Q2. AI Audio Intelligence + Chat System

1. Problem Statement

You are building a system where users upload audio files (calls, meetings). The system extracts insights and allows users to chat with the content.

2. Requirements

1. Fetch audio from a URL.
2. Run speech-to-text and store transcript.
3. Generate summary and key insights.
4. Allow users to ask questions on the transcript.
5. Maintain chat history per user/session.
6. Support streaming responses for chat answers.
7. Handle long audio via chunking + parallel processing.

3. Follow-up Questions

• How will you design schema for transcripts, chunks, and chat history?
• How will you store embeddings and retrieve context?
• How will you manage context window limits?
• How will you handle multi-user concurrency?

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4. Schema Design (Fields)

• AudioSources: id, user_id, source_url, duration_secs, status
• Transcripts: id, audio_id, raw_text, summary_json
• TranscriptChunks: id, transcript_id, start_timestamp, chunk_text, embedding_vector (VECTOR(1536) - pgvector)
• ChatSessions: id, user_id, audio_id, created_at
• ChatMessages: id, session_id, role (user/ai), content, cited_chunk_ids (List), created_at

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5. High-Level Design (HLD) & Explanatory Walkthrough

graph TD
    Client -->|Audio URL| API[Upload Gateway]
    API -->|Async Event| Splitter[Audio Chunking Worker]
    
    Splitter -->|10 min segments| STT[Transcribe Worker Pool]
    STT -->|Merge Text| DB[(PostgreSQL master)]
    
    DB -->|Text Context| Summarizer[LLM Map-Reduce]
    DB -->|Raw Text Array| Embedder[OpenAI Embedding Generator]
    
    Embedder -->|Upsert Chunks & Vectors| VDB[(PostgreSQL + pgvector Plugin)]
    
    Client -->|Ask 'What was the budget?'| ChatAPI[Chat Engine]
    ChatAPI -->|SQL Semantic Query| VDB
    VDB -->|Top 5 Paragraphs| ChatAPI
    ChatAPI -->|Prompt + Context| LLM_Chat[ChatGPT API]

Explanatory Walkthrough (Teaching Notes)

When architects design Audio Intelligence platforms, the biggest trap is treating a 3-hour audio file as one monolithic block.

1. Parallelizing STT Integration: When a user submits an audio URL, our pipeline downloads the asset. A Chunking Worker physically splits the MP3 into dozens of 5-minute clips. We place 20 concurrent messages onto a Queue. Effectively, 20 parallel Speech-To-Text GPU instances spin up. A 3-hour file transcribes as fast as a 5-minute file.

2. Managing Knowledge Extraction (Vector DB over SQL): Now we have a giant text wall. Rather than paying for Pinecone, we install the pgvector extension natively into Postgres. We instruct OpenAI to convert the text chunks into mathematical arrays (Embeddings). We store the text and the vectors side-by-side in PostgreSQL.

3. The Chat Loop: When the user asks a question, we convert their physical question into a Vector. We query Postgres natively utilizing Cosine Similarity equations against the vector column to pull back the 5 closest matching transcript paragraphs. We stuff these paragraphs invisibly into the LLM prompt, and the AI streams back a contextual reply (RAG).

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6. LLD, Thought Process & Failure Handling

• Managing Context Window Limits: If a podcast transcript is 60,000 words, you cannot pass it into an LLM blindly. By chunking the transcript into 500-word blocks and only sending the Top 5 most relevant blocks (via pgvector) to the LLM, we keep context usage tightly under 3,000 tokens while retaining total accuracy.

• Handling Multi-User Concurrency: Each WebSocket stream is stateless. When the LLM generates a streamed token, the backend routes it via Redis Pub/Sub directly to the user socket, dropping the memory payload immediately.

• Vector DB Leaks (Multi-tenant isolation): Operating Pinecone requires complex metadata filtering. Using pgvector natively in Postgres is incredibly safe because tenant isolation uses standard SQL relationships: JOIN audio_sources WHERE user_id = $1. It is mathematically impossible for data to leak across users at the query layer.

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7. Follow-up SQL Queries

1. RAG Vector DB Execution (Cosine Semantic Search):
Instead of Pinecone APIs, you execute standard SQL that performs vector math natively inside Postgres to pull the 5 most relevant paragraphs.

SELECT chunk_text, 1 - (embedding_vector <=> '[0.012, 0.441, ...]') AS semantic_similarity
FROM transcript_chunks
WHERE transcript_id = 'active-transcript-uuid'
ORDER BY embedding_vector <=> '[0.012, 0.441, ...]' ASC
LIMIT 5;

2. Hydrating Chat Context:
Pull the most recent Chat History to ground the LLM in the current conversation logic alongside the RAG injected above.

SELECT role, content 
FROM chat_messages 
WHERE session_id = 'active-session-uuid' 
ORDER BY created_at DESC 
LIMIT 10;

3. Temporal Reconstruction:
Find the chronological transcript blocks required to rebuild the transcription page UI fully.

SELECT start_timestamp, chunk_text
FROM transcript_chunks
WHERE transcript_id = 'transcript-uuid'
ORDER BY start_timestamp ASC;

4. Engagement Analytics:
Which audio transcript uploads have sparked the most intense AI chat sessions from users?

SELECT a.id, COUNT(c.id) AS total_chat_sessions
FROM audio_sources a
JOIN chat_sessions c ON a.id = c.audio_id
GROUP BY a.id
ORDER BY total_chat_sessions DESC
LIMIT 5;

5. RAG Quality Tuning:
By storing ‘cited_chunk_ids’, we can track exactly which Vector DB chunks the LLM keeps referencing. This identifies the most critical components of meetings natively.

SELECT c.chunk_id, COUNT(*) as citation_count
FROM chat_messages m, unnest(m.cited_chunk_ids) AS c(chunk_id)
GROUP BY c.chunk_id
ORDER BY citation_count DESC 
LIMIT 10;