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#https://docs.google.com/document/d/1hY5ItC8Mewyk-90Q--CGr50wBbZBjPrkYu4NtiBVre4/edit?usp=sharing
#Inference takes 6-7 mins per query
import logging
import sys
import gradio as gr
from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.llms import LlamaCPP
from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
# Set up logging
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
def configure_llama_model():
#model_url = 'https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q8_0.gguf'
model_url = 'https://huggingface.co/TheBloke/stablelm-zephyr-3b-GGUF/resolve/main/stablelm-zephyr-3b.Q4_K_M.gguf'
llm = LlamaCPP(
model_url=model_url,
temperature=0.3,
max_new_tokens=256,
context_window=3900,
model_kwargs={"n_gpu_layers": -1},
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
verbose=True,
)
return llm
def configure_embeddings():
embed_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
return embed_model
def configure_service_context(llm, embed_model):
return ServiceContext.from_defaults(chunk_size=250, llm=llm, embed_model=embed_model)
def initialize_vector_store_index(data_path, service_context):
documents = SimpleDirectoryReader("./").load_data()
index = VectorStoreIndex.from_documents(documents, service_context=service_context)
return index
# Configure and initialize components
llm = configure_llama_model()
embed_model = configure_embeddings()
service_context = configure_service_context(llm, embed_model)
index = initialize_vector_store_index("./", service_context)
query_engine = index.as_query_engine()
# Define a function for Gradio to use
def get_response(text, username):
# For simplicity, we are only using the 'text' argument
response = str(query_engine.query(text))
return response
gr.ChatInterface(get_response).launch(debug=True,share=True) |