> ## Documentation Index > Fetch the complete documentation index at: https://docs.ragbuddy.ai/llms.txt > Use this file to discover all available pages before exploring further. # RAG-Buddy Quickstart > Add RAG-Buddy to your RAG pipeline efficiently in five minutes or less. Jumpstart your integration with RAG-Buddy in proxy mode, designed to enhance your existing RAG setup with minimal effort. This Quickstart is tailored for a swift and straightforward experience, enabling you to witness the improvements RAG-Buddy brings to your system promptly. For alternative configurations and detailed instructions, our comprehensive [Guidebook](./guidebook) is available. The Quickstart below is designed to get you up and running with RAG-Buddy in the shortest time possible. We do however recommend that you use the REST API version of RAG-Buddy. It is more reliable and offers additional advantages over the method described below. That having said, please do follow the quickstart below to get a feel of how RAG-Buddy works with a minimum of effort. ## Assumptions This Quickstart assumes the following prerequisites are met: * You have an active OpenAI API key. If you do not possess one, please obtain it from [OpenAI](https://platform.openai.com/api-keys). * You are using the [OpenAI Python client](https://github.com/openai/openai-python). Installation and setup instructions can be found in the repository. * Your use case is: * Text Classification If your setup or requirements differ from the above, please consult our [Guidebook](./guidebook) for a path tailored to your needs. ## Create a Project To begin using RAG-Buddy, a new project must be created. If you haven't set up a project yet, follow these steps: 1. Visit the console at [RAG-Buddy Console](https://ragbuddy.ai/) and sign up for an account if you don't already have one. The first project of your account is a trial project, meaning you get to experience the Business plan for a week. After the week passes your project's plan will be updated to the Free plan and the cache's data will be reset. If you wish to keep your data, subscribe to the Business plan for your project. 2. Once you're signed in: * On your Console, click `[Create a new project]`. * Name your project. * The `OpenAI:LARGE3-1024` model is pre-selected as your embedding model. * `OpenAI Chat Completions API` is pre-selected as your LLM provider. * Select the `Text-Classification` cache type. * Click `Next` to move on to the next step. * Add the classes you wish to use in your `Text-Classification` requests in the `Classes list`. * Click `Create Project` to create your project. 3. Next, you'll need an API key for your cache: * Navigate to the `Services` tab. * Scroll down to the `API Keys for Cache` section. * Click on `Create new key`. * Assign a name to your key and select `Create API Key`. * Copy the newly generated API key for use in the next step. ## Integrate RAG-Buddy Here, we demonstrate how to integrate RAG-Buddy into your existing LLM Pipeline. As this is the Quickstart guide we will create the most basic and most effortless integration possible, with the Proxy Endpoint for the Text-Classification use case. For more advanced configurations, please refer to our [Guidebook](./guidebook). For the RAG+Citatio use case please refer to the [Proxy Endpoint](./guidebook/proxy-endpoint) in the Guidebook. Below we continue with the Text-Classification use case exclusively. ### Using the OpenAI API in a TC Context In this section, we demonstrate a typical use case of the OpenAI API within a Text Classification context. The focus is on leveraging the LLM to preprocess and classify the user's question with an appropriate intent/class. We start by showcasing a standard OpenAI API call, where the system is fed with a set of pre-selected classes. These system instructions provide the necessary context for the AI to understand and classify the question into the appropriate class/intent without requiring any prior training or feature extraction. **Code Example:** ```python Typical TC OpenAI API call theme={null} import openai # Your OpenAI API key openai_api_key = "sk-abc123" # Replace with your actual API key # System messages system_instructions = """You are an expert assistant in the field of customer service. Your task is to help workers in the customer service department of a company.\nYour task is to classify the customer's question in order to help the customer service worker to answer the question. In order to help the worker, you MUST respond with the name of one of the following classes you know.\nIn case you reply with something else, you will be penalized.\nThe classes are the following:""" # Classes classes = [ "activate_my_card", "age_limit", "apple_pay_or_google_pay", "atm_support", "automatic_top_up", "balance_not_updated_after_bank_transfer", "balance_not_updated_after_cheque_or_cash_deposit", "beneficiary_not_allowed", "cancel_transfer", "card_about_to_expire", "card_acceptance", "card_arrival", "card_delivery_estimate", "card_linking", "card_not_working", "card_payment_fee_charged", "card_payment_not_recognised", "card_payment_wrong_exchange_rate", "cash_withdrawal_charge", "cash_withdrawal_not_recognised", "change_pin", "compromised_card", "contactless_not_working", "country_support", "declined_card_payment", "declined_cash_withdrawal", "declined_transfer", "direct_debit_payment_not_recognised", "disposable_card_limits", "edit_personal_details", "exchange_charge", "exchange_rate", "exchange_via_app", "extra_charge_on_statement", "failed_transfer", "fiat_currency_support", "get_disposable_virtual_card", "get_physical_card", "getting_spare_card", "getting_virtual_card", "lost_or_stolen_card", "lost_or_stolen_phone", "order_physical_card", "passcode_forgotten", "pending_card_payment", "pending_cash_withdrawal", "pending_top_up", "pending_transfer", "pin_blocked", "receiving_money", "Refund_not_showing_up", "request_refund", "reverted_card_payment?", "supported_cards_and_currencies", "terminate_account", "top_up_by_bank_transfer_charge", "top_up_by_card_charge", "top_up_by_cash_or_cheque", "top_up_failed", "top_up_limits", "top_up_reverted", "topping_up_by_card", "transaction_charged_twice", "transfer_fee_charged", "transfer_into_account", "transfer_not_received_by_recipient", "transfer_timing", "unable_to_verify_identity", "verify_my_identity", "verify_source_of_funds", "verify_top_up", "virtual_card_not_working", "visa_or_mastercard", "why_verify_identity", "wrong_amount_of_cash_received", "wrong_exchange_rate_for_cash_withdrawal", ] # User