Introduction

If you’ve ever found yourself digging through product manuals, company wikis, or lengthy documents just to find a simple answer, you know the pain. The fact you’re reading this suggests you’re interested in how Generative AI can make that process less painful. Stick around for a few minutes, and I’ll walk you through how we built a smarter FAQ bot using Google’s Gemini API, Retrieval Augmented Generation (RAG), and structured output. This isn’t just another chatbot; it’s designed to give reliable, context-aware answers based only on provided information, minimizing the risk of making things up (hallucination). This example uses Google Car manuals, but the principles apply anywhere you have a set of documents you need to query effectively. I’m sharing my journey building this; it’s a practical demonstration, not a definitive guide, so adapt the ideas to your needs!

The Problem: Dumb Bots and Information Overload

Traditional search methods or basic chatbots often fall short when dealing with specific document sets:

  • Information Overload: Manually searching large documents is time-consuming and inefficient.
  • Generic LLM Limitations: Large Language Models (LLMs) are powerful, but they lack specific, up-to-date knowledge about your documents unless explicitly trained on them (which is often impractical).
  • Hallucination Risk: When asked about information outside their training data, LLMs might confidently invent answers that sound plausible but are incorrect. This is unacceptable for reliable FAQ systems.
  • Inconsistent Outputs: Getting answers in a usable, predictable format can be challenging with free-form text generation.

We need a system that answers questions accurately based only on a given set of documents and provides answers in a consistent, structured way.

The Solution: RAG + Gemini API

Our approach combines Retrieval Augmented Generation (RAG) with the capabilities of the Gemini API. At a high level, the user interacts with the system like this:

High-Level RAG Flow Diagram: User Query -> RAG System -> Grounded Answer
Figure 1: High-Level RAG Interaction Flow.

This involves three main steps in the underlying RAG pipeline:

1. Indexing: Convert the source documents (Google Car manuals) into numerical representations (embeddings) using the Gemini text-embedding-004 model and store them in a vector database (ChromaDB). This allows for efficient similarity searches. This setup process is crucial for enabling fast retrieval later.

Indexing Flow Diagram: Documents -> Gemini Embedding -> Vector Embeddings -> ChromaDB Vector Store
Figure 2: The Document Indexing Flow.

2. Retrieval: When a user asks a question, embed the question using the same model and search the vector database to find the most relevant document chunks based on semantic similarity.

Retrieval Flow Diagram: User Query -> Gemini Embedding -> Query Vector -> ChromaDB -> Relevant Document Chunks
Figure 3: The Query Retrieval Flow.

3. Generation: Pass the original question and the retrieved document chunks as context to a powerful LLM (like gemini-2.0-flash). Instruct the model to answer the question based only on the provided context.

Alongside the RAG structure, we leverage specific Gemini API Features:

  • High-Quality Embeddings: text-embedding-004 provides embeddings suitable for finding semantically similar text.
  • Powerful Generation: gemini-2.0-flash can synthesize answers based on the retrieved context.
  • Structured Output (JSON Mode): We instruct Gemini to return the answer and a confidence score in a predictable JSON format, making it easy for applications to use the output.
  • Optional Grounding: We can even add Google Search as a tool if the local documents don’t suffice (though our primary goal here is document-based Q&A).

Implementation Highlights

1. Custom Embedding Function for ChromaDB:

We need to tell ChromaDB how to generate embeddings using the Gemini API.

# --- 4. Define Gemini Embedding Function for ChromaDB ---
from chromadb import Documents, EmbeddingFunction, Embeddings
from google.api_core import retry
from google import genai
from google.genai import types

is_retriable = lambda e: (isinstance(e, genai.errors.APIError) and e.code in {429, 503})

class GeminiEmbeddingFunction(EmbeddingFunction):
    document_mode = True  # Toggle between indexing docs and embedding queries

    @retry.Retry(predicate=is_retriable)
    def __call__(self, input_texts: Documents) -> Embeddings:
        task = "retrieval_document" if self.document_mode else "retrieval_query"
        print(f"Embedding {'documents' if self.document_mode else 'query'} ({len(input_texts)})...")
        try:
            # Assuming 'client' is initialized Google GenAI client
            response = client.models.embed_content(
                model="models/text-embedding-004",
                contents=input_texts,
                config=types.EmbedContentConfig(task_type=task),
            )
            return [e.values for e in response.embeddings]
        except Exception as e:
            print(f"Error during embedding: {e}")
            return [[] for _ in input_texts]

2. Setting up ChromaDB and Indexing:

We create a ChromaDB collection and add our documents. get_or_create_collection makes this idempotent.

