**Introduction to DeepSeek R1** DeepSeek R1 is an exciting open-source AI model that performs as well as many top proprietary models. This guide will help you set up a Retrieval-Augmented Generation (RAG) system using DeepSeek R1, covering everything from installation to running queries. **What is RAG?** RAG combines two techniques: it retrieves relevant information from a knowledge base and generates accurate answers to user questions. **Prerequisites** - **Python**: Version 3.7 or higher. - **Ollama**: A framework to run models like DeepSeek R1 on your local machine. **Step-by-Step Implementation** 1. **Install Ollama** Visit the Ollama website for installation instructions. Check if it’s installed correctly by running: `ollama --version` 2. **Run DeepSeek R1 Model** Open your terminal and run: `ollama run deepseek-r1:1.5b` This starts the 1.5 billion parameter version of DeepSeek R1. 3. **Prepare Your Knowledge Base** Collect documents or relevant text data for your retrieval system. - **Load Your Documents** Use the following code to load documents from text files: ```python import os def load_documents(directory): documents = [] for filename in os.listdir(directory): if filename.endswith('.txt'): with open(os.path.join(directory, filename), 'r') as file: documents.append(file.read()) return documents documents = load_documents('path/to/your/documents') ``` 4. **Create a Vector Store for Retrieval** Use a vector store like FAISS for efficient document retrieval. - **Install Required Libraries** Run: `pip install faiss-cpu huggingface-hub` - **Generate Embeddings and Set Up FAISS** Use the following code: ```python from huggingface_hub import HuggingFaceEmbeddings import faiss import numpy as np embeddings_model = HuggingFaceEmbeddings() document_embeddings = [embeddings_model.embed(doc) for doc in documents] document_embeddings = np.array(document_embeddings).astype('float32') index = faiss.IndexFlatL2(document_embeddings.shape[1]) index.add(document_embeddings) ``` 5. **Set Up the Retriever** Create a retriever to fetch relevant documents based on user queries: ```python class SimpleRetriever: def __init__(self, index, embeddings_model): self.index = index self.embeddings_model = embeddings_model def retrieve(self, query, k=3): query_embedding = self.embeddings_model.embed(query) distances, indices = self.index.search(np.array([query_embedding]).astype('float32'), k) return [documents[i] for i in indices[0]] retriever = SimpleRetriever(index, embeddings_model) ``` 6. **Configure DeepSeek R1 for RAG** Set up a prompt template for DeepSeek R1: ```python from ollama import Ollama from string import Template llm = Ollama(model="deepseek-r1:1.5b") prompt_template = Template(""" Use ONLY the context below. If unsure, say "I don't know". Keep answers under 4 sentences. Context: $context Question: $question Answer: """) ``` 7. **Implement Query Handling Functionality** Create a function to combine retrieval and generation: ```python def answer_query(question): context = retriever.retrieve(question) combined_context = "\n".join(context) response = llm.generate(prompt_template.substitute(context=combined_context, question=question)) return response.strip() ``` 8. **Running Your RAG System** Test your RAG system by calling the `answer_query` function: ```python if __name__ == "__main__": user_question = "What are the key features of DeepSeek R1?" answer = answer_query(user_question) print("Answer:", answer) ``` **Conclusion** By following these steps, you can create a Retrieval-Augmented Generation (RAG) system using DeepSeek R1. This setup enables efficient information retrieval and accurate response generation. Explore how DeepSeek R1 can meet your specific needs. **AI Solutions for Your Business** To enhance your business with AI, consider these steps: - **Identify Automation Opportunities**: Look for customer interaction points that could benefit from AI. - **Define KPIs**: Set measurable goals to track business impact. - **Select an AI Solution**: Choose tools that fit your needs and allow for customization. - **Implement Gradually**: Start with a pilot project, gather data, and expand AI use wisely. For advice on AI KPI management, reach out to us. For ongoing insights, follow us on Telegram or Twitter.
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