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"StyleView", "description_width": "" } } } }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "source": [ "#install with specific versions that satisfy both Pinecone and Colab's default tools\n", "!pip install -qU \\\n", " \"protobuf<6.0.0dev,>=3.20.3\" \\\n", " \"grpcio>=1.67.1\" \\\n", " pinecone[grpc] \\\n", " pinecone-text \\\n", " sentence-transformers \\\n", " datasets \\\n", " langchain-text-splitters \\\n", " huggingface_hub" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "adW0s_bzFD44", "outputId": "83d86b79-a76b-4c4d-d807-4b7b124e932d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/5.9 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/5.9 MB\u001b[0m \u001b[31m61.2 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kB\u001b[0m \u001b[31m46.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "grpcio-status 1.71.2 requires grpcio>=1.71.2, but you have grpcio 1.67.1 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0m" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "from pinecone import Pinecone, ServerlessSpec\n", "from datasets import load_dataset\n", "from sentence_transformers import SentenceTransformer, CrossEncoder\n", "from pinecone_text.sparse import BM25Encoder\n", "from langchain_text_splitters import RecursiveCharacterTextSplitter, CharacterTextSplitter\n", "\n", "# Enter your API Keys here\n", "PINECONE_API_KEY = \"\"\n", "HF_TOKEN = \"\"\n", "\n", "# Initialize Pinecone client\n", "pc = Pinecone(api_key=PINECONE_API_KEY)\n", "try:\n", " # List your indexes to see if the connection is live\n", " active_indexes = pc.list_indexes()\n", " print(\"Successfully connected to Pinecone!\")\n", " print(f\"Current Indexes: {active_indexes.names()}\")\n", "except Exception as e:\n", " print(f\"Connection failed: {e}\")\n" ], "metadata": { "id": "1lk0TqnJFGPM", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d4e81925-b6c0-4adb-85f5-c16726921d0c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Successfully connected to Pinecone!\n", "Current Indexes: ['medrag-hybrid-index']\n" ] } ] }, { "cell_type": "code", "source": [ "!pip install datasets" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Co7U8Fw-1nTu", "outputId": "ffe31b22-df7c-49c8-8f49-cd8c73d29afd" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.8.4)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from datasets) (3.25.2)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from datasets) (2.0.2)\n", 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/usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface-hub<2.0,>=0.25.0->datasets) (0.1.2)\n" ] } ] }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "# 1. Load MedRAG (pubmed) - Professional Medical Corpus\n", "print(\"Fetching dataset...\")\n", "dataset = load_dataset(\"MedRAG/pubmed\", split=\"train\", streaming=True)\n", "\n", "# Pull 100 documents (will result in 500+ chunks)\n", "raw_texts = []\n", "for i, entry in enumerate(dataset):\n", " if i >= 200: break\n", " raw_texts.append(f\"{entry['title']}: {entry['content']}\")\n", "\n", "# 2. Strategy A: Fixed-Size Chunking (Ablation Case 1)\n", "# By setting separator to \"\", we force it to split exactly at 500 chars\n", "# or at the nearest character if it exceeds the limit.\n", "fixed_splitter = CharacterTextSplitter(\n", " separator=\"\",\n", " chunk_size=500,\n", " chunk_overlap=50,\n", " is_separator_regex=False\n", ")\n", "docs_fixed = fixed_splitter.create_documents(raw_texts)\n", "\n", "# 3. Strategy B: Recursive Character Splitting (Ablation Case 2 - Requirement)\n", "# This is already smart, but we ensure it uses the standard medical delimiters\n", "recursive_splitter = RecursiveCharacterTextSplitter(\n", " chunk_size=500,\n", " chunk_overlap=50,\n", " separators=[\"\\n\\n\", \"\\n\", \".\", \" \", \"\"]\n", ")\n", "docs_recursive = recursive_splitter.create_documents(raw_texts)\n", "\n", "# Convert to list of strings for processing\n", "# (Note: You should use docs_fixed for Strategy A and docs_recursive for Strategy B\n", "# in your ablation tests to see the difference in count!)