diff --git a/.github/workflows/integration_test.yml b/.github/workflows/integration_test.yml index 4ca3d0d..1ad00f2 100644 --- a/.github/workflows/integration_test.yml +++ b/.github/workflows/integration_test.yml @@ -28,7 +28,7 @@ jobs: matrix: php-versions: [ '8.3' ] databases: [ 'sqlite' ] - server-versions: [ 'master', 'stable33', 'stable32', 'stable31', 'stable30' ] + server-versions: [ 'master', 'stable34', 'stable33', 'stable32', 'stable31', 'stable30' ] name: Integration test on ☁️${{ matrix.server-versions }} 🐘${{ matrix.php-versions }} @@ -106,13 +106,21 @@ jobs: ./occ maintenance:install --verbose --database=${{ matrix.databases }} --database-name=nextcloud --database-host=127.0.0.1 --database-port=$PGSQL_PORT --database-user=root --database-pass=rootpassword --admin-user admin --admin-pass password composer run serve & + - name: Checkout app_api + uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0 + with: + path: apps/app_api/ + repository: nextcloud/app_api + ref: ${{ matrix.server-versions == 'master' && 'main' || matrix.server-versions }} + persist-credentials: false + - name: Enable app and app_api run: ./occ app:enable -vvv -f app_api - name: Setup python 3.10 uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 with: - python-version: '3.10' + python-version: '3.13' cache: 'pip' cache-dependency-path: context_chat_backend/requirements.txt @@ -122,6 +130,9 @@ jobs: sudo apt install pipx pipx install poetry poetry install + # pyproject.toml pins xllamacpp to the cu128 wheel, which needs libcuda.so.1 at import + # time. The CI runner has no NVIDIA driver, so we swap in the PyPI CPU build for tests. + poetry run pip install --force-reinstall --no-deps xllamacpp - name: Cache llm2 models uses: actions/cache/restore@v5 @@ -137,6 +148,7 @@ jobs: env: APP_VERSION: ${{ fromJson(steps.appinfo.outputs.result).version }} run: | + mkdir -p "$(pwd)/../../llm2-persistent_storage/" APP_PERSISTENT_STORAGE="$(pwd)/../../llm2-persistent_storage/" poetry run python3 main.py > ../backend_logs 2>&1 & - name: Register backend diff --git a/.github/workflows/publish-docker-cpu.yml b/.github/workflows/publish-docker.yml similarity index 76% rename from .github/workflows/publish-docker-cpu.yml rename to .github/workflows/publish-docker.yml index 904ddba..9e62c30 100644 --- a/.github/workflows/publish-docker-cpu.yml +++ b/.github/workflows/publish-docker.yml @@ -1,7 +1,7 @@ # SPDX-FileCopyrightText: 2025 Nextcloud GmbH and Nextcloud contributors # SPDX-License-Identifier: AGPL-3.0-or-later -name: Publish CPU Image +name: Publish Docker Images on: push: @@ -15,9 +15,13 @@ permissions: jobs: push_to_registry: - name: Build image + name: Build ${{ matrix.variant }} image runs-on: ubuntu-22.04 if: ${{ github.repository_owner == 'nextcloud' }} + strategy: + fail-fast: false + matrix: + variant: [cuda, rocm, cpu] steps: - name: Set app env run: | @@ -93,14 +97,29 @@ jobs: run: | echo "Extracted version: ${{ env.VERSION }}" + # AppAPI selects ExApp images by tag suffix per the deploy daemon's computeDevice: + # CPU is the unsuffixed default (`:`), CUDA is `:-cuda`, + # ROCm is `:-rocm`. + - name: Compute image tags + id: tags + run: | + if [ "${{ matrix.variant }}" = "cpu" ]; then + SUFFIX="" + else + SUFFIX="-${{ matrix.variant }}" + fi + { + echo "tags<> "$GITHUB_OUTPUT" + - name: Build container image uses: docker/build-push-action@f9f3042f7e2789586610d6e8b85c8f03e5195baf # v7.2.0 with: push: true context: ./${{ env.APP_NAME }} + file: ./${{ env.APP_NAME }}/Dockerfile.${{ matrix.variant }} platforms: linux/amd64 - tags: | - ghcr.io/nextcloud/${{ env.APP_NAME }}:${{ env.VERSION }} - ghcr.io/nextcloud/${{ env.APP_NAME }}:latest - build-args: | - BUILD_TYPE=cpu + tags: ${{ steps.tags.outputs.tags }} diff --git a/Dockerfile.cpu b/Dockerfile.cpu new file mode 100644 index 0000000..ba667b5 --- /dev/null +++ b/Dockerfile.cpu @@ -0,0 +1,53 @@ +# SPDX-FileCopyrightText: 2024 Nextcloud GmbH and Nextcloud contributors +# SPDX-License-Identifier: AGPL-3.0-or-later +FROM docker.io/ubuntu:22.04 + +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt update +RUN apt-get update --fix-missing +RUN apt install -y python3 python3-venv pipx build-essential git vim curl +RUN pipx install poetry + +# Download and install FRP client into /usr/local/bin. +RUN set -ex; \ + ARCH=$(uname -m); \ + if [ "$ARCH" = "aarch64" ]; then \ + FRP_URL="https://raw.githubusercontent.com/nextcloud/HaRP/main/exapps_dev/frp_0.61.1_linux_arm64.tar.gz"; \ + else \ + FRP_URL="https://raw.githubusercontent.com/nextcloud/HaRP/main/exapps_dev/frp_0.61.1_linux_amd64.tar.gz"; \ + fi; \ + echo "Downloading FRP client from $FRP_URL"; \ + curl -L "$FRP_URL" -o /tmp/frp.tar.gz; \ + tar -C /tmp -xzf /tmp/frp.tar.gz; \ + mv /tmp/frp_0.61.1_linux_* /tmp/frp; \ + cp /tmp/frp/frpc /usr/local/bin/frpc; \ + chmod +x /usr/local/bin/frpc; \ + rm -rf /tmp/frp /tmp/frp.tar.gz + +ENV DEBIAN_FRONTEND=dialog +ENV PATH="/root/.local/bin:${PATH}" +ENV COMPUTE_DEVICE=CPU + +WORKDIR /app + +# Install requirements +COPY pyproject.toml . +COPY poetry.lock . +COPY healthcheck.sh . +COPY --chmod=775 start.sh / + +RUN poetry install +# pyproject.toml pins the cu128 wheel for GPU production; swap to the PyPI CPU build +# so this image runs without libcuda.so.1 / CUDA runtime libraries. +RUN poetry run pip install --force-reinstall --no-deps xllamacpp + +ADD lib /app/lib +ADD models /app/models +ADD default_config /app/default_config + +WORKDIR /app/lib +ENTRYPOINT ["/start.sh", "poetry", "run", "python3", "main.py"] + +LABEL org.opencontainers.image.source=https://github.com/nextcloud/llm2 +HEALTHCHECK --interval=2s --timeout=2s --retries=300 CMD /app/healthcheck.sh diff --git a/Dockerfile b/Dockerfile.cuda similarity index 100% rename from Dockerfile rename to Dockerfile.cuda diff --git a/Dockerfile.rocm b/Dockerfile.rocm new file mode 100644 index 0000000..bd514c1 --- /dev/null +++ b/Dockerfile.rocm @@ -0,0 +1,52 @@ +# SPDX-FileCopyrightText: 2024 Nextcloud GmbH and Nextcloud contributors +# SPDX-License-Identifier: AGPL-3.0-or-later +FROM docker.io/rocm/dev-ubuntu-22.04:6.4.1 + +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt update +RUN apt-get update --fix-missing +RUN apt install -y pipx build-essential git vim curl +RUN pipx install poetry + +# Download and install FRP client into /usr/local/bin. +RUN set -ex; \ + ARCH=$(uname -m); \ + if [ "$ARCH" = "aarch64" ]; then \ + FRP_URL="https://raw.githubusercontent.com/nextcloud/HaRP/main/exapps_dev/frp_0.61.1_linux_arm64.tar.gz"; \ + else \ + FRP_URL="https://raw.githubusercontent.com/nextcloud/HaRP/main/exapps_dev/frp_0.61.1_linux_amd64.tar.gz"; \ + fi; \ + echo "Downloading FRP client from $FRP_URL"; \ + curl -L "$FRP_URL" -o /tmp/frp.tar.gz; \ + tar -C /tmp -xzf /tmp/frp.tar.gz; \ + mv /tmp/frp_0.61.1_linux_* /tmp/frp; \ + cp /tmp/frp/frpc /usr/local/bin/frpc; \ + chmod +x /usr/local/bin/frpc; \ + rm -rf /tmp/frp /tmp/frp.tar.gz + +ENV DEBIAN_FRONTEND=dialog +ENV PATH="/root/.local/bin:${PATH}" + +WORKDIR /app + +# Install requirements +COPY pyproject.toml . +COPY poetry.lock . +COPY healthcheck.sh . +COPY --chmod=775 start.sh / + +RUN poetry install +# pyproject.toml pins the cu128 wheel; swap to the matching ROCm wheel. +RUN poetry run pip install --force-reinstall --no-deps \ + --index-url https://xorbitsai.github.io/xllamacpp/whl/rocm-6.4.1 xllamacpp + +ADD lib /app/lib +ADD models /app/models +ADD default_config /app/default_config + +WORKDIR /app/lib +ENTRYPOINT ["/start.sh", "poetry", "run", "python3", "main.py"] + +LABEL org.opencontainers.image.source=https://github.com/nextcloud/llm2 +HEALTHCHECK --interval=2s --timeout=2s --retries=300 CMD /app/healthcheck.sh diff --git a/README.md b/README.md index b3c078a..09528dc 100644 --- a/README.md +++ b/README.md @@ -70,14 +70,16 @@ See [the Nextcloud admin documentation](https://docs.nextcloud.com/server/latest ## Development installation using Docker > [!NOTE] -> Currently, running the Docker image requires that your host system have CUDA/NVIDIA drivers installed and is equipped with a GPU capable of performing the required tasks. +> The CUDA image requires that your host system have CUDA/NVIDIA drivers installed and is equipped with a GPU capable of performing the required tasks. The CPU image runs anywhere but is slower. -0. [Install Nvidia drivers and CUDA on your host system](https://gist.github.com/denguir/b21aa66ae7fb1089655dd9de8351a202) and [install NVIDIA Docker toolkit](https://stackoverflow.com/questions/25185405/using-gpu-from-a-docker-container). +0. (CUDA image only) [Install Nvidia drivers and CUDA on your host system](https://gist.github.com/denguir/b21aa66ae7fb1089655dd9de8351a202) and [install NVIDIA Docker toolkit](https://stackoverflow.com/questions/25185405/using-gpu-from-a-docker-container). -1. Build the Docker image: +1. Build the Docker image (pick one). Per the [AppAPI image convention](https://docs.nextcloud.com/server/latest/developer_manual/exapp_development/faq/GpuSupport.html), CPU is the unsuffixed default and GPU variants are suffixed: ```sh - docker build --no-cache -f Dockerfile -t llm2:latest . + docker build --no-cache -f Dockerfile.cpu -t llm2:latest . + docker build --no-cache -f Dockerfile.cuda -t llm2:latest-cuda . + docker build --no-cache -f Dockerfile.rocm -t llm2:latest-rocm . ``` 2. Run the Docker image: diff --git a/default_config/config.json b/default_config/config.json index 9850a28..7b2cafd 100644 --- a/default_config/config.json +++ b/default_config/config.json @@ -62,6 +62,7 @@ "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>\n{user_prompt}<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n", "loader_config": { "n_ctx": 24000, + "n_batch": 8, "max_tokens": 8192, "stop": ["<|eot_id|>"], "temperature": 0.7 @@ -86,11 +87,21 @@ } }, "Olmo-3-7B-Instruct-Q4_K_M": { - "prompt": "<|endoftext|><|system|>\n{system_prompt}\n<|user|>\n{system_prompt}\n{user_prompt}\n<|assistant|>\n", "loader_config": { "n_ctx": 4096, "max_tokens": 2048, "stop": ["<|endoftext|>"], + "temperature": 0.4, + "enable_chat_template": 1, + "chat_template": "{%- set has_system = messages|selectattr('role', 'equalto', 'system')|list|length > 0 -%}{%- if not has_system -%}{{- '<|im_start|>system\nYou are a helpful AI assistant.<|im_end|>\n' -}}{%- endif -%}{%- for message in messages -%}{%- if message['role'] == 'system' -%}{{- '<|im_start|>system\n' + message['content'] + '<|im_end|>\n' -}}{%- elif message['role'] == 'user' -%}{{- '<|im_start|>user\n' + message['content'] + '<|im_end|>\n' -}}{%- elif message['role'] == 'assistant' -%}{{- '<|im_start|>assistant\n' -}}{%- if message.get('content', none) is not none -%}{{- message['content'] -}}{%- endif -%}{%- if not loop.last -%}{{- '<|im_end|>\n' -}}{%- else -%}{{- eos_token -}}{%- endif -%}{%- endif -%}{%- if loop.last and add_generation_prompt -%}{{- '<|im_start|>assistant\n' -}}{%- endif -%}{%- endfor -%}" + } + }, + "Olmo-3-7B-Think-Q4_K_M": { + "prompt": "<|endoftext|><|system|>\n{system_prompt}\n<|user|>\n{system_prompt}\n{user_prompt}\n<|assistant|>\n", + "loader_config": { + "n_ctx": 2096, + "max_tokens": 2048, + "stop": ["<|endoftext|>"], "temperature": 0.4 } }, @@ -102,6 +113,16 @@ "stop": ["<|endoftext|>"], "temperature": 0.3 } + }, + "gemma-4-E4B-it-Q4_K_S": { + "prompt": "<|endoftext|><|system|>\n{system_prompt}\n<|user|>\n{system_prompt}\n{user_prompt}\n<|assistant|>\n", + "loader_config": { + "n_parallel": 1, + "n_ctx": 4096, + "max_tokens": 2048, + "stop": ["<|endoftext|>"], + "temperature": 0.7 + } }, "default": { "prompt": "<|im_start|> system\n{system_prompt}\n<|im_end|>\n<|im_start|> user\n{user_prompt}\n<|im_end|>\n<|im_start|> assistant\n", diff --git a/lib/change_tone.py b/lib/change_tone.py index cb2e771..7405de8 100644 --- a/lib/change_tone.py +++ b/lib/change_tone.py @@ -35,10 +35,16 @@ class ChangeToneProcessor: def __init__(self, runnable: Runnable): self.runnable = runnable - def __call__(self, input_data: dict, context: StreamContext | None = None) -> dict[str, Any]: - """Process a single input""" + async def __call__(self, input_data: dict, context: StreamContext | None = None) -> dict[str, Any]: messages = [ SystemMessage(content=self.system_prompt), HumanMessage(content=self.user_prompt.format_prompt(text=input_data['input'], tone=input_data['tone']).to_string()) ] - return {'output': run_runnable_with_streaming(self.runnable, messages, context)} + reasoning_sink: dict[str, str] = {} + output = await run_runnable_with_streaming( + self.runnable, + messages, + context, + reasoning_sink=reasoning_sink, + ) + return {'output': output, 'reasoning': reasoning_sink.get('reasoning', '')} diff --git a/lib/chat.py b/lib/chat.py index d2c2815..49f5961 100644 --- a/lib/chat.py +++ b/lib/chat.py @@ -20,7 +20,7 @@ class ChatProcessor: def __init__(self, runner: Runnable): self.runnable = runner - def __call__( + async def __call__( self, inputs: dict[str, Any], context: StreamContext | None = None, @@ -32,10 +32,14 @@ def __call__( (message['role'], message['content']) for message in [json.loads(message) for message in inputs['history']] ] + [('human', inputs['input'])] + reasoning_sink: dict[str, str] = {} + output = await run_runnable_with_streaming( + self.runnable, + messages, + context, + reasoning_sink=reasoning_sink, + ) return { - 'output': run_runnable_with_streaming( - self.runnable, - messages, - context, - ) + 'output': output, + 'reasoning': reasoning_sink.get('reasoning', ''), } diff --git a/lib/chatwithtools.py b/lib/chatwithtools.py index 461b501..f5d9f17 100644 --- a/lib/chatwithtools.py +++ b/lib/chatwithtools.py @@ -2,13 +2,14 @@ # SPDX-License-Identifier: AGPL-3.0-or-later """A chat chain """ +import ast import json import hashlib import pprint import re from typing import Any -from langchain_community.chat_models import ChatLlamaCpp +from langchain_core.language_models import BaseChatModel from langchain_core.messages import SystemMessage, HumanMessage from langchain_core.messages.ai import AIMessage @@ -31,6 +32,32 @@ def generate_tool_call_id(tool_call: dict) -> str: ) return hashlib.sha1(stable_payload.encode("utf-8")).hexdigest()[:16] +def _parse_python_function_call(call_str: str) -> dict | None: + """Parse a Python-style call like `name(a="x", b=1)` into {name, arguments}.""" + try: + tree = ast.parse(call_str.strip(), mode='eval') + except SyntaxError: + return None + if not isinstance(tree.body, ast.Call): + return None + func = tree.body.func + if isinstance(func, ast.Name): + name = func.id + elif isinstance(func, ast.Attribute): + name = func.attr + else: + return None + args: dict[str, Any] = {} + for kw in tree.body.keywords: + if kw.arg is None: + continue + try: + args[kw.arg] = ast.literal_eval(kw.value) + except (ValueError, SyntaxError): + return None + return {'name': name, 'arguments': args} + + def try_parse_tool_calls(content: str): """Try parse the tool calls.""" tool_calls = [] @@ -38,7 +65,7 @@ def try_parse_tool_calls(content: str): found = False # Qwen-style tool call, works with Llama 3.1 # {"name": "function_name", "arguments": {"param1": "value1", "param2": "value2"}} - for i, m in enumerate(re.finditer(r"\w*?