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h
hi
t
Thanks for reaching out, manish. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Hi — welcome! How can I help you today? If you’re reporting an issue, please include: • What you’re trying to do (e.g., join a room, publish audio/video, record) • Any error messages or unexpected behavior • SDK/SDK version, platform (web/iOS/Android), and Room/Project IDs if relevant If you’re exploring features, tell me which area (realtime, recording, TURN, auth, pricing) and I’ll point you to next steps.
h
how to stop chain-of-thaought content of an gemini llm into the transcript.
t
You're asking how to stop Gemini LLM "chain-of-thought" (internal reasoning) from appearing in the transcript. I can help — quick clarifying question first: Do you run Gemini as a non-realtime LLM plugin (Gemini via the LLM plugin) or as a realtime model (Gemini Live / RealtimeModel)? The best approach differs: • If you use the LLM plugin (non-realtime): override 
llm_node
in your Agent to intercept the LLM response stream and remove/replace chain-of-thought tokens (e.g., 
<tool_call>
, “Found”, or similar) before the text is sent to TTS or the transcript. I can provide a concise Python example that shows how to stream, detect those tokens, and modify chunk content if you want. • If you use Gemini as a realtime model: you have less ability to modify intermediate reasoning. Disable intermediate "thinking" output via the model's 
thinking_config
or switch to Gemini LLM (non-realtime) + separate TTS so you can fully filter responses before transcription. I can show the config options or an example for that path. Tell me which setup you use (non-realtime LLM plugin vs realtime Gemini) and whether you want a code example in Python or another language. Sources: Modifying LLM output before TTS | LiveKit docs | Pipeline nodes and hooks | LiveKit docs | Gemini Live API plugin | LiveKit docs
h
gemini as live api
t
You're using Gemini as a realtime (Live API) model — to prevent chain-of-thought from appearing in transcripts, disable the model's intermediate "thinking" output and/or remove internal reasoning tokens before audio/transcript generation. Two practical options: 1) Disable intermediate thinking via 
thinking_config
• Set the model's 
thinking_config
to not emit intermediate reasoning. For example, disable or set fields that control "thought" or "intermediate" output (turn off streaming of internal steps). This prevents the model from sending chain-of-thought tokens into the output stream that becomes transcript/TTS input. 2) If disabling thinking isn't sufficient or available, post-filter streamed output • Intercept the realtime output stream as it arrives and drop or replace common chain-of-thought markers (e.g., 
<tool_call>
, "Found", internal reasoning phrases) before sending text to TTS or your transcript storage. Which would you like next? • I can show a concise config snippet for 
thinking_config
for Gemini Live API (if you want code), or • I can show a short example of streaming output filtering for your ingestion pipeline. Tell me which (config snippet or streaming filter) and your preferred language (Python/Node).
h
Copy code
llm=google.beta.realtime.RealtimeModel(
    model="gemini-2.5-flash-native-audio-preview-12-2025",
    voice="leda",
    temperature=0.4,
    thinking_config=types.ThinkingConfig(
        include_thoughts=False,
    ),
is this is a right way to prevent that?
t
Yes — setting 
thinking_config.include_thoughts = False
in your 
RealtimeModel
configuration is the correct way to suppress intermediate chain‑of‑thought output from Gemini Live API. This prevents the model from emitting internal "thinking" tokens that would otherwise be sent to TTS or included in transcripts. A couple brief considerations: • If you still see unwanted tokens after this, filter the realtime text stream as a fallback (drop/replace markers like 
<tool_call>
or "Found") before sending to TTS or saving the transcript. • Using lower temperature (you already set 0.4) reduces creative/verbose outputs but does not replace disabling thinking. If you want, I can show a short Node or Python example that applies 
thinking_config
and includes a simple stream filter. Which language do you prefer?
h
okay, go ahead
t
You're on the right track — 
thinking_config.include_thoughts = False
is the correct primary control. Below is a concise Python example showing: 1) RealtimeModel configured to disable thinking. 2) A simple streaming filter fallback that removes common chain-of-thought markers before sending text to TTS or saving transcripts.
Copy code
from google.beta.realtime import RealtimeModel
from google.beta import types

