feat: Chunk visual

.env.example

@@ -5,4 +5,4 @@ COHERE_API_KEY=
 # Optional for walkthrough
 PINECONE_API_KEY=
 PINECONE_HOST=
-PINECONE_INDEX=
+PINECONE_INDEX=

dev/embedding.ipynb

@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from models.file import File, FileType\n",
+    "from service.embedding import EmbeddingService\n",
+    "from termcolor import colored\n",
+    "\n",
+    "PINECONE_INDEX = os.getenv(\"PINECONE_INDEX\", \"\")\n",
+    "PINECONE_API_KEY = os.getenv(\"PINECONE_API_KEY\", \"\")\n",
+    "PINECONE_HOST = os.getenv(\"PINECONE_HOST\", \"\")\n",
+    "\n",
+    "file = File(\n",
+    "    type=FileType.pdf,\n",
+    "    url=\"https://arxiv.org/pdf/2402.05131.pdf\"\n",
+    ")\n",
+    "vector_credentials = {\n",
+    "        \"type\": \"pinecone\",\n",
+    "        \"config\": {\n",
+    "            \"api_key\": PINECONE_API_KEY,\n",
+    "            \"host\": PINECONE_HOST,\n",
+    "        }\n",
+    "    },\n",
+    "\n",
+    "embedding_service = EmbeddingService(\n",
+    "        files=[file],\n",
+    "        index_name=PINECONE_INDEX,\n",
+    "        vector_credentials=vector_credentials\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = await embedding_service.generate_documents()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chunks = await embedding_service.generate_chunks(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = ['red', 'green', 'yellow', 'blue', 'magenta', 'cyan', 'white']\n",
+    "\n",
+    "concatenated_document = \"\"\n",
+    "\n",
+    "for i, chunk in enumerate(chunks):\n",
+    "    color = colors[i % len(colors)]\n",
+    "    colored_text = colored(chunk.text, color)\n",
+    "    print(colored_text)\n",
+    "    concatenated_document += chunk.text + \" \"\n",
+    "\n",
+    "print(\"\\nConcatenated Document:\\n\", concatenated_document)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

models/ingest.py

@@ -7,15 +7,13 @@
 from models.vector_database import VectorDatabase
 
 
-# Step 1: Define the Encoder Enum
 class EncoderEnum(str, Enum):
     cohere = "cohere"
     openai = "openai"
     huggingface = "huggingface"
     fastembed = "fastembed"
 
 
-# Step 2: Use the Enum in RequestPayload
 class RequestPayload(BaseModel):
     files: List[File]
     encoder: EncoderEnum

pyproject.toml

@@ -33,6 +33,9 @@ python-dotenv = "^1.0.1"
 [tool.poetry.extras]
 fastembed = ["fastembed"]
 
+[tool.poetry.group.dev.dependencies]
+termcolor = "^2.4.0"
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"