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removed cohere dependency , update codes . (#261)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -113,5 +113,4 @@ reportlab==3.6.13 | |
| easygui==0.98.3 | ||
| xhtml2pdf==0.2.11 | ||
| fastembed~=0.2.2 | ||
| cohere==5.3.2 | ||
| qdrant-client==1.8.2 | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,226 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "id": "initial_id", | ||
| "metadata": { | ||
| "collapsed": true, | ||
| "ExecuteTime": { | ||
| "end_time": "2024-05-14T22:31:25.398358Z", | ||
| "start_time": "2024-05-14T22:31:22.567855Z" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "import json\n", | ||
| "import logging\n", | ||
| "import os\n", | ||
| "\n", | ||
| "import yaml\n", | ||
| "from qdrant_client import QdrantClient\n", | ||
| "from scripts.utils.logger import init_logging_config\n" | ||
| ], | ||
| "outputs": [ | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "\u001B[32m2024-05-14 15:31:22.994\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mfastembed.embedding\u001B[0m:\u001B[36m<module>\u001B[0m:\u001B[36m7\u001B[0m - \u001B[33m\u001B[1mDefaultEmbedding, FlagEmbedding, JinaEmbedding are deprecated.Use from fastembed import TextEmbedding instead.\u001B[0m\n" | ||
| ] | ||
| } | ||
| ], | ||
| "execution_count": 1 | ||
| }, | ||
| { | ||
| "metadata": { | ||
| "ExecuteTime": { | ||
| "end_time": "2024-05-14T22:31:28.571615Z", | ||
| "start_time": "2024-05-14T22:31:28.569033Z" | ||
| } | ||
| }, | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "init_logging_config(basic_log_level=logging.INFO)\n", | ||
| "# Get the logger\n", | ||
| "logger = logging.getLogger(__name__)\n", | ||
| "\n", | ||
| "# Set the logging level\n", | ||
| "logger.setLevel(logging.INFO)" | ||
| ], | ||
| "id": "a69d1b16785bf17b", | ||
| "outputs": [], | ||
| "execution_count": 2 | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "def find_path(folder_name):\n", | ||
| " curr_dir = os.getcwd()\n", | ||
| " while True:\n", | ||
| " if folder_name in os.listdir(curr_dir):\n", | ||
| " return os.path.join(curr_dir, folder_name)\n", | ||
| " else:\n", | ||
| " parent_dir = os.path.dirname(curr_dir)\n", | ||
| " if parent_dir == \"/\":\n", | ||
| " break\n", | ||
| " curr_dir = parent_dir\n", | ||
| " raise ValueError(f\"Folder '{folder_name}' not found.\")\n", | ||
| "\n", | ||
| "\n", | ||
| "def read_config(filepath):\n", | ||
| " try:\n", | ||
| " with open(filepath) as f:\n", | ||
| " config = yaml.safe_load(f)\n", | ||
| " return config\n", | ||
| " except FileNotFoundError as e:\n", | ||
| " logger.error(f\"Configuration file {filepath} not found: {e}\")\n", | ||
| " except yaml.YAMLError as e:\n", | ||
| " logger.error(\n", | ||
| " f\"Error parsing YAML in configuration file {filepath}: {e}\", exc_info=True\n", | ||
| " )\n", | ||
| " except Exception as e:\n", | ||
| " logger.error(f\"Error reading configuration file {filepath}: {e}\")\n", | ||
| " return None\n", | ||
| "\n", | ||
| "\n", | ||
| "def read_doc(path):\n", | ||
| " with open(path) as f:\n", | ||
| " try:\n", | ||
| " data = json.load(f)\n", | ||
| " except Exception as e:\n", | ||
| " logger.error(f\"Error reading JSON file: {e}\")\n", | ||
| " data = {}\n", | ||
| " return data\n" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "ExecuteTime": { | ||
| "end_time": "2024-05-14T22:31:31.783421Z", | ||
| "start_time": "2024-05-14T22:31:31.763842Z" | ||
