diff --git "a/eval/e5connector.ipynb" "b/eval/e5connector.ipynb" new file mode 100644--- /dev/null +++ "b/eval/e5connector.ipynb" @@ -0,0 +1,411 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 109, + "id": "e88b5452-f21c-4f4c-8760-30294ebd3292", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain.document_loaders import TextLoader\n", + "from langchain.vectorstores import Chroma\n", + "import os\n", + "from langchain.document_loaders import DirectoryLoader\n", + "from dotenv import load_dotenv\n", + "from langchain.llms import OpenAI\n", + "from langchain.embeddings.openai import OpenAIEmbeddings\n", + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.chains import RetrievalQA\n", + "from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings\n", + "import openai" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "8fcacf3d-6497-447b-a0d5-99100f251efd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "page_content='Iron Pillar and Uncorrelated Ventures led the round, with participation from existing investors Nexus Venture Partners, Chiratae Ventures and Next47. CEO and founder Nitin Jayakrishnan says that the new capital will be put toward expanding Pando’s global sales, marketing and delivery capabilities.' metadata={'source': 'new_articles/05-03-ai-powered-supply-chain-startup-pando-lands-30m-investment.txt'}\n" + ] + } + ], + "source": [ + "loader = DirectoryLoader('./new_articles/', glob=\"./*.txt\", loader_cls=TextLoader)\n", + "documents = loader.load()\n", + "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=200)\n", + "texts = text_splitter.split_documents(documents)\n", + "print(texts[1]) " + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "2823e7a7-c133-4055-b8dc-b60752eb5c89", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" + ] + } + ], + "source": [ + "print(len(texts))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f75e34ce-e644-4b51-9db3-93a073685341", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "b4ecc980-7833-4bb6-b41e-4b265e950933", + "metadata": {}, + "outputs": [], + "source": [ + "metadata_list = [doc.metadata for doc in texts]\n", + "text_content_list = [doc.page_content for doc in texts]" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "14c492ac-357e-41f7-a2cb-86eec63e401c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(type(text_content_list[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "c335b748-4e5a-4f4c-a14b-f11561c549a5", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_ids(i):\n", + " ids = []\n", + " for a in range(0, i):\n", + " id = \"doc\" + str(a) \n", + " ids.append(id)\n", + " return ids\n", + "\n", + "id_list=generate_ids(len(texts))" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "8c501c35-0790-4d3b-95fe-1d6ec2d84228", + "metadata": {}, + "outputs": [], + "source": [ + "# from gradio_client import Client\n", + "# client = Client(\"https://tonic-e5.hf.space/--replicas/h4q86/\")\n", + "# result = client.predict(\n", + "# \t\t\"ArguAna\",\t# Literal['ArguAna', 'ClimateFEVER', 'DBPedia', 'FEVER', 'FiQA2018', 'HotpotQA', 'MSMARCO', 'NFCorpus', 'NQ', 'QuoraRetrieval', 'SCIDOCS', 'SciFact', 'Touche2020', 'TRECCOVID'] in 'Select a Task' Dropdown component\n", + "# \t\ttext_content_list[1],\t# str in '📖Input Text' Textbox component\n", + "# \t\tapi_name=\"/generate_and_format_embeddings\"\n", + "# )\n", + "# print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "2f7c4796-5656-4074-b5dc-3fc2042926ae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4096\n" + ] + } + ], + "source": [ + "print(len(result['data'][0]['embedding'][0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "e645ebd5-8988-439c-98ec-eaa6c2fa8346", + "metadata": {}, + "outputs": [], + "source": [ + "from chromadb import Documents, EmbeddingFunction, Embeddings\n", + "\n", + "class E5EmbeddingFunction(EmbeddingFunction):\n", + " def __init__(self, api_link: str, model_name: str):\n", + " if not api_link:\n", + " raise ValueError(\"Please provide a api end point.\")\n", + "\n", + " if not model_name:\n", + " raise ValueError(\"Please provide the model name.\")\n", + " self._api_link = api_link\n", + " self._model_name = model_name\n", + "\n", + " \n", + " def __call__(self, input: Documents) -> Embeddings:\n", + " embeddings = []\n", + " for text in input:\n", + " try:\n", + " from gradio_client import Client\n", + " client = Client(self._api_link)\n", + " \n", + " response = client.predict(\n", + " self._model_name,\n", + " text, \n", + " api_name=\"/generate_and_format_embeddings\"\n", + " )\n", + " \n", + " # Extracting the embeddings from the response\n", + " embeddings_data = response.get('data', [])\n", + " if embeddings_data:\n", + " embedding_list = embeddings_data[0].get('embedding', None)\n", + " print(\"Embeddings:\", embedding_list)\n", + "\n", + " if embedding_list is None:\n", + " print(\"Warning: Gradio client returned None.\")\n", + " return None \n", + "\n", + " # Extending the embeddings list with the nested embeddings\n", + " embeddings.extend(embedding_list)\n", + " else:\n", + " print(\"Warning: 'data' not found in response.\")\n", + " return None\n", + " except Exception as e:\n", + " print(f\"Error in E5EmbeddingFunction: {e}\")\n", + " return None\n", + "\n", + " return embeddings\n" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "78c9ef12-f7a7-463a-a2c9-dbf864fde1e7", + "metadata": {}, + "outputs": [], + "source": [ + "embedding_function= embedding_functions.OpenAIEmbeddingFunction(\n", + " api_key=\"None\",\n", + " api_base=\"https://tonic-e5.hf.space/--replicas/ax7dg/generate_and_format_embeddings\",\n", + " api_type=\"None\",\n", + " api_version=\"123\",\n", + " model_name=\"ClimateFEVER\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2bffa27-95e1-40f3-a647-651538832341", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "4f61eeb4-1d2d-4691-b3ff-e067e5aca3d8", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "type object 'EmbeddingFunction' has no attribute 'E5EmbeddingFunction'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[127], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mchromadb\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01membedding_functions\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01membedding_functions\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m