What is the best way to add a knowledge base to agents? We usually use GPT-4o Mini.

Thanks for reaching out, Waleed. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

We do sales calls, and I wanted to know the best providers/platforms for this. Also, how can we achieve better success with sales calls? We usually use GPT-4o Mini, and I want the agent to sound natural and human-like — someone who understands customer psychology, builds rapport, handles objections well, and guides the conversation smoothly instead of sounding robotic.

Thanks — I’m responding to your questions about platform recommendations for sales calls and how to make your GPT-4o Mini agent sound natural and effective. I can help most with building and tuning the agent (voice, convo design, RAG, live audio flow) rather than recommending specific commercial providers. To give targeted, actionable guidance, could you tell me: • Which pipeline do you use today: STT→LLM→TTS (live voice) or text-first with call bridging? • Where do you host your KB and CRM data (vector DB, Google Drive, Salesforce, etc.)? • Are you running calls at scale (many concurrent calls) or pilot/low volume? • Do you need real-time supervisor intervention, recordings, or analytics integrated? • What are your main failure modes now (robotic tone, bad objection handling, latency, hallucinations)? Based on your answers I’ll provide: • A recommended LiveKit-based architecture for natural voice sales agents using GPT-4o Mini (including RAG, filler speech, and tooling). • Specific prompt patterns, persona and rapport techniques, and short code examples for injecting KB/context or using tools. • Practical tuning steps to improve naturalness, objection-handling, and conversion., with interactive elements

We are currently using GPT-4o Mini, Cartesia, Deepgram, PGVector, and Supabase, and we are running around 10 concurrent calls. Right now, we are facing issues with hallucinations and the agent sounding too robotic during conversations. We want the voice agent to sound more natural, conversational, and human-like while staying accurate and context-aware during sales calls.

Thanks — I’m responding to your setup (GPT-4o Mini + Cartesia TTS, Deepgram STT, PGVector + Supabase, ~10 concurrent calls) and your goals: reduce hallucinations and make the agent sound natural, rapport-building, and effective on sales calls. Below is a focused plan you can implement, plus quick actions to try immediately. 1) Grounding & hallucination reduction (high impact) • Use RAG with your PGVector/Supabase vector store. Do lookups per user turn and inject the top 3–5 retrieved snippets into the chat context before generation. ◦ Recommended hook: implement the on_user_turn_completed lifecycle hook to run the vector search and call

turn_ctx.add_message()

so the LLM sees the retrieved facts in the same call that generates the reply. • Prefer concise, source-attributed context (one-paragraph snippets + source ID) rather than dumping long docs. • Add guardrails: ◦ If no confident KB hits, have the agent say: “I don’t have that info right now — would you like me to check?” instead of guessing. ◦ Limit reasoning/tool-call loops to 3 attempts and fall back to human-sounding safe responses. • Add a background observer (optional, higher accuracy): a more capable LLM reviews candidates and injects corrections when the primary reply risks hallucination. 2) Make the voice sound natural (low-latency, human-like) • Persona as audible behavior: in the system prompt define concrete speech patterns (short sentences, occasional fillers, small clarifying questions, empathy lines). Example rules: ◦ Start with a warm opener: “Hi — I’m Alex. Thanks for taking a minute.” ◦ Use short interjections: “Mhm,” “Got it,” “Let me check that,” then a 200–400ms pause. ◦ If unsure, ask a clarifying question rather than fill with facts. • Use SSML and Cartesia features: ◦ Insert controlled pauses with

