RAG Implementation with LLMs from Scratch
Implementing Retrieval-Augmented Generation (RAG) can significantly enhance the capabilities of large language models (LLMs), making them more accurate and contextually relevant. In this blog, we will guide you through the process of RAG implementation with LLM, discuss the RAG framework, and explore its applications. This step-by-step guide will help you understand the RAG approach to LLMs and how to effectively integrate it into your projects. Explore its application in platforms like LangChain and CustomGPT. Let’s get started!
What is RAG?
Retrieval-Augmented Generation (RAG) is an AI approach that combines information retrieval techniques with generative models to enhance the accuracy and relevance of AI-generated content. The RAG framework operates in two main steps: Retrieval and Generation.
The working of Retrieval-Augmented Generation involves two main steps:
Retrieval: RAG Implementation
In the retrieval step, relevant information is sourced from external knowledge bases using techniques such as keyword-based search, semantic similarity search, or neural network-based retrieval. This process involves scanning through a collection of documents and identifying the most relevant ones.
Generation: RAG Implementation
After retrieving the relevant information, it is used to augment the generation process. The generative model then incorporates this information to produce more accurate, contextually relevant, and fluent responses. Usually, a transformer-based model such as BERT, GPT-2, or GPT-3, produces text resembling human language using the retrieved documents.
RAG Framework: A Technical Deep Dive
As we know RAG model operates in a two-step process:
Retrieval Step
When a query is asked, it is converted into numerical vectors called embeddings using Query Encoder (q). During the retrieval step, the system scans through the corpus and selects the N most relevant documents, typically employing similarity metrics such as cosine similarity.
Here’s an explanation of the code snippet:
- Import necessary modules as shown in the first two lines of the above code snippet.
- The modules will be used to convert user queries into vectors using TfidfVectorizer and to find the most relevant document to the user query among the collection of documents using cosine_similarity.
- vectorizer = TfidfVectorizer() creates an instance of the TfidfVectorizer class, which will be used to convert text data into TF-IDF features.
- TF-IDF stands for Term Frequency-Inverse Document Frequency. It is a numerical statistic used in natural language processing and information retrieval to reflect the importance of a word in a document relative to a collection of documents, typically a corpus.
- tfidf_matrix = vectorizer.fit_transform(corpus) Convert the corpus to TF-IDF matrix.
- query_vector = vectorizer.transform([query]) Transform the query into TF-IDF vector.
- similarity_scores = cosine_similarity(query_vector, tfidf_matrix) computes the cosine similarity between the TF-IDF vector of the query and the TF-IDF matrix of the corpus.
- The resulting similarity_scores array contains the cosine similarity scores between the query and each document in the corpus
This way the most relevant document of the query will be retrieved using retrieval code snippet.
Generation Step
Now the generator processes these N-retrieved documents along with the original query to generate the relevant response to the user query.
Here’s an explanation of the provided code in points:
- The code imports necessary modules in the first line from the transformers library to work with RAG models.
- Initializing Tokenizer, Retriever, and Model: It initializes three components required for RAG: tokenizer, retriever, and model.
- RagTokenizer.from_pretrained() initializes the tokenizer for RAG models.
- RagRetriever.from_pretrained() initializes the retriever component for RAG models. It specifies the index_name as “exact” and uses a dummy dataset for demonstration purposes.
- RagTokenForGeneration.from_pretrained() initializes the RAG model for text generation, specifying the previously initialized retriever.
- Tokenizing the Query: The query is tokenized using the initialized tokenizer. tokenizer() method takes the query as input and returns the tokenized representation as input_ids.
- Generating Response: The model generates a response based on the tokenized query and retrieved documents.
- generate() method of the model generates the response based on the input_ids.
- The generated response is decoded into human-readable text using the decode() method of the tokenizer, skipping special tokens.
This code essentially sets up an RAG model for generation and generates a response for a given query. After defining the RAG model now you can set up this RAG model with the large language model.
Setting Up RAG with LLM
Before configuring RAG for Large Language Models (LLMs) you will require:
Data Corpus
Gather a dataset in various formats such as SQL databases, Elasticsearch, or JSON files. This corpus serves as the knowledge base for retrieving relevant information.
Machine Learning Framework
Choose a machine learning framework like TensorFlow or PyTorch to implement and train the RAG model.
Computational Resources
Ensure access to sufficient computational resources, including CPUs or GPUs, for both training and inference tasks. These resources are necessary to handle the computational demands of RAG implementations.
RAG Approach with LLM: Steps to Implement RAG in LLMs
To implement the RAG technique with LLMs, you need to follow a series of steps. Here’s how you can set up the RAG model with LLM:
Data preparation
Ensure your dataset is in a searchable format. If utilizing Elasticsearch, index your data appropriately.
Select Model
Choose the retriever and generator models. You can opt for pre-trained models or train your own based on your specific requirements.
Train Model
Train the retriever and generator models separately.
- retriever.train()
- generator.train()
Integrate LLM Models
Combine the trained retriever and generator models to create a unified RAG model.
- rag_model = RagModel(retriever, generator)
Test Your Model
Validate the model’s performance using metrics such as BLEU for text generation quality and recall for retrieval accuracy.
By following these straightforward steps, you can develop a robust RAG model ready to enhance your LLMs for improved performance.