(.+)?\w*?", content)): + for i, m in enumerate(re.finditer(r"\s*(.+?)\s*", content, re.DOTALL)): if i == 0: offset = m.start() try: @@ -61,12 +88,28 @@ def try_parse_tool_calls(content: str): print(f"Failed to parse tool calls: the content is {m.group(1)} and {e}") pass + if not found: + # Olmo-style tool call + # function_name(param1="value1", param2="value2") + for i, m in enumerate(re.finditer(r"\s*(.+?)\s*", content, re.DOTALL)): + if i == 0: + offset = m.start() + func = _parse_python_function_call(m.group(1)) + if func is None: + print(f"Failed to parse tool call: the content is {m.group(1)}") + continue + func['args'] = func.pop('arguments', {}) + func.setdefault('id', generate_tool_call_id(func)) + func.setdefault('type', 'tool_call') + tool_calls.append(func) + found = True + if not found: # Gemma-style tool call # ```tool_call # {"name": "function_name", "arguments": {"param1": "value1", "param2": "value2"}} # ``` - for i, m in enumerate(re.finditer(r"```tool_call\n(.+)?\n```", content)): + for i, m in enumerate(re.finditer(r"```tool_call\s*\n(.+?)\n\s*```", content, re.DOTALL)): if i == 0: offset = m.start() try: @@ -99,9 +142,14 @@ def try_parse_tool_calls(content: str): def strip_tool_calls_for_streaming(content: str) -> str: sanitized = re.sub(r".*?", "", content, flags=re.DOTALL) + sanitized = re.sub(r".*?", "", sanitized, flags=re.DOTALL) sanitized = re.sub(r"```tool_call\n.*?\n```", "", sanitized, flags=re.DOTALL) - partial_markers = [index for index in (sanitized.find(" dict[str, Any]: + async def _process_single_input(self, input_data: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: system_prompt = """ {downstream_system_prompt} @@ -192,20 +240,23 @@ def _process_single_input(self, input_data: dict[str, Any], context: StreamConte messages.append(HumanMessage(content='')) pprint.pprint(messages) - response_content = run_runnable_with_streaming( + reasoning_sink: dict[str, str] = {} + response_content = await run_runnable_with_streaming( self.model, messages, context, stream_payload_transform=build_streaming_payload, suppress_empty_stream_updates=True, + reasoning_sink=reasoning_sink, ) response = AIMessage(**try_parse_tool_calls(response_content)) return { 'output': response.content, - 'tool_calls': json.dumps(response.tool_calls) + 'tool_calls': json.dumps(response.tool_calls), + 'reasoning': reasoning_sink.get('reasoning', ''), } - def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: - return self._process_single_input(inputs, context) + async def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: + return await self._process_single_input(inputs, context) diff --git a/lib/contextwrite.py b/lib/contextwrite.py index 8fe62fc..5292288 100644 --- a/lib/contextwrite.py +++ b/lib/contextwrite.py @@ -38,7 +38,7 @@ class ContextWriteProcessor: def __init__(self, runnable: Runnable): self.runnable = runnable - def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: + async def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: messages = [ SystemMessage(content=self.system_prompt), HumanMessage(content=self.user_prompt.format( @@ -46,5 +46,11 @@ def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) source_input=inputs['source_input'] )) ] - output = run_runnable_with_streaming(self.runnable, messages, context) - return {'output': output} + reasoning_sink: dict[str, str] = {} + output = await run_runnable_with_streaming( + self.runnable, + messages, + context, + reasoning_sink=reasoning_sink, + ) + return {'output': output, 'reasoning': reasoning_sink.get('reasoning', '')} diff --git a/lib/free_prompt.py b/lib/free_prompt.py index cb5ca25..3690984 100644 --- a/lib/free_prompt.py +++ b/lib/free_prompt.py @@ -19,13 +19,19 @@ class FreePromptProcessor: def __init__(self, runnable: Runnable): self.runnable = runnable - def __call__( + async def __call__( self, inputs: dict[str, Any], context: StreamContext | None = None, ) -> dict[str, Any]: - output = run_runnable_with_streaming(self.runnable, [ - SystemMessage(self.system_prompt), - HumanMessage(inputs['input']) - ], context) - return {'output': output} \ No newline at end of file + reasoning_sink: dict[str, str] = {} + output = await run_runnable_with_streaming( + self.runnable, + [ + SystemMessage(self.system_prompt), + HumanMessage(inputs['input']) + ], + context, + reasoning_sink=reasoning_sink, + ) + return {'output': output, 'reasoning': reasoning_sink.get('reasoning', '')} \ No newline at end of file diff --git a/lib/headline.py b/lib/headline.py index ac88832..27853f5 100644 --- a/lib/headline.py +++ b/lib/headline.py @@ -35,12 +35,18 @@ class HeadlineProcessor: def __init__(self, runnable: Runnable): self.runnable = runnable - def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: + async def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: messages = [ SystemMessage(content=self.system_prompt), HumanMessage(content=self.user_prompt.format( text=inputs['input'] )) ] - output = run_runnable_with_streaming(self.runnable, messages, context) - return {'output': output} + reasoning_sink: dict[str, str] = {} + output = await run_runnable_with_streaming( + self.runnable, + messages, + context, + reasoning_sink=reasoning_sink, + ) + return {'output': output, 'reasoning': reasoning_sink.get('reasoning', '')} diff --git a/lib/llama_server.py b/lib/llama_server.py new file mode 100644 index 0000000..36e765c --- /dev/null +++ b/lib/llama_server.py @@ -0,0 +1,57 @@ +# SPDX-FileCopyrightText: 2024 Nextcloud GmbH and Nextcloud contributors +# SPDX-License-Identifier: AGPL-3.0-or-later +"""Entry point for the llama-cpp server subprocess. + +Spawned by task_processors.generate_chat_model with a JSON config blob as argv[1]. +Any key in SERVER_KEYS that is present in the config is applied to xllamacpp's +CommonParams; client-side keys (temperature, max_tokens, stop) are ignored here +and consumed by the LangChain client instead. +""" +import json +import sys +import time + +import xllamacpp as xlc + +SERVER_KEYS = ( + # Context / batching + "n_ctx", "n_batch", "n_ubatch", "n_parallel", "n_predict", "n_keep", + "ctx_shift", "kv_unified", "swa_full", "cont_batching", + # GPU / hardware + "n_gpu_layers", "main_gpu", "split_mode", "tensor_split", + "no_kv_offload", "no_op_offload", "flash_attn_type", + # Memory + "use_mmap", "use_mlock", + "cache_type_k", "cache_type_v", + "cache_prompt", "cache_idle_slots", "cache_ram_mib", + # RoPE / YaRN + "rope_freq_base", "rope_freq_scale", "rope_scaling_type", + "yarn_attn_factor", "yarn_beta_fast", "yarn_beta_slow", + "yarn_ext_factor", "yarn_orig_ctx", + # Chat / reasoning + "reasoning_format", "enable_reasoning", + "use_jinja", "chat_template", "enable_chat_template", + # Server + "hostname", "port", "n_threads_http", + "timeout_read", "timeout_write", + "api_keys", "api_prefix", + # Misc + "model_alias", "verbosity", +) + + +def main() -> None: + cfg = json.loads(sys.argv[1]) + p = xlc.CommonParams() + p.model.path = cfg["model_path"] + for k in SERVER_KEYS: + if k in cfg: + setattr(p, k, cfg[k]) + + server = xlc.Server(p) # noqa: F841 — held to keep the C++ server thread alive + while True: + time.sleep(3600) + + +if __name__ == "__main__": + main() diff --git a/lib/main.py b/lib/main.py index 652b200..166c17c 100644 --- a/lib/main.py +++ b/lib/main.py @@ -6,15 +6,15 @@ import asyncio import os import logging +import time import traceback from contextlib import asynccontextmanager from json import JSONDecodeError -from threading import Event, Thread -from time import perf_counter, sleep, strftime +from threading import Event