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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## 📄 **Hybrid-RAG: Combining Retrieval and Generation for Intelligent Responses** 2 | 3 | ## 📌 About the Project 4 | 5 | This project implements a **hybrid Retrieval-Augmented Generation (RAG)** system that uses **BERT and GPT-2** to deliver more accurate and contextually relevant responses. 6 | 7 | ### 🎯 **Key Features** 8 | ✅ **Intelligent Information Retrieval:** Uses **BERT + FAISS** for fast and precise document search. 9 | ✅ **Optimized Response Generation:** Combines retrieved information with the original query and leverages **GPT-2** for response generation. 10 | ✅ **Efficient Vector Processing:** Stores and searches documents in a **vector index** using **FAISS**. 11 | 12 | --- 13 | 14 | ## 🔧 Installation & Requirements 15 | 16 | ### Dependencies 17 | **Libraries used in this project:** 18 | - `torch` 19 | - `transformers` 20 | - `faiss-gpu` 21 | - `numpy` 22 | - `scipy` 23 | 24 | 25 | ### Run the Notebook 26 | ```bash 27 | jupyter notebook hybrid-rag.ipynb 28 | ``` 29 | 30 | --- 31 | 32 | ## 🔧 **How the System Works** 33 | 34 | 1️⃣ **Embedding:** 35 | - Scientific texts and documents are converted into **embeddings**. 36 | 2️⃣ **Indexing:** 37 | - Vectors are stored in a **FAISS Index** for searchability. 38 | 3️⃣ **Retrieval:** 39 | - When a query is received, the most relevant documents are **searched and retrieved**. 40 | 4️⃣ **Augmentation:** 41 | - The original query + retrieved texts are combined. 42 | 5️⃣ **Generation:** 43 | - The **GPT-2** model uses the new input to generate a **precise and relevant response**. 44 | 45 | --- 46 | 47 | - If you have suggestions for improving the project, please submit a **Pull Request**. 48 | - To report issues, please open an **Issue**. 49 | 50 | --- 51 | -------------------------------------------------------------------------------- /hybrid-rag.ipynb: -------------------------------------------------------------------------------- 1 | {"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Retrieval-Augmented Generation (RAG) combines the power of large language models (LLMs) with external knowledge sources to generate more accurate and contextually relevant responses. Here’s an overview of how to create a RAG model using a vector graph (for efficient retrieval) and a hybrid approach:\n\n1. Overview of RAG Hybrid Model:\n\nRetrieval Component: Retrieves relevant documents or snippets from a knowledge base using a vector graph or embeddings.\nAugmentation Component: The retrieved documents are used to augment the input to the language model, which generates a more informed response.\nGeneration Component: The language model (e.g., GPT-3, BERT) generates the final response, leveraging both the original query and the retrieved documents.\n\n2. Vector Graph for Efficient Retrieval:\n\nEmbedding Space: Documents or knowledge base entries are converted into embeddings using a pre-trained model like BERT, SBERT, or similar. These embeddings capture the semantic meaning of the text.\n\nVector Graph/Index: The embeddings are stored in a vector graph or a vector index (like FAISS or Annoy) for fast retrieval. This graph organizes the embeddings in a way that allows for efficient nearest neighbor search.\nRetrieval Process: Given a query, it is converted into an embedding, and the vector graph is queried to find the nearest neighbors (i.e., the most semantically similar documents).\n\n3. Hybrid RAG Model:\n\nQuery Embedding: The user query is first converted into an embedding.\nRetrieval: This embedding is used to query the vector graph to retrieve relevant documents.\nAugmentation: The retrieved documents are concatenated with the original query, forming an augmented input.