Integrate OpenCode CLI with ollama-cloud/gemma4:31b
This commit is contained in:
Adolfo Reyna
@@ -153,14 +153,14 @@ def parse_args() -> argparse.Namespace:
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)
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parser.add_argument(
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"--llm-engine",
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choices=["ollama", "opencode", "apple", "macos", "claude"],
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default="ollama",
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help="LLM engine to use: ollama/opencode for Ollama/Cloud models, apple/macos for local MLX, claude for Claude Code.",
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choices=["opencode", "ollama", "apple", "macos", "claude"],
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default="opencode",
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help="LLM engine to use: opencode/ollama for OpenCode Cloud models (gemma4:31b), apple/macos for local MLX, claude for Claude Code.",
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)
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parser.add_argument(
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"--ollama-model",
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default="gemma4:31b",
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help="Ollama / OpenCode model name (default gemma4:31b).",
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"--opencode-model",
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default="ollama-cloud/gemma4:31b",
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help="OpenCode Cloud model (default ollama-cloud/gemma4:31b).",
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)
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parser.add_argument(
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"--ollama-host",
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@@ -497,18 +497,17 @@ def build_claude_options(args: argparse.Namespace, vocabulary=None, brain=None)
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def build_llm(args: argparse.Namespace, vocabulary=None, brain=None, observer=None):
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if args.llm_engine in ("ollama", "opencode"):
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from ollama_llm import OllamaLLM, probe_ollama, get_default_ollama_host
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if args.llm_engine in ("opencode", "ollama"):
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from opencode_llm import OpenCodeLLM, probe_opencode
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host = args.ollama_host or get_default_ollama_host()
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logger.info(f"LLM: Ollama / OpenCode ({args.ollama_model} @ {host})")
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available, reason = probe_opencode(args.opencode_model)
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logger.info(f"LLM: OpenCode Cloud ({reason})")
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personality = read_personality(args.cwd) or "You are a helpful voice assistant."
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system_prompt = personality + "\n\n" + VOICE_STYLE
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if brain and (memory := brain.prompt_block()):
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system_prompt += "\n\n" + memory
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return OllamaLLM(
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model=args.ollama_model,
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host=host,
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return OpenCodeLLM(
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model=args.opencode_model,
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system_prompt=system_prompt,
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observer=observer,
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)
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+200
@@ -0,0 +1,200 @@
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"""A Pipecat processor that puts OpenCode (with ollama-cloud/gemma4:31b) in the LLM slot.
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Drives the installed OpenCode CLI (`opencode run -m ollama-cloud/gemma4:31b`) to stream
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cloud responses to text-to-speech downstream with zero local GPU overhead.
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"""
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import asyncio
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import json
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import os
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import re
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import shutil
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from pathlib import Path
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from loguru import logger
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from pipecat.frames.frames import (
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CancelFrame,
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EndFrame,
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Frame,
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InterruptionFrame,
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LLMContextFrame,
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LLMFullResponseEndFrame,
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LLMFullResponseStartFrame,
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LLMTextFrame,
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StartFrame,
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)
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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_NOISE_TRANSCRIPTS = {
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"",
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".",
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"thank you.",
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"thanks for watching!",
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"you",
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"bye.",
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"okay.",
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"[blank_audio]",
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"[silence]",
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}
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def _clean_spoken_text(text: str) -> str:
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"""Clean text for speech output and truncate fake turn generations."""
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if not text:
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return ""
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# Truncate if model hallucinates fake turn markers
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for marker in ("User:", "Human:", "Assistant:", "\nUser", "\nHuman", "\nAssistant"):
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if marker in text:
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text = text.split(marker)[0]
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# Remove markdown code blocks
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text = re.sub(r"```[\s\S]*?```", "", text)
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# Remove inline code ticks
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text = re.sub(r"`[^`]*`", "", text)
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# Remove markdown syntax characters
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text = re.sub(r"[\#\*\_\~]", "", text)
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# Flatten newlines into clear speech
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lines = [line.strip() for line in text.splitlines() if line.strip()]
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return " ".join(lines).strip()
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def find_opencode_cli() -> str | None:
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return shutil.which("opencode") or (
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"/Users/adolforeyna/.opencode/bin/opencode"
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if os.path.exists("/Users/adolforeyna/.opencode/bin/opencode")
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else None
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)
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def probe_opencode(model: str = "ollama-cloud/gemma4:31b") -> tuple[bool, str]:
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cli = find_opencode_cli()
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if not cli:
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return False, "OpenCode CLI binary not found"
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return True, f"OpenCode CLI available ({cli}) with model {model}"
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class OpenCodeLLM(FrameProcessor):
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"""Runs user turns through the OpenCode CLI driving OpenCode Cloud models."""
