Basic RAG Pipeline Implementation

Having explored the individual components of Retrieval Augmented Generation (RAG) , loading documents, splitting them, creating embeddings, storing them in vector stores, and retrieving relevant chunks , we can now assemble these parts into a functional pipeline. This section demonstrates how to connect these components to create a system that answers user queries based on the provided external data, rather than solely relying on the LLM's internal knowledge.

The core idea is straightforward: when a user asks a question, we first retrieve relevant information from our document store and then provide this information, along with the original question, to the Large Language Model to generate an informed answer.

The RAG Workflow

Let's visualize the typical flow of information in a basic RAG pipeline:

The diagram illustrates the sequence of steps in a RAG pipeline, from receiving the user query to generating the final answer using retrieved context and an LLM.

Implementing the Steps

Let's break down the implementation of this pipeline, assuming you have already set up the necessary components as discussed in previous sections: a document loader, text splitter, embedding model interface, a populated vector store, and an LLM interface.

1. Receive User Query: The process begins when the application receives a query from the user.

   user_query = "What are the main challenges with LLM output consistency?"
   

2. Embed the Query: Use the same embedding model that was used to embed the document chunks to convert the user's query into a vector representation. Consistency here is important for the semantic search to work correctly.

   # Assume 'embedding_model' is an initialized interface
   query_vector = embedding_model.embed_query(user_query)
   # query_vector might look like [-0.012, 0.987, ..., -0.345]
   

3. Perform Vector Search: Use the generated query vector to search the vector store. The goal is to find the document chunks whose embeddings are most similar (e.g., using cosine similarity or dot product) to the query embedding. Typically, you retrieve the top k most relevant chunks.

   # Assume 'vector_store' is an initialized and populated vector store interface
   # Retrieve the top 3 most relevant document chunks
   k = 3
   retrieved_chunks = vector_store.similarity_search(query_vector, k=k)
   # retrieved_chunks would be a list of Document objects/strings
   # Example content of a chunk:
   # "LLM outputs can be unpredictable. Variability arises from..."
   

4. Augment the Prompt: Combine the retrieved document chunks with the original user query to create a new, context-rich prompt for the LLM. A common approach is to use a prompt template.

   # Format the retrieved chunks into a single context string
   context_string = "\n\n".join([chunk.page_content for chunk in retrieved_chunks])
   
   # Define a prompt template
   prompt_template = """
   Based on the following context, please answer the question. If the context doesn't contain the answer, state that.
   
   Context:
   {context}
   
   Question:
   {question}
   
   Answer:
   """
   
   # Create the final augmented prompt
   augmented_prompt = prompt_template.format(context=context_string, question=user_query)
   

   This template clearly instructs the LLM on how to use the provided context to answer the question.

5. Generate the Answer: Send the augmented prompt to the LLM. The model will use both its internal knowledge and the specific context provided to formulate an answer.

   # Assume 'llm' is an initialized LLM interface
   response = llm.invoke(augmented_prompt)
   # response might be: "Based on the provided context, the main challenges with LLM output
   # consistency include unpredictability and variability stemming from model parameters..."
   

6. Return the Response: Present the LLM's generated answer to the user.

Putting It Together (Conceptual Example)

Here's how these steps might look combined in a simplified function:

# Assume necessary components (embedding_model, vector_store, llm) are initialized

def answer_query_with_rag(user_query: str, k: int = 3) -> str:
    """
    Answers a user query using a basic RAG pipeline.
    """
    # 1. Embed the query
    query_vector = embedding_model.embed_query(user_query)

    # 2. Retrieve relevant documents
    retrieved_chunks = vector_store.similarity_search(query_vector, k=k)

    # 3. Format context
    context_string = "\n\n".join([chunk.page_content for chunk in retrieved_chunks])

    # 4. Create augmented prompt
    prompt_template = """
    Context:
    {context}

    Question: {question}

    Answer based only on the provided context:
    """
    augmented_prompt = prompt_template.format(context=context_string, question=user_query)

    # 5. Generate response using LLM
    response = llm.invoke(augmented_prompt)

    return response

# Example usage:
user_question = "What methods are used for document splitting in RAG?"
final_answer = answer_query_with_rag(user_question)
print(final_answer)

This basic pipeline forms the foundation of most RAG systems. By retrieving relevant information dynamically, it allows the LLM to answer questions about specific, external data sources, overcoming the limitations of its static training knowledge. The next sections and chapters will build upon this foundation, exploring more advanced techniques and considerations for building robust RAG applications. Remember that the quality of the retrieval step directly impacts the quality of the final generated answer. Therefore, optimizing document preparation, embedding choices, and retrieval strategies is significant for effective RAG implementation.