from niquests import RequestException from streaming import StreamContext -from task_processors import generate_task_processors +from task_processors import generate_task_processors, get_n_parallel, stop_all_servers from fastapi import FastAPI from nc_py_api import AsyncNextcloudApp, NextcloudApp, NextcloudException from nc_py_api.ex_app import LogLvl, persistent_storage, run_app, set_handlers @@ -27,49 +27,67 @@ class NextcloudTaskStreamResult: - def __init__(self, nc: NextcloudApp, task_id: int, enabled: bool): + def __init__(self, nc: AsyncNextcloudApp, task_id: int, enabled: bool): self.nc = nc self.task_id = task_id self.enabled = enabled - def send(self, output: dict) -> None: + async def send(self, output: dict) -> None: if not self.enabled: return - try: - self.nc.ocs( + await self.nc.ocs( "POST", f"/ocs/v2.php/taskprocessing/tasks_provider/{self.task_id}/stream-result", json={"output": output}, ) except (NextcloudException, RequestException, JSONDecodeError) as e: - log(self.nc, LogLvl.WARNING, f"Streaming intermediate task output failed for task {self.task_id}: {e}") + logger.warning(f"Streaming intermediate task output failed for task {self.task_id}: {e}") self.enabled = False - def set_progress(self, progress: float) -> bool: + async def set_progress(self, progress: float) -> bool: try: - self.nc.providers.task_processing.set_progress(self.task_id, progress) + await self.nc.providers.task_processing.set_progress(self.task_id, progress) except (NextcloudException, RequestException, JSONDecodeError) as e: - log(self.nc, LogLvl.WARNING, f"Updating progress failed for task {self.task_id}: {e}") + logger.warning(f"Updating progress failed for task {self.task_id}: {e}") return False return True -def log(nc, level, content): - logger.log((level+1)*10, content) + +async def log(nc: AsyncNextcloudApp, level, content): + logger.log((level + 1) * 10, content) if level < LogLvl.WARNING: return try: - asyncio.run(nc.log(level, content)) - except: + await nc.log(level, content) + except Exception: pass + models_to_fetch = { - "https://huggingface.co/unsloth/Qwen3.5-9B-GGUF/resolve/3885219b6810b007914f3a7950a8d1b469d598a5/Qwen3.5-9B-Q4_K_M.gguf": { "save_path": os.path.join(persistent_storage(), "Qwen3.5-9B-Q4_K_M.gguf") }, - "https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/resolve/4f0c246f125fc7594238ebe7beb1435a8335f519/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf": { "save_path": os.path.join(persistent_storage(), "Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf") }, - "https://huggingface.co/unsloth/Olmo-3-7B-Instruct-GGUF/resolve/86844f8ff856ba9cc8a22c7b9edd7cf95129a580/Olmo-3-7B-Instruct-Q4_K_M.gguf": { "save_path": os.path.join(persistent_storage(), "Olmo-3-7B-Instruct-Q4_K_M.gguf") }, + "https://huggingface.co/unsloth/Qwen3.5-9B-GGUF/resolve/3885219b6810b007914f3a7950a8d1b469d598a5/Qwen3.5-9B-Q4_K_M.gguf": {"save_path": os.path.join(persistent_storage(), "Qwen3.5-9B-Q4_K_M.gguf")}, + "https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/resolve/4f0c246f125fc7594238ebe7beb1435a8335f519/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf": {"save_path": os.path.join(persistent_storage(), "Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf")}, + "https://huggingface.co/unsloth/Olmo-3-7B-Instruct-GGUF/resolve/86844f8ff856ba9cc8a22c7b9edd7cf95129a580/Olmo-3-7B-Instruct-Q4_K_M.gguf": {"save_path": os.path.join(persistent_storage(), "Olmo-3-7B-Instruct-Q4_K_M.gguf")}, } + +# threading.Event: set from the async enabled_handler, checked in the async polling loop app_enabled = Event() -trigger = Event() +# asyncio.Event: set from the async trigger_handler and on shutdown +trigger = asyncio.Event() +# Signals the polling loop to re-scan models on the next iteration — set by enabled_handler +# so newly available providers are picked up immediately instead of waiting up to SCAN_INTERVAL. +REFRESH_PROCESSORS = asyncio.Event() + +NUM_RUNNING_TASKS = 0 +NUM_RUNNING_TASKS_LOCK = asyncio.Lock() + +# Per-model in-flight counter, bounded by each model's n_parallel. Keeps NC from +# marking tasks "running" while they are merely queued in llama-cpp-server. +MODEL_INFLIGHT: dict[str, int] = {} +MODEL_INFLIGHT_LOCK = asyncio.Lock() +MODEL_SLOT_FREED = asyncio.Event() + +SHUTDOWN_EVENT = asyncio.Event() try: CHECK_INTERVAL = float(os.getenv('TASK_POLLING_INTERVAL', '5')) @@ -82,124 +100,182 @@ def log(nc, level, content): CHECK_INTERVAL_WITH_TRIGGER = 5 * 60 CHECK_INTERVAL_ON_ERROR = 10 -SHUTDOWN_EVENT_RECEIVED = Event() -SHUTDOWN_CLEAR = Event() +SCAN_INTERVAL = 5 * 60 -@asynccontextmanager -async def lifespan(_app: FastAPI): - set_handlers( - APP, - enabled_handler, # type: ignore - models_to_fetch=models_to_fetch, - trigger_handler=trigger_handler - ) - nc = NextcloudApp() - if nc.enabled_state: - app_enabled.set() - start_bg_task() - yield - print("\nSIGTERM received. Processing last task and stopping to fetch and process new tasks..") - SHUTDOWN_EVENT_RECEIVED.set() - trigger.set() - SHUTDOWN_CLEAR.wait() + +async def wait_for_tasks(interval: float | None = None) -> None: + global CHECK_INTERVAL + actual_interval = CHECK_INTERVAL if interval is None else interval + try: + await asyncio.wait_for(trigger.wait(), timeout=actual_interval) + CHECK_INTERVAL = CHECK_INTERVAL_WITH_TRIGGER + except asyncio.TimeoutError: + pass + trigger.clear() -APP = FastAPI(lifespan=lifespan) +def _model_of(processor_name: str) -> str: + return processor_name.split(":", 1)[0] -def background_thread_task(): - nc = NextcloudApp() +async def available_provider_ids(task_processors: dict) -> list[str]: + async with MODEL_INFLIGHT_LOCK: + return [ + "llm2:" + name + for name in task_processors + if MODEL_INFLIGHT.get(_model_of(name), 0) < get_n_parallel(_model_of(name)) + ] - while not app_enabled.is_set(): - sleep(5) - provider_ids = set() - task_type_ids = set() - task_processors = generate_task_processors() - for task_processor_name, _ in task_processors.items(): - provider_ids.add("llm2:" + task_processor_name) - (model, task) = task_processor_name.split(":", 1) - task_type_ids.add(task) - - while True: - if not app_enabled.is_set(): - sleep(30) - continue - - if SHUTDOWN_EVENT_RECEIVED.is_set(): - break - - current_minute = int(strftime("%M")) - if current_minute % 5 == 0: - # scan dir and load new models every 5 minutes - provider_ids = set() - task_type_ids = set() - task_processors = generate_task_processors(task_processors) - for task_processor_name, _ in task_processors.items(): - provider_ids.add("llm2:" + task_processor_name) - (model, task) = task_processor_name.split(":", 1) - task_type_ids.add(task) +async def handle_task(task: dict, provider: dict, nc: AsyncNextcloudApp, task_processors: dict) -> None: + global NUM_RUNNING_TASKS + task_processor_name = provider["name"][5:] + model_name = _model_of(task_processor_name) + + async with NUM_RUNNING_TASKS_LOCK: + NUM_RUNNING_TASKS += 1 + + try: + await log(nc, LogLvl.INFO, f"Processing: {task_processor_name}") + + task_processor_loader = task_processors.get(task_processor_name) + if task_processor_loader is None: + await nc.providers.task_processing.report_result( + task["id"], error_message="Requested model is not available" + ) + return + + # task_processor_loader() calls generate_chat_model() which may start the llama-cpp-server + # on first use (blocking I/O). Run it in the executor to avoid stalling the event loop. + loop = asyncio.get_running_loop() + processor = await loop.run_in_executor(None, task_processor_loader) + + stream_result = NextcloudTaskStreamResult(nc, task["id"], bool(task.get("preferStreaming"))) + stream_context = StreamContext( + stream_result=stream_result.send if stream_result.enabled else None, + progress_callback=stream_result.set_progress if stream_result.enabled else None, + ) + + time_start = time.perf_counter() + result = await processor(task.get("input"), context=stream_context) + await log(nc, LogLvl.INFO, f"Done in {round(time.perf_counter() - time_start, 2)}s: {result}") + await nc.providers.task_processing.report_result(task["id"], result) + + except (NextcloudException, RequestException, JSONDecodeError) as e: + tb_str = ''.join(traceback.format_exception(e)) + await log(nc, LogLvl.ERROR, f"Network error handling task: {tb_str}") + except Exception as e: + tb_str = ''.join(traceback.format_exception(e)) + await log(nc, LogLvl.ERROR, f"Error handling task: {tb_str}") try: - response = nc.providers.task_processing.next_task(list(provider_ids), list(task_type_ids)) - if not response: - wait_for_tasks() + await nc.providers.task_processing.report_result(task["id"], error_message=str(e)) + except (NextcloudException, RequestException): + pass + finally: + async with NUM_RUNNING_TASKS_LOCK: + NUM_RUNNING_TASKS -= 1 + async with MODEL_INFLIGHT_LOCK: + MODEL_INFLIGHT[model_name] = max(0, MODEL_INFLIGHT.get(model_name, 0) - 1) + MODEL_SLOT_FREED.set() + + +async def background_task_loop() -> None: + try: + await _background_task_loop_inner() + except Exception: + # Without this, an unhandled exception in the bg task is silenced until shutdown awaits it, + # leaving the polling loop dead with no log line — backend looks healthy but never claims tasks. + logger.exception("background_task_loop crashed") + raise + + +async def _background_task_loop_inner() -> None: + nc = AsyncNextcloudApp() + task_processors = generate_task_processors() + last_scan = time.monotonic() + + task_type_ids = {n.split(":", 1)[1] for n in task_processors} + + while not app_enabled.is_set(): + await asyncio.sleep(5) + + async with asyncio.TaskGroup() as tg: + while not SHUTDOWN_EVENT.is_set(): + if not app_enabled.is_set(): + await asyncio.sleep(5) continue - except (NextcloudException, RequestException, JSONDecodeError) as e: - log(nc, LogLvl.ERROR, f"Network error fetching the next task {e}") - wait_for_tasks(CHECK_INTERVAL_ON_ERROR) - continue - task = response["task"] - provider = response["provider"] + if REFRESH_PROCESSORS.is_set() or time.monotonic() - last_scan >= SCAN_INTERVAL: + task_processors = generate_task_processors(task_processors) + task_type_ids = {n.split(":", 1)[1] for n in task_processors} + last_scan = time.monotonic() + REFRESH_PROCESSORS.clear() + + available = await available_provider_ids(task_processors) + if not available: + # Every model is at its n_parallel cap. Wait for a slot to free + # rather than pulling tasks NC would otherwise mark "running". + try: + await asyncio.wait_for(MODEL_SLOT_FREED.wait(), timeout=CHECK_INTERVAL) + except asyncio.TimeoutError: + pass + MODEL_SLOT_FREED.clear() + continue - try: - task_processor_name = provider["name"][5:] - log(nc, LogLvl.INFO, f"chain: {task_processor_name}") - task_processor_loader = task_processors.get(task_processor_name) - if task_processor_loader is None: - NextcloudApp().providers.task_processing.report_result( - task["id"], error_message="Requested model is not available" + try: + response = await nc.providers.task_processing.next_task( + available, list(task_type_ids) ) + except (NextcloudException, RequestException, JSONDecodeError) as e: + await log(nc, LogLvl.ERROR, f"Network error fetching the next task: {e}") + await wait_for_tasks(CHECK_INTERVAL_ON_ERROR) continue - task_processor = task_processor_loader() - log(nc, LogLvl.INFO, "Generating reply") - time_start = perf_counter() - log(nc, LogLvl.INFO, task.get("input")) - stream_result = NextcloudTaskStreamResult(nc, task["id"], bool(task.get("preferStreaming", None))) - stream_context = StreamContext( - stream_result=stream_result.send if stream_result.enabled else None, - progress_callback=stream_result.set_progress, - ) - result = task_processor(task.get("input"), context=stream_context) - log(nc, LogLvl.INFO, f"reply generated: {round(float(perf_counter() - time_start), 2)}s") - log(nc, LogLvl.INFO, result) - nc.providers.task_processing.report_result( - task["id"], - result, - ) - except (NextcloudException, RequestException, JSONDecodeError) as e: - # Error when reporting the result - exception_info = traceback.format_exception(type(e), e, e.__traceback__) - log(nc, LogLvl.ERROR, f"Error: {''.join(exception_info)}") - except Exception as e: # noqa - exception_info = traceback.format_exception(type(e), e, e.__traceback__) - log(nc, LogLvl.ERROR, f"Error: {''.join(exception_info)}") - try: - log(nc, LogLvl.ERROR, str(e)) - nc.providers.task_processing.report_result(task["id"], error_message=str(e)) - except (NextcloudException, RequestException) as net_err: - log(nc, LogLvl.INFO, f"Network error in reporting the error: {net_err}") - SHUTDOWN_CLEAR.set() -def start_bg_task(): - t = Thread(target=background_thread_task, args=()) - t.start() + if not response: + async with NUM_RUNNING_TASKS_LOCK: + no_tasks_running = NUM_RUNNING_TASKS == 0 + if no_tasks_running: + await wait_for_tasks() + else: + await asyncio.sleep(2) + continue + + # Reserve the slot synchronously, before yielding to the loop again, + # so the next available_provider_ids() call sees this task counted. + pulled_model = _model_of(response["provider"]["name"][5:]) + async with MODEL_INFLIGHT_LOCK: + MODEL_INFLIGHT[pulled_model] = MODEL_INFLIGHT.get(pulled_model, 0) + 1 + + tg.create_task(handle_task(response["task"], response["provider"], nc, task_processors)) + # TaskGroup exits only after all spawned handle_task coroutines finish — graceful drain on shutdown + + +@asynccontextmanager +async def lifespan(_app: FastAPI): + set_handlers( + APP, + enabled_handler, # type: ignore + models_to_fetch=models_to_fetch, + trigger_handler=trigger_handler, + ) + nc = NextcloudApp() + if nc.enabled_state: + app_enabled.set() + bg = asyncio.get_event_loop().create_task(background_task_loop()) + yield + print("\nSIGTERM received. Finishing in-flight tasks before shutdown...") + SHUTDOWN_EVENT.set() + trigger.set() # wake up any wait_for_tasks() call so the loop checks SHUTDOWN_EVENT + await bg + stop_all_servers() + + +APP = FastAPI(lifespan=lifespan) async def enabled_handler(enabled: bool, nc: AsyncNextcloudApp) -> str: - global app_enabled - log(nc, LogLvl.INFO, f"enabled={enabled}") + await log(nc, LogLvl.INFO, f"enabled={enabled}") task_processors = generate_task_processors() @@ -212,49 +288,45 @@ async def enabled_handler(enabled: bool, nc: AsyncNextcloudApp) -> str: name="Local Large language Model: " + model, task_type=task, expected_runtime=30, - input_shape_enum_values= { + input_shape_enum_values={ "tone": [ - ShapeEnumValue(name= "Friendlier", value= "friendlier"), - ShapeEnumValue(name= "More formal", value= "more formal"), - ShapeEnumValue(name= "Funnier", value= "funnier"), - ShapeEnumValue(name= "More casual", value= "more casual"), - ShapeEnumValue(name= "More urgent", value= "more urgent"), + ShapeEnumValue(name="Friendlier", value="friendlier"), + ShapeEnumValue(name="More formal", value="more formal"), + ShapeEnumValue(name="Funnier", value="funnier"), + ShapeEnumValue(name="More casual", value="more casual"), + ShapeEnumValue(name="More urgent", value="more urgent"), ], } if task == "core:text2text:changetone" else {}, optional_input_shape=[ ShapeDescriptor(name="memories", description="Memories to inject into