| } | ||
| }, | ||
| "id": "899904f870d3f720", | ||
| "outputs": [], | ||
| "execution_count": 3 | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "def get_score(resume_string, job_description_string):\n", | ||
| " logger.info(\"Started getting similarity score\")\n", | ||
| "\n", | ||
| " documents = [resume_string]\n", | ||
| " client = QdrantClient(\":memory:\")\n", | ||
| " client.set_model(\"BAAI/bge-base-en\")\n", | ||
| "\n", | ||
| " client.add(\n", | ||
| " collection_name=\"demo_collection\",\n", | ||
| " documents=documents,\n", | ||
| " )\n", | ||
| "\n", | ||
| " search_result = client.query(\n", | ||
| " collection_name=\"demo_collection\", query_text=job_description_string\n", | ||
| " )\n", | ||
| " logger.info(\"Finished getting similarity score\")\n", | ||
| " return search_result" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "ExecuteTime": { | ||
| "end_time": "2024-05-14T22:31:33.976354Z", | ||
| "start_time": "2024-05-14T22:31:33.972943Z" | ||
| } | ||
| }, | ||
| "id": "c54a86727f436747", | ||
| "outputs": [], | ||
| "execution_count": 4 | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "cwd = find_path('Resume-Matcher')\n", | ||
| "READ_RESUME_FROM = os.path.join(cwd, 'Data', 'Processed', 'Resumes')\n", | ||
| "READ_JOB_DESCRIPTION_FROM = os.path.join(cwd, 'Data', 'Processed', 'JobDescription')\n", | ||
| "config_path = os.path.join(cwd, \"scripts\", \"similarity\")" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "ExecuteTime": { | ||
| "end_time": "2024-05-14T22:31:40.945947Z", | ||
| "start_time": "2024-05-14T22:31:40.934732Z" | ||
| } | ||
| }, | ||
| "id": "ae15b96094e7c460", | ||
| "outputs": [], | ||
| "execution_count": 5 | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "# To give your custom resume use this code\n", | ||
| "resume_dict = read_config(\n", | ||
| " READ_RESUME_FROM\n", | ||
| " + \"/Resume-alfred_pennyworth_pm.pdf83632b66-5cce-4322-a3c6-895ff7e3dd96.json\"\n", | ||
| ")\n", | ||
| "job_dict = read_config(\n", | ||
| " READ_JOB_DESCRIPTION_FROM\n", | ||
| " + \"/JobDescription-job_desc_product_manager.pdf6763dc68-12ff-4b32-b652-ccee195de071.json\"\n", | ||
| ")\n", | ||
| "resume_keywords = resume_dict[\"extracted_keywords\"]\n", | ||
| "job_description_keywords = job_dict[\"extracted_keywords\"]\n", | ||
| "\n", | ||
| "resume_string = \" \".join(resume_keywords)\n", | ||
| "jd_string = \" \".join(job_description_keywords)\n", | ||
| "final_result = get_score(resume_string, jd_string)\n", | ||
| "for r in final_result:\n", | ||
| " print(r.score)" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "ExecuteTime": { | ||
| "end_time": "2024-05-14T22:32:29.666678Z", | ||
| "start_time": "2024-05-14T22:32:28.781841Z" | ||
| } | ||
| }, | ||
| "id": "2f531db076cc1f59", | ||
| "outputs": [ | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "\u001B[32;10m2024-05-14 15:32:28,825 (1835371807.py:2) - INFO: \u001B[0mStarted getting similarity score\n", | ||
| "\u001B[32;10m2024-05-14 15:32:29,664 (1835371807.py:16) - INFO: \u001B[0mFinished getting similarity score\n" | ||
| ] | ||
| }, | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "0.8572231574198962\n" | ||
| ] | ||
| } | ||
| ], | ||
| "execution_count": 6 | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 2 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython2", | ||
| "version": "2.7.6" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |