App_Function_Libraries/RAG/ChromaDB_Library.py

# ChromaDB_Library.py
# Description: Functions for managing embeddings in ChromaDB
#
# Imports:
import logging
from typing import List, Dict, Any
# 3rd-Party Imports:
import chromadb
from chromadb import Settings
from itertools import islice
#
# Local Imports:
from App_Function_Libraries.Chunk_Lib import chunk_for_embedding, chunk_options
from App_Function_Libraries.DB.DB_Manager import get_unprocessed_media, mark_media_as_processed
from App_Function_Libraries.DB.SQLite_DB import process_chunks
from App_Function_Libraries.RAG.Embeddings_Create import create_embeddings_batch
# FIXME - related to Chunking
from App_Function_Libraries.RAG.Embeddings_Create import create_embedding
from App_Function_Libraries.Summarization.Summarization_General_Lib import summarize
from App_Function_Libraries.Utils.Utils import get_database_path, ensure_directory_exists, \
    load_comprehensive_config
#
#######################################################################################################################
#
# Config Settings for ChromaDB Functions
#
# FIXME - Refactor so that all globals are set in summarize.py
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
#
# Load config
config = load_comprehensive_config()
#
# ChromaDB settings
chroma_db_path = config.get('Database', 'chroma_db_path', fallback=get_database_path('chroma_db'))
ensure_directory_exists(chroma_db_path)
chroma_client = chromadb.PersistentClient(path=chroma_db_path, settings=Settings(anonymized_telemetry=False))
#
# Embedding settings
embedding_provider = config.get('Embeddings', 'embedding_provider', fallback='openai')
embedding_model = config.get('Embeddings', 'embedding_model', fallback='text-embedding-3-small')
embedding_api_key = config.get('Embeddings', 'api_key', fallback='')
embedding_api_url = config.get('Embeddings', 'api_url', fallback='')
#
# End of Config Settings
#######################################################################################################################
#
# Functions:


# Function to preprocess and store all existing content in the database
def preprocess_all_content(database, create_contextualized=True, api_name="gpt-3.5-turbo"):
    unprocessed_media = get_unprocessed_media(db=database)
    total_media = len(unprocessed_media)

    for index, row in enumerate(unprocessed_media, 1):
        media_id, content, media_type, file_name = row
        collection_name = f"{media_type}_{media_id}"

        logger.info(f"Processing media {index} of {total_media}: ID {media_id}, Type {media_type}")

        try:
            process_and_store_content(
                database=database,
                content=content,
                collection_name=collection_name,
                media_id=media_id,
                file_name=file_name or f"{media_type}_{media_id}",
                create_embeddings=True,
                create_contextualized=create_contextualized,
                api_name=api_name
            )

            # Mark the media as processed in the database
            mark_media_as_processed(database, media_id)

            logger.info(f"Successfully processed media ID {media_id}")
        except Exception as e:
            logger.error(f"Error processing media ID {media_id}: {str(e)}")

    logger.info("Finished preprocessing all unprocessed content")


def batched(iterable, n):
    "Batch data into lists of length n. The last batch may be shorter."
    it = iter(iterable)
    while True:
        batch = list(islice(it, n))
        if not batch:
            return
        yield batch


def situate_context(api_name, doc_content: str, chunk_content: str) -> str:
    doc_content_prompt = f"""

    <document>

    {doc_content}

    </document>

    """

    chunk_context_prompt = f"""

    \n\n\n\n\n

    Here is the chunk we want to situate within the whole document

    <chunk>

    {chunk_content}

    </chunk>



    Please give a short succinct context to situate this chunk within the overall document for the purposes of improving search retrieval of the chunk.

    Answer only with the succinct context and nothing else.