# --- 5. Setup ChromaDB Vector Store ---
import chromadb
import time

print("Setting up ChromaDB...")
DB_NAME = "googlecar_faq_db"
embed_fn = GeminiEmbeddingFunction()
chroma_client = chromadb.Client()  # In-memory client

try:
    db = chroma_client.get_or_create_collection(name=DB_NAME, embedding_function=embed_fn)
    print(f"Collection '{DB_NAME}' ready. Current count: {db.count()}")
    # Assuming 'documents' and 'doc_ids' are defined earlier
    if db.count() < len(documents):
        print(f"Adding/Updating documents in '{DB_NAME}'...")
        embed_fn.document_mode = True  # Set mode for indexing
        db.upsert(documents=documents, ids=doc_ids)  # Use upsert for safety
        time.sleep(2)  # Allow indexing to settle
        print(f"Documents added/updated. New count: {db.count()}")
    else:
        print("Documents already seem to be indexed.")
except Exception as e:
    print(f"Error setting up ChromaDB collection: {e}")
    raise SystemExit("ChromaDB setup failed. Exiting.")

3. Retrieving Relevant Documents:

This function takes the user query, embeds it (using document_mode=False), and searches ChromaDB.

# --- 6. Define Retrieval Function ---
def retrieve_documents(query: str, n_results: int = 1) -> list[str]:
    print(f"\nRetrieving documents for query: '{query}'")
    embed_fn.document_mode = False  # Switch to query mode
    try:
        results = db.query(query_texts=[query], n_results=n_results)
        if results and results.get("documents"):
            retrieved_docs = results["documents"][0]
            print(f"Retrieved {len(retrieved_docs)} documents.")
            return retrieved_docs
        else:
            print("No documents retrieved.")
            return []
    except Exception as e:
        print(f"Error querying ChromaDB: {e}")
        return []

4. Generating the Structured Answer:

Here’s the core logic combining the query, retrieved context, and instructions for the LLM, specifying JSON output with a confidence score.

# --- 7. Define Structured Output Schema ---
from typing_extensions import Literal
from pydantic import BaseModel

class AnswerWithConfidence(BaseModel):
    answer: str
    confidence: Literal["High", "Medium", "Low"]

# --- 8. Define Augmented Generation Function ---
def generate_structured_answer(query: str, context_docs: list[str]) -> dict | None:
    if not context_docs:
        print("No context provided, cannot generate answer.")
        return {
            "answer": "I couldn't find relevant information in the provided documents to answer this question.",
            "confidence": "Low",
        }

    context = "\n---\n".join(context_docs)

    prompt = f"""You are an AI assistant answering questions about a Google car based ONLY on the provided documents.
Context Documents:
---
{context}
---
Question: {query}
Based *only* on the information in the context documents above, answer the question.
Also, assess your confidence in the answer based *only* on the provided text:
- "High" if the answer is directly and clearly stated in the documents.
- "Medium" if the answer can be inferred but isn't explicitly stated.
- "Low" if the documents don't seem to contain the answer or are ambiguous.
Return your response ONLY as a JSON object with the keys "answer" and "confidence". Example format:
{{
  "answer": "Your answer here.",
  "confidence": "High/Medium/Low"
}}
"""
    try:
        generation_config = types.GenerateContentConfig(
            temperature=0.2,
            response_mime_type="application/json",  # Request JSON
            response_schema=AnswerWithConfidence,  # Provide the schema
        )
        # Assuming 'client' is initialized Google GenAI client
        response = client.models.generate_content(
            model="gemini-2.0-flash",
            contents=prompt,
            generation_config=generation_config,  # Pass the config object
        )

