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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Notebook for creating/updating the dense and sparse indices"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from ipynb.fs.defs.preprocess_data import preprocess_data\n",
"from ipynb.fs.defs.preprocess_data import get_documents_from_files\n",
"from ipynb.fs.defs.preprocess_data import split_docs\n",
"from ipynb.fs.defs.preprocess_data import clean_and_process_chunked_documents\n",
"from ipynb.fs.defs.preprocess_data import store_documents\n",
"import chromadb\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.docstore.document import Document\n",
"from typing import List\n",
"import os\n",
"\n",
"\n",
"def build_or_update_index_vector_db(documents: List[Document], embeddings, collection_name: str, dist_function: str, collection_metadata: dict):\n",
" '''\n",
" Builds the index vector DB from documents with the specified embeddings and collection_name\n",
" If it already exists, updates the index with the new documents\n",
" '''\n",
" new_client = chromadb.PersistentClient(path=os.environ.get(\"CHROMA_PATH\"))\n",
"\n",
" print(\"Starting to build index for: \", collection_metadata)\n",
"\n",
" # Check if collection already exists\n",
" collection_exists = True\n",
" try:\n",
" collection = new_client.get_collection(collection_name)\n",
" except ValueError as e:\n",
" collection_exists = False\n",
"\n",
" if not collection_exists:\n",
" print(\"Collection is new\")\n",
" # If collection does not exist, create it\n",
" collection = new_client.create_collection(collection_name)\n",
" # Each document needs an ID\n",
" ids = [str(i) for i in range(1, len(documents) + 1)]\n",
"\n",
" # Store the text of the document and metadata separately in order to insert it into Chroma\n",
" texts = []\n",
" metadata_docs = []\n",
" for document in documents:\n",
" texts.append(document.page_content)\n",
" metadata_docs.append(document.metadata)\n",
"\n",
" # Add them in batches (otherwise Chroma error)\n",
" for start_idx in range(0, len(embeddings), 1000):\n",
" end_idx = start_idx + 1000\n",
" # Ensure not to go out of bounds\n",
" embeddings_batch = embeddings[start_idx : min(end_idx, len(embeddings))]\n",
" texts_batch = texts[start_idx : min(end_idx, len(embeddings))]\n",
" ids_batch = ids[start_idx : min(end_idx, len(embeddings))]\n",
" metadatas_batch = metadata_docs[start_idx : min(end_idx, len(embeddings))]\n",
"\n",
" collection.add(embeddings=embeddings_batch, documents=texts_batch, ids=ids_batch, metadatas=metadatas_batch)\n",
" print(f\"Added embeddings from {start_idx} to {min(end_idx, len(embeddings))-1}\")\n",
"\n",
" vectordb = Chroma(\n",
" client=new_client,\n",
" collection_name=collection_name,\n",
" collection_metadata={\n",
" \"embedding_model_provider\": collection_metadata[\"embedding_model_provider\"],\n",
" \"embedding_model_name\": collection_metadata[\"embedding_model_name\"],\n",
" \"chunk_size\": collection_metadata[\"chunk_size\"],\n",
" \"chunk_overlap\": collection_metadata[\"chunk_overlap\"],\n",
" \"hnsw:space\": dist_function, # either \"l2\" or \"ip\" or \"cosine\"\n",
" },\n",
" )\n",
" print(f\"Collection {collection_name} successfully created.\")\n",
" print(\"There are\", vectordb._collection.count(), \"entries in the collection.\")\n",
"\n",
" return new_client, vectordb\n",
"\n",
" else:\n",
" print(\"Collection already exists\")\n",
" vectordb = Chroma(client=new_client, collection_name=collection_name)\n",
"\n",
" collection_count = vectordb._collection.count()\n",
" print(f\"There are {collection_count} entries in the collection {collection_name} prior to updating.\")\n",
"\n",
" # Continue the IDs from the last ID\n",
" ids = [str(i) for i in range(collection_count + 1, collection_count + len(documents) + 1)]\n",
" # Store the text of the document and metadata separately in order to insert it into Chroma\n",