embedding_function \u001b[38;5;241m=\u001b[39m \u001b[43mEmbeddingFunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mE5EmbeddingFunction\u001b[49m(api_link\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://tonic-e5.hf.space/--replicas/ax7dg/generate_and_format_embeddings\u001b[39m\u001b[38;5;124m\"\u001b[39m, model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDBPedia\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: type object 'EmbeddingFunction' has no attribute 'E5EmbeddingFunction'" + ] + } + ], + "source": [ + "import chromadb.utils.embedding_functions as embedding_functions\n", + "embedding_function = EmbeddingFunction.E5EmbeddingFunction(api_link=\"https://tonic-e5.hf.space/--replicas/ax7dg/\", model=\"DBPedia\")" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "aed98dc5-74bc-4de6-94e8-ca54c0f360b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No collection found\n" + ] + } + ], + "source": [ + "collection_name = \"texts\"\n", + "existing_collections = [collection.name for collection in chroma_client.list_collections()]\n", + "\n", + "if collection_name in existing_collections:\n", + " chroma_client.delete_collection(collection_name)\n", + " print(f\"Info: Existing collection '{collection_name}' deleted.\")\n", + "else:\n", + " print(\"No collection found\")" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "90512a49-af0c-4d85-a39b-673b55d1a6ae", + "metadata": {}, + "outputs": [ + { + "ename": "NotFoundError", + "evalue": "Error code: 404 - {'detail': 'Not Found'}", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[131], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m vector_store \u001b[38;5;241m=\u001b[39m chroma_client\u001b[38;5;241m.\u001b[39mget_or_create_collection(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtexts\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 2\u001b[0m embedding_function\u001b[38;5;241m=\u001b[39membedding_function)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mvector_store\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mid_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdocuments\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtext_content_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmetadatas\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata_list\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/chromadb/api/models/Collection.py:154\u001b[0m, in \u001b[0;36mCollection.add\u001b[0;34m(self, ids, embeddings, metadatas, documents, images, uris)\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m embeddings \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 152\u001b[0m \u001b[38;5;66;03m# At this point, we know that one of documents or images are provided from the validation above\u001b[39;00m\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m documents \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 154\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_embed\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdocuments\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m images \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 156\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_embed(\u001b[38;5;28minput\u001b[39m\u001b[38;5;241m=\u001b[39mimages)\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/chromadb/api/models/Collection.py:633\u001b[0m, in \u001b[0;36mCollection._embed\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 628\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_embedding_function \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 629\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 630\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou must provide an embedding function to compute embeddings.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 631\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://docs.trychroma.com/embeddings\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 632\u001b[0m )\n\u001b[0;32m--> 633\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_embedding_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/chromadb/utils/embedding_functions.py:177\u001b[0m, in \u001b[0;36mOpenAIEmbeddingFunction.__call__\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;66;03m# Call the OpenAI Embedding API\u001b[39;00m\n\u001b[1;32m 176\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_v1:\n\u001b[0;32m--> 177\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 178\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_deployment_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_model_name\u001b[49m\n\u001b[1;32m 179\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdata\n\u001b[1;32m 181\u001b[0m \u001b[38;5;66;03m# Sort resulting embeddings by index\u001b[39;00m\n\u001b[1;32m 182\u001b[0m sorted_embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(\n\u001b[1;32m 183\u001b[0m embeddings, key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m e: e\u001b[38;5;241m.\u001b[39mindex\n\u001b[1;32m 184\u001b[0m ) \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/openai/resources/embeddings.py:106\u001b[0m, in \u001b[0;36mEmbeddings.create\u001b[0;34m(self, input, model, encoding_format, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m 100\u001b[0m embedding\u001b[38;5;241m.\u001b[39membedding \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfrombuffer( \u001b[38;5;66;03m# type: ignore[no-untyped-call]\u001b[39;00m\n\u001b[1;32m 101\u001b[0m base64\u001b[38;5;241m.\u001b[39mb64decode(data), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfloat32\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 102\u001b[0m )\u001b[38;5;241m.