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# Rag_Chat_tab.py
# Description: This file contains the code for the RAG Chat tab in the Gradio UI
#
# Imports
import logging
#
# External Imports
import gradio as gr
#
# Local Imports
from App_Function_Libraries.DB.DB_Manager import get_all_content_from_database
from App_Function_Libraries.RAG.ChromaDB_Library import chroma_client, \
check_embedding_status, store_in_chroma
from App_Function_Libraries.RAG.Embeddings_Create import create_embedding
from App_Function_Libraries.RAG.RAG_Libary_2 import enhanced_rag_pipeline
#
########################################################################################################################
#
# Functions:
def create_rag_tab():
with gr.TabItem("RAG Search"):
gr.Markdown("# Retrieval-Augmented Generation (RAG) Search")
with gr.Row():
with gr.Column():
search_query = gr.Textbox(label="Enter your question", placeholder="What would you like to know?")
keyword_filtering_checkbox = gr.Checkbox(label="Enable Keyword Filtering", value=False)
keywords_input = gr.Textbox(
label="Enter keywords (comma-separated)",
value="keyword1, keyword2, ...",
visible=False
)
keyword_instructions = gr.Markdown(
"Enter comma-separated keywords to filter your search results.",
visible=False
)
api_choice = gr.Dropdown(
choices=["Local-LLM", "OpenAI", "Anthropic", "Cohere", "Groq", "DeepSeek", "Mistral", "OpenRouter", "Llama.cpp", "Kobold", "Ooba", "Tabbyapi", "VLLM", "ollama", "HuggingFace"],
label="Select API for RAG",
value="OpenAI"
)
search_button = gr.Button("Search")
with gr.Column():
result_output = gr.Textbox(label="Answer", lines=10)
context_output = gr.Textbox(label="Context", lines=10, visible=True)
def toggle_keyword_filtering(checkbox_value):
return {
keywords_input: gr.update(visible=checkbox_value),
keyword_instructions: gr.update(visible=checkbox_value)
}
keyword_filtering_checkbox.change(
toggle_keyword_filtering,
inputs=[keyword_filtering_checkbox],
outputs=[keywords_input, keyword_instructions]
)
def perform_rag_search(query, keywords, api_choice):
if keywords == "keyword1, keyword2, ...":
keywords = None
result = enhanced_rag_pipeline(query, api_choice, keywords)
return result['answer'], result['context']
search_button.click(perform_rag_search, inputs=[search_query, keywords_input, api_choice], outputs=[result_output, context_output])
# FIXME - under construction
def create_embeddings_tab():
with gr.TabItem("Create Embeddings"):
gr.Markdown("# Create Embeddings for All Content")
with gr.Row():
with gr.Column():
embedding_provider = gr.Radio(
choices=["openai", "local", "huggingface"],
label="Select Embedding Provider",
value="openai"
)
embedding_model = gr.Textbox(
label="Embedding Model",
value="text-embedding-3-small"
)
embedding_api_url = gr.Textbox(
label="API URL (for local provider)",
value="http://localhost:8080/embedding",
visible=False
)
create_button = gr.Button("Create Embeddings")
with gr.Column():
status_output = gr.Textbox(label="Status", lines=10)
def update_provider_options(provider):
return gr.update(visible=provider == "local")
embedding_provider.change(
fn=update_provider_options,
inputs=[embedding_provider],
outputs=[embedding_api_url]
)
def create_all_embeddings(provider, model, api_url):
try:
all_content = get_all_content_from_database()
if not all_content:
return "No content found in the database."
collection_name = "all_content_embeddings"
collection = chroma_client.get_or_create_collection(name=collection_name)
for item in all_content:
media_id = item['id']
text = item['content']
existing = collection.get(ids=[f"doc_{media_id}"])
if existing['ids']:
continue
embedding = create_embedding(text, provider, model, api_url)
store_in_chroma(collection_name, [text], [embedding], [f"doc_{media_id}"], [{"media_id": media_id}])
return "Embeddings created and stored successfully for all new content."