    """

    response = summarize(chunk_context_prompt, doc_content_prompt, api_name, api_key=None, temp=0, system_message=None)
    return response


# FIXME - update all uses to reflect 'api_name' parameter
def process_and_store_content(database, content: str, collection_name: str, media_id: int, file_name: str,

                              create_embeddings: bool = True, create_contextualized: bool = True, api_name: str = "gpt-3.5-turbo",

                              chunk_options = None, embedding_provider: str = None,

                              embedding_model: str = None, embedding_api_url: str = None):
    try:
        logger.info(f"Processing content for media_id {media_id} in collection {collection_name}")

        chunks = chunk_for_embedding(content, file_name, chunk_options)

        # Process chunks synchronously
        process_chunks(database, chunks, media_id)

        if create_embeddings:
            texts = []
            contextualized_chunks = []
            for chunk in chunks:
                chunk_text = chunk['text']
                if create_contextualized:
                    context = situate_context(api_name, content, chunk_text)
                    contextualized_text = f"{chunk_text}\n\nContextual Summary: {context}"
                    contextualized_chunks.append(contextualized_text)
                else:
                    contextualized_chunks.append(chunk_text)
                texts.append(chunk_text)  # Store original text for database

            embeddings = create_embeddings_batch(contextualized_chunks, embedding_provider, embedding_model, embedding_api_url)
            ids = [f"{media_id}_chunk_{i}" for i in range(1, len(chunks) + 1)]
            metadatas = [{
                "media_id": str(media_id),
                "chunk_index": i,
                "total_chunks": len(chunks),
                "start_index": int(chunk['metadata']['start_index']),
                "end_index": int(chunk['metadata']['end_index']),
                "file_name": str(chunk['metadata']['file_name']),
                "relative_position": float(chunk['metadata']['relative_position']),
                "contextualized": create_contextualized,
                "original_text": chunk['text'],
                "contextual_summary": contextualized_chunks[i-1].split("\n\nContextual Summary: ")[-1] if create_contextualized else ""
            } for i, chunk in enumerate(chunks, 1)]

            store_in_chroma(collection_name, contextualized_chunks, embeddings, ids, metadatas)

            # Mark the media as processed
            mark_media_as_processed(database, media_id)

        # Update full-text search index
        database.execute_query(
            "INSERT OR REPLACE INTO media_fts (rowid, title, content) SELECT id, title, content FROM Media WHERE id = ?",
            (media_id,)
        )

        logger.info(f"Finished processing and storing content for media_id {media_id}")

    except Exception as e:
        logger.error(f"Error in process_and_store_content for media_id {media_id}: {str(e)}")
        raise

# Usage example:
# process_and_store_content(db, content, "my_collection", 1, "example.txt", create_embeddings=True, create_summary=True, api_name="gpt-3.5-turbo")


def check_embedding_status(selected_item, 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}", ""

        item_title = selected_item.rsplit(' (', 1)[0]
        collection = chroma_client.get_or_create_collection(name="all_content_embeddings")

        result = collection.get(ids=[f"doc_{item_id}"], include=["embeddings", "metadatas"])
        logging.info(f"ChromaDB result for item '{item_title}' (ID: {item_id}): {result}")

        if not result['ids']:
            return f"No embedding found for item '{item_title}' (ID: {item_id})", ""

        if not result['embeddings'] or not result['embeddings'][0]:
            return f"Embedding data missing for item '{item_title}' (ID: {item_id})", ""

        embedding = result['embeddings'][0]
        metadata = result['metadatas'][0] if result['metadatas'] else {}
        embedding_preview = str(embedding[:50])
        status = f"Embedding exists for item '{item_title}' (ID: {item_id})"
        return status, f"First 50 elements of embedding:\n{embedding_preview}\n\nMetadata: {metadata}"

    except Exception as e:
        logging.error(f"Error in check_embedding_status: {str(e)}")
        return f"Error processing item: {selected_item}. Details: {str(e)}", ""

def reset_chroma_collection(collection_name: str):
    try:
        chroma_client.delete_collection(collection_name)
        chroma_client.create_collection(collection_name)
        logging.info(f"Reset ChromaDB collection: {collection_name}")
    except Exception as e:
        logging.error(f"Error resetting ChromaDB collection: {str(e)}")


def store_in_chroma(collection_name: str, texts: List[str], embeddings: List[List[float]], ids: List[str], metadatas: List[Dict[str, Any]]):
    try:
        collection = chroma_client.get_or_create_collection(name=collection_name)