\u001b[39mtolist()\n\u001b[1;32m 104\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\n\u001b[0;32m--> 106\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 107\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/embeddings\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membedding_create_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEmbeddingCreateParams\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 110\u001b[0m \u001b[43m \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 111\u001b[0m \u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 112\u001b[0m \u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 113\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 114\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_parser\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 115\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 116\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCreateEmbeddingResponse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 117\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/openai/_base_client.py:1088\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m 1074\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[1;32m 1075\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1076\u001b[0m path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1083\u001b[0m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1084\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m 1085\u001b[0m opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[1;32m 1086\u001b[0m method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[1;32m 1087\u001b[0m )\n\u001b[0;32m-> 1088\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/openai/_base_client.py:853\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m 844\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\n\u001b[1;32m 845\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 846\u001b[0m cast_to: Type[ResponseT],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 851\u001b[0m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 852\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[0;32m--> 853\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 854\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 855\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 856\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 857\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 858\u001b[0m \u001b[43m \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/openai/_base_client.py:930\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m 927\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mis_closed:\n\u001b[1;32m 928\u001b[0m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m--> 930\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 932\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[1;32m 933\u001b[0m cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[1;32m 934\u001b[0m options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 937\u001b[0m stream_cls\u001b[38;5;241m=\u001b[39mstream_cls,\n\u001b[1;32m 938\u001b[0m )\n", + "\u001b[0;31mNotFoundError\u001b[0m: Error code: 404 - {'detail': 'Not Found'}" + ] + } + ], + "source": [ + "vector_store = chroma_client.get_or_create_collection(name=\"texts\",\n", + " embedding_function=embedding_function)\n", + "vector_store.add(ids=id_list, documents=text_content_list,metadatas=metadata_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "fd49d201-cd25-494b-bcf0-ec9691943227", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded as API: https://tonic-e5.hf.space/--replicas/ax7dg/ ✔\n", + "Embeddings: [[0.01154327392578125, 0.0170135498046875, -0.007244110107421875, 0.005615234375, 0.006069183349609375, -0.025238037109375, -0.0026092529296875, 0.03656005859375, 0.019561767578125, -0.01270294189453125, -0.0009469985961914062, 0.0004642009735107422, 0.0227508544921875, -0.0011005401611328125, 0.01023101806640625, -0.028900146484375, 0.00013828277587890625, 0.010498046875, 0.00547027587890625, -0.02496337890625, -0.0017957687377929688, -0.03411865234375, -0.001293182373046875, 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'new_articles/05-03-ai-powered-supply-chain-startup-pando-lands-30m-investment.txt'}, {'source': 'new_articles/05-03-ai-powered-supply-chain-startup-pando-lands-30m-investment.txt'}]], 'embeddings': None, 'documents': [['Iron Pillar and Uncorrelated Ventures led the round, with participation from existing investors Nexus Venture Partners, Chiratae Ventures and Next47. CEO and founder Nitin Jayakrishnan says that the new capital will be put toward expanding Pando’s global sales, marketing and delivery capabilities.', '“We will not expand into new industries or adjacent product areas,” he told TechCrunch in an email interview. “Great talent is the foundation of the business — we will continue to augment our teams at all levels of the organization. Pando is also open to exploring strategic partnerships and acquisitions with this round of funding.”']], 'uris': None, 'data': None}\n" + ] + } + ], + "source": [ + "results = vector_store.query(\n", + " query_texts=\"Who is Nitin Jayakrishnan\",\n", + " n_results=2\n", + ")\n", + "print(results)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "b69719cc-c5a9-4ae0-a6b9-6227c88959a4", + "metadata": {}, + "outputs": [], + "source": [ + "load_dotenv()\n", + "OPENAI_API_KEY= os.getenv(\"OPENAI_API_KEY\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "1086b696-b020-4bf1-83d3-1d0978c580d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nitin Jayakrishnan is the CEO and founder of Pando, an AI-powered supply chain startup.\n" + ] + } + ], + "source": [ + "query=\"Who is Nitin Jayakrishnan\"\n", + "openai.api_key = OPENAI_API_KEY\n", + "completion = openai.chat.completions.create(\n", + "model=\"gpt-3.5-turbo\",\n", + "temperature=0,\n", + "messages=\n", + "[\n", + " {\"role\": \"user\",\n", + " \"content\": \n", + " f\"We have provided context information below. \\n\"\n", + " f\"---------------------\\n\"\n", + " f\"{results}\"\n", + " f\"\\n---------------------\\n\"\n", + " f\"Given this information, please answer the question: {query}\"\n", + " }\n", + "]\n", + ").choices[0].message.content\n", + "print(completion)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "065bb651-b5be-4647-8fdf-4020a13d8d37", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}