except Exception as e:
logging.error(f"Error during embedding creation: {str(e)}")
return f"Error: {str(e)}"
create_button.click(
fn=create_all_embeddings,
inputs=[embedding_provider, embedding_model, embedding_api_url],
outputs=status_output
)
def create_view_embeddings_tab():
with gr.TabItem("View/Update Embeddings"):
gr.Markdown("# View and Update Embeddings")
item_mapping = gr.State({})
with gr.Row():
with gr.Column():
item_dropdown = gr.Dropdown(label="Select Item", choices=[], interactive=True)
refresh_button = gr.Button("Refresh Item List")
embedding_status = gr.Textbox(label="Embedding Status", interactive=False)
embedding_preview = gr.Textbox(label="Embedding Preview", interactive=False, lines=5)
with gr.Column():
create_new_embedding_button = gr.Button("Create New Embedding")
embedding_provider = gr.Radio(
choices=["openai", "local", "huggingface"],
label="Embedding Provider",
value="openai"
)
embedding_model = gr.Textbox(
label="Embedding Model",
value="text-embedding-3-small",
visible=True
)
embedding_api_url = gr.Textbox(
label="API URL (for local provider)",
value="http://localhost:8080/embedding",
visible=False
)
def get_items_with_embedding_status():
try:
items = get_all_content_from_database()
collection = chroma_client.get_or_create_collection(name="all_content_embeddings")
choices = []
new_item_mapping = {}
for item in items:
try:
result = collection.get(ids=[f"doc_{item['id']}"])
embedding_exists = result is not None and result.get('ids') and len(result['ids']) > 0
status = "Embedding exists" if embedding_exists else "No embedding"
except Exception as e:
print(f"Error checking embedding for item {item['id']}: {str(e)}")
status = "Error checking"
choice = f"{item['title']} ({status})"
choices.append(choice)
new_item_mapping[choice] = item['id']
return gr.update(choices=choices), new_item_mapping
except Exception as e:
print(f"Error in get_items_with_embedding_status: {str(e)}")
return gr.update(choices=["Error: Unable to fetch items"]), {}
def update_provider_options(provider):
return gr.update(visible=provider == "local")
def create_new_embedding_for_item(selected_item, provider, model, api_url, item_mapping):
if not selected_item:
return "Please select an item", ""
try:
item_id = item_mapping.get(selected_item)
if item_id is None:
return f"Invalid item selected: {selected_item}", ""
items = get_all_content_from_database()
item = next((item for item in items if item['id'] == item_id), None)
if not item:
return f"Item not found: {item_id}", ""
embedding = create_embedding(item['content'], provider, model, api_url)
collection_name = "all_content_embeddings"
metadata = {"media_id": item_id, "title": item['title']}
store_in_chroma(collection_name, [item['content']], [embedding], [f"doc_{item_id}"],
[{"media_id": item_id, "title": item['title']}])
embedding_preview = str(embedding[:50])
status = f"New embedding created and stored for item: {item['title']} (ID: {item_id})"
return status, f"First 50 elements of new embedding:\n{embedding_preview}\n\nMetadata: {metadata}"
except Exception as e:
logging.error(f"Error in create_new_embedding_for_item: {str(e)}")
return f"Error creating embedding: {str(e)}", ""
refresh_button.click(
get_items_with_embedding_status,
outputs=[item_dropdown, item_mapping]
)
item_dropdown.change(
check_embedding_status,
inputs=[item_dropdown, item_mapping],
outputs=[embedding_status, embedding_preview]
)
create_new_embedding_button.click(
create_new_embedding_for_item,
inputs=[item_dropdown, embedding_provider, embedding_model, embedding_api_url, item_mapping],
outputs=[embedding_status, embedding_preview]
)
embedding_provider.change(
update_provider_options,
inputs=[embedding_provider],
outputs=[embedding_api_url]
)
return item_dropdown, refresh_button, embedding_status, embedding_preview, create_new_embedding_button, embedding_provider, embedding_model, embedding_api_url
#
# End of file
########################################################################################################################
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