\n", "fixed_chunk_texts = [d.page_content for d in docs_fixed]\n", "recursive_chunk_texts = [d.page_content for d in docs_recursive]\n", "\n", "print(f\"Strategy A (Fixed): {len(fixed_chunk_texts)} chunks\")\n", "print(f\"Strategy B (Recursive): {len(recursive_chunk_texts)} chunks\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 101, "referenced_widgets": [ "12a5c31425f14f54a4ef13b5bc221034", "a8ef8535971d408fb4b5057aca4f6305", "f7d837f74b37405985aa5d5ae51c4fdb", "b7a7ff37e7ea4d18aba5fbc9817d190a", "0cbae854c4794c5286bdfc4b67aae13d", "604de396834a4999aa8d58e3ef7bf9a0", "d1da1e4ee12b4e0b8361aca0e81bb613", "2043185aac164645ae4da8a49db0b020", "43ba62481ef54d5ba274c0b531bc8486", "7df71ce491c74dfda2cfe41f9edac0f8", "2749d00b3c414c418def52f5cdf8e396" ] }, "id": "MCd-_go0HUIu", "outputId": "402ed860-ffc5-4b74-8896-1a499628b5a2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fetching dataset...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Resolving data files: 0%| | 0/1166 [00:00 5]\n", " else:\n", " alt_queries = []\n", "\n", " return faith_result, alt_queries\n", "\n", "print(\"Evaluation pipeline ready.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GCY0xcHmM1j5", "outputId": "41d7ffe2-8f39-4b14-ddb6-01f8e7cb6a6b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Evaluation pipeline ready.\n" ] } ] }, { "cell_type": "code", "source": [ "'''\n", "test_queries = [\n", " \"What is the effect of (--)-alpha-bisabolol on the proteolytic activity of pepsin?\",\n", " \"Does alpha-bisabolol alter the pH-value to achieve its antipeptic action?\",\n", " \"Compare the recovery rates of BA 1 and ifosfamide in rats with DS carcinosarcoma.\",\n", " \"What role does hyperglycemia play in the effectiveness of the substance BA 1?\",\n", " \"How does etafenone affect the transmural gradient of myocardial blood flow in dogs?\",\n", " \"What is the difference in myocardial blood flow between normoxia and hypoxia?\",\n", " \"How does the virostatic compound 5531 affect the induction of tryptophan-pyrrolase?\",\n", " \"At what dosage does compound 5531 inhibit tyrosine-aminotransferase in tissue culture?\",\n", " \"What are the pharmacological properties of RMI 61 140 compared to chlorpromazine?\",\n", " \"Do RMI 61 140 and RMI 61 144 show activity on electroshock tests?\",\n", " \"How does the combination of Sch 1000 and oxazepam affect gastric juice volume?\",\n", " \"What were the findings regarding oxazepam's effect on stimulated acid secretion?\",\n", " \"Which lysosomal glycosidases show high activity in the guinea-pig epidermis?\",\n", " \"List the distinct ester hydrolases characterized in the guinea-pig epidermis.\",\n", " \"What inhibits the serum haemagglutinating property dependent upon polycarboxyl groups?\",\n", " \"How does human erythrocyte stromata influence complement activation in PNH cells?\",\n", " \"What is the effect of salicylic acid on the pentose phosphate pathway in G6PD-deficient cells?\",\n", " \"How does pelleted barley affect rumen pH compared to whole barley in sheep?\",\n", " \"Describe the ordered structure of Poly(8-aminoguanylic acid) in neutral solution.\",\n", " \"What are the estimated pKa values for the active center groups of human liver alanine aminopeptidase?\"\n", "]" ], "metadata": { "id": "h6_Q1Wh6NzT1" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "!pip install openai" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "NyI5JrKd_p2K", "outputId": "5ead7114-eb1b-48d0-fcdc-146a9ac1998a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: openai in /usr/local/lib/python3.12/dist-packages (2.30.0)\n", "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.12/dist-packages (from openai) (4.13.0)\n", "Requirement already satisfied: distro<2,>=1.7.0 in /usr/local/lib/python3.12/dist-packages (from openai) (1.9.0)\n", "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from openai) (0.28.1)\n", "Requirement already satisfied: jiter<1,>=0.10.0 in /usr/local/lib/python3.12/dist-packages (from openai) (0.13.0)\n", "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.12/dist-packages (from openai) (2.12.3)\n", "Requirement already satisfied: sniffio in /usr/local/lib/python3.12/dist-packages (from openai) (1.3.1)\n", "Requirement