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Browse files- README.md +8 -8
- requirements.txt +3 -0
- run.py +117 -0
README.md
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---
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title: RAG
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emoji:
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sdk: gradio
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sdk_version:
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app_file:
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: RAG-Interface-to-Hub
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emoji: 🔥
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colorFrom: indigo
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.47.1
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app_file: run.py
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pinned: false
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hf_oauth: false
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---
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requirements.txt
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llama-cpp-python[server]
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chromadb
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sentence_transformers
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run.py
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#########################################################################################
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# Title: Gradio Interface to LLM-chatbot with RAG-funcionality and ChromaDB on HF-Hub
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# Author: Andreas Fischer
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# Date: December 29th, 2023
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# Last update: December 29th, 2023
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##########################################################################################
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# Chroma-DB
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#-----------
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import os
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import chromadb
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dbPath="/home/af/Schreibtisch/gradio/Chroma/db"
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if(os.path.exists(dbPath)==False):
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dbPath="/home/user/app/db"
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print(dbPath)
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#client = chromadb.Client()
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path=dbPath
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client = chromadb.PersistentClient(path=path)
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print(client.heartbeat())
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print(client.get_version())
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print(client.list_collections())
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from chromadb.utils import embedding_functions
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default_ef = embedding_functions.DefaultEmbeddingFunction()
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sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="T-Systems-onsite/cross-en-de-roberta-sentence-transformer")
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#instructor_ef = embedding_functions.InstructorEmbeddingFunction(model_name="hkunlp/instructor-large", device="cuda")
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print(str(client.list_collections()))
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global collection
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if("name=ChromaDB1" in str(client.list_collections())):
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print("ChromaDB1 found!")
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collection = client.get_collection(name="ChromaDB1", embedding_function=sentence_transformer_ef)
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else:
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print("ChromaDB1 created!")
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collection = client.create_collection(
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"ChromaDB1",
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embedding_function=sentence_transformer_ef,
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metadata={"hnsw:space": "cosine"})
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collection.add(
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documents=["The meaning of life is to love.", "This is a sentence", "This is a sentence too"],
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metadatas=[{"source": "notion"}, {"source": "google-docs"}, {"source": "google-docs"}],
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ids=["doc1", "doc2", "doc3"],
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)
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print("Database ready!")
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print(collection.count())
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# Model
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#-------
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from huggingface_hub import InferenceClient
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import gradio as gr
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client = InferenceClient(
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"mistralai/Mixtral-8x7B-Instruct-v0.1"
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#"mistralai/Mistral-7B-Instruct-v0.1"
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)
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# Gradio-GUI
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#------------
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import gradio as gr
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import json
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def format_prompt(message, history):
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prompt = "<s>"
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for user_prompt, bot_response in history:
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prompt += f"[INST] {user_prompt} [/INST]"
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prompt += f" {bot_response}</s> "
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prompt += f"[INST] {message} [/INST]"
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return prompt
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def response(
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prompt, history, temperature=0.9, max_new_tokens=500, top_p=0.95, repetition_penalty=1.0,
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):
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temperature = float(temperature)
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if temperature < 1e-2: temperature = 1e-2
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top_p = float(top_p)
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generate_kwargs = dict(
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temperature=temperature,
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max_new_tokens=max_new_tokens,
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top_p=top_p,
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repetition_penalty=repetition_penalty,
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do_sample=True,
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seed=42,
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)
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addon=""
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results=collection.query(
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query_texts=[prompt],
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n_results=2,
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#where={"source": "google-docs"}
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#where_document={"$contains":"search_string"}
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)
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dists=["<small>(relevance: "+str(round((1-d)*100/100))+";" for d in results['distances'][0]]
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sources=["source: "+s["source"]+")</small>" for s in results['metadatas'][0]]
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results=results['documents'][0]
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combination = zip(results,dists,sources)
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combination = [' '.join(triplets) for triplets in combination]
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print(combination)
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if(len(results)>1):
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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)
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system="Du bist ein KI-basiertes Assistenzsystem."+addon+"\n\nUser-Anliegen:"
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#body={"prompt":system+"### Instruktion:\n"+message+"\n\n### Antwort:","max_tokens":500, "echo":"False","stream":"True"} #e.g. SauerkrautLM
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formatted_prompt = format_prompt(system+"\n"+prompt, history)
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stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
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output = ""
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for response in stream:
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output += response.token.text
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yield output
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output=output+"\n\n<br><details open><summary><strong>Sources</strong></summary><br><ul>"+ "".join(["<li>" + s + "</li>" for s in combination])+"</ul></details>"
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yield output
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gr.ChatInterface(response, chatbot=gr.Chatbot(render_markdown=True),title="RAG-Interface").queue().launch(share=True) #False, server_name="0.0.0.0", server_port=7864)
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print("Interface up and running!")
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