query (can be replaced with any relevant question) user_query = "I lost my card!" # Format the template classes_string = "\n".join(classes) template = f"{system_instructions}####\n{classes_string}\n####" # Initialize OpenAI client client = openai.OpenAI( api_key=openai_api_key, timeout=10, ) # Prepare the messages messages = [ { "role": "system", "content": template, }, {"role": "user", "content": user_query}, ] # Call OpenAI API completion = client.chat.completions.create( model="gpt-4o", # Replace with the specific model name messages=messages, temperature=0.0, ) # Read the response response = completion.choices[0].message print(response.content) ``` **Open this code example in a notebook:** [Colab notebook](https://colab.research.google.com/drive/1S1oJ7sl4hX043gYYj7CyeLkiGxOUqzvC#scrollTo=9c590a5d) ### Transitioning to RAG-Buddy Usage To further enhance the efficiency and relevance of responses, we integrate the RAG-Buddy services. RAG-Buddy acts as a proxy, caching user queries and selected articles, reducing the context size sent to the LLM. To utilize RAG-Buddy, just a few modifications in the existing code are necessary: 1. Set the base URL to the RAG-Buddy Proxy Endpoint: Modify the `base_url` parameter to point to the RAG-Buddy Proxy Endpoint: `base_url = "https://api.ragbuddy.ai/proxy/ragc/v1"` This directs the API calls to the RAG-Buddy Proxy instead of directly to OpenAI. 2. Add the RAG-Buddy Key as a Header: Incorporate the RAG-Buddy key into the headers for authentication and tracking purposes. Your headers configuration will look like this: `headers = {"Helvia-RAG-Buddy-Token": "your-rag-buddy-key"}` Replace "your-rag-buddy-key" with the actual key provided during your project setup. 3. Remove the `Classes` provided in the request. RAG-Buddy utilizes the predefined `Classes` that the user set while creating their project so that they don't have to supply the `Classes` for every request to their Text-Classification cache. By making these changes, your API calls will now be routed through the RAG-Buddy proxy, leveraging its caching capabilities for decreasing the context size. **Complete Code with RAG-Buddy Integration:** ```RAG-Buddy TC Integration theme={null} import openai # Your OpenAI API key openai_api_key = "sk-abc123" # Replace with your actual API key # Your RAG Buddy key rag_buddy_key = "RAG_CA_abc123" # Replace with your actual RAG Buddy key # Needed for RAG Cache integration base_url = "https://api.dev.ragbuddy.ai/proxy/tc/v1" headers = {"Helvia-RAG-Buddy-Token": rag_buddy_key} # System messages system_instructions = """You are an expert assistant in the field of customer service. Your task is to help workers in the customer service department of a company.\nYour task is to classify the customer's question in order to help the customer service worker to answer the question. In order to help the worker, you MUST respond with the name of one of the following classes you know.\nIn case you reply with something else, you will be penalized.\nThe classes are the following:""" # User query (can be replaced with any relevant question) user_query = "I lost my card!" # Format the template template = f"{system_instructions}" # Initialize OpenAI client client = openai.OpenAI( api_key=openai_api_key, timeout=10, default_headers=headers, base_url=base_url, ) # Prepare the messages messages = [ { "role": "system", "content": template, }, {"role": "user", "content": user_query}, ] # Call OpenAI API completion = client.chat.completions.create( model="gpt-4o", # Replace with the specific model name messages=messages, ) # Read the response response = completion.choices[0].message print(response.content) ``` **Open this code example in a notebook:** [Colab notebook](https://colab.research.google.com/drive/1a2mmk9rDLd8WpbFDmQMM5xzb0lwrVWQ9) ## What's Next? This quickstart guide is designed to get you up and running with RAG-Buddy in the shortest time possible. For more advanced configurations and detailed instructions, please refer to our [Guidebook](./guidebook) where you can: * Add cache control * Inspect the response header for cache hit information * Use the REST API instead of this proxy setup * Integrate for different use cases * And more... {/* ## Setup your development Learn how to update your docs locally and and deploy them to the public. ### Edit and preview During the onboarding process, we created a repository on your Github with your docs content. You can find this repository on our [dashboard](https://dashboard.mintlify.com). To clone the repository locally, follow these [instructions](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository) in your terminal. Previewing helps you make sure your changes look as intended. 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If you push and don't see that the Github app successfully deployed your changes, you can also manually update your docs through our [dashboard](https://dashboard.mintlify.com). ## Update your docs Add content directly in your files with MDX syntax and React components. You can use any of our components, or even build your own. Add flair to your docs with personalized branding. Implement your OpenAPI spec and enable API user interaction. Draw insights from user interactions with your documentation. Keep your docs on your own website's subdomain. */}