Build a Searchable Podcast Database with Gemini: Audio Transcription, Embeddings, and Semantic RAG

The System Architecture: From Audio to Answer

A robust podcast RAG pipeline requires two distinct stages: Ingestion (one-time processing) and Retrieval (on-demand answering).

Step 1: Transcription with Gemini 2.0 Flash

Gemini 2.0 Flash can "listen" to audio files up to 2 hours long and provide high-fidelity transcripts with speaker diarization.

# transcribe.py
import google.generativeai as genai
import os

genai.configure(api_key=os.environ["GEMINI_API_KEY"])

def transcribe_podcast(audio_path: str):
    # 1. Upload to Files API
    print(f"Uploading {audio_path}...")
    audio_file = genai.upload_file(path=audio_path)
    
    # 2. Wait for processing
    while audio_file.state.name == "PROCESSING":
        time.sleep(5)
        audio_file = genai.get_file(audio_file.name)

    # 3. Transcribe with 2.0 Flash
    model = genai.GenerativeModel("gemini-2.0-flash")
    prompt = "Transcribe this podcast. Include speaker names and timestamps. Format as JSON list of objects: {speaker, text, start_time, end_time}."
    
    response = model.generate_content([prompt, audio_file])
    return response.text

Step 2: Indexing in ChromaDB

Once we have the transcript, we break it into chunks and store them in a vector database.

# indexer.py
import chromadb
from chromadb.utils.embedding_functions import GoogleGenerativeAiEmbeddingFunction

def index_transcript(transcript_json: list, episode_title: str):
    client = chromadb.PersistentClient(path="./podcast_db")
    
    # Use Gemini's high-performance embedding model
    embed_fn = GoogleGenerativeAiEmbeddingFunction(
        api_key=os.environ["GEMINI_API_KEY"],
        model_name="models/text-embedding-004"
    )
    
    collection = client.get_or_create_collection(
        name="podcast_chunks", 
        embedding_function=embed_fn
    )

    # Chunking: Group 5 sentences together with 1 sentence overlap
    chunks = create_semantic_chunks(transcript_json)
    
    collection.add(
        documents=[c['text'] for c in chunks],
        metadatas=[{"title": episode_title, "time": c['start_time']} for c in chunks],
        ids=[f"{episode_title}_{i}" for i in range(len(chunks))]
    )

Step 3: Semantic RAG Query

The final piece is the QueryEngine that finds the right chunks and generates the answer.

# query.py
def ask_podcast(question: str):
    # 1. Retrieve
    results = collection.query(query_texts=[question], n_results=5)
    context = "\n\n".join(results['documents'][0])
    sources = results['metadatas'][0]

    # 2. Generate
    model = genai.GenerativeModel("gemini-2.0-flash")
    system_prompt = f"Answer using ONLY the provided context. Context: {context}"
    
    response = model.generate_content(f"{system_prompt}\n\nQuestion: {question}")
    
    # 3. Add Citations
    answer = response.text
    citation_summary = "\n".join([f"- {s['title']} at {s['time']}" for s in sources])
    return f"{answer}\n\n**Sources:**\n{citation_summary}"

Performance & Cost Analysis (Mid-2026)

Common Pitfalls to Avoid

Key Takeaways