Merge pull request #281 from rmusser01/dev

Fixes + RAG Enhancements

App_Function_Libraries/Audio/Audio_Transcription_Lib.py

@@ -50,7 +50,7 @@
 
 class WhisperModel(OriginalWhisperModel):
     tldw_dir = os.path.dirname(os.path.dirname(__file__))
-    default_download_root = os.path.join(tldw_dir, 'App_Function_Libraries', 'models', 'Whisper')
+    default_download_root = os.path.join(tldw_dir, 'models', 'Whisper')
 
     valid_model_sizes = [
         "tiny.en", "tiny", "base.en", "base", "small.en", "small", "medium.en", "medium",
@@ -207,8 +207,8 @@ def speech_to_text(audio_file_path, selected_source_lang='en', whisper_model='me
 
     try:
         _, file_ending = os.path.splitext(audio_file_path)
-        out_file = audio_file_path.replace(file_ending, ".segments.json")
-        prettified_out_file = audio_file_path.replace(file_ending, ".segments_pretty.json")
+        out_file = audio_file_path.replace(file_ending, "-whisper_model-"+whisper_model+".segments.json")
+        prettified_out_file = audio_file_path.replace(file_ending, "-whisper_model-"+whisper_model+".segments_pretty.json")
         if os.path.exists(out_file):
             logging.info("speech-to-text: Segments file already exists: %s", out_file)
             with open(out_file) as f:

App_Function_Libraries/Chunk_Lib.py

@@ -476,22 +476,22 @@ def semantic_chunk_long_file(file_path, max_chunk_size=1000, overlap=100, unit='
 #
 #  Embedding Chunking
 
-def chunk_for_embedding(text: str, file_name: str, full_summary: str, custom_chunk_options: Dict[str, Any] = None) -> List[Dict[str, Any]]:
+def chunk_for_embedding(text: str, file_name: str, custom_chunk_options: Dict[str, Any] = None) -> List[Dict[str, Any]]:
     options = chunk_options.copy()
     if custom_chunk_options:
         options.update(custom_chunk_options)
 
+    logging.info(f"Chunking options: {options}")
     chunks = improved_chunking_process(text, options)
     total_chunks = len(chunks)
+    logging.info(f"Total chunks created: {total_chunks}")
 
     chunked_text_with_headers = []
     for i, chunk in enumerate(chunks, 1):
         chunk_text = chunk['text']
         chunk_position = determine_chunk_position(chunk['metadata']['relative_position'])
-
         chunk_header = f"""
         Original Document: {file_name}
-        Full Document Summary: {full_summary or "Full document summary not available."}
         Chunk: {i} of {total_chunks}
         Position: {chunk_position}
 

App_Function_Libraries/DB/DB_Manager.py

@@ -50,7 +50,7 @@
     check_media_and_whisper_model as sqlite_check_media_and_whisper_model, \
     create_document_version as sqlite_create_document_version,
     get_document_version as sqlite_get_document_version, sqlite_search_db, add_media_chunk as sqlite_add_media_chunk,
-    sqlite_update_fts_for_media, sqlite_get_unprocessed_media, fetch_item_details as sqlite_fetch_item_details, \
+    sqlite_update_fts_for_media, get_unprocessed_media as sqlite_get_unprocessed_media, fetch_item_details as sqlite_fetch_item_details, \
     search_media_database as sqlite_search_media_database, mark_as_trash as sqlite_mark_as_trash, \
     get_media_transcripts as sqlite_get_media_transcripts, get_specific_transcript as sqlite_get_specific_transcript, \
     get_media_summaries as sqlite_get_media_summaries, get_specific_summary as sqlite_get_specific_summary, \
@@ -68,7 +68,7 @@
     get_workflow_chat as sqlite_get_workflow_chat, update_media_content_with_version as sqlite_update_media_content_with_version, \
     check_existing_media as sqlite_check_existing_media, get_all_document_versions as sqlite_get_all_document_versions, \
     fetch_paginated_data as sqlite_fetch_paginated_data, get_latest_transcription as sqlite_get_latest_transcription, \
-
+    mark_media_as_processed as sqlite_mark_media_as_processed,
 )
 #
 # Local Imports
@@ -417,7 +417,7 @@ def update_fts_for_media(media_id: int):
         raise ValueError(f"Unsupported database type: {db_type}")
 
 
-def get_unprocessed_media():
+def get_unprocessed_media(*args, **kwargs):
     if db_type == 'sqlite':
         return sqlite_get_unprocessed_media(db)
     elif db_type == 'elasticsearch':
@@ -427,6 +427,16 @@ def get_unprocessed_media():
         raise ValueError(f"Unsupported database type: {db_type}")
 
