A Step-by-Step Guide to Building a RAG Pipeline

Introduction to RAG

Retrieval-Augmented Generation (RAG) is a powerful technique that combines the strengths of large language models (LLMs) with external knowledge bases. While LLMs are excellent at generating human-like text, their knowledge is limited to their training data, which can become outdated or lack domain-specific information. RAG addresses this by allowing LLMs to retrieve relevant, up-to-date information from an external source before generating a response. This significantly reduces "hallucinations" and improves the factual accuracy and relevance of the output.

Building a RAG pipeline involves several key stages, from preparing your data to deploying and monitoring your system. This guide will walk you through each step with practical examples.

1. Data Ingestion and Chunking

The first step in building a RAG pipeline is to prepare your external knowledge base. This involves ingesting your data and breaking it down into manageable pieces, or "chunks," that can be effectively searched and retrieved.

1.1 Data Collection

Gather all the relevant documents, articles, internal wikis, or any text data that your LLM should reference. This could be in various formats (PDFs, Markdown files, web pages, database records, etc.).

1.2 Data Loading and Cleaning

Load your raw data into a structured format. This often involves parsing different file types and cleaning the text (e.g., removing HTML tags, special characters, or irrelevant boilerplate text). Libraries like Unstructured or custom parsers can be very useful here.

1.3 Text Chunking

Large documents need to be split into smaller, semantically meaningful chunks. This is crucial because LLMs have context window limitations, and smaller chunks are easier to embed and retrieve accurately. There's no one-size-fits-all chunking strategy; it often depends on the nature of your data and the queries you expect.

• Fixed-size chunking: Splitting text into chunks of a predefined number of characters or tokens.
• Recursive character text splitter: A more advanced method that attempts to split text using a list of separators (e.g., paragraphs, sentences) and recursively splits smaller chunks if they are still too large.
• Semantic chunking: Identifying natural breaks in the text based on meaning, which can be more complex but yield better retrieval.

Example: Basic Text Chunking with LangChain's RecursiveCharacterTextSplitter

from langchain_text_splitters import RecursiveCharacterTextSplitter

# Sample document text
document_text = """
The quick brown fox jumps over the lazy dog. This is a very common phrase used for testing.
It contains all letters of the alphabet. This makes it useful for typography and keyboard testing.

Another paragraph here. This one talks about the benefits of RAG in LLMs.
RAG helps reduce hallucinations and provides up-to-date information.
"""

# Initialize the text splitter
# chunk_size: maximum size of each chunk
# chunk_overlap: number of characters to overlap between chunks to maintain context
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=100,
    chunk_overlap=20,
    length_function=len,
    is_separator_regex=False,
)

# Create chunks
chunks = text_splitter.create_documents([document_text])

# Print the chunks
for i, chunk in enumerate(chunks):
    print(f"Chunk {i+1}:\n{chunk.page_content}\n---")

2. Embedding and Vector Database

Once your data is chunked, the next step is to convert these text chunks into numerical representations called "embeddings" and store them in a specialized database optimized for similarity search.

2.1 Embedding Models

An embedding model (also known as a text encoder) transforms text into high-dimensional vectors. Texts with similar meanings will have embeddings that are close to each other in this vector space. Popular embedding models include OpenAI's `text-embedding-ada-002`, Google's `text-embedding-004`, or open-source models like `Sentence-BERT` variants.

2.2 Vector Database (Vector Store)

A vector database is designed to efficiently store and query these high-dimensional vectors. When a user asks a question, its embedding can be compared against all stored chunk embeddings to find the most semantically similar ones. Popular vector databases include Pinecone, Weaviate, Chroma, Milvus, and FAISS (for local use).

Example: Embedding Chunks and Storing in a Vector Database (Conceptual with Chroma)

from langchain_community.embeddings import OpenAIEmbeddings # Or any other embedding model
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document

# Assuming 'chunks' are already created from the previous step
# Convert chunks (from RecursiveCharacterTextSplitter) to Document objects if not already
documents = [Document(page_content=chunk.page_content) for chunk in chunks]

# Initialize embedding model (replace with your actual API key or local model)
# For demonstration, using a placeholder for API key. In a real app, load securely.
embeddings_model = OpenAIEmbeddings(openai_api_key="YOUR_OPENAI_API_KEY")

# Create a Chroma vector store from the documents and embeddings
# This will embed the documents and store them in the Chroma database
vectorstore = Chroma.from_documents(
    documents=documents,
    embedding=embeddings_model,
    persist_directory="./chroma_db" # Directory to store the database
)

# To load an existing database:
# vectorstore = Chroma(persist_directory="./chroma_db", embedding_function=embeddings_model)

print("Chunks embedded and stored in Chroma DB.")

3. Retrieval

The retrieval step is where the RAG system finds the most relevant information from your knowledge base based on the user's query. This is typically done by converting the user's query into an embedding and then performing a similarity search in the vector database.

3.1 Query Embedding

The user's input question is first converted into a vector embedding using the same embedding model that was used for the document chunks. This ensures that the query and document embeddings are in the same vector space, allowing for accurate comparison.

3.2 Similarity Search

The query embedding is then used to search the vector database for the top-K (e.g., top 3 or 5) most similar document chunks. The similarity is usually measured using cosine similarity or dot product.

