Building a RAG Pipeline: A Step-by-Step Guide for Beginners#

Documents and Knowledge Bases#

A RAG pipeline begins and ends with data. Before you can generate meaningful answers, you need a collection of documents or knowledge entries. This data can exist in various forms:

• PDF files
• Wikipedia articles
• Text transcripts
• Database entries
• Web pages

Each document is typically broken into chunks (or paragraphs) for more granular embedding and retrieval. Choosing the right data — ensuring it is relevant, correct, and comprehensive — is a critical first step.

Embeddings and Vector Stores#

Instead of relying on string-based matching or keyword searches, RAG pipelines use embeddings to represent the semantic meaning of each document chunk as a vector. This vector representation allows a model to handle synonyms and paraphrased content more effectively than classic keyword approaches.

The typical process:

1. Chunk your documents into smaller segments (e.g., 200-500 words).
2. Embed these segments using an embedding model like Sentence-BERT or other transformer-based encoders.
3. Store the embedded vectors in a vector database or vector store (e.g., FAISS, Milvus, Pinecone, or Weaviate).

During retrieval, you convert the user query into a vector using the same embedding model. You then perform a similarity search in the vector database to fetch the closest matching chunks, which are then provided to the language model as context.

Retriever#

The retriever’s job is to take a user query (or partially formed prompt), embed it, and perform a k-nearest neighbors (k-NN) search in the vector store to find highly relevant document chunks. Some approaches involve more sophisticated techniques like sparse-dense hybrid retrieval, but most beginners start with a simple vector-based nearest neighbor search.

Generator (Large Language Model)#

After gathering the top relevant snippets, the pipeline uses an LLM to synthesize a response. The LLM generates an answer conditioned on both the query and the retrieved context. This fused approach helps produce responses that are more accurate, anchored to the provided text, and verifiable.

Step 1: Data Collection and Ingestion#

Goal: Gather all relevant reference documents or textual resources.

1. Identify your data sources. For instance, if you’re building a financial Q&A system, you might collect annual reports, market analyses, or regulatory documents.
2. Convert documents to text format. Tools like Apache Tika or PyPDF2 can help extract text from PDFs. For websites, you might use web scraping solutions like Beautiful Soup.
3. Store or record metadata. Keep track of each file’s source, publication date, or author. This metadata can be crucial for advanced retrieval and re-ranking steps.

It’s helpful to keep your data in a standardized directory structure or database table, along with pointers to each file’s origin, to maintain traceability and version control.

Step 2: Document Preprocessing and Chunking#

After collecting raw text, the next step is to make it “consumable” by your embedding model and retriever.

1. Cleaning: Remove boilerplate text (headers, footers), fix broken encodings, and standardize whitespace.
2. Language detection: If you’re dealing with multilingual content, separate or tag items by language.
3. Chunking: Break the text into smaller segments. A frequently used approach is splitting documents by paragraphs or by a fixed token/window size. For instance:
   • 200-300 token chunks (ideal for many transformer-based embeddings).
   • Overlapping windows (e.g., each chunk overlaps 50 tokens with the previous one) can improve retrieval recall.

Here’s a basic Python snippet to chunk a large text into smaller segments of ~500 words each:

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def chunk_text(text, chunk_size=500, overlap=50):
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    words = text.split()
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    chunks = []
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    start = 0
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    while start < len(words):
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        end = min(start + chunk_size, len(words))
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        chunk_words = words[start:end]
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        chunks.append(" ".join(chunk_words))
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        start = end - overlap  # move the window with overlap
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    return chunks

Step 3: Embedding and Vector Storage#

Goal: Convert each chunk into a vector representation and store them in a vector database.

1. Select an embedding model. Popular choices include:

   • Sentence-BERT: Good for general-purpose sentence embeddings.
   • OpenAI API (e.g., text-embedding-ada-002).
   • Other specialized domain models (e.g., BioBERT if you’re working on biomedical text).

2. Generate embeddings. Each chunk gets transformed into a numerical vector (usually 512-768 dimensions for many embeddings). For example:

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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer('all-MiniLM-L6-v2')  # or your chosen model
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embeddings = model.encode(list_of_text_chunks)

3. Store embeddings in a vector database. A minimal example using FAISS might look like this:

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import faiss
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import numpy as np
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dimension = 384  # depends on your embedding model
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faiss_index = faiss.IndexFlatL2(dimension)
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# Convert embeddings to float32
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emb_array = np.array(embeddings, dtype='float32')
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faiss_index.add(emb_array)

In production settings, you’d often integrate a managed service like Pinecone or Milvus for scalability, real-time updates, and easy query abilities.

Step 4: Retriever Implementation#

Goal: Given a user query, retrieve the top-k relevant chunks.

1. Embed the user query using the same embedding model from Step 3.
2. Use the vector database to find the closest vectors to the query.
3. Sort the results by distance (similarity) and pick the top k chunks.

In FAISS, you might do:

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def retrieve_top_k(query, k=3):
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    query_vector = model.encode([query])
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    query_vector = np.array(query_vector, dtype='float32')
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    distances, indices = faiss_index.search(query_vector, k)
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    # gather the chunks and return them
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    relevant_chunks = [list_of_text_chunks[i] for i in indices[0]]
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    return relevant_chunks

Step 5: Setting up the Generator (LLM)#

Goal: Feed the retrieved chunks to a large language model for context-aware generation.

Choices for the LLM:

• OpenAI’s GPT: GPT-3.5, GPT-4 or other mainline GPT models.
• Local LLM: Hugging Face’s Bloom, LLaMA, or other open models.
• Hosted solutions: AI21, Cohere, etc.