the prompt", shape_type=ShapeType.LIST_OF_TEXTS) ] if task == "core:text2text:chat" else [], + optional_output_shape=[ + ShapeDescriptor(name="reasoning", description="Reasoning trace produced by the model, if any", shape_type=ShapeType.TEXT) + ] if task != "core:text2text:summary" else [], ) await nc.providers.task_processing.register(provider) - log(nc, LogLvl.INFO, f"Registered {task_processor_name}") + await log(nc, LogLvl.INFO, f"Registered {task_processor_name}") app_enabled.set() + REFRESH_PROCESSORS.set() except Exception as e: - log(nc, LogLvl.ERROR, f"Failed to register {model} - {task}, Error: {e}\n") + await log(nc, LogLvl.ERROR, f"Failed to register {model} - {task}, Error: {e}\n") break else: app_enabled.clear() for task_processor_name in task_processors: try: await nc.providers.task_processing.unregister("llm2:" + task_processor_name) - log(nc, LogLvl.INFO, f"Unregistered {task_processor_name}") + await log(nc, LogLvl.INFO, f"Unregistered {task_processor_name}") except Exception as e: - log(nc, LogLvl.ERROR, f"Failed to unregister {task_processor_name}, Error: {e}\n") + await log(nc, LogLvl.ERROR, f"Failed to unregister {task_processor_name}, Error: {e}\n") break return "" -def trigger_handler(providerId: str): - print('TRIGGER called') +async def trigger_handler(providerId: str): trigger.set() -def wait_for_tasks(interval = None): - global CHECK_INTERVAL - global CHECK_INTERVAL_WITH_TRIGGER - actual_interval = CHECK_INTERVAL if interval is None else interval - if trigger.wait(timeout=actual_interval): - CHECK_INTERVAL = CHECK_INTERVAL_WITH_TRIGGER - trigger.clear() if __name__ == "__main__": run_app("main:APP", log_level="trace") diff --git a/lib/proofread.py b/lib/proofread.py index 1d4a715..c699794 100644 --- a/lib/proofread.py +++ b/lib/proofread.py @@ -35,12 +35,18 @@ class ProofreadProcessor: def __init__(self, runnable: Runnable): self.runnable = runnable - def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: + async def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: messages = [ SystemMessage(content=self.system_prompt), HumanMessage(content=self.user_prompt.format( text=inputs['input'] )) ] - output = run_runnable_with_streaming(self.runnable, messages, context) - return {'output': output} + reasoning_sink: dict[str, str] = {} + output = await run_runnable_with_streaming( + self.runnable, + messages, + context, + reasoning_sink=reasoning_sink, + ) + return {'output': output, 'reasoning': reasoning_sink.get('reasoning', '')} diff --git a/lib/reformulate.py b/lib/reformulate.py index d3392c0..3336d6e 100644 --- a/lib/reformulate.py +++ b/lib/reformulate.py @@ -35,12 +35,18 @@ class ReformulateProcessor: def __init__(self, runnable: Runnable): self.runnable = runnable - def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: + async def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: messages = [ SystemMessage(content=self.system_prompt), HumanMessage(content=self.user_prompt.format( text=inputs['input'] )) ] - output = run_runnable_with_streaming(self.runnable, messages, context) - return {'output': output} + reasoning_sink: dict[str, str] = {} + output = await run_runnable_with_streaming( + self.runnable, + messages, + context, + reasoning_sink=reasoning_sink, + ) + return {'output': output, 'reasoning': reasoning_sink.get('reasoning', '')} diff --git a/lib/simplify.py b/lib/simplify.py index bff3f32..7d6cd7e 100644 --- a/lib/simplify.py +++ b/lib/simplify.py @@ -38,12 +38,18 @@ class SimplifyProcessor: def __init__(self, runnable: Runnable): self.runnable = runnable - def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: + async def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: messages = [ SystemMessage(content=self.system_prompt), HumanMessage(content=self.user_prompt.format( text=inputs['input'] )) ] - output = run_runnable_with_streaming(self.runnable, messages, context) - return {'output': output} + reasoning_sink: dict[str, str] = {} + output = await run_runnable_with_streaming( + self.runnable, + messages, + context, + reasoning_sink=reasoning_sink, + ) + return {'output': output, 'reasoning': reasoning_sink.get('reasoning', '')} diff --git a/lib/streaming.py b/lib/streaming.py index 2cddbb5..9f35194 100644 --- a/lib/streaming.py +++ b/lib/streaming.py @@ -2,9 +2,39 @@ # SPDX-License-Identifier: AGPL-3.0-or-later from __future__ import annotations +import asyncio from dataclasses import dataclass, field from time import monotonic -from typing import Any, Callable +from typing import Any, Awaitable, Callable + +# langchain-openai's converters drop `reasoning_content` (emitted by llama.cpp's +# deepseek reasoning format) from both streaming deltas and non-streaming +# responses. Patch them to preserve it under additional_kwargs so downstream +# code can read it back out. +try: + from langchain_openai.chat_models import base as _lc_openai_base + + _original_delta_to_chunk = _lc_openai_base._convert_delta_to_message_chunk + _original_dict_to_message = _lc_openai_base._convert_dict_to_message + + def _delta_to_chunk_with_reasoning(_dict, default_class): + chunk = _original_delta_to_chunk(_dict, default_class) + reasoning = _dict.get("reasoning_content") if hasattr(_dict, "get") else None + if reasoning and hasattr(chunk, "additional_kwargs"): + chunk.additional_kwargs["reasoning_content"] = reasoning + return chunk + + def _dict_to_message_with_reasoning(_dict): + message = _original_dict_to_message(_dict) + reasoning = _dict.get("reasoning_content") if hasattr(_dict, "get") else None + if reasoning and hasattr(message, "additional_kwargs"): + message.additional_kwargs["reasoning_content"] = reasoning + return message + + _lc_openai_base._convert_delta_to_message_chunk = _delta_to_chunk_with_reasoning + _lc_openai_base._convert_dict_to_message = _dict_to_message_with_reasoning +except Exception: + pass def extract_text_content(value: Any) -> str: @@ -32,10 +62,28 @@ def extract_text_content(value: Any) -> str: return str(content) +def extract_reasoning_content(value: Any) -> str: + if value is None: + return "" + + additional_kwargs = getattr(value, "additional_kwargs", None) + if isinstance(additional_kwargs, dict): + reasoning = additional_kwargs.get("reasoning_content") + if isinstance(reasoning, str): + return reasoning + + if isinstance(value, dict): + reasoning = value.get("reasoning_content") + if isinstance(reasoning, str): + return reasoning + + return "" + + @dataclass class StreamContext: - stream_result: Callable[[dict[str, Any]], None] | None = None - progress_callback: Callable[[float], Any] | None = None + stream_result: Callable[[dict[str, Any]], Awaitable[None] | None] | None = None + progress_callback: Callable[[float], Awaitable[Any] | Any] | None = None stream_interval_seconds: float = 0.75 current_output: dict[str, Any] = field(default_factory=dict) _last_emit_at: float = field(default=0.0, init=False) @@ -80,17 +128,24 @@ def emit(self, *, force: bool = False) -> None: if payload == self._last_emitted_output: return - self.stream_result(payload) + if asyncio.iscoroutinefunction(self.stream_result): + asyncio.ensure_future(self.stream_result(payload)) + else: + self.stream_result(payload) self._last_emit_at = now self._last_emitted_output = payload def set_progress(self, progress: float) -> Any: if self.progress_callback is None: return True - return self.progress_callback(progress) + result = self.progress_callback(progress) + if asyncio.iscoroutine(result): + asyncio.ensure_future(result) + return True + return result -def run_runnable_with_streaming( +async def