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import os
import gradio as gr
import copy
from llama_cpp import Llama
from huggingface_hub import hf_hub_download
import chromadb
from sentence_transformers import SentenceTransformer
# Initialize the Llama model
llm = Llama(
model_path=hf_hub_download(
repo_id="microsoft/Phi-3-mini-4k-instruct-gguf",
filename="Phi-3-mini-4k-instruct-q4.gguf",
),
n_ctx=2048,
n_gpu_layers=50, # Adjust based on your VRAM
)
# Initialize ChromaDB Vector Store
class VectorStore:
def __init__(self, collection_name):
self.embedding_model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
self.chroma_client = chromadb.Client()
self.collection = self.chroma_client.create_collection(name=collection_name)
def populate_vectors(self, texts, ids):
embeddings = self.embedding_model.encode(texts, batch_size=32).tolist()
for text, embedding, doc_id in zip(texts, embeddings, ids):
self.collection.add(embeddings=[embedding], documents=[text], ids=[doc_id])
def search_context(self, query, n_results=1):
query_embedding = self.embedding_model.encode([query]).tolist()
results = self.collection.query(query_embeddings=query_embedding, n_results=n_results)
return results['documents']
# Example initialization (assuming you've already populated the vector store)
vector_store = VectorStore("embedding_vector")
# Populate with your data if not already done
# vector_store.populate_vectors(your_texts, your_ids)
def generate_text(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Retrieve context from vector store
context_results = vector_store.search_context(message, n_results=1)
context = context_results[0] if context_results else ""
input_prompt = f"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n {context}\n"
for interaction in history:
input_prompt += f"{interaction[0]} [/INST] {interaction[1]} </s><s> [INST] "
input_prompt += f"{message} [/INST] "
temp = ""
output = llm(
input_prompt,
temperature=temperature,
top_p=top_p,
top_k=40,
repeat_penalty=1.1,
max_tokens=max_tokens,
stop=["", " \n", "ASSISTANT:", "USER:", "SYSTEM:"],
stream=True,
)
for out in output:
temp += out["choices"][0]["text"]
yield temp
# Define the Gradio interface without timeout
demo = gr.Interface(
fn=generate_text,
inputs="text",
outputs="text",
title="llama-cpp-python on GPU with ChromaDB",
description="Running LLM with context retrieval from ChromaDB",
examples=[
["I have leftover rice, what can I make out of it?"],
["Can I make lunch for two people with this?"],
],
cache_examples=False,
retry_btn=None,
undo_btn="Delete Previous",
clear_btn="Clear",
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()
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