3.3 Contextual Information

The retrieved chunks, which are just raw text, will serve as the "context" for the LLM. It's important that these chunks contain enough information to answer the user's question without being too long or irrelevant.

Example: Retrieving Relevant Chunks with Chroma

# Assuming 'vectorstore' is already initialized and populated
user_query = "What is the purpose of RAG?"

# Retrieve relevant documents based on the user query
# 'k' specifies the number of top similar documents to retrieve
retrieved_docs = vectorstore.similarity_search(user_query, k=2)

print(f"Retrieved documents for query: '{user_query}'")
for i, doc in enumerate(retrieved_docs):
    print(f"Document {i+1}:\n{doc.page_content}\n---")

4. Generation

With the relevant context retrieved, the next step is to use an LLM to generate a coherent and accurate answer. This involves crafting a prompt that combines the user's original query with the retrieved information.

4.1 Prompt Construction

A well-engineered prompt is crucial. It typically instructs the LLM to answer the user's question using *only* the provided context. This helps prevent the LLM from "hallucinating" or relying on its general training knowledge when specific information is available.

A common prompt structure looks like this:

"Use the following context to answer the question. If the answer is not in the context, state that you don't know.

Context:
{retrieved_context}

Question: {user_query}

Answer:"

4.2 LLM Inference

The constructed prompt is then sent to the LLM. The LLM processes the prompt, synthesizes the information from the retrieved context, and generates a final answer. This can be done using various LLMs, such as those from OpenAI (GPT-3.5, GPT-4), Google (Gemini), or open-source models (Llama, Mistral).

Example: Generating a Response with an LLM (Conceptual with Gemini API)

# Assuming 'retrieved_docs' and 'user_query' are available from previous steps
# Concatenate the content of retrieved documents into a single context string
context = "\n\n".join([doc.page_content for doc in retrieved_docs])

# Construct the prompt for the LLM
prompt = f"""
Use the following context to answer the question. If the answer is not in the context, state that you don't know.

Context:
{context}

Question: {user_query}

Answer:
"""

async function generateResponse(prompt_text) {
    let chatHistory = [];
    chatHistory.push({ role: "user", parts: [{ text: prompt_text }] });
    const payload = { contents: chatHistory };
    const apiKey = ""; // Canvas will provide this at runtime
    const apiUrl = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-preview-05-20:generateContent?key=${apiKey}`;

    try {
        const response = await fetch(apiUrl, {
            method: 'POST',
            headers: { 'Content-Type': 'application/json' },
            body: JSON.stringify(payload)
        });
        const result = await response.json();
        if (result.candidates && result.candidates.length > 0 &&
            result.candidates[0].content && result.candidates[0].content.parts &&
            result.candidates[0].content.parts.length > 0) {
            return result.candidates[0].content.parts[0].text;
        } else {
            console.error("Unexpected API response structure:", result);
            return "Could not generate a response.";
        }
    } catch (error) {
        console.error("Error calling Gemini API:", error);
        return "An error occurred while generating the response.";
    }
}

// Example usage (in an async context)
// (async () => {
//     const llm_response = await generateResponse(prompt);
//     console.log("LLM Response:", llm_response);
// })();

5. Evaluation and Iteration

Building a RAG pipeline is an iterative process. After setting up the initial system, it's crucial to evaluate its performance and make improvements.

5.1 Evaluation Metrics

• Relevance: How well do the retrieved chunks answer the question?
• Faithfulness: Does the generated answer only use information from the retrieved context?
• Answer Correctness: Is the final answer accurate and complete?
• Latency: How quickly does the system respond?

Evaluation can involve both automated metrics (e.g., RAGAS framework) and human review, especially for subjective quality assessments.

5.2 Iteration and Improvement

Based on evaluation results, you can iterate on various components:

• Chunking strategy: Adjust chunk size, overlap, or splitting logic.
• Embedding model: Experiment with different embedding models.
• Retrieval method: Explore advanced retrieval techniques like reranking or hybrid search.
• Prompt engineering: Refine the prompt to guide the LLM more effectively.
• Knowledge base expansion: Add more diverse or specialized data.

6. Deployment and Monitoring

Once your RAG pipeline performs satisfactorily, you can deploy it for production use and continuously monitor its performance.

6.1 Deployment

Deploy your RAG system as an API service using frameworks like FastAPI or integrate it into an existing application. Ensure your vector database and LLM inference endpoints are scalable and robust.

6.2 Monitoring

• Performance Metrics: Track latency, throughput, and error rates.
• Usage Patterns: Monitor the types of queries users are making and which documents are being retrieved most often.
• Drift Detection: Continuously evaluate the quality of responses on new data to detect any degradation in performance over time, which might signal a need for data updates or model re-training.
• User Feedback: Implement mechanisms for users to provide feedback on the quality of answers, which can be invaluable for ongoing improvement.

Conclusion

Building a RAG pipeline is an effective way to enhance the capabilities of LLMs, making them more factual, up-to-date, and reliable for specific applications. By carefully managing data ingestion, embedding, retrieval, and generation, you can create powerful AI systems that leverage the best of both pre-trained knowledge and external information. As the field of AI continues to evolve, RAG will remain a critical technique for developing robust and intelligent applications.