already satisfied: tqdm>4 in /usr/local/lib/python3.12/dist-packages (from openai) (4.67.3)\n", "Requirement already satisfied: typing-extensions<5,>=4.11 in /usr/local/lib/python3.12/dist-packages (from openai) (4.15.0)\n", "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.12/dist-packages (from anyio<5,>=3.5.0->openai) (3.11)\n", "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->openai) (2026.2.25)\n", "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->openai) (1.0.9)\n", "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.16.0)\n", "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=1.9.0->openai) (0.7.0)\n", "Requirement already satisfied: pydantic-core==2.41.4 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=1.9.0->openai) (2.41.4)\n", "Requirement already satisfied: typing-inspection>=0.4.2 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=1.9.0->openai) (0.4.2)\n" ] } ] }, { "cell_type": "code", "source": [ "'''\n", "from openai import OpenAI\n", "import time\n", "\n", "# 1. PASTE YOUR NEW KEY HERE\n", "NEW_KEY = \"\"\n", "\n", "client = OpenAI(\n", " base_url=\"https://openrouter.ai/api/v1\",\n", " api_key=NEW_KEY,\n", ")\n", "\n", "def get_llm_response(prompt):\n", " try:\n", " completion = client.chat.completions.create(\n", " # 'openrouter/auto' is the safest way to find a working free model\n", " model=\"openrouter/auto\",\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", " extra_headers={\n", " \"HTTP-Referer\": \"http://localhost:3000\",\n", " \"X-Title\": \"IBA NLP Assignment\",\n", " },\n", " temperature=0.1,\n", " max_tokens=500\n", " )\n", "\n", " res = completion.choices[0].message.content\n", " if res:\n", " print(f\"✅ Success: {res[:50]}...\")\n", " return res\n", " else:\n", " print(\"⚠️ API returned empty string.\")\n", " return \"\"\n", "\n", " except Exception as e:\n", " print(f\"❌ API ERROR: {e}\")\n", " return \"\"\n", "\n", "# --- MANDATORY TEST ---\n", "print(\"Testing API connection...\")\n", "test = get_llm_response(\"Say 'Connection Successful'\")\n", "if \"Successful\" in str(test):\n", " print(\"\\n🚀 CONNECTION VERIFIED. You can now run your loop.\")\n", "else:\n", " print(\"\\n🛑 STOP! Your API key is still not working. Check OpenRouter.ai/keys\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BAXSdEJS_1Y0", "outputId": "53f57c79-043f-433c-debb-b64de6905b4d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Testing API connection...\n", "✅ Success: Connection Successful....\n", "\n", "🚀 CONNECTION VERIFIED. You can now run your loop.\n" ] } ] }, { "cell_type": "code", "source": [ "'''\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.metrics.pairwise import cosine_similarity\n", "import time\n", "\n", "results_data = []\n", "\n", "print(\"🚀 Starting Final RAG Evaluation with Real Relevancy Scores...\")\n", "\n", "for i, query in enumerate(test_queries):\n", " print(f\"Processing Query {i+1}/20...\")\n", "\n", " # 1. Retrieval\n", " retrieved = hybrid_query(query, top_k=3)\n", " context = \" \".join([r.get('metadata', {}).get('text', r.get('text', '')) for r in retrieved])\n", "\n", " # 2. Generation (The Answer)\n", " ans_prompt = f\"Context: {context}\\n\\nQuestion: {query}\\n\\nAnswer based ONLY on context:\"\n", " answer = get_llm_response(ans_prompt)\n", "\n", " # 3. Evaluation (The Judge)\n", " eval_prompt = f\"Context: {context}\\nAnswer: {answer}\\nEvaluate Faithfulness. Provide Score % and short Rationale.\"\n", " faith_report = get_llm_response(eval_prompt)\n", "\n", " # --- 4. NEW: CALCULATE ACTUAL RELEVANCY ---\n", " try:\n", " # We compare the Query embedding to the Answer embedding\n", " query_vec = dense_model.encode([query])\n", " answer_vec = dense_model.encode([answer])\n", "\n", " # Calculate Cosine Similarity\n", " sim_score = cosine_similarity(query_vec, answer_vec)[0][0]\n", " except Exception as e:\n", " print(f\" ⚠️ Relevancy Calc Error: {e}\")\n", " sim_score = 0.0\n", "\n", " # 5. Data Collection\n", " results_data.append({\n", " \"query_no\": i + 1,\n", " \"query\": query,\n", " \"answer\": answer.strip(),\n", " \"faithfulness\": faith_report.strip(),\n", " \"relevancy_score\": round(float(sim_score), 4) # This will now be a decimal between 0 and 1\n", " })\n", "\n", " time.sleep(1)\n", "\n", "# 6. Export\n", "df_final = pd.DataFrame(results_data)\n", "df_final.to_csv(\"rag_results_final.csv\", index=False)\n", "print(f\"\\n✅ DONE! Relevancy scores are now calculated.