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def __init__(
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self,
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*,
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model: str = "ollama-cloud/gemma4:31b",
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system_prompt: str | None = None,
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observer=None,
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**kwargs,
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):
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super().__init__(**kwargs)
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self._model = model
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self._system_prompt = system_prompt or "You are a helpful spoken voice assistant. Keep answers brief and conversational."
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self._on_reply = observer
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self._turn_task: asyncio.Task | None = None
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self._history: list[dict[str, str]] = []
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self._cli_path = find_opencode_cli() or "opencode"
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, StartFrame):
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await self.push_frame(frame, direction)
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logger.info(f"OpenCode LLM engine ready: CLI={self._cli_path}, model={self._model}")
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elif isinstance(frame, (EndFrame, CancelFrame)):
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await self._cancel_turn()
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await self.push_frame(frame, direction)
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elif isinstance(frame, InterruptionFrame):
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await self._cancel_turn()
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await self.push_frame(frame, direction)
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elif isinstance(frame, LLMContextFrame):
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text = self._latest_user_text(frame.context)
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await self._maybe_start_turn(text)
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else:
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await self.push_frame(frame, direction)
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def _latest_user_text(self, context) -> str:
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if not context or not hasattr(context, "messages"):
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return ""
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for msg in reversed(context.messages):
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if isinstance(msg, dict) and msg.get("role") == "user":
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content = msg.get("content", "")
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if isinstance(content, str):
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return content
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elif isinstance(content, list):
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text_parts = [c.get("text", "") for c in content if isinstance(c, dict)]
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return " ".join(text_parts)
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return ""
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async def _maybe_start_turn(self, text: str):
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utterance = text.strip()
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if utterance.lower() in _NOISE_TRANSCRIPTS or len(utterance) < 2:
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logger.debug(f"Ignoring noise transcript: {utterance!r}")
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return
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await self._cancel_turn()
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logger.info(f"You: {utterance}")
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self._turn_task = self.create_task(self._run_turn(utterance))
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async def _cancel_turn(self):
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if not self._turn_task:
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return
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task, self._turn_task = self._turn_task, None
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await self.cancel_task(task)
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async def _run_turn(self, utterance: str):
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self._history.append({"role": "user", "content": utterance})
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# Format prompt with system instructions and recent conversation history
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recent_history = self._history[-6:]
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conv_text = "\n".join(
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f"{'User' if m['role']=='user' else 'Assistant'}: {m['content']}"
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for m in recent_history
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)
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prompt_str = f"{self._system_prompt}\n\n{conv_text}\nAssistant:"
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await self.push_frame(LLMFullResponseStartFrame())
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chunks: list[str] = []
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try:
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proc = await asyncio.create_subprocess_exec(
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self._cli_path,
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"run",
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"-m", self._model,
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"--pure",
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prompt_str,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE,
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stdin=asyncio.subprocess.DEVNULL,
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)
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while True:
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line = await proc.stdout.readline()
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if not line:
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break
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text_line = line.decode("utf-8")
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cleaned = _clean_spoken_text(text_line)
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if cleaned:
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chunks.append(cleaned)
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await self.push_frame(LLMTextFrame(cleaned))
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await proc.wait()
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except asyncio.CancelledError:
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logger.info("OpenCode turn cancelled mid-response.")
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if proc and proc.returncode is None:
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try:
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proc.kill()
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except Exception:
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pass
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raise
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except Exception as e:
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logger.error(f"OpenCode LLM error: {e}")
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err_msg = "Sorry, I ran into an error generating a response."
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chunks.append(err_msg)
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await self.push_frame(LLMTextFrame(err_msg))
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finally:
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await self.push_frame(LLMFullResponseEndFrame())
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full_reply = _clean_spoken_text(" ".join(chunks))
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if full_reply:
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self._history.append({"role": "assistant", "content": full_reply})
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logger.info(f"OpenCode LLM ({self._model}): {full_reply}")
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if self._on_reply:
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self._on_reply(full_reply)
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+5
-5
@@ -171,11 +171,11 @@ def check_mlx_whisper(synthesized):
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record("mlx whisper (optional)", matches(heard), f"heard {heard!r}")
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async def check_ollama_llm():
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from ollama_llm import probe_ollama
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def check_opencode_llm():
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from opencode_llm import probe_opencode
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available, reason = await probe_ollama(model="gemma4:31b")
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record("ollama llm (optional)", available, reason)
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available, reason = probe_opencode(model="ollama-cloud/gemma4:31b")
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record("opencode llm", available, reason)
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def check_macos_llm():
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@@ -238,7 +238,7 @@ async def main():
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check_apple_stt(synthesized)
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check_mlx_whisper(synthesized)
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check_macos_llm()
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await check_ollama_llm()
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check_opencode_llm()
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await check_claude()
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required_failures = [
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