Running Queries Through the Pipeline

With the retriever and generator components assembled and linked, potentially using a framework like LangChain or LlamaIndex as discussed previously, our basic Retrieve-Augmented Generation pipeline is ready for use. The purpose of this pipeline is to answer queries by first finding relevant information within our specific document set and then using that information to generate a coherent and contextually grounded response. Let's see how to interact with it.

The Journey of a Query

When you submit a query to the RAG pipeline, it initiates a sequence of operations designed to leverage your external knowledge base:

1. Query Input: You provide the pipeline with a question or prompt as a string.
2. Retrieval Step: The pipeline first passes your query to the retriever component. The retriever searches the vector database (which contains the indexed document chunks) for chunks whose embeddings are most similar to the query's embedding. It returns a set of these relevant document chunks.
3. Context Augmentation: The retrieved chunks (the context) are combined with your original query. This often involves formatting them into a specific structure within a larger prompt template that instructs the Language Model (LLM) on how to use the context.
4. Generation Step: This augmented prompt (containing the original query and the retrieved context) is sent to the LLM (the generator component).
5. Final Response: The LLM processes the augmented prompt and generates a response. Ideally, this response directly answers your query, drawing information primarily from the provided context rather than solely relying on its internal, pre-trained knowledge.

This flow ensures that the generated answer is informed by the specific documents you provided during the data preparation phase.

A diagram illustrating the path a query takes through the RAG pipeline, from initial input to the final generated response.

Executing a Query in Practice

Assuming you have instantiated your combined RAG pipeline in an object (let's call it rag_chain for consistency, though the actual name depends on the framework or code used in previous steps), running a query is typically straightforward.

You pass the query string to the appropriate method of your rag_chain object. Frameworks like LangChain often use methods like invoke() or stream() for this purpose.

# Assume 'rag_chain' is your previously configured RAG pipeline object
# (e.g., created using LangChain or LlamaIndex)

# Define your query
user_query = "What are the main challenges mentioned in the latest project status report?"

# Execute the query through the pipeline
try:
    response = rag_chain.invoke(user_query)
    print("Pipeline Response:")
    print(response)

except Exception as e:
    print(f"An error occurred: {e}")

# Example with potential streaming output (if supported by the chain/LLM)
# try:
#     print("Streaming Pipeline Response:")
#     for chunk in rag_chain.stream(user_query):
#         # Process each chunk as it arrives (e.g., print it)
#         print(chunk, end="", flush=True)
#     print("\n--- End of Stream ---")
# except Exception as e:
#     print(f"\nAn error occurred during streaming: {e}")

Understanding the Output

The response variable in the code above will contain the final answer generated by the LLM. Let's consider a hypothetical scenario where our indexed documents contain status reports for "Project Alpha".

Query: "What are the main challenges mentioned in the latest Project Alpha status report?"

Possible Retrieved Context (Simplified):

• Chunk 1: "...Project Alpha Status - Oct 26... integration with the legacy payment system remains complex..."
• Chunk 2: "...resource allocation for Q4 is tighter than anticipated, potentially impacting the testing timeline..."
• Chunk 3: "...dependency on Team Beta's API delivery schedule introduces risk..."

Possible rag_chain Response: "According to the latest Project Alpha status report, the main challenges mentioned are the complexity of integrating with the legacy payment system, tighter than anticipated resource allocation for Q4 potentially impacting timelines, and a dependency risk related to Team Beta's API delivery schedule."

Notice how this response directly addresses the query and synthesizes information found in the hypothetical retrieved chunks. It is specific and grounded in the supposed content of the documents. Without RAG, a standard LLM might provide a generic answer about project challenges or state it doesn't have access to specific, real-time project reports.

Experimentation and Next Steps

Running a single query confirms the pipeline works, but true understanding comes from experimentation. Try different types of queries:

• Specific Fact Retrieval: Ask for precise details you know are in the documents.
• Summarization: Ask for a summary of a specific document section or topic covered in multiple documents.
• Comparison: Ask how two concepts discussed in the documents differ.
• Queries with No Direct Answer: Input a query whose answer is unlikely to be in the indexed documents. Observe how the pipeline responds. Does it indicate the information isn't available in the context, or does it attempt to answer based solely on the LLM's general knowledge? The desired behavior often depends on the prompt engineering used when creating the pipeline.

Executing queries is the moment of truth for your RAG system. It demonstrates the practical value of combining targeted information retrieval with the generative capabilities of LLMs to produce relevant, context-aware answers based on your specific data sources. In the next chapter, we will look into methods for evaluating how well your pipeline performs and strategies for improving its effectiveness.