich 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 Studienberater Assistenzsystem, das zu jedem Anliegen möglichst geeignete Studieninformationen empfiehlt."+addon+"\n\nUser-Anliegen:"   
+    formatted_prompt = format_prompt(system+"\n"+prompt,history)
+    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<br><details open><summary><strong>Sources</strong></summary><br><ul>"+ "".join(["<li>" + s + "</li>" for s in combination])+"</ul></details>"
+    yield output
+
+gr.ChatInterface(response, chatbot=gr.Chatbot(value=[[None,"Herzlich willkommen! Ich bin Chätti ein KI-basiertes Studienassistenzsystem, das für jede Anfrage die am besten Studieninformationen empfiehlt.<br>Erzähle mir, was du gerne tust!"]],render_markdown=True),title="German BERUFENET-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!")
+
+