 
+def mark_media_as_processed(*args, **kwargs):
+    if db_type == 'sqlite':
+        return sqlite_mark_media_as_processed(*args, **kwargs)
+    elif db_type == 'elasticsearch':
+        # Implement Elasticsearch version
+        raise NotImplementedError("Elasticsearch version of mark_media_as_processed not yet implemented")
+    else:
+        raise ValueError(f"Unsupported database type: {db_type}")
+
+
 #
 # End of DB-Ingestion functions
 ############################################################################################################

App_Function_Libraries/DB/SQLite_DB.py

@@ -301,7 +301,8 @@ def create_tables(db) -> None:
             is_trash BOOLEAN DEFAULT 0,
             trash_date DATETIME,
             vector_embedding BLOB,
-            chunking_status TEXT DEFAULT 'pending'
+            chunking_status TEXT DEFAULT 'pending',
+            vector_processing INTEGER DEFAULT 0
         )
         ''',
         '''
@@ -564,11 +565,14 @@ def sqlite_update_fts_for_media(db, media_id: int):
         conn.commit()
 
 
-def sqlite_get_unprocessed_media(db):
-    with db.get_connection() as conn:
-        cursor = conn.cursor()
-        cursor.execute("SELECT id, content, type FROM Media WHERE id NOT IN (SELECT DISTINCT media_id FROM MediaChunks)")
-        return cursor.fetchall()
+def get_unprocessed_media(db):
+    query = """
+    SELECT id, content, type, COALESCE(title, '') as file_name
+    FROM Media 
+    WHERE vector_processing = 0
+    ORDER BY id
+    """
+    return db.execute_query(query)
 
 def get_next_media_id():
     try:
@@ -580,8 +584,18 @@ def get_next_media_id():
     finally:
         conn.close()
 
+
+def mark_media_as_processed(database, media_id):
+    try:
+        query = "UPDATE Media SET vector_processing = 1 WHERE id = ?"
+        database.execute_query(query, (media_id,))
+        logger.info(f"Marked media_id {media_id} as processed")
+    except Exception as e:
+        logger.error(f"Error marking media_id {media_id} as processed: {str(e)}")
+        raise
+
 #
-# End of Media-related Functions
+# End of Vector-chunk-related Functions
 #######################################################################################################################
 
 
@@ -2896,6 +2910,23 @@ def update_media_table(db):
     # Add chunking_status column if it doesn't exist
     add_missing_column_if_not_exists(db, 'Media', 'chunking_status', "TEXT DEFAULT 'pending'")
 
+# Vector check FIXME/Delete later
+def alter_media_table(db):
+    alter_query = '''
+    ALTER TABLE Media ADD COLUMN vector_processing INTEGER DEFAULT 0
+    '''
+    try:
+        db.execute_query(alter_query)
+        logging.info("Media table altered successfully to include vector_processing column.")
+    except Exception as e:
+        logging.error(f"Error altering Media table: {str(e)}")
+        # If the column already exists, SQLite will throw an error, which we can safely ignore
+        if "duplicate column name" not in str(e).lower():
+            raise
+
+# Vector check FIXME/Delete later
+alter_media_table(db)
+
 #
 # End of Functions to manage media chunks
 #######################################################################################################################

App_Function_Libraries/Gradio_UI/Chat_ui.py

@@ -214,7 +214,7 @@ def create_chat_interface():
                                                  value="You are a helpful AI assitant",
                                                  lines=3,
                                                  visible=False)
-            with gr.Column():
+            with gr.Column(scale=2):
                 chatbot = gr.Chatbot(height=600, elem_classes="chatbot-container")
                 msg = gr.Textbox(label="Enter your message")
                 submit = gr.Button("Submit")

App_Function_Libraries/Gradio_UI/Embeddings_tab.py

@@ -7,14 +7,17 @@
 #
 # External Imports
 import gradio as gr
+from tqdm import tqdm
 
