File size: 2,253 Bytes
6d6b66e 98b6610 6d6b66e 0ddbfad 6d6b66e 0ddbfad 6d6b66e 98b6610 6d6b66e 0ddbfad 6d6b66e 98b6610 6d6b66e 98b6610 6d6b66e 98b6610 6d6b66e 98b6610 |
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 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import os
import gradio as gr
import json
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from langchain_community.embeddings import HuggingFaceEmbeddings
from llama_index.llms.llama_cpp import LlamaCPP
from llama_index.llms.llama_cpp.llama_utils import (
messages_to_prompt,
completion_to_prompt,
)
model_url = 'https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF/resolve/main/Llama-3.2-3B-Instruct-Q4_K_M.gguf'
llm = LlamaCPP(
model_url=model_url,
temperature=0.1,
max_new_tokens=256,
context_window=2048,
model_kwargs={"n_gpu_layers": 1},
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
verbose=True,
)
# Initialize embeddings and LLM
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-small-en-v1.5")
def initialize_index():
"""Initialize the vector store index from JSON data."""
# Load JSON data
with open("dummy_users_with_tasks.json", "r") as file:
json_data = json.load(file)
# Convert JSON data to plain text for embedding
documents = []
for user in json_data:
tasks_summary = "\n".join(
[f"Task: {task['title']} | Start: {task['start_date']} | Due: {task['due_date']}"
for task in user["tasks"]]
)
doc_text = f"User: {user['name']} | Email: {user['email']}\nTasks:\n{tasks_summary}"
documents.append(doc_text)
# Create index
index = VectorStoreIndex.from_documents(
documents,
embed_model=embeddings,
)
# Return query engine with Llama
return index.as_query_engine(llm=llm)
# Initialize the query engine at startup
query_engine = initialize_index()
def process_query(
message: str,
history: list[tuple[str, str]],
) -> str:
"""Process a query using the RAG system."""
try:
# Get response from the query engine
response = query_engine.query(
message,
)
return response
except Exception as e:
return f"Error processing query: {str(e)}"
# Gradio interface (if needed)
interface = gr.Interface(
fn=process_query,
inputs=["text", "state"],
outputs="text",
)
if __name__ == "__main__":
interface.launch()
|