Retrieval Augmented Generation (RAG) System Overview and Implementation with LangChain, Redis, and llama.cpp

RAG Overview

Retrieval Augmented Generation (RAG) is a mainstream technique that mitigates the hallucination problem of large language models (LLMs) by incorporating external knowledge.

System Architecture

The RAG system consists of two main components:

Indexing : builds a knowledge base.

Retrieval & Generation : fetches relevant information from the knowledge base and generates the final answer.

Indexing Process

The indexing pipeline includes four steps:

Load : ingest PDFs, docs, markdown, web pages, etc.

Split : chunk documents to fit LLM context windows.

Embedding : convert text chunks into vector embeddings.

store to VectorDB : persist texts and vectors in a vector database (knowledge base).

Retrieval & Generation Process

This pipeline also has four steps:

Embedding : encode the user query into a vector.

search VectorDB : retrieve semantically similar text passages.

prompt : combine retrieved passages with the user question to form a prompt.

LLM : feed the prompt to a large language model to obtain the answer.

Practical Example

The example builds a Kubernetes knowledge‑base Q&A system using langchain , Redis as the vector store, and llama.cpp as the LLM runtime, all orchestrated with Docker‑compose.

Code Snippet

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence"]
model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)

The code demonstrates loading a model, encoding sentences, and printing the resulting embeddings.

Implementation Details

Load documents: langchain fetches Kubernetes official documentation pages.

Split documents: langchain performs chunking.

Embedding: sentence_transformers converts text to vectors.

Store vectors: vectors are saved in Redis (any vector DB can be used).

Query embedding: the same model encodes user questions.

Similarity search: retrieve relevant passages from the vector DB.

Prompt construction: merge retrieved passages with the question.

LLM inference: submit the prompt to the LLM (e.g., llama.cpp ) to obtain the answer.

Results

Testing shows that adding contextual knowledge via RAG yields more accurate answers from the LLM.

Challenges

Loading diverse document formats (generally straightforward).

Effective chunking, which influences prompt context and LLM output quality.

Selecting appropriate embedding models.

Choosing a suitable vector database.

Designing prompt composition strategies.

Picking the LLM and its runtime (e.g., Llama series, Ollama, HuggingFace/transformers, vLLM, etc.).

Conclusion

RAG enhances LLMs by integrating a knowledge base, improving their ability to answer domain‑specific and real‑time queries accurately and efficiently.

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References

https://python.langchain.com/docs/tutorials/rag/

https://huggingface.co/sentence-transformers/all-mpnet-base-v2

https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md

https://redis.io/docs/latest/develop/interact/search-and-query/query/vector-search/