How to Build a RAG Pipeline with LangChain

TL;DR

RAG improves an LLM’s answers by retrieving relevant information from an external authoritative knowledge base before generating a response. Its pipeline parses and chunks source data, converts each chunk into vector embeddings, stores them in a vector database, and retrieves similar content for user queries, extending the model to current or private information without repeated fine-tuning.

Transcript

Hello all, my name is Krishna and I am super excited to announce this amazing crash course on rag that is retrieval augmented generation. Uh in this specific crash course it'll be somewhere around 2.5 to 3 hours but we are going to discuss everything that is related to rack completely from scratch. Uh we'll be talking about the entire pipeline from... Read More

Key Insights

• RAG is a process for optimizing LLM output by referencing an authoritative knowledge base outside the model’s original training data before producing a response. This approach can make generated answers more relevant, accurate, and useful for a particular context or domain.
• An LLM can hallucinate when asked about events or information absent from its training data. The model may still produce a convincing answer despite lacking the necessary knowledge, which makes reliance on the model alone unsuitable for questions requiring unavailable or newer information.
• RAG can connect an LLM to private organizational information such as HR, finance, and company policies. These protected sources may not appear in public training data, but an external knowledge base allows the application to use them without directly retraining the model.
• Fine-tuning is presented as an expensive and tedious alternative because an LLM can contain billions of parameters that must be adjusted. Frequently changing company information also makes repeated fine-tuning impractical, while a RAG knowledge base can accommodate updated source material.
• The data injection pipeline consists of source data, parsing, chunking, embedding, and storage in a vector database. Completing this pipeline transforms the original content into searchable numerical representations that can later be retrieved in response to a user’s query.
• Data parsing is a critical RAG development step because source information may be structured or unstructured and can appear in PDF, HTML, Excel, SQL database, or other formats. Parsing must read that information and divide it into usable chunks.
• Embeddings are numerical vector representations of text that are applied to individual chunks before storage. These vectors make it possible to use retrieval algorithms, including similarity search and cosine similarity, to locate content related to a specific query.
• Embedding models can come from Google Gemini, OpenAI, or Hugging Face, and the available models can have different costs. Open-source embedding models are also available for converting text into vectors that can be stored and searched in a vector database.

Questions & Answers

Q: What is retrieval-augmented generation?

Retrieval-augmented generation, or RAG, is a process that improves the output of a large language model by referencing an authoritative knowledge base outside the model’s training sources before generating a response. It extends an LLM to specific domains or internal organizational information without requiring the model to be retrained, offering a cost-effective way to produce more relevant, accurate, and useful answers.

Q: Why does an LLM hallucinate when used without RAG?

An LLM can hallucinate when a question concerns information that was not included in its training data, such as events occurring after the training period. Even without the required knowledge, the model may attempt to generate a convincing response. RAG addresses this limitation by retrieving related information from an external authoritative knowledge base before the model prepares its answer.

Q: How does RAG use private company information?

RAG places private company information, such as HR, finance, and organizational policies, into an external knowledge pipeline instead of depending on public model training data. The content is parsed, divided into chunks, converted into vector embeddings, and stored in a vector database. Relevant material can then be retrieved for a user query and used to support the LLM’s generated response.

Q: Why use RAG instead of repeatedly fine-tuning an LLM?

Fine-tuning can be expensive and tedious because an LLM may contain billions of parameters that need to be adjusted. It is also inconvenient when company policies and other internal information change regularly, since the model would need repeated updates. RAG avoids retraining by keeping additional knowledge in an external database that participates in retrieval before response generation.

Q: What are the stages of a RAG data injection pipeline?

A RAG data injection pipeline begins with source data and then performs parsing to read its contents. The parsed material is divided into chunks, and an embedding model converts each text chunk into a numerical vector. Those vectors are finally saved in a vector database or vector store, where they become available for similarity-based retrieval when users submit queries.

Q: Why is chunking important in a RAG application?

Chunking divides parsed source material into smaller units that can be processed by an embedding model and stored in a vector database. Each chunk receives its own numerical representation, allowing the retrieval process to identify portions of the source that are similar to a user’s query. The course treats parsing and chunking strategy as especially important parts of developing a RAG application.

Q: What are embeddings in a RAG pipeline?

Embeddings are numerical vector representations of text. After source data is parsed and divided into chunks, an embedding model converts each chunk from text into a vector and stores it in a vector database. This representation allows algorithms such as similarity search and cosine similarity to compare a query with stored content and retrieve related results for the generation process.

Q: What source formats and embedding models can a RAG pipeline use?

The described RAG pipeline can accept information in PDF, HTML, Excel, SQL database, structured, and unstructured formats. After parsing and chunking that content, it can use embedding models from Google Gemini, OpenAI, or Hugging Face to convert text into vectors. The models have different costs, and open-source embedding options are also available for this conversion task.

Summary & Key Takeaways

• Retrieval-augmented generation addresses two limitations of using an LLM alone: hallucinations about information outside its training data and lack of access to protected organizational knowledge. Rather than changing billions of model parameters, RAG connects the model to an external authoritative source that can contain current, private, or domain-specific information.

• The data injection pipeline accepts structured or unstructured sources, including PDF, HTML, Excel, SQL database, and other formats. It parses the content, divides it into appropriate chunks, applies an embedding model that converts text into numerical vectors, and stores those embeddings in a vector database or vector store.

• The retrieval and generation workflow uses a user query to find similar stored content through techniques such as similarity search and cosine similarity. Retrieved information can then support the LLM’s response. The course progresses from theoretical concepts and basic implementation toward modular code intended to clarify and connect the complete RAG pipeline.