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John Wakaba
John Wakaba

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RETRIEVAL AUGMENTED GENERATION

Retrieval augmented generation (RAG) enhances language models by integrating external data sources.

RAG solves the limitations of large language models such as

  • Outdated Knowledge
  • Hallucinations
  • Generic responses

This article will look at how RAG works, why it was brought about, why it is a game changer within the realms of AI applications the best practices associated with RAG's challenges.

RAG

It is a technique that enhances large language models(LLMs) by integrating them with external data sources.

RAG enables AI systems to produce more accurate and circumstantial relevant responses by combining the generative capabilities of models like GPT-4 with precise information retrieval mechanisms.

Image description

RAG addresses LLMs implicit limitations by allowing models retrieve up to date and domain specific information from structured and unstructured data sources like APIs, databases and documentation.

RAG can help avoid hallucinations

RAG allows the system to refuse to answer.

RAG cites data sources where the answer is based rendering the system, more trustworthy.

LARGE LANGUAGE MODEL LIMITATIONS

  • Lack of specific information : They are limited to providing generic answers based on their training data.

  • Hallucinate : Tend to confidently generate false responses based on imagined facts or responses that are off topic if they do not have an accurate answer.

  • Generic Responses : They oftern provide generic responses that are not tailored to specific contexts.

  • RAG effectively bridges the above stated gaps by providing you with a way to integrate the general knowledge base of LLMs with the ability to access specific information

HOW RAG WORKS

Data Collection

First one must gather all the data that is needed for your application.

Data Chunking

Involves breaking your data down into smaller more manageable pieces. Each chunk of data is focused on a specific topic.

  • This step is crucial in making sure irrelevant information from the entire document is not included whenever a piece of information is retrieved from the data source. 
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500,
                                            chunk_overlap=200,
                                            length_function=len,
                                            separators=["\n\n", "\n", " "])
chunks = text_splitter.split_documents(pages)
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Document Embeddings

Once the source data has been broken down into smaller parts it is necessary to convert it into embeddings.

  • Involves transferring text data into embeddings which are numeric representations that capture the semantic meaning behind text. > - Embeddings ensure the responses are relevant and aligned with the user's query. 
embed_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vectorstore = Chroma.from_documents(
    documents=chunks,
    embedding=embed_model,
    persist_directory="./Vectordb",
)
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Handling User Queries

The same model ought to be used for both the document and query embedding to ensure uniformity.

Cosine similarity and euclidean distance measures are used to identify and retrieve chunks whose embeddings are most similar to the query embedded.

Generating Responses With an LLM

Retrieved text chunks along with the initial user query are fed into a language model.

This information will be utilized by the algorithm to generate a coherent response to the user's questions through a chat interface.

PRACTICAL APPLICATION OF RAG

Application Description
Text Summarization RAG can use content from external sources to provide accurate summaries resulting in considerable time savings
Customer chatbot
Personalized Recommendations RAG can be utilized to analyze customer data so as to generate product recommendations
Business Intelligence (BI) RAG applications can be used in keeping an eye on customer behavior and analyzing customer trends

CHALLENGES AND BEST PRACTICES

  1. Integration Complexity: It can be difficult to integrate a retrieval system with an LLM particularly where there are multiple sources of external data in varying formats.
  • The best practice in this scenario is separate modules can be designed to handle different data sources independently.
  1. Scalability: As the amount of data increases it gets more challenging to maintain the efficiency of the RAG system.
  • Distributing computational load across different servers and investing in robust hardware infrastructure can help.
  • Coding queries that are frequently asked can aid in improving response time.
  1. Data Quality: Effectiveness of a RAG system depends heavily on the quality of data being fed into it.
  • Investing in a diligent content curation and fine tuning process.

Final Thoughts

RAG is currently the best known technique leveraging the language capabilities of LLMs alongside a specialized DB. Having and including human oversight in the process is key in getting the most out of RAG systems.

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