# 1) RealtimeModel config: disable intermediate "thinking"
llm = RealtimeModel(
    model="gemini-2.5-flash-native-audio-preview-12-2025",
    voice="leda",
    temperature=0.4,
    thinking_config=types.ThinkingConfig(
        include_thoughts=False,
    ),
)

# 2) Example stream handler with a simple filter fallback
CHAIN_OF_THOUGHT_MARKERS = ["<tool_call>", "</tool_call>", "Found", "[THOUGHT]", "<reasoning>"]

def clean_chunk_text(text: str) -> str:
    if not text:
        return text
    cleaned = text
    for marker in CHAIN_OF_THOUGHT_MARKERS:
        cleaned = cleaned.replace(marker, "")
    # Optional: normalize repeated whitespace
    return " ".join(cleaned.split())

async def handle_realtime_stream(stream):
    async for event in stream:
        # event may have .text or similar depending on SDK; adapt accordingly
        raw_text = getattr(event, "text", None) or getattr(event, "content", None) or str(event)
        cleaned_text = clean_chunk_text(raw_text)

        # Send cleaned_text to TTS or append to transcript storage
        if cleaned_text.strip():
            await send_to_tts_or_store_transcript(cleaned_text)

# Usage: start the model and pass its output to the handler (pseudo-code)
async with llm.start_streaming(...) as stream:
    await handle_realtime_stream(stream)
Notes: • Use 
thinking_config.include_thoughts=False
first — it prevents most internal reasoning from being emitted. • Keep the simple filter as a fallback for any residual markers. Adjust 
CHAIN_OF_THOUGHT_MARKERS
to match tokens you observe. • Lower temperature helps reduce tangential or verbose outputs but doesn't replace disabling thoughts. Want the same example in Node.js or a full end-to-end snippet wiring this into your TTS/transcript pipeline?
h
Okay, in the STT-LLM-TTS pipeline, getting a warning, and the agent is not switching. 110057.505 WARNI… livekit.…ns.google no candidates in the response: sdk_http_response=HttpResponse( headers=<dict len=11> ) candidates=[Candidate( content=Content( role='model' ), finish_reason=<FinishReason.STOP: 'STOP'>, index=0 )] create_time=None model_version='gemini-2.5-flash' prompt_feedback=None response_id='ke5AafPBFdinjuMP_s-JgQk' usage_metadata=GenerateContentResponseUsageMetadata( prompt_token_count=5175, prompt_tokens_details=[ ModalityTokenCount( modality=<MediaModality.TEXT: 'TEXT'>, token_count=5175 ), ], total_token_count=5175 ) automatic_function_calling_history=None parsed=None
t
You're seeing a warning that the Gemini realtime response contained no text candidates — in other words, the model returned an empty/no-content response. I'll explain likely causes and give targeted steps to fix this so your agent can switch correctly. Why this happens • The response shows 0 text candidates even though the request consumed tokens (prompt_token_count=5175). Large prompts near model limits can lead to empty outputs or the model stopping early. • Realtime models may also return empty candidates when thinking is disabled but the model decides there is nothing to emit, or if an internal error/timeout occurred. What to try next 1. Handle empty responses gracefully ◦ In your pipeline, detect responses with no text candidates and treat them as a recoverable case: retry the request, ask the model a shorter follow-up prompt, or emit a default reply so the agent can switch state. 1. Reduce prompt size / trim context ◦ Drop older conversation history or summarize it before sending. 5k prompt tokens is high; trimming will reduce risk of empty responses. 1. Add explicit final-answer instruction ◦ Include a concise instruction in the prompt like: “Provide a short final answer. Do not output internal reasoning or tool traces.” This helps the model produce usable candidates. 1. Backoff & retry with fewer tokens ◦ If you get an empty response, retry once with a pared-down prompt and the same 
thinking_config.include_thoughts=False
. 1. Log and correlate SDK/transport errors ◦ Capture the full SDK response and any transport/timeout errors. If you see transient network issues or timeouts, increase timeouts or retry policy. 1. Keep your fallback filter in place ◦ Continue using your stream filter to drop any residual chain-of-thought markers if they appear. What I can do for you now • Review the exact STT→LLM request/response handling code to suggest where to detect empty candidates and implement retries or default replies. • Provide a short Python or Node patch that: ◦ Detects no candidates, ◦ Retries with trimmed context, ◦ Emits a default agent reply so the agent state advances. Which would you like me to provide — a Python or Node code snippet to handle empty Gemini realtime responses in your pipeline? Also, paste the small portion of your handler where you process model responses if you want a tailored patch.
h
Copy code
stt=google.STT(
    languages="te-IN",
    model="chirp_3",
    location="asia-southeast1",
    enable_word_time_offsets=False,
    enable_voice_activity_events=True,  # optional, improves real-time responsiveness
     ),
llm=google.LLM(
    model="gemini-2.5-flash",
    temperature=0.4,
    thinking_config=types.ThinkingConfig(
        include_thoughts=False,
    ),
),
tts=google.TTS(
    language="te-IN",
    gender="female",
    voice_name="te-IN-Chirp3-HD-Despina",
),
t
You're correctly using 
thinking_config.include_thoughts=False
. Below is a concise Python pattern you can drop into your STT→LLM→TTS pipeline to: • Detect Gemini realtime responses with no text candidates • Retry once with trimmed context • Emit a default reply so the agent can switch state • Keep your thinking config and fallback filtering
Copy code
# Pseudocode / pattern — adapt to your SDK event shapes
MAX_PROMPT_TOKENS = 4000  # choose under model limit
RETRY_TRIM_TOKENS = 2000