run_runnable_with_streaming( runnable: Any, messages: list[Any], context: StreamContext | None = None, @@ -99,23 +154,42 @@ def run_runnable_with_streaming( stream_text_transform: Callable[[str], str] | None = None, stream_payload_transform: Callable[[str], dict[str, Any] | None] | None = None, suppress_empty_stream_updates: bool = False, + reasoning_sink: dict[str, str] | None = None, **extra_output: Any, ) -> str: + capture_reasoning = reasoning_sink is not None + if context and context.enabled: - chunks: list[str] = [] + text_parts: list[str] = [] + reasoning_parts: list[str] = [] - for chunk in runnable.stream(messages): + async for chunk in runnable.astream(messages): text_chunk = extract_text_content(chunk) - if not text_chunk: + reasoning_chunk = extract_reasoning_content(chunk) if capture_reasoning else "" + + if not text_chunk and not reasoning_chunk: continue - chunks.append(text_chunk) - output = "".join(chunks) + + if text_chunk: + text_parts.append(text_chunk) + if reasoning_chunk: + reasoning_parts.append(reasoning_chunk) + + output = "".join(text_parts) + reasoning = "".join(reasoning_parts) + + stream_extra = dict(extra_output) + if capture_reasoning and reasoning: + stream_extra["reasoning"] = reasoning + if stream_payload_transform: streamed_payload = stream_payload_transform(output) if streamed_payload is None: + if capture_reasoning and reasoning: + context.update_output({"reasoning": reasoning, **extra_output}) continue - if extra_output: - streamed_payload.update(extra_output) + if stream_extra: + streamed_payload.update(stream_extra) if suppress_empty_stream_updates and not streamed_payload: continue context.update_output(streamed_payload) @@ -123,20 +197,32 @@ def run_runnable_with_streaming( streamed_output = stream_text_transform(output) if stream_text_transform else output if suppress_empty_stream_updates and streamed_output == "": + if capture_reasoning and reasoning: + context.update_output({"reasoning": reasoning, **extra_output}) continue context.update_text( streamed_output, key=output_key, - **extra_output, + **stream_extra, ) - output = "".join(chunks) + output = "".join(text_parts) + reasoning_final = "".join(reasoning_parts) + if capture_reasoning: + reasoning_sink["reasoning"] = reasoning_final + + stream_extra_final = dict(extra_output) + if capture_reasoning and reasoning_final: + stream_extra_final["reasoning"] = reasoning_final + if stream_payload_transform: streamed_payload = stream_payload_transform(output) if streamed_payload is None: + if capture_reasoning and reasoning_final: + context.update_output({"reasoning": reasoning_final, **extra_output}, force=True) return output - if extra_output: - streamed_payload.update(extra_output) + if stream_extra_final: + streamed_payload.update(stream_extra_final) if not suppress_empty_stream_updates or streamed_payload: context.update_output(streamed_payload, force=True) return output @@ -146,9 +232,11 @@ def run_runnable_with_streaming( streamed_output, key=output_key, force=True, - **extra_output, + **stream_extra_final, ) return output - return extract_text_content(runnable.invoke(messages)) - + result = await runnable.ainvoke(messages) + if capture_reasoning: + reasoning_sink["reasoning"] = extract_reasoning_content(result) + return extract_text_content(result) diff --git a/lib/summarize.py b/lib/summarize.py index ebcc86b..2e02674 100644 --- a/lib/summarize.py +++ b/lib/summarize.py @@ -48,7 +48,7 @@ def __init__(self, runnable: Runnable, n_ctx: int = 8000): length_function=len, ) - def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: + async def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: # Split text if needed splits = self.text_splitter.split_text(inputs['input']) total_splits = len(splits) @@ -58,7 +58,7 @@ def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) SystemMessage(content=self.system_prompt), HumanMessage(content=self.user_prompt.format(input=splits[0])) ] - output = run_runnable_with_streaming( + output = await run_runnable_with_streaming( self.runnable, messages, context, @@ -72,7 +72,7 @@ def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) SystemMessage(content=self.system_prompt), HumanMessage(content=self.user_prompt.format(input=split)) ] - output = self.runnable.invoke(messages) + output = await self.runnable.ainvoke(messages) summaries.append(output.content) if context: context.set_progress(index / (total_splits + 1) * 50) @@ -85,7 +85,7 @@ def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) if context: context.set_progress(total_splits / (total_splits + 1) * 50 + 50) - final_output = run_runnable_with_streaming( + final_output = await run_runnable_with_streaming( self.runnable, messages, context, diff --git a/lib/task_processors.py b/lib/task_processors.py index 045ed0b..952c012 100644 --- a/lib/task_processors.py +++ b/lib/task_processors.py @@ -4,13 +4,18 @@ """ import json +import logging import os +import socket +import subprocess +import sys +import time +from collections import deque from functools import cache +from threading import Thread -from langchain_community.chat_models import ChatLlamaCpp - -from langchain.prompts import PromptTemplate -from langchain_community.llms import LlamaCpp +import niquests +from langchain_openai import ChatOpenAI from nc_py_api.ex_app import persistent_storage from chat import ChatProcessor @@ -28,6 +33,31 @@ dir_path = os.path.dirname(os.path.realpath(__file__)) models_folder_path = os.path.join(dir_path , "../models/") +# Maps file_name -> (Popen, port) for cleanup on shutdown +_server_processes: dict[str, tuple[subprocess.Popen, int]] = {} + +logger = logging.getLogger(__name__) + + +class _ServerLogPipe: + """Forward a subprocess pipe to the logger, keeping the last lines for error context.""" + def __init__(self, model_name: str, tail_lines: int = 50) -> None: + self.prefix = f"[llama-cpp-server:{model_name}] " + self._tail: deque[str] = deque(maxlen=tail_lines) + + def consume(self, stream) -> None: + try: + for line in stream: + line = line.rstrip() + self._tail.append(line) + logger.info(self.prefix + line) + except Exception: + pass + + def tail(self) -> str: + return "\n".join(self._tail) + + def get_model_config(file_name): file_name = file_name.split('.gguf')[0] if os.path.exists(os.path.join(models_folder_path, file_name + ".json")): @@ -50,64 +80,126 @@ def get_model_config(file_name): return model_config -@cache -def generate_llm(file_name): - model_config = get_model_config(file_name) +def get_n_parallel(model_name: str) -> int: + return get_model_config(model_name)["loader_config"].get("n_parallel", 1) - path = os.path.join(models_folder_path, file_name) - if not os.path.exists(path): - path = os.path.join(persistent_storage(), file_name) +def _find_free_port() -> int: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(("127.0.0.1", 0)) + return s.getsockname()[1] - compute_device = os.getenv("COMPUTE_DEVICE", "CUDA") - try: - llm = LlamaCpp( - model_path=path, - **{ - "n_gpu_layers": (0, -1)[compute_device != "CPU"], - **model_config["loader_config"], - }, - ) - except Exception as e: - print(f"Failed to load model '{path}' with compute device '{compute_device}'") - raise e - return llm +def _wait_for_server(proc: subprocess.Popen, port: int, log_pipe: _ServerLogPipe, timeout: float = 300.0) -> None: + url = f"http://127.0.0.1:{port}/health" + deadline = time.monotonic() + timeout + while time.monotonic() < deadline: + exit_code = proc.poll() + if exit_code is not None: + raise RuntimeError( + f"llama-server exited with code {exit_code} before