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+FROM python:3.10-slim
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+USER root
+RUN apt-get update && apt-get install -y \
+    gcc \
+    g++ \
+    cmake \
+    build-essential \
+    libstdc++6 \
+    && rm -rf /var/lib/apt/lists/*
+
+USER user
+
+COPY --chown=user ./requirements.txt requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from sentence_transformers import SentenceTransformer
+from qdrant_client import QdrantClient
+import torch
+from llama_cpp import Llama
+
+llm = Llama.from_pretrained(
+	repo_id="Suku0/mistral-7b-instruct-v0.3-bnb-4bit-GGUF",
+	filename="mistral-7b-instruct-v0.3-bnb-4bit.Q4_K_M.gguf",
+    n_ctx=16384
+)
+embedding_model = SentenceTransformer('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True)
+qdrant_client = QdrantClient(
+    url="https://9a5cbf91-7dac-4dd0-80f6-13e512da1060.europe-west3-0.gcp.cloud.qdrant.io:6333",
+    api_key="1M-sCCVolJOOJeRXMBUh4wHfj8bkY4nZyHiau0LBllFr1vsXb1oDPg",
+)
+
+def retrieve_context(query):
+    query_vector = embedding_model.encode(query).tolist()
+
+    search_result = qdrant_client.search(
+        collection_name="ctx_collection",
+        query_vector=query_vector,
+        limit=10,
+        with_payload=True
+    )
+
+    context = " ".join([hit.payload["text"] for hit in search_result])
+    return context
+
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    context = retrieve_context(message)
+    prompt = f"""You are a helpful assistant. Please answer the user's question based on the given context. If the context doesn't provide any answer, say the context doesn't provide the answer.
+
+### Context:
+{context}
+
+### Question:
+{message}
+
+### Answer:
+"""
+
+    response = llm(prompt.format(ctx=context, question=message), max_tokens=243)
+
+    return response["choices"][0]["text"]
+
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+gradio
+transformers
+sentence-transformers
+qdrant-client
+llama-cpp-python
+einops