async def call_gemini_realtime(llm, prompt_chunks):
    # prompt_chunks: list[str] pieces of context (recent last)
    prompt = " ".join(prompt_chunks)
    # ensure prompt not too large (implement your tokenizer check)
    if token_count(prompt) > MAX_PROMPT_TOKENS:
        prompt = summarize_or_trim(prompt, target_tokens=MAX_PROMPT_TOKENS)

    resp = await llm.generate(prompt=prompt)  # adapt to your RealtimeModel call
    candidates = getattr(resp, "candidates", None) or []

    # Case: no usable text candidate
    if not candidates or all((getattr(c, "content", None) is None or not getattr(c.content, "text", "").strip()) for c in candidates):
        # 1) Retry with trimmed context once
        trimmed_prompt = trim_prompt_tokens(prompt, target_tokens=RETRY_TRIM_TOKENS)
        retry_resp = await llm.generate(prompt=trimmed_prompt)
        retry_candidates = getattr(retry_resp, "candidates", None) or []

        if retry_candidates and any(getattr(c.content, "text", "").strip() for c in retry_candidates):
            return extract_text_from_candidates(retry_candidates)
        else:
            # 2) Fallback: return a short default reply so agent can switch state
            return "Sorry, I couldn't generate a response. Could you please rephrase?"

    return extract_text_from_candidates(candidates)

# Helper examples (implement according to your SDK/tokenizer)
def extract_text_from_candidates(candidates):
    texts = []
    for c in candidates:
        txt = getattr(c.content, "text", None)
        if txt:
            texts.append(txt)
    return " ".join(texts).strip()

def trim_prompt_tokens(prompt, target_tokens):
    # Simple heuristics: drop oldest history until below target
    # Replace with real token-based trimming or summarization
    parts = prompt.split("\n\n")
    while token_count(" ".join(parts)) > target_tokens and len(parts) > 1:
        parts.pop(0)
    return " ".join(parts)

def token_count(text):
    # Replace with an actual tokenizer count for Gemini
    return len(text.split())

# Usage within your streaming handler:
# - keep thinking_config.include_thoughts=False on your RealtimeModel
# - still filter residual markers before TTS (your clean_chunk_text)
# - ensure you treat empty responses as transient and advance agent state via fallback reply
Practical notes: • Large prompts (your logs show ~5175 prompt tokens) frequently cause empty/no‑content responses. Trim or summarize history before sending. • Keep 
thinking_config.include_thoughts=False
(you already have it). • Continue to run your fallback stream filter for any leftover markers. • If you prefer, I can convert this to a concrete snippet using the exact SDK method names you call (paste the function where you currently invoke the LLM).
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