\n\nGeneration: The augmented input is fed into the language model, which generates a response that integrates the retrieved information.\n\n4. Implementing RAG Hybrid Model:\n\nHere’s a basic implementation outline using Python with PyTorch and Hugging Face:","metadata":{}},{"cell_type":"code","source":"!pip install faiss-gpu","metadata":{"execution":{"iopub.status.busy":"2024-08-16T00:43:47.658865Z","iopub.execute_input":"2024-08-16T00:43:47.659538Z","iopub.status.idle":"2024-08-16T00:44:00.113852Z","shell.execute_reply.started":"2024-08-16T00:43:47.659509Z","shell.execute_reply":"2024-08-16T00:44:00.112842Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Requirement already satisfied: faiss-gpu in /opt/conda/lib/python3.10/site-packages (1.7.2)\n","output_type":"stream"}]},{"cell_type":"code","source":"import torch\nfrom transformers import BertTokenizer, BertModel, GPT2LMHeadModel, GPT2Tokenizer\nimport faiss\nimport numpy as np\n\n# Load the models\ntokenizer_bert = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel_bert = BertModel.from_pretrained('bert-base-uncased')\ntokenizer_gpt2 = GPT2Tokenizer.from_pretrained('gpt2')\nmodel_gpt2 = GPT2LMHeadModel.from_pretrained('gpt2')\n\n# Sample documents for vector indexing\ndocuments = [\n \"info about skin cancer...\",\n \"treatment options for skin cancer...\",\n \"explaining symptoms of skin cancer...\"\n]\n\n# Convert documents to embeddings\nembeddings = []\nfor doc in documents:\n inputs = tokenizer_bert(doc, return_tensors='pt', max_length=512, truncation=True)\n with torch.no_grad():\n embedding = model_bert(**inputs).pooler_output\n embeddings.append(embedding.squeeze().numpy())\n\n# Create a vector index (FAISS)\ndimension = embeddings[0].shape[0]\nindex = faiss.IndexFlatL2(dimension)\nindex.add(np.array(embeddings))\n\n# Function to get embeddings for a query\ndef get_query_embedding(query):\n query_inputs = tokenizer_bert(query, return_tensors='pt', max_length=512, truncation=True)\n with torch.no_grad():\n query_embedding = model_bert(**query_inputs).pooler_output.squeeze().numpy()\n return query_embedding\n\n# Retrieve top-k documents\ndef retrieve_documents(query, k=2):\n query_embedding = get_query_embedding(query)\n D, I = index.search(np.array([query_embedding]), k)\n return [documents[i] for i in I[0]]\n\n# Generate a response using GPT-2\ndef generate_response(query, retrieved_docs):\n augmented_query = query + \" \" + \" \".join(retrieved_docs)\n inputs_gpt2 = tokenizer_gpt2(augmented_query, return_tensors='pt', max_length=512, truncation=True)\n response = model_gpt2.generate(**inputs_gpt2)\n return tokenizer_gpt2.decode(response[0], skip_special_tokens=True)\n\n# Example usage\nquery = \"What are the symptoms of skin cancer?\"\nretrieved_docs = retrieve_documents(query, k=2)\ngenerated_response = generate_response(query, retrieved_docs)\n\nprint(\"Query:\", query)\nprint(\"Retrieved Documents:\")\nfor doc in retrieved_docs:\n print(f\"- {doc}\")\nprint(\"Generated Response:\", generated_response)\n","metadata":{"execution":{"iopub.status.busy":"2024-08-16T00:44:00.116194Z","iopub.execute_input":"2024-08-16T00:44:00.116619Z","iopub.status.idle":"2024-08-16T00:44:01.863527Z","shell.execute_reply.started":"2024-08-16T00:44:00.116580Z","shell.execute_reply":"2024-08-16T00:44:01.862558Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stderr","text":"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","output_type":"stream"},{"name":"stdout","text":"Query: What are the symptoms of skin cancer?\nRetrieved Documents:\n- info about skin cancer...\n- treatment options for skin cancer...\nGenerated Response: What are the symptoms of skin cancer? info about skin cancer... treatment options for skin cancer...\n\n","output_type":"stream"}]}]} --------------------------------------------------------------------------------