        # Safe access to parsed output
        if (
            response.candidates
            and response.candidates[0].content
            and response.candidates[0].content.parts
        ):
            parsed_output = response.candidates[0].content.parts[0].function_call
            # Fallback check if .parsed is used
            if not parsed_output and hasattr(
                response.candidates[0].content.parts[0], "parsed"
            ):
                parsed_output = response.candidates[0].content.parts[0].parsed

            if isinstance(parsed_output, dict) and "answer" in parsed_output and "confidence" in parsed_output:
                print("Generated Answer:", parsed_output)
                return parsed_output
            else:
                print("Warning: Could not extract valid JSON from response.")
                print("Raw response part:", response.candidates[0].content.parts[0])
                # Attempt to parse the text part if it exists and looks like JSON
                try:
                    import json

                    text_part = response.candidates[0].content.parts[0].text
                    if text_part and text_part.strip().startswith("{") and text_part.strip().endswith("}"):
                        parsed_json = json.loads(text_part)
                        if isinstance(parsed_json, dict) and "answer" in parsed_json and "confidence" in parsed_json:
                            print("Recovered JSON from text part:", parsed_json)
                            return parsed_json
                except Exception as json_e:
                    print(f"Could not parse text part as JSON: {json_e}")

        print("Error: Could not generate/parse structured response correctly.")
        return {"answer": "Error: Could not generate or parse the structured response from the AI.", "confidence": "Low"}

    except Exception as e:
        print(f"Error during content generation call: {e}")
        return {"answer": f"Error during generation API call: {e}", "confidence": "Low"}

Tip: Ensure your API key is correctly set up in Kaggle Secrets (GOOGLE_API_KEY). Also, ChromaDB setup might require specific permissions or setup depending on the environment (here we use an in-memory one for simplicity).

Limitations and Future Work

This implementation is a great starting point, but it has limitations:

  • Document Quality: The RAG system’s effectiveness heavily depends on the quality, relevance, and comprehensiveness of the indexed documents. Garbage in, garbage out.
  • Retrieval Accuracy: Simple similarity search might not always retrieve the perfect chunk of text, especially for complex queries. More advanced retrieval strategies (like hybrid search or re-ranking) could improve this.
  • Structured Output Failures: While JSON mode is robust, the LLM might occasionally fail to generate perfectly valid JSON matching the schema. More robust error handling and potentially retries could be added.
  • Limited Context Handling (within LLM): While RAG provides context, the LLM itself still has limits on how much context it can process effectively in a single generation step. Very long retrieved passages might need summarization or chunking before being sent to the LLM.
  • Static Knowledge: The bot only knows what’s in the ChromaDB index. It doesn’t learn automatically. Updates require re-indexing.

Future Enhancements

  • Implement Google Search grounding as a fallback when confidence is low or documents are missing.
  • Add conversation memory for multi-turn interactions.
  • Explore more sophisticated retrieval techniques.
  • Build a simple UI (e.g., using Gradio or Streamlit).
  • Fine-tune an embedding model specifically for the car manual domain (though text-embedding-004 is quite capable).

Conclusion

Building this FAQ bot demonstrates how combining RAG with Gemini’s embedding and generation capabilities, especially its structured output mode, can create powerful and reliable AI-driven Q&A systems. By grounding the LLM’s responses in specific source documents and requesting a confidence score, we significantly mitigate hallucination and provide a more trustworthy user experience.

Key Takeaways:

  • RAG grounds LLM answers in your specific data.
  • Gemini Embeddings + ChromaDB enable efficient document retrieval.
  • Structured Output (JSON Mode) enhances reliability and integrability.
  • Confidence Scores add a layer of trustworthiness.

This approach is versatile and can be adapted for various knowledge bases, from customer support FAQs to internal documentation search.

I hope this walkthrough provides a clear picture of how this smarter FAQ bot works! Feel free to ask questions or leave a comment with your thoughts or own implementations!