Combining Retrieval and Generation

Connecting a retriever component, which fetches relevant information, with a generator (LLM), which produces text, forms the primary logic of a RAG pipeline. The retrieved information directly influences the final output generated by the LLM.

The fundamental process involves taking the user's input query, using it to retrieve context, and then presenting both the original query and the retrieved context to the LLM within a carefully structured prompt.

The RAG Sequence

Let's visualize the typical flow of information when combining retrieval and generation:

A standard data flow in a RAG system. The user query initiates retrieval, the retrieved context is added to the query to form an augmented prompt, which is then processed by the generator LLM.

Implementing the Connection

At its core, combining retrieval and generation involves a sequence of function calls. You'll typically create a function or method that orchestrates this flow. Let's assume you have:

1. A retriever object with a method like search(query: str) -> list[str] that returns a list of relevant text chunks.
2. A generator object (representing the LLM interface) with a method like generate(prompt: str) -> str that takes a prompt and returns the generated text.

The combining logic would look something like this in Python:

# Assume 'retriever' and 'generator' objects are already initialized
# as described in previous sections.

def create_augmented_prompt(query: str, context_chunks: list[str]) -> str:
    """
    Formats the prompt string to include the query and retrieved context.
    """
    # Simple concatenation strategy
    context = "\n\n".join(context_chunks)

    # Example prompt template
    prompt = f"""Based on the following context, please answer the query. If the context doesn't contain the answer, state that.

Context:
{context}

Query: {query}

Answer:"""
    return prompt

def execute_rag_pipeline(query: str) -> str:
    """
    Runs the query through the retrieval and generation steps.
    """
    # 1. Retrieve relevant context
    try:
        retrieved_chunks = retriever.search(query)
        if not retrieved_chunks:
            # Handle cases where no relevant chunks are found
            # Option 1: Return a specific message
            # return "I couldn't find relevant information to answer your query."
            # Option 2: Proceed without context (falls back to standard LLM behavior)
            retrieved_chunks = []
            print("Warning: No relevant context found.") # Or use logging

    except Exception as e:
        print(f"Error during retrieval: {e}")
        # Handle retrieval errors appropriately
        return "An error occurred during information retrieval."

    # 2. Construct the augmented prompt
    augmented_prompt = create_augmented_prompt(query, retrieved_chunks)

    # Optional: Check prompt length against model limits
    # (Implementation depends on the specific LLM and tokenizer)
    # if len(tokenizer.encode(augmented_prompt)) > MAX_CONTEXT_LENGTH:
    #     # Handle context overflow (e.g., truncate context, use a different strategy)
    #     print("Warning: Prompt exceeds maximum length. Truncation may occur.")
    #     # Add logic here to shorten the prompt if necessary

    # 3. Generate the response using the LLM
    try:
        final_response = generator.generate(augmented_prompt)
    except Exception as e:
        print(f"Error during generation: {e}")
        # Handle generation errors
        return "An error occurred while generating the response."

    return final_response

# Example usage:
user_query = "What are the main benefits of using RAG compared to fine-tuning?"
response = execute_rag_pipeline(user_query)
print(f"Query: {user_query}")
print(f"Response: {response}")

Combination Step Strategies

• Prompt Formatting: As discussed in Chapter 4 ("The Generation Component and Augmentation"), the way you structure the prompt (create_augmented_prompt function in the example) is significant. It guides the LLM on how to use the provided context. The example uses a simple template, but more sophisticated templates might instruct the LLM to cite sources or handle conflicting information.
• Context Handling: When the retriever returns multiple chunks, you need a strategy to combine them. Simple concatenation is common, but be mindful of the LLM's context window limit. If the combined context is too long, you might need to truncate it, select only the top-k most relevant chunks, or use more advanced summarization or filtering techniques.
• Error Handling: Implement strong error handling for both the retrieval and generation steps. Network issues, API errors, or problems during data processing can occur. Decide how the pipeline should behave in these cases (e.g., return an error message, fall back to non-augmented generation).
• Framework Abstractions: While this section shows the manual combination logic, remember that frameworks like LangChain or LlamaIndex (mentioned in the overview-rag-frameworks section) often provide higher-level abstractions like "Chains" or "Query Engines". These abstractions encapsulate this retrieval-augmentation-generation sequence, simplifying the implementation considerably by handling prompt formatting, component linking, and sometimes context management automatically. However, understanding the underlying sequence, as shown here, is important for debugging and customization.

By connecting the retriever and generator, you create the pathway for external knowledge to inform the LLM's output, moving from isolated components to a functional RAG system. The next section focuses on running queries through this assembled pipeline.