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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import documents exported from Evernote to a vectorstore\n",
"### Use OpenAI file search with responses API\n",
"#### Prerequisite steps\n",
"* exported notes from your Evernote notebook as html \n",
"* converted the notes further to md-files and remove broken image links (use python/AI)\n",
"* the files are named with note titles\n",
"\n",
"Files are in one folder.\n",
"\n",
"\n",
"##### Query ChromaDB vectorstore\n",
"I tried to accomplish this task with RAG like the example by https://github.com/ed-donner/llm_engineering/commits?author=dinorrusso.\n",
"\n",
"I thought this to be a trivial task, but it was not 😃 That example uses Ollama running locally.\n",
"Even though the retriever had the information required, it was dropped from the answer.\n",
"\n",
"I tried then to use Chroma + OpenAI. After several attemps succeeded to create a vectorstore and query it. That's it for this time.\n",
"\n",
"##### Openai vectorstore, see bottom of the notebook\n",
"One attempt was to use OpenAI's fileSearch-tool which seemed pretty straightforward.\n",
"The con: loading files was not working always. Code is left though as reference."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Imports\n",
"from dotenv import load_dotenv\n",
"import gradio as gr\n",
"import openai\n",
"import chromadb\n",
"from chromadb.config import Settings\n",
"import os"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load files to vectorstore"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"load_dotenv(override=True)\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
"openai.api_key = os.environ['OPENAI_API_KEY']\n",
"\n",
"def chunk_text(text, max_tokens=2000):\n",
" words = text.split()\n",
" chunks = []\n",
" current_chunk = []\n",
" current_length = 0\n",
"\n",
" for word in words:\n",
" current_length += len(word) + 1 # +1 for the space\n",
" if current_length > max_tokens:\n",
" chunks.append(\" \".join(current_chunk))\n",
" current_chunk = [word]\n",
" current_length = len(word) + 1\n",
" else:\n",
" current_chunk.append(word)\n",
"\n",
" if current_chunk:\n",
" chunks.append(\" \".join(current_chunk))\n",
"\n",
" return chunks\n",
"\n",
"\n",
"# # Set up OpenAI API key\n",
"# openai.api_key = \"your_openai_api_key\" # Replace with your API key\n",
"chroma_client = chromadb.Client()\n",
"\n",
"# Create or get the existing collection\n",
"collection_name = \"EverNotes\"\n",
"\n",
"try:\n",
" existing_collection = chroma_client.get_collection(name=collection_name)\n",
" if existing_collection.count() > 0:\n",
" chroma_client.delete_collection(name=collection_name)\n",
"except:\n",
" print(f\"Collection {collection_name} does not exist. Creating a new one.\")\n",
"\n",
"# Create a collection in ChromaDB\n",
"collection = chroma_client.get_or_create_collection(name=collection_name)\n",
"\n",
"# Define your data\n",
"# it should be like this\n",
"# documents = [\"OpenAI is revolutionizing AI.\", \"ChromaDB makes embedding storage easy.\"]\n",
"# metadata = [{\"id\": 1}, {\"id\": 2}]\n",
"\n",
"folder_path = os.getenv('EVERNOTE_EXPORT')\n",
"documents = []\n",
"\n",
"for root, dirs, files in os.walk(folder_path):\n",
" for file in files:\n",
" if file.endswith('.md'): # Change this to the file extension you need\n",
" with open(os.path.join(root, file), 'r') as f:\n",
" documents.append(f.read())\n",
"\n",
"metadata = [{\"id\": i + 1} for i in range(len(documents))]\n",
"\n",
"# Generate embeddings using OpenAI\n",
"def get_embedding(text, model=\"text-embedding-ada-002\"):\n",
" response = openai.embeddings.create(input=text, model=model)\n",
" return response.data[0].embedding\n",
"\n",
"# Add documents and embeddings to ChromaDB in chunks\n",
"for doc, meta in zip(documents, metadata):\n",
" chunks = chunk_text(doc)\n",
" for chunk in chunks:\n",
" embedding = get_embedding(chunk)\n",
" collection.add(\n",
" documents=[chunk],\n",
" embeddings=[embedding],\n",
" metadatas=[meta],\n",
" ids=[str(meta[\"id\"])]\n",
" )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Query ChromaDB"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# \n",
"query_text = \"Is there a video for Fitting the Shimano speed hub 7\"\n",
"query_embedding = get_embedding(query_text)\n",
"\n",
"results = collection.query(\n",
" query_embeddings=[query_embedding],\n",
" n_results=2\n",
")\n",
"\n",
"print(\"Query Results:\", results)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Gradio interface"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Function to query ChromaDB\n",
"def query_chromadb(query_text):\n",
" query_embedding = get_embedding(query_text)\n",
" results = collection.query(\n",
" query_embeddings=[query_embedding],\n",
" n_results=2\n",
" )\n",
" return results\n",
"\n",
"# Gradio interface\n",
"def gradio_interface(query_text):\n",
" results = query_chromadb(query_text)\n",
" return results\n",
"\n",
"# Create Gradio app\n",
"iface = gr.Interface(\n",
" fn=gradio_interface,\n",
" inputs=\"text\",\n",
" outputs=\"text\",\n",
" title=\"ChromaDB Query Interface\",\n",
" description=\"Enter your query to search the ChromaDB collection.\"\n",
")\n",
"\n",
"iface.launch()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Below OpenAI filesearch variant which had some failures in file uploads."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import glob\n",
"folder_path = os.environ['EVERNOTE_EXPORT'] \n",
"# Filter out other except .md-files\n",
"md_files = glob.glob(os.path.join(folder_path, '*.md'))\n",
"file_paths = [os.path.join(folder_path, file) for file in md_files]\n",
"file_streams = [open(path, 'rb') for path in file_paths]\n",
"\n",
"# Create vector store\n",
"vector_store = openai.vector_stores.create(\n",
" name=\"Evernote notes\",\n",
")\n",
"\n",
"# Batch Upload Limit: You can upload up to 100 files in a single batch\n",
"# https://community.openai.com/t/max-100-files-in-vector-store/729876/4\n",
"batch_size = 90\n",
"for i in range(0, len(file_streams), batch_size):\n",
" batch = file_streams[i:i + batch_size]\n",
" file_batch = openai.vector_stores.file_batches.upload_and_poll(\n",
" vector_store_id=vector_store.id,\n",
" files=batch\n",
" )\n",
" print(file_batch.status)\n",
" print(file_batch.file_counts)\n",
"\n",
"# There can be some fails in file counts:\n",
"# \"FileCounts(cancelled=0, completed=89, failed=1, in_progress=0, total=90)\"\"\n",
"# Usually 1 % fails. Did not find solution for improving that yet"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"response = openai.responses.create(\n",
" model=\"gpt-4o-mini\",\n",
" input=\"Is there a video for Fitting the Shimano speed hub 7?\",\n",
" tools=[{\n",
" \"type\": \"file_search\",\n",
" \"vector_store_ids\": [vector_store.id]\n",
" }],\n",
" include=None\n",
")\n",
"print(response)"
]
}
],
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