Spaces:
Running
Running
File size: 10,842 Bytes
41ba402 |
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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 |
# 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
########################################################################################################################
|