" texts = []\n",
" metadata_docs = []\n",
" for document in documents:\n",
" texts.append(document.page_content)\n",
" metadata_docs.append(document.metadata)\n",
"\n",
" # Add them in batches (otherwise Chroma error)\n",
" for start_idx in range(0, len(embeddings), 1000):\n",
" end_idx = start_idx + 1000\n",
" # Ensure not to go out of bounds\n",
" embeddings_batch = embeddings[start_idx : min(end_idx, len(embeddings))]\n",
" texts_batch = texts[start_idx : min(end_idx, len(embeddings))]\n",
" ids_batch = ids[start_idx : min(end_idx, len(embeddings))]\n",
" metadatas_batch = metadata_docs[start_idx : min(end_idx, len(embeddings))]\n",
"\n",
" collection.add(embeddings=embeddings_batch, documents=texts_batch, ids=ids_batch, metadatas=metadatas_batch)\n",
" print(f\"Added embeddings from {start_idx} to {min(end_idx, len(embeddings))-1}\")\n",
"\n",
" collection_count = vectordb._collection.count()\n",
" print(f\"There are {collection_count} entries in the collection {collection_name} after updating.\")\n",
" return new_client, 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chunk_size = 1536\n",
"chunk_overlap = 264\n",
"# If update is needed, set to False\n",
"all_docs = True\n",
"\n",
"documents, embedding_model, embeddings = preprocess_data(chunk_size, chunk_overlap, all_docs)\n",
"collection_name = \"ISO_27001_Collection\"\n",
"collection_metadata = {\n",
"\"embedding_model_provider\": \"Fine-tuned\",\n",
"\"embedding_model_name\": \"finetuned-BGE-large-ISO-27001\",\n",
"\"chunk_size\": str(chunk_size),\n",
"\"chunk_overlap\": str(chunk_overlap),\n",
"}\n",
"\n",
"build_or_update_index_vector_db(documents, embeddings, collection_name, \"l2\", collection_metadata)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def store_documents_for_sparse_retrieval(chunk_size: int, chunk_overlap: int):\n",
" \"\"\"\n",
" Stores the documents for sparse retrieval in a basic text file\n",
" \"\"\"\n",
" documents = get_documents_from_files(True)\n",
" chunked_documents = split_docs(documents, chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n",
" chunked_cleaned_documents = clean_and_process_chunked_documents(chunked_documents)\n",
"\n",
" store_documents(chunked_cleaned_documents, f\"./../sparse_index/sparse_1536_264\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create the actual sparse index\n",
"store_documents_for_sparse_retrieval(chunk_size, chunk_overlap)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Helper methods for Chroma"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Returns the vectorDB based on the collection name if it exists\n",
"def get_index_vector_db(collection_name: str):\n",
" new_client = chromadb.PersistentClient(path=os.environ.get(\"CHROMA_PATH\"))\n",
"\n",
" # Check if collection already exists\n",
" collection_exists = True\n",
" try:\n",
" new_client.get_collection(collection_name)\n",
" except ValueError as e:\n",
" collection_exists = False\n",
"\n",
" if not collection_exists:\n",
" raise Exception(\"Error, raised exception: Collection does not exist.\")\n",
" else:\n",
" vectordb = Chroma(client=new_client, collection_name=collection_name)\n",
"\n",
" return new_client, vectordb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def delete_collection(collection_name: str):\n",
" new_client = chromadb.PersistentClient(path=os.environ.get(\"CHROMA_PATH\"))\n",
"\n",
" try:\n",
" new_client.delete_collection(collection_name)\n",
" except ValueError as e:\n",
" print(\"Collection could not be deleted.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def return_collections():\n",
" new_client = chromadb.PersistentClient(path=os.environ.get(\"CHROMA_PATH\"))\n",
" collections = new_client.list_collections()\n",
" return collections"
]
}
],
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"display_name": "venv",
"language": "python",
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