<break time="300ms"/>

where you want breath/processing. ◦ Add occasional soft filler words (

um

,

so

) per explicit rules in prompts to sound more conversational. ◦ Pick and pin a voice UUID in Cartesia for consistency. • Timing & prosody: ◦ Engineer pause placement in examples, not as abstract instructions. Provide 6–8 example utterances in the system prompt demonstrating pacing and fillers. ◦ Employ small emotional constraints (calm, confident) via TTS tags if supported. 3) Conversation design & objections handling (behavioral changes) • Pre-classify common sales intents (qualify lead, pricing, demo scheduling, objections) so simple flows are handled by deterministic templates; escalate to full LLM reasoning only for open-ended scenarios. • Build short objection-response templates in prompt memory and let the LLM adapt them with retrieved facts. Example flows: acknowledge → validate → counter-proposal → close/check next step. • Teach the model rapid rapport moves: mirror the customer tone, use their name, reference prior notes, and summarize decisions before next steps. 4) Latency, scale, and UX (practical for ~10 concurrent calls) • Keep the RAG retrieval small and fast (top-k=3–5). Co-locate services or use low-latency regions for Supabase/PGVector and your inference endpoints. • Use preemptive generation / partial streaming to reduce perceived latency and start filler speech while retrieval completes. • Provide short status utterances during longer lookups: “Give me a second, I’m pulling that up.” This avoids dead air and feels human. • Monitor and log failed KB lookups and hallucination incidents to iteratively expand the KB and refine prompts. 5) Quick implementation checklist (start today) 1. Add on_user_turn_completed hook to perform PGVector search for each user turn. 2. Inject top-3 snippets into turn_ctx with source IDs and persist those only when useful. 3. Update system prompt with 6–8 concrete sample utterances showing pacing, fillers, and objection-handling. 4. Configure Cartesia to use a pinned voice UUID and include SSML breaks per example outputs. 5. Add a fallback rule: if KB confidence < threshold, respond with an explicit offer to check or transfer. 6. Run A/B tests: measure conversion, call length, interruptions, and hallucination rate. If you want, I can: • Draft the exact system prompt + 6 example utterances (SSML included). • Provide a short code snippet for the on_user_turn_completed hook that queries PGVector/Supabase and injects results. Which would you like first? Sources: External data and RAG | LiveKit Documentation | The ReAct Pattern for Voice Agents and How AI Agents Think, Act, and Respon... | Sequential Pipeline Architecture for Voice Agents | LiveKit | Prompting voice agents to sound more realistic | LiveKit | Building Performant Voice Agents in India | LiveKit, with interactive elements

Should I go for this or not ? 1. Switch from OpenAI embedding → local FastEmbed — saves the 800ms HTTP roundtrip. Biggest single latency win (~40% improvement). Requires deploying a small embedding model (BAAI/bge-small or similar) in the backend container — adds ~100MB to image size.

Thanks — I’m responding to your proposal to replace OpenAI embeddings with a local FastEmbed (BAAI/bge-small) to save ~800ms per lookup. Short answer: Yes — switch if low latency is a top priority for call quality and you can accept the added operational cost. The latency win is the largest single improvement you can get and will meaningfully reduce perceived lag on live calls. Pros • ~800ms saved per embedding call → biggest single latency improvement (~40% for your pipeline). • More predictable latency and reduced external dependency. • Cheaper at scale (no per-call embedding API cost). Cons / Operational trade-offs • You must run and maintain a small model (~100MB added to your backend image). This increases deployment complexity and resource usage. • Slightly more infra responsibility: model updates, monitoring, and potential memory/CPU utilization during peak concurrent calls. • Embedding quality parity depends on model choice; FastEmbed/BAAI bge-small is usually adequate but test for your KB. Practical recommendations • Prototype first: run A/B on a subset of calls to compare latency, embedding similarity (MAP/recall on retrievals), and downstream hallucination/quality. • Use batching + async embedding to maximize throughput and avoid blocking inference during concurrent calls. • Co-locate the embedding service with your Supabase/PGVector instance or agent workers to minimize intra-cluster latency. • Cache embeddings for static KB docs to eliminate repeated work. • Monitor: track retrieval latency, recall/precision, and conversion changes after the switch. • Rollback plan: keep OpenAI embedding keys available so you can revert quickly if quality or stability problems appear. If you want, I can: • Draft a short A/B test plan (metrics and thresholds) and a minimal on‑container deployment snippet for bge-small + batching. Which would you like?, with interactive elements

I am using telnyx SIP with Live kit. The agent voice is being cut down. lot what can be teh reason for this ?

