File size: 2,214 Bytes
3d6d112 fa83d85 d8640b0 ab9c135 d8640b0 fa83d85 6b00926 33159f9 fa83d85 6b00926 6437d7f fa83d85 dde5236 fa83d85 dde5236 fa83d85 7c07353 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
#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
from langchain_community.llms import Cohere
from langchain_community.llms import FakeListLLM
from langchain_community.llms import *
# 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/stablelm-zephyr-3b-GGUF/resolve/main/stablelm-zephyr-3b.Q4_0.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) |