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EvB-uQxqAKzx", "outputId": "e9c29f44-0469-4385-9ba3-fba88861a428" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "🚀 Starting Final RAG Evaluation with Real Relevancy Scores...\n", "Processing Query 1/20...\n", "✅ Success: The proteolytic activity of pepsin is reduced by 5...\n", "✅ Success: **Score: 100%**\n", "\n", "**Rationale:** The answer directl...\n", "Processing Query 2/20...\n", "✅ Success: No, (--)-alpha-bisabolol's primary antipeptic acti...\n", "✅ Success: **Faithfulness Score: 100%**\n", "\n", "**Rationale:** The p...\n", "Processing Query 3/20...\n", "✅ Success: The provided text states that BA 1, at a dosage of...\n", "✅ Success: **Score: 100%**\n", "\n", "**Rationale:** The answer accurat...\n", "Processing Query 4/20...\n", "✅ Success: Hyperglycemia, in one test, resulted in an 80 perc...\n", "✅ Success: **Score: 100%**\n", "\n", "**Rationale:** The answer accurat...\n", "Processing Query 5/20...\n", "✅ Success: Etafenone causes a marked redistribution of region...\n", "✅ Success: Score: 100%\n", "\n", "Rationale: The answer directly and ac...\n", "Processing Query 6/20...\n", "✅ Success: In normoxia, a transmural gradient of flow was obs...\n", "✅ Success: **Score: 100%**\n", "\n", "**Rationale:** The provided answe...\n", "Processing Query 7/20...\n", "✅ Success: The induction of tryptophan-pyrrolase by cortisone...\n", "✅ Success: Score: 100%\n", "\n", "Rationale: The provided answer direct...\n", "Processing Query 8/20...\n", "✅ Success: The induction of tyrosine-aminotransferase by dexa...\n", "✅ Success: Score: 100%\n", "\n", "Rationale: The answer directly quotes...\n", "Processing Query 9/20...\n", "✅ Success: RMI 61 140 has pharmacological properties similar ...\n", "✅ Success: Score: 100%\n", "\n", "Rationale: The answer accurately refl...\n", "Processing Query 10/20...\n", "✅ Success: No, RMI 61 140 and RMI 61 144 do not show any acti...\n", "✅ Success: **Score: 100%**\n", "\n", "**Rationale:** The provided text ...\n", "Processing Query 11/20...\n", "✅ Success: Sch 1000 and Sch 1000 + oxazepam were equal in eff...\n", "✅ Success: Score: 100%\n", "\n", "Rationale: The answer directly quotes...\n", "Processing Query 12/20...\n", "✅ Success: None of the three active preparations was capable ...\n", "✅ Success: **Score: 100%**\n", "\n", "**Rationale:** The provided answe...\n", "Processing Query 13/20...\n", "✅ Success: beta-galactosidase and beta-acetylglucosaminidase...\n", "✅ Success: **Faithfulness Score: 100%**\n", "\n", "**Rationale:** The a...\n", "Processing Query 14/20...\n", "✅ Success: The distinct ester hydrolases characterized in the...\n", "✅ Success: **Score: 100%**\n", "\n", "**Rationale:** The answer directl...\n", "Processing Query 15/20...\n", "✅ Success: The provided context does not explicitly state wha...\n", "✅ Success: **Score: 0%**\n", "\n", "**Rationale:** The provided context...\n", "Processing Query 16/20...\n", "✅ Success: Human erythrocyte stromata induce lysis of PNH-lik...\n", "✅ Success: **Score: 100%**\n", "\n", "**Rationale:** The answer directl...\n", "Processing Query 17/20...\n", "✅ Success: Salicylic acid inhibits the pentose phosphate path...\n", "✅ Success: **Faithfulness Score: 100%**\n", "\n", "**Rationale:** The a...\n", "Processing Query 18/20...\n", "✅ Success: With whole barley, there was little variation in r...\n", "✅ Success: Score: 100%\n", "\n", "Rationale: The answer directly and ac...\n", "Processing Query 19/20...\n", "✅ Success: In neutral solution, Poly(8-aminoguanylic acid) ha...\n", "✅ Success: **Score: 100%**\n", "\n", "**Rationale:** The provided answe...\n", "Processing Query 20/20...\n", "✅ Success: The estimated pKa values for the active center gro...\n", "✅ Success: **Score: 100%**\n", "\n", "**Rationale:** The answer directl...\n", "\n", "✅ DONE! Relevancy scores are now calculated.\n" ] } ] } ] }