Sorry you’re seeing the agent’s voice get cut off — I’ll help troubleshoot. A few likely causes and quick checks given you’re using Telnyx SIP with LiveKit: Possible causes (and what to check) • Noise-cancellation mismatch ◦ Ensure SIP participants use the BVCTelephony filter (not the default WebRTC BVC). Wrong filter can suppress or chop SIP audio. • Server-side audio processing / transcoding ◦ LiveKit may decrypt audio briefly to run noise suppression/transcoding. Aggressive processing (Krisp or codecs) can cut audio. Try disabling those processing steps briefly. • Codec or packetization mismatch between Telnyx and LiveKit ◦ Verify SIP codec negotiation (sample rates, payload types) and ensure no frequent re-negotiations or packet loss. • Adaptive interruption/VAD interactions ◦ Confirm the agent’s audio isn’t being misclassified as user speech or noise by VAD. Check whether cutting happens when other audio/noise occurs. • Network packet loss or jitter on the server or Telnyx leg ◦ Inspect packet loss, jitter, and RTCP reports during affected calls; packet loss can cause audio dropouts. • Resource contention or concurrency limits ◦ At peak load, CPU spikes or audio encoding overload can truncate audio. Check host CPU/memory and TTS worker logs. Quick isolation steps 1. Reproduce with noise cancellation disabled for the SIP participant to see if cutoffs stop. 2. Test direct WebRTC-LiveKit call (no Telnyx) to isolate whether issue is SIP trunk-related. 3. Temporarily disable server-side Krisp/noise-suppression/transcoding. 4. Capture logs and RTP stats (jitter, loss, round-trip) for a failing call. 5. Confirm Cartesia TTS output is complete before sending into LiveKit (play back saved audio locally). 6. Try a different codec or force a stable codec to avoid re-negotiation issues. What I need to dig deeper • A short call timestamp where the issue occurred (UTC) and a meeting/room ID. • Relevant LiveKit logs around that call (errors, warnings) and RTP/RTCP stats if available. • Your noise-cancellation setting for SIP participants (are you using BVCTelephony?). • Whether the cut happens mid-sentence, fades out, or is immediate drop. If you share those details I’ll help pinpoint the root cause and suggest a targeted fix. Sources: Telnyx LLM plugin guide | LiveKit Documentation | Using Noise Cancellation for SIP and WebRTC Participants | SIP Telephony and Encryption | Solving unwanted interruptions with Adaptive Interruption Handling | LiveKi... | Agents telephony integration | LiveKit Documentation, with interactive elements

Now also check the segment synchronizer warnings from earlier — those

_SegmentSynchronizerImpl.on_play

warnings I saw on call ce1a95ff and others are the gap-during-speech symptom:

Thanks — I’m responding to your note that the

_SegmentSynchronizerImpl.on_play

warnings on call ce1a95ff are the gap-during-speech symptom. Short answer — those warnings indicate audio synchronization/drift during playback and are very likely the root cause of the agent voice being cut. Here’s a concise diagnosis and action plan. Why this causes gaps •

_SegmentSynchronizerImpl.on_play

warnings point to A/V sync drift or missed/late audio segments, which manifest as mid-sentence gaps or dropped audio. • Common contributors: oversized synchronizer buffer, blocking pushes between audio/video, incorrect capture timestamps/durations, or network/jitter causing late RTP packets. Immediate fixes to try 1. Reduce synchronizer buffer ◦ Lower

queue_size_ms

from large values (e.g., 1500ms) toward ~200ms to reduce rebuffering and faster corrections. 1. Push audio/video in parallel ◦ Ensure your publisher code doesn’t serialize audio and video pushes; send them in parallel tasks to avoid blocking drift. 1. Validate timestamps/durations ◦ Confirm

capture_time_ns

and

duration

for segments are correct and monotonically increasing. 1. Test with noise cancellation off ◦ Temporarily disable BVCTelephony/Krisp/transcoding to rule out processing-induced dropouts. 1. Check RTP stats & network ◦ Collect jitter, packet loss, and latency (RTCP) for ce1a95ff; high jitter/loss correlates with segment gaps. 1. Verify TTS output ◦ Play the generated TTS audio locally to ensure Cartesia output is complete before sending into LiveKit. 1. Co-locate services / reduce hops ◦ Move Supabase/PGVector/agent workers and Telnyx egress closer to LiveKit to reduce RTT if trunk location causes long hops. Logs & info I need to help further • The LiveKit room/room_id and timestamp range for call ce1a95ff (UTC). • The exact

queue_size_ms

(synchronizer buffer) and noise-cancellation setting used for SIP participants. • RTP/RTCP stats (jitter, packet loss) or LiveKit logs around the warning messages. • A short excerpt of the

_SegmentSynchronizerImpl.on_play

warning lines (full stack/log text helps). If you share those details I’ll pinpoint the likely root cause and provide the exact config changes or code diffs to fix it. Sources: Debugging Audio/Video Sync Issues in LiveKit Publishing | Agents telephony integration | LiveKit Documentation | Understand and Improve Agent Latency | LiveKit | The ReAct Pattern for Voice Agents and How AI Agents Think, Act, and Respon... | The ReAct Pattern for Voice Agents and How AI Agents Think, Act, and Respon..., with interactive elements