becoming ready. " + f"Last output:\n{log_pipe.tail()}" + ) + try: + resp = niquests.get(url, timeout=5) + if resp.status_code == 200: + return + except Exception: + pass + time.sleep(2) + raise RuntimeError( + f"llama-server on port {port} did not become ready within {timeout}s. " + f"Last output:\n{log_pipe.tail()}" + ) -@cache -def generate_llm_chain(file_name): - model_config = get_model_config(file_name) - print(model_config) - llm = generate_llm(file_name) - prompt = PromptTemplate.from_template(model_config['prompt']) +_SERVER_SCRIPT_PATH = os.path.join(dir_path, "llama_server.py") - return prompt | llm @cache -def generate_chat_chain(file_name): +def generate_chat_model(file_name: str) -> ChatOpenAI: model_config = get_model_config(file_name) + loader_config = model_config["loader_config"] path = os.path.join(models_folder_path, file_name) - if not os.path.exists(path): path = os.path.join(persistent_storage(), file_name) compute_device = os.getenv("COMPUTE_DEVICE", "CUDA") + n_gpu_layers = -1 if compute_device != "CPU" else 0 + + port = _find_free_port() + model_alias = file_name.split(".gguf")[0] + + server_config = json.dumps({ + "model_path": path, + "hostname": "127.0.0.1", + "port": port, + "n_gpu_layers": n_gpu_layers, + "n_batch": loader_config.get("n_batch", 512), + "n_parallel": loader_config.get("n_parallel", 1), + "cont_batching": True, + **loader_config, + }) + + logger.info(f"Starting llama-server for {file_name} on port {port}") try: - llm = ChatLlamaCpp( - model_path=path, - n_batch=1, - **{ - "n_gpu_layers": (0, -1)[compute_device != "CPU"], - **model_config["loader_config"], - }, + proc = subprocess.Popen( + [sys.executable, _SERVER_SCRIPT_PATH, server_config], + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, ) - except Exception as e: - print(f"Failed to load model '{path}' with compute device '{compute_device}'") - raise e + except OSError as e: + raise RuntimeError(f"Failed to spawn llama-server subprocess for {file_name}: {e}") from e - return llm + log_pipe = _ServerLogPipe(model_alias) + Thread(target=log_pipe.consume, args=(proc.stdout,), daemon=True).start() + _server_processes[file_name] = (proc, port) + + try: + _wait_for_server(proc, port, log_pipe) + except Exception: + _server_processes.pop(file_name, None) + try: + proc.terminate() + proc.wait(timeout=5) + except Exception: + try: + proc.kill() + except Exception: + pass + raise + logger.info(f"llama-cpp-server for {file_name} ready on port {port}") + + model_kwargs: dict = {} + if loader_config.get("stop"): + model_kwargs["stop"] = loader_config["stop"] + + return ChatOpenAI( + base_url=f"http://127.0.0.1:{port}/v1", + api_key="not-needed", + model=model_alias, + max_tokens=loader_config.get("max_tokens", 2048), + temperature=loader_config.get("temperature", 0.7), + model_kwargs=model_kwargs, + ) + + +def stop_all_servers() -> None: + for file_name, (proc, port) in _server_processes.items(): + logger.info(f"Stopping llama-cpp-server for {file_name} (port {port})") + try: + proc.terminate() + proc.wait(timeout=15) + except Exception as e: + logger.warning(f"Error stopping server for {file_name}: {e}") + try: + proc.kill() + except Exception: + pass + _server_processes.clear() def generate_task_processors(task_processors = {}): @@ -130,16 +222,14 @@ def generate_task_processors_for_model(file_name, task_processors): model_name = file_name.split('.gguf')[0] n_ctx = get_model_config(file_name)["loader_config"]["n_ctx"] - task_processors[model_name + ":core:text2text:summary"] = lambda: SummarizeProcessor(generate_chat_chain(file_name), n_ctx) - task_processors[model_name + ":core:text2text:headline"] = lambda: HeadlineProcessor(generate_chat_chain(file_name)) - task_processors[model_name + ":core:text2text:topics"] = lambda: TopicsProcessor(generate_chat_chain(file_name)) - task_processors[model_name + ":core:text2text:simplification"] = lambda: SimplifyProcessor(generate_chat_chain(file_name)) - task_processors[model_name + ":core:text2text:reformulation"] = lambda: ReformulateProcessor(generate_chat_chain(file_name)) - task_processors[model_name + ":core:contextwrite"] = lambda: ContextWriteProcessor(generate_chat_chain(file_name)) - task_processors[model_name + ":core:text2text"] = lambda: FreePromptProcessor(generate_chat_chain(file_name)) - task_processors[model_name + ":core:text2text:chat"] = lambda: ChatProcessor(generate_chat_chain(file_name)) - task_processors[model_name + ":core:text2text:proofread"] = lambda: ProofreadProcessor(generate_chat_chain(file_name)) - task_processors[model_name + ":core:text2text:changetone"] = lambda: ChangeToneProcessor(generate_chat_chain(file_name)) - task_processors[model_name + ":core:text2text:chatwithtools"] = lambda: ChatWithToolsProcessor(generate_chat_chain(file_name)) - - # chains[model_name + ":core:contextwrite"] = lambda: ContextWriteChain(llm_chain=llm_chain()) + task_processors[model_name + ":core:text2text:summary"] = lambda: SummarizeProcessor(generate_chat_model(file_name), n_ctx) + task_processors[model_name + ":core:text2text:headline"] = lambda: HeadlineProcessor(generate_chat_model(file_name)) + task_processors[model_name + ":core:text2text:topics"] = lambda: TopicsProcessor(generate_chat_model(file_name)) + task_processors[model_name + ":core:text2text:simplification"] = lambda: SimplifyProcessor(generate_chat_model(file_name)) + task_processors[model_name + ":core:text2text:reformulation"] = lambda: ReformulateProcessor(generate_chat_model(file_name)) + task_processors[model_name + ":core:contextwrite"] = lambda: ContextWriteProcessor(generate_chat_model(file_name)) + task_processors[model_name + ":core:text2text"] = lambda: FreePromptProcessor(generate_chat_model(file_name)) + task_processors[model_name + ":core:text2text:chat"] = lambda: ChatProcessor(generate_chat_model(file_name)) + task_processors[model_name + ":core:text2text:proofread"] = lambda: ProofreadProcessor(generate_chat_model(file_name)) + task_processors[model_name + ":core:text2text:changetone"] = lambda: ChangeToneProcessor(generate_chat_model(file_name)) + task_processors[model_name + ":core:text2text:chatwithtools"] = lambda: ChatWithToolsProcessor(generate_chat_model(file_name)) diff --git a/lib/topics.py b/lib/topics.py index 82f25f2..50d0c03 100644 --- a/lib/topics.py +++ b/lib/topics.py @@ -36,10 +36,16 @@ class TopicsProcessor(): def __init__(self, runnable: Runnable): self.runnable = runnable - def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: + async def __call__(self, inputs: dict[str, Any], context: StreamContext | None = None) -> dict[str, Any]: messages = [ SystemMessage(content=self.system_prompt), HumanMessage(content=self.user_prompt.format_prompt(text=inputs['input']).to_string()) ] - output = run_runnable_with_streaming(self.runnable, messages, context) - return {'output': output} \ No newline at end of file + reasoning_sink: dict[str, str] = {} + output = await run_runnable_with_streaming( + self.runnable, + messages, + context, + reasoning_sink=reasoning_sink, + ) + return {'output': output, 'reasoning': reasoning_sink.get('reasoning', '')} \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 0ff990e..d7aa9d1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,186 +1,15 @@ -# This file is automatically @generated by Poetry 2.4.1 and should not be changed by hand. - -[[package]] -name = "aiohappyeyeballs" -version = "2.6.1" -description = "Happy Eyeballs for asyncio" -optional = false -python-versions = ">=3.9" -groups = ["main"] -files = [ - 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