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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="1F4q1oo0rB5oU5OYOXcuzJLxACEkeGR87ioXwR-Jg617vsctJaPrOw",
)
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() |