        # Log the inputs for debugging
        logging.debug(f"Storing in ChromaDB - Collection: {collection_name}")
        logging.debug(f"Texts (first 100 chars): {texts[0][:100]}...")
        logging.debug(f"Embeddings (first 5 values): {embeddings[0][:5]}")
        logging.debug(f"IDs: {ids}")
        logging.debug(f"Metadatas: {metadatas}")

        # Use upsert instead of add/update
        collection.upsert(
            documents=texts,
            embeddings=embeddings,
            ids=ids,
            metadatas=metadatas
        )

        # Verify storage
        for doc_id in ids:
            result = collection.get(ids=[doc_id], include=["documents", "embeddings", "metadatas"])
            if not result['embeddings'] or result['embeddings'][0] is None:
                logging.error(f"Failed to store embedding for {doc_id}")
            else:
                logging.info(f"Embedding stored successfully for {doc_id}")
                logging.debug(f"Stored document: {result['documents'][0][:100]}...")
                logging.debug(f"Stored metadata: {result['metadatas'][0]}")

    except Exception as e:
        logging.error(f"Error storing embeddings in ChromaDB: {str(e)}")
        raise


# Function to perform vector search using ChromaDB + Keywords from the media_db
def vector_search(collection_name: str, query: str, k: int = 10) -> List[Dict[str, Any]]:
    try:
        query_embedding = create_embedding(query, embedding_provider, embedding_model, embedding_api_url)
        collection = chroma_client.get_collection(name=collection_name)
        results = collection.query(
            query_embeddings=[query_embedding],
            n_results=k,
            include=["documents", "metadatas"]
        )
        return [{"content": doc, "metadata": meta} for doc, meta in zip(results['documents'][0], results['metadatas'][0])]
    except Exception as e:
        logging.error(f"Error in vector_search: {str(e)}")
        raise

def schedule_embedding(media_id: int, content: str, media_name: str):
    try:
        chunks = chunk_for_embedding(content, media_name, chunk_options)
        texts = [chunk['text'] for chunk in chunks]
        embeddings = create_embeddings_batch(texts, embedding_provider, embedding_model, embedding_api_url)
        ids = [f"{media_id}_chunk_{i}" for i in range(len(chunks))]
        metadatas = [{
            "media_id": str(media_id),
            "chunk_index": i,
            "total_chunks": len(chunks),
            "start_index": chunk['metadata']['start_index'],
            "end_index": chunk['metadata']['end_index'],
            "file_name": media_name,
            "relative_position": chunk['metadata']['relative_position']
        } for i, chunk in enumerate(chunks)]

        store_in_chroma("all_content_embeddings", texts, embeddings, ids, metadatas)

    except Exception as e:
        logging.error(f"Error scheduling embedding for media_id {media_id}: {str(e)}")


# Function to process content, create chunks, embeddings, and store in ChromaDB and SQLite
# def process_and_store_content(content: str, collection_name: str, media_id: int):
#     # Process the content into chunks
#     chunks = improved_chunking_process(content, chunk_options)
#     texts = [chunk['text'] for chunk in chunks]
#
#     # Generate embeddings for each chunk
#     embeddings = [create_embedding(text) for text in texts]
#
#     # Create unique IDs for each chunk using the media_id and chunk index
#     ids = [f"{media_id}_chunk_{i}" for i in range(len(texts))]
#
#     # Store the texts, embeddings, and IDs in ChromaDB
#     store_in_chroma(collection_name, texts, embeddings, ids)
#
#     # Store the chunk metadata in SQLite
#     for i, chunk in enumerate(chunks):
#         add_media_chunk(media_id, chunk['text'], chunk['start'], chunk['end'], ids[i])
#
#     # Update the FTS table
#     update_fts_for_media(media_id)


#
# End of Functions for ChromaDB
#######################################################################################################################