Utility Function to evaluate RAG model performance
Enhancing the performance of your RAG model in LLMs involves leveraging utility functions like get_retrieval_score(). This function evaluates the effectiveness of the retriever by utilizing metrics such as Precision or NDCG (Normalized Discounted Cumulative Gain).
- from sklearn.metrics import ndcg_score
- ndcg = ndcg_score(y_true, y_score)
By employing this function, you can efficiently fine-tune your retriever’s performance, ensuring it accurately retrieves the most relevant documents from the corpus.
Technologies to implement RAG with LLM
Several technologies are available to facilitate the implementation of RAG. Notable platforms include LangChain and CustomGPT, which offer innovative solutions for integrating RAG into various applications.
Technologies available to implement RAG:
- LangChain
- CustomGPT
Implementing RAG with LangChain
LangChain is a decentralized platform designed to integrate diverse machine learning models, including RAG. To implement RAG in LangChain, follow these steps:.
Implementing RAG in langChain:
Installation: Begin by installing the langChain SDK and configuring your development environment.
- pip install langChain
Model Upload: Upload your pre-trained RAG model to the langChain platform using the provided command-line interface.
- langChain upload –model my_rag_model
API Integration: Utilize langChain’s API to seamlessly integrate your RAG model into your application code.
- from langChain import RagService
- Initialize the RagService with your API key
- rag_service = RagService(api_key=”your_api_key”)
Query Execution: Execute queries through the langChain platform, which will leverage your integrated RAG model to generate responses.
- response = rag_service.query(“your query”)
By following these steps, you can effectively integrate RAG into langChain, harnessing the platform’s decentralized architecture to enhance performance and scalability.
CustomGPT using RAG: A no-code platform
CustomGPT is an AI platform that implemented the RAG framework into its work. RAG enables CustomGPT to enhance its conversational AI capabilities by retrieving relevant information from external knowledge bases and using it to augment the generation of AI responses.
Its biggest strength lies in its no-code convenience features making it a versatile choice for large number of audience.
- CustomGPT is designed to be a no-code platform, making it accessible to both non-technical and technical users. This versatility allows users from various backgrounds to leverage CustomGPT’s advanced AI capabilities without requiring extensive programming knowledge.
- The availability of CustomGPT as a no-code platform enhances its versatility, making it suitable for a wide range of applications and users. Whether it’s for research assistance, translation, or creative writing, CustomGPT offers a user-friendly interface for harnessing the power of RAG technology.
Read the full blog on How you can train the chatbot with external data sources with no coding.
Conclusion
In conclusion, the adoption of RAG architecture underscores its potential to reshape the landscape of AI-driven content generation applications. As the technology continues to evolve, we can expect further advancements in its integration with LLMs and broader applications across diverse platforms, paving the way for more sophisticated and contextually relevant AI interactions.
FAQs: Setting Up RAG with LLM
1. What is RAG and how does it work?
RAG, or Retrieval-Augmented Generation, is a framework in artificial intelligence that enhances AI-generated content by combining information retrieval techniques with generative models. The RAG implementation involves retrieving relevant information from external knowledge sources and using this information to augment the generation process.
2. How do you implement RAG with LLMs?
Implementing RAG with LLMs involves a two-step process: retrieval and generation. The retrieval step uses algorithms like BM25 or Dense Retriever to scan documents and identify relevant ones. The generation step then uses these retrieved documents to produce more accurate and contextually relevant responses with a transformer-based model like BERT, GPT-2, or GPT-3.
3. What are the main benefits of using RAG with LLMs?
Using RAG with LLMs enhances the contextual relevance and accuracy of AI-generated content. This combination of retrieval and generation improves the performance of applications that require high-quality information retrieval and content generation.
4. What are the key components of a RAG model LLM?
The key components of a RAG model LLM include the retriever and the generator. The retriever selects the most relevant documents based on a query, and the generator uses these documents to produce a coherent and contextually relevant response.
5. How can the RAG framework be applied in real-world scenarios?
The RAG framework can be applied in various real-world scenarios such as customer service, research assistance, and content creation. By enhancing generative capabilities with accurate information retrieval, the RAG application becomes highly versatile.
6. What are the steps involved in the RAG implementation process?
The RAG implementation process involves several steps: data preparation, model selection, training of retriever and generator models, integration of these models, and performance validation. Each step is crucial to ensure the effectiveness of the RAG technique LLM.
7. Can I use pre-trained models for RAG LLM implementation?
Yes, you can use pre-trained models for RAG LLM implementation. Frameworks like Hugging Face provide pre-trained RAG models that can be fine-tuned to fit specific requirements, making it easier to implement RAG with LLM.
8. What computational resources are required for RAG LLM models?
Implementing RAG LLM models typically requires substantial computational resources, including CPUs or GPUs, for both training and inference tasks. Ensuring adequate computational power is essential for efficient RAG implementation.
9. How do you evaluate the performance of a LLM RAG model?
The performance of an LLM RAG model can be evaluated using metrics such as BLEU for text generation quality and recall for retrieval accuracy. Utility functions like get_retrieval_score() can also be used to assess the retriever’s performance.
10. What are some common challenges in using RAG with LLM?
Common challenges in using RAG with LLM include managing large datasets, ensuring efficient retrieval, maintaining high-quality generation, and integrating the models seamlessly. Overcoming these challenges is essential for successful RAG model LLM implementation.