RAG & LLM: Pioneering Dynamic Language Model Frontier | Qwak

Discover how RAG and LLM are revolutionizing AI language models for more dynamic, context-aware interactions.

By: Pavel Klushin, Head of Solution Architecture at Qwak

What challenges do LLMs bring? Traditional language models, such as GPT-4 and Llama2, face inherent limitations. Their static nature binds them to a fixed knowledge cut-off, leaving them unaware of developments post their last training date. While they encapsulate vast amounts of data, there’s a cap on their knowledge. Infusing fresh information often means an exhaustive retraining cycle — both in terms of computational resources and time. Additionally, their generalistic approach sometimes lacks the precision needed for specialized domains. This is where Retrieval-Augmented Generation (RAG) comes into play.‍

Introducing RAG (Retrieval Augmented Generation)

RAG is a revolutionary blend of two AI powerhouses: a retriever and a generator. It empowers a language model to dynamically fetch pertinent data from a vast external corpus and then craft a coherent answer based on this information.‍

The Power of RAG in Everyday Applications

Let’s see an example Retrieval Augmented Generation use case in real life. Consider a customer support chatbot scenario. Initially, I query a LLM Model about Qwak, a simple question of “how to install the Qwak SDK?”. In the subsequent approach, I enhance this with insights from Qwak’s official documentation which were ingested to a Vector Store.‍

As you can see, without RAG & Vector Store data the model couldnt generate a professional answer, With RAG, the answer is more detailed and enriched with additional relevant information. The combination of retrieval and generation gives the model the capability to “pull” from recent sources and provide a more comprehensive answer.

How Does RAG Enhance LLM?

• Tackling Static Knowledge: RAG breaks free from the constraints of static knowledge by dynamically sourcing information from ever-evolving external corpora.
• Knowledge Expansion: Unlike standalone models like GPT-4, RAG leverages external databases, amplifying its knowledge horizon.
• Minimizing Retraining: RAG reduces the need for periodic retraining. Instead, you can refresh the external database, keeping the AI system up-to-date without overhauling the model.
• Boosting Domain-Specific Responses: RAG can draw from domain-specific databases, e.g., medical repositories, to provide detailed, accurate answers.
• Balancing Breadth with Depth: RAG merges the strength of retrieval and generation. While its generative side ensures contextual relevance, the retrieval facet dives deep for detailed insights.‍

Retrieval Augmented Generation — Architecture‍

• Data Ingestion Pipeline Step: In this phase, the system orchestrates the gathering of relevant data and converts it into embeddings. These processed embeddings are subsequently structured to provide the LLM model with the necessary context for generating responses.
• Retrieval Step: At this step, the retrieval mechanism comes into play, pinpointing the segments of data that are most relevant from the available datasets.
• Generation Step: Subsequently, the generation component, utilizing models akin to LLM, synthesizes a response that is both informed and contextually aligned with the data retrieved.‍

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Data Pipeline:

The data pipeline is the initial phase where raw data is acquired, processed, and prepared for further use in the system. This usually involves:‍

1. Data Collection: Obtaining raw data from various sources.
2. Pre-processing: Cleaning the data to remove any inconsistencies, irrelevant information, or errors. This step may involve normalization, tokenization, and other data transformation techniques.
3. Transformation using Embedding model: Converting data into a format that’s amenable for use in the subsequent layers, converting text data into numerical vectors or embeddings. The main goal is to capture semantic relationships between words/phrases so that words with similar meanings are close in the embedding space.
4. Vector Store Insertion: Before insertion, vectors are often indexed to facilitate efficient retrieval. Finally, the indexed vectors are stored in the vector database.‍

Retrieval Step:

• Input: Could be text, image, etc.
• Preprocessing: Similar to the data insertion pipeline, query data is preprocessed to match the format expected by the embedding model.

1. Query Embedding: The preprocessed query is converted into an embedding vector using the same model (or compatible one) that was used for generating embeddings during the insertion pipeline.
2. Similarity Search: The query embedding is then used to search the vector store for the nearest neighbors.
3. Candidate Generation: Based on the nearest neighbors, the system generates a set of candidate data points that could be relevant to the query.
4. Filtering & Ranking: Further filtering and ranking might be applied to the retrieved neighbors to select the best candidates.‍

• LLM: A model such as Llama2, GPT, Mistral could take the candidates and generate new data
• Aggregation: In cases like recommendations, the candidates are often aggregated to form a single coherent response.

Generation Step:

In some systems, additional processing is applied to the candidates to generate the final output.

• Formatting: Ensuring the data is in a user-friendly format.
• Personalization: Tailoring the output to the user’s preferences.

Post-Processing

The generated data or response might require post-processing before being presented to the user.

• Prompt Design: Designing prompts that guide the model to generate desired outputs. This can involve iterating and refining based on the model’s responses.
• Sequential Interaction: Some tasks might require multiple prompts to be sent sequentially, with the model’s output from one prompt being used as input for the next. This “chaining” can help in guiding the model towards a more refined or specific output.
• Feedback Loop: The chaining prompts layer might incorporate a feedback mechanism, where the model’s output is analyzed, and subsequent prompts are adapted accordingly.

Chaining Prompts:

This layer manages how prompts are fed into the LLM to control its output or guide its generation process.

The interplay between these objects and layers forms a cohesive system where raw data is transformed into actionable insights, answers, or other desired outputs using the power of language models.‍

For constructing such a chaining process, platforms like Langchain, LlamaIndex, and AutoGPT are among the prevalent solutions.

Wrapping up

The integration of RAG into the realm of LLM’s marks a significant milestone in our journey towards more dynamic and informed AI interactions. This advancement transforms the capabilities of language models like GPT-4, allowing them to access and utilize the most current information without the constraints of their initial training data. RAG achieves this by seamlessly incorporating real-time data into the conversation, ensuring that the ML models remain relevant and knowledgeable. The implications for professional fields are profound; whether it’s delivering expert advice or providing support, RAG enables ML models to offer insights that are both precise and timely. It’s a sophisticated leap forward, ensuring that Models communication remains at the forefront of innovation, accuracy, and adaptability.‍

In our next blog we’ll walk through how to build a Production ready RAG & LLM flow, stay tuned!

Originally published at https://www.qwak.com.