Principle:Neuml Txtai Retrieval Augmented Generation
| Knowledge Sources | |
|---|---|
| Domains | NLP, Information_Retrieval, RAG |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Retrieval-Augmented Generation (RAG) pipeline configuration is the process of combining a retrieval component (embeddings search) with a generative component (language model) into a single pipeline that can answer questions grounded in a knowledge base.
Description
A RAG pipeline bridges two distinct capabilities: information retrieval and text generation. The retrieval component searches an embeddings index to find passages relevant to a user's question. The generative component -- typically a large language model (LLM) -- receives those passages as context and produces a natural-language answer grounded in the retrieved evidence.
Configuring a RAG pipeline involves selecting and connecting three core elements. First, the retrieval backend, which is a content-enabled embeddings index or a similarity pipeline that can return ranked text passages. Second, the generative model, which can be a HuggingFace transformer, a llama.cpp model, a LiteLLM-compatible API, or an extractive question-answering model. Third, the prompt template, which defines how the question and retrieved context are assembled into the input that the model receives.
The prompt template is a critical design decision. It must contain placeholders for both the question and the context, and its phrasing directly influences the quality and faithfulness of generated answers. A well-crafted template instructs the model to answer based only on the provided context, reducing hallucination. An optional system prompt can further constrain the model's behavior, establishing persona, tone, or factuality requirements.
Usage
Configure a RAG pipeline when you need to:
- Build a question-answering system grounded in a specific document corpus.
- Connect an existing embeddings search index to a language model for generative answers.
- Create a chatbot or assistant that retrieves evidence before responding.
- Experiment with different LLM backends (local models, API-based models) for the same knowledge base.
Theoretical Basis
A RAG pipeline R is defined as a composition of a retrieval function and a generation function:
R(q) = G(T(q, C(q)))
where:
- q is the input question.
- C(q) is the context retrieval function that returns the top-k most relevant passages from the knowledge base.
- T(q, c) is the template function that merges the question and context into a prompt string.
- G(p) is the generation function that produces an answer given a prompt.
The template function T is typically parameterized as:
T(q, c) = template.format(question=q, context=c)
where template is a string containing {question} and {context} placeholders.
Key configuration parameters and their effects:
- Context window size (top-k): controls how many retrieved passages are included. Larger values provide more evidence but may exceed the model's context window or dilute focus.
- Minimum score threshold: filters out low-confidence retrieval results, ensuring only relevant passages reach the model.
- Model selection: determines the generation quality, speed, and cost trade-off. Local models offer privacy and low latency; API models offer higher capability.
- System prompt: establishes behavioral constraints for the model, such as "Answer only based on the provided context."
The RAG approach offers several advantages over pure generation:
- Grounding: answers are anchored to specific evidence, reducing hallucination.
- Updatability: the knowledge base can be refreshed without retraining the model.
- Transparency: retrieved passages can be shown alongside answers for verification.
In pseudocode:
FUNCTION configure_rag(index, model_path, template, top_k):
retriever = index
generator = load_model(model_path)
RETURN RAGPipeline(retriever, generator, template, top_k)