Typical prompt engineering approach:

1. Construct a prompt that includes the user query and some or all retrieved chunks.
2. Ask the model to generate the final response, citing or referencing context from the retrieved text.

A rudimentary prompt structure might be:

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You have access to the following context:
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1) ...
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2) ...
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3) ...
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Answer the user query based on the above context. If you cannot find the answer, say "I don't know."
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User query: {USER_QUERY}
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Your answer:

Step 6: Putting It All Together#

Combining all these pieces:

1. User input: “What are the key financial risks for Company XYZ in 2023?”
2. Retriever: Convert the query to an embedding and find relevant chunks from the knowledge base.
3. Generator: Provide the relevant chunks alongside the user query to the LLM. The LLM generates a final summarized or direct answer.

In practice, you’d wrap these steps in a single pipeline function or script, orchestrating the flow from ingestion to output. Tools like LangChain or Haystack offer higher-level abstractions for chaining these steps, making your RAG pipeline more modular.

Minimal RAG Example in Python#

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import faiss
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import numpy as np
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from sentence_transformers import SentenceTransformer
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from typing import List
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# 1) Initialize the embedding model
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model = SentenceTransformer('all-MiniLM-L6-v2')  # example model
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# 2) Example chunked documents
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documents = [
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    "Company XYZ reported an increased risk in 2023 due to market volatility.",
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    "Regulatory changes in 2023 have impacted financial reporting standards.",
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    "Company XYZ also faces cybersecurity threats, especially after expansion."
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]
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# 3) Embed and store in FAISS
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doc_embeddings = model.encode(documents)
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dimension = doc_embeddings.shape[1]
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faiss_index = faiss.IndexFlatL2(dimension)
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faiss_index.add(np.array(doc_embeddings, dtype='float32'))
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def retrieve_top_k(query: str, k: int = 2) -> List[str]:
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    # Embed query
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    query_embedding = model.encode([query])
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    query_embedding = np.array(query_embedding, dtype='float32')
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    # Search in FAISS
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    distances, indices = faiss_index.search(query_embedding, k)
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    # Return relevant document chunks
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    return [documents[i] for i in indices[0]]
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# 4) Query -> Retrieve -> Generate
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def rag_pipeline(query: str, k: int = 2) -> str:
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    retrieved_docs = retrieve_top_k(query, k)
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    prompt = (
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        "You have access to the following context:\n"
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        + "\n".join(f"- {doc}" for doc in retrieved_docs)
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        + f"\n\nAnswer the user query based on the above context. "
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        + f"User query: {query}\nYour answer:"
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    )
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    # Placeholder for LLM call
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    # For demonstration, we’ll mimic an LLM by just providing a naive answer
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    # In a real system, you'd call your LLM API or model here.
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    return f"Mock LLM answer based on retrieved docs: {retrieved_docs}"
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# 5) Test the pipeline
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user_query = "What are the key financial risks for Company XYZ in 2023?"
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answer = rag_pipeline(user_query)
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print(answer)

In a production environment:

• Replace the # Placeholder for LLM call section with actual code to interface with an LLM.
• Handle chunking more systematically for long documents.
• Add caching mechanisms for embeddings.
• Incorporate error handling and logging.

Reranking and Hybrid Retrieval#

Sometimes the semantics of direct vector matching might not perfectly capture the user’s intent, especially if the query includes domain-specific jargon or if your knowledge base is highly specialized. Reranking aids in refining retrieval:

• Cross-Encoder Reranking: Use a BERT-based model to score the relevance of each candidate chunk after initial retrieval.
• Sparse-Dense Hybrid: Combine a sparse retrieval system (like BM25) with a dense retrieval system (like FAISS). This can capture both keyword precision and semantic relationships.

Ensuring High-Quality Context#

If your retrieved context includes irrelevant text or text from misleading sources, the final generated output can suffer. Here are some professional best practices for improving context:

1. Metadata Filtering: Segment your knowledge base by domain, date, or author. Only retrieve from subsets relevant to the user’s question.
2. Confidence Scoring: Discard chunks if their similarity to the query is below a certain threshold, reducing off-topic context.
3. Chunk Ranking: Retrieve more candidate chunks than you need, then rank them by quality, trustworthiness, or timeliness.

Evaluating Your RAG Pipeline#

A well-defined evaluation strategy ensures you’re continuously improving. Consider:

1. Human-in-the-loop: Domain experts or end users rate the correctness of generated answers.
2. Quantitative QA metrics: If your pipeline handles factual questions, measure accuracy, precision, or F1 against a labeled dataset.
3. A/B testing: For large-scale deployments, systematically compare different retrieval or generation configurations.

Real-Time and Incremental Updates#

For dynamic knowledge sources (like news articles or streaming data):

• Incremental Embedding: As new documents arrive, embed them on-the-fly and update the vector store.
• Index Rebuilding Schedules: Periodically rebuild or reorganize indexes for efficiency, especially if your knowledge base grows significantly.
• Cache Invalidation: If you discover some texts are outdated or inaccurate, remove or flag them to prevent retrieval errors.

Beyond the Basics#

Once you’ve mastered initial pipeline building, consider these more advanced topics:

1. Prompt Engineering: Fine-tune your prompts or use advanced templates (Chain-of-Thought, ReAct, etc.) for improved reasoning.
2. Multimodal Retrieval: If your data includes images, audio, or other modalities, you can extend your pipeline to handle them with specialized embedding models.
3. Generative Agents: Some setups continuously query and store new information in a knowledge base, effectively “learning” in near real-time.
4. Neural QA: Integrate your pipeline with specialized neural approaches that do end-to-end answer generation and retrieval simultaneously.