removed cohere dependency , api codes .

CONTRIBUTING.md

@@ -167,34 +167,6 @@ The full stack Next.js (React and FastAPI) web application allows users to inter
 
 To run the full stack web application (frontend client and backend api servers), follow the instructions over on the [webapp README](/webapp/README.md) file.
 
-<br/>
-
-### Cohere and Qdrant
-
-1.  Visit [Cohere website registration](https://dashboard.cohere.ai/welcome/register) and create an account.
-2.  Go to API keys and copy your cohere api key.
-3.  Visit [Qdrant website](https://cloud.qdrant.io/) and create an account.
-4.  Get your api key and cluster url.
-5.  Go to open dashboard in qdrant and enter your api key **for only the first time**
-
-<img src="Assets/img/quadrant_cloud.png" height="60%" width="60%"/>
-
-6.  Now create a yaml file named config.yml in Scripts/Similarity/ folder.
-
-7.  The format for the conifg file should be as below:
-    ```yaml
-    cohere:
-      api_key: cohere_key
-    qdrant:
-      api_key: qdrant_api_key
-      url: qdrant_cluster_url
-    ```
-8.  Please replace your values without any quotes.
-
-*Note: Please make sure that Qdrant_client's version is higher than v1.1*
-
-<br/>
-
 ## Code Formatting
 
 This project uses [Black](https://black.readthedocs.io/en/stable/) for code formatting. We believe this helps to keep the code base consistent and reduces the cognitive load when reading code.

README.md

@@ -198,35 +198,6 @@ To run the full stack web application (frontend client and backend api servers),
 5. Copy the url and open it in your browser.
 6. ![img_2.png](img_2.png)
 
-### Cohere and Qdrant
-
-1.  Visit [Cohere website registration](https://dashboard.cohere.ai/welcome/register) and create an account.
-2.  Go to API keys and copy your cohere api key.
-3.  Visit [Qdrant website](https://cloud.qdrant.io/) and create an account.
-4.  Get your api key and cluster url.
-5.  Go to open dashboard in qdrant and enter your api key **for only the first time**
-<img src="Assets/img/quadrant_cloud.png" height="60%" width="60%"/>
-1.  Now create a yaml file named config.yml in Scripts/Similarity/ folder.
-2.  The format for the conifg file should be as below:
-    ```yaml
-    cohere:
-      api_key: cohere_key
-    qdrant:
-      api_key: qdrant_api_key
-      url: qdrant_cluster_url
-    ```
-3.  Please replace your values without any quotes.
-
-*Note: Please make sure that Qdrant_client's version is higher than v1.1*
-
-*Note: This part needs updating w.r.t to the new FastEmbed changes.*
-
-
-<br/>
-
-<div align="center">
-
-
 ## Code Formatting
 
 This project uses [Black](https://black.readthedocs.io/en/stable/) for code formatting. We believe this helps to keep the code base consistent and reduces the cognitive load when reading code.
@@ -253,7 +224,6 @@ Now, the pre-commit hooks will automatically run every time you commit your chan
 
 ## Join Us, Contribute!
 
-</div>
 
 Pull Requests & Issues are not just welcomed, they're celebrated! Let's create together.
 

requirements.txt

@@ -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

scripts/similarity/get_score.ipynb

@@ -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
+}