-from App_Function_Libraries.Chunk_Lib import improved_chunking_process, determine_chunk_position
+from App_Function_Libraries.Chunk_Lib import improved_chunking_process, chunk_for_embedding
 #
 # 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, \
-    store_in_chroma
-from App_Function_Libraries.RAG.Embeddings_Create import create_embedding
+    store_in_chroma, situate_context
+from App_Function_Libraries.RAG.Embeddings_Create import create_embedding, create_embeddings_batch
+
+
 #
 ########################################################################################################################
 #
@@ -174,17 +177,23 @@ def create_view_embeddings_tab():
                     value="words"
                 )
                 max_chunk_size = gr.Slider(
-                    minimum=1, maximum=8000, step=1, value=500,
+                    minimum=1, maximum=8000, step=5, value=500,
                     label="Max Chunk Size"
                 )
                 chunk_overlap = gr.Slider(
-                    minimum=0, maximum=5000, step=1, value=200,
+                    minimum=0, maximum=5000, step=5, value=200,
                     label="Chunk Overlap"
                 )
                 adaptive_chunking = gr.Checkbox(
                     label="Use Adaptive Chunking",
                     value=False
                 )
+                contextual_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 Contextualized Embeddings",
+                    value="OpenAI"
+                )
+                contextual_api_key = gr.Textbox(label="API Key", lines=1)
 
         def get_items_with_embedding_status():
             try:
@@ -242,7 +251,7 @@ def check_embedding_status(selected_item, item_mapping):
                 logging.error(f"Error in check_embedding_status: {str(e)}")
                 return f"Error processing item: {selected_item}. Details: {str(e)}", "", ""
 
-        def create_new_embedding_for_item(selected_item, provider, model, api_url, method, max_size, overlap, adaptive, item_mapping):
+        def create_new_embedding_for_item(selected_item, provider, model, api_url, method, max_size, overlap, adaptive, item_mapping, contextual_api_choice=None):
             if not selected_item:
                 return "Please select an item", "", ""
 
@@ -263,31 +272,30 @@ def create_new_embedding_for_item(selected_item, provider, model, api_url, metho
                     'adaptive': adaptive
                 }
 
-                chunks = improved_chunking_process(item['content'], chunk_options)
+                logging.info(f"Chunking content for item: {item['title']} (ID: {item_id})")
+                chunks = chunk_for_embedding(item['content'], item['title'], chunk_options)
                 collection_name = "all_content_embeddings"
                 collection = chroma_client.get_or_create_collection(name=collection_name)
 
                 # Delete existing embeddings for this item
                 existing_ids = [f"doc_{item_id}_chunk_{i}" for i in range(len(chunks))]
                 collection.delete(ids=existing_ids)
+                logging.info(f"Deleted {len(existing_ids)} existing embeddings for item {item_id}")
 
-                for i, chunk in enumerate(chunks):
+                texts, ids, metadatas = [], [], []
+                chunk_count = 0
+                logging.info("Generating contextual summaries and preparing chunks for embedding")
+                for i, chunk in tqdm(enumerate(chunks), total=len(chunks), desc="Processing chunks"):
                     chunk_text = chunk['text']
                     chunk_metadata = chunk['metadata']
-                    chunk_position = determine_chunk_position(chunk_metadata['relative_position'])
-
-                    chunk_header = f"""
-                    Original Document: {item['title']}
-                    Chunk: {i + 1} of {len(chunks)}
-                    Position: {chunk_position}
-                    Header: {chunk_metadata.get('header_text', 'N/A')}
-        
-                    --- Chunk Content ---
-                    """
-
-                    full_chunk_text = chunk_header + chunk_text
+                    if chunk_count == 0:
+                        chunk_count = 1
+                    # Generate contextual summary
+                    logging.debug(f"Generating contextual summary for chunk {chunk_count}")
+                    context = situate_context(contextual_api_choice, item['content'], chunk_text)
+                    contextualized_text = f"{chunk_text}\n\nContextual Summary: {context}"
+
                     chunk_id = f"doc_{item_id}_chunk_{i}"
-                    embedding = create_embedding(full_chunk_text, provider, model, api_url)
                     metadata = {
                         "media_id": str(item_id),
                         "chunk_index": i,
@@ -298,13 +306,26 @@ def create_new_embedding_for_item(selected_item, provider, model, api_url, metho
                         "adaptive_chunking": adaptive,
                         "embedding_model": model,
                         "embedding_provider": provider,
+                        "original_text": chunk_text,
+                        "contextual_summary": context,
                         **chunk_metadata
                     }
-                    store_in_chroma(collection_name, [full_chunk_text], [embedding], [chunk_id], [metadata])
 
-                embedding_preview = str(embedding[:50])
-                status = f"New embeddings created and stored for item: {item['title']} (ID: {item_id})"
-                return status, f"First 50 elements of new embedding:\n{embedding_preview}", json.dumps(metadata, indent=2)
+                    texts.append(contextualized_text)
+                    ids.append(chunk_id)
+                    metadatas.append(metadata)
+                    chunk_count = chunk_count+1
+
+                # Create embeddings in batch
+                logging.info(f"Creating embeddings for {len(texts)} chunks")
+                embeddings = create_embeddings_batch(texts, provider, model, api_url)
+
+                # Store in Chroma
+                store_in_chroma(collection_name, texts, embeddings, ids, metadatas)
+
+                embedding_preview = str(embeddings[0][:50]) if embeddings else "No embeddings created"
+                status = f"New contextual embeddings created and stored for item: {item['title']} (ID: {item_id})"
+                return status, f"First 50 elements of new embedding:\n{embedding_preview}", json.dumps(metadatas[0], indent=2)
             except Exception as e:
                 logging.error(f"Error in create_new_embedding_for_item: {str(e)}")
                 return f"Error creating embedding: {str(e)}", "", ""
@@ -321,7 +342,7 @@ def create_new_embedding_for_item(selected_item, provider, model, api_url, metho
         create_new_embedding_button.click(
             create_new_embedding_for_item,
             inputs=[item_dropdown, embedding_provider, embedding_model, embedding_api_url,
-                    chunking_method, max_chunk_size, chunk_overlap, adaptive_chunking, item_mapping],
+                    chunking_method, max_chunk_size, chunk_overlap, adaptive_chunking, item_mapping, contextual_api_choice],
             outputs=[embedding_status, embedding_preview, embedding_metadata]
         )
         embedding_provider.change(

App_Function_Libraries/Gradio_UI/RAG_QA_Chat_tab.py

@@ -15,7 +15,7 @@
 from App_Function_Libraries.Books.Book_Ingestion_Lib import read_epub
 from App_Function_Libraries.DB.DB_Manager import DatabaseError, get_paginated_files, add_media_with_keywords
 from App_Function_Libraries.PDF.PDF_Ingestion_Lib import extract_text_and_format_from_pdf
-from App_Function_Libraries.RAG.RAG_Libary_2 import generate_answer
+from App_Function_Libraries.RAG.RAG_Libary_2 import generate_answer, enhanced_rag_pipeline
 from App_Function_Libraries.RAG.RAG_QA_Chat import search_database, rag_qa_chat
 # Eventually... FIXME
 from App_Function_Libraries.RAG.RAG_QA_Chat import load_chat_history, save_chat_history
@@ -31,9 +31,9 @@ def create_rag_qa_chat_tab():
         with gr.Row():
             with gr.Column(scale=1):
                 context_source = gr.Radio(
-                    ["Existing File", "Search Database", "Upload File"],
+                    ["All Files in the Database", "Search Database", "Upload File"],
                     label="Context Source",
-                    value="Existing File"
+                    value="All Files in the Database"
                 )
                 existing_file = gr.Dropdown(label="Select Existing File", choices=[], interactive=True)
                 file_page = gr.State(value=1)
@@ -127,9 +127,10 @@ def rag_qa_chat_wrapper(message, history, context_source, existing_file, search_
                     rephrased_question = message
                     logging.info(f"First question, no rephrasing: {message}")
 
-                if context_source == "Existing File":
-                    context = f"media_id:{existing_file.split('(ID: ')[1][:-1]}"
-                    logging.info(f"Using existing file with context: {context}")
+                if context_source == "All Files in the Database":
+                    # Use the enhanced_rag_pipeline to search the entire database
+                    context = enhanced_rag_pipeline(rephrased_question, api_choice)
+                    logging.info(f"Using enhanced_rag_pipeline for database search")
                 elif context_source == "Search Database":
                     context = f"media_id:{search_results.split('(ID: ')[1][:-1]}"
                     logging.info(f"Using search result with context: {context}")

App_Function_Libraries/Gradio_UI/Search_Tab.py

@@ -66,6 +66,8 @@ def update_content_for_version(selected_item, item_mapping, selected_version):
     return "", "", ""
 
 def format_as_html(content, title):
+    if content is None:
+        content = "No content available"
     escaped_content = html.escape(content)
     formatted_content = escaped_content.replace('\n', '<br>')
     return f"""
@@ -79,9 +81,9 @@ def format_as_html(content, title):
 
 def create_search_tab():
     with gr.TabItem("Search / Detailed View"):
+        gr.Markdown("# Search across all ingested items in the Database")
         with gr.Row():
             with gr.Column(scale=1):
-                gr.Markdown("# Search across all ingested items in the Database")
                 gr.Markdown("by Title / URL / Keyword / or Content via SQLite Full-Text-Search")
                 search_query_input = gr.Textbox(label="Search Query", placeholder="Enter your search query here...")
                 search_type_input = gr.Radio(choices=["Title", "URL", "Keyword", "Content"], value="Title",