From abcf17bd44225cb91ffefdb4643ec5ce615aa73e Mon Sep 17 00:00:00 2001 From: wwxxzz Date: Tue, 6 Aug 2024 19:35:09 +0800 Subject: [PATCH] Replace PaiEas LLM with LLI-integration and upgrade python to 3.11 (#148) * Replace PaiEas LLM with LLI-integration and upgrade python version to 3.11 * Replace MyFCDashScope with OpenAILike class * Fix pyproject dependency --- .github/workflows/ci.yml | 4 +- .github/workflows/main.yml | 4 +- .github/workflows/main_gpu.yml | 4 +- Dockerfile | 4 +- Dockerfile_gpu | 4 +- Dockerfile_ui | 4 +- README.md | 4 +- README_zh.md | 4 +- poetry.lock | 1311 +++++++++-------- pyproject.toml | 13 +- pyproject_gpu.toml | 13 +- src/pai_rag/config/settings_fc_demo.toml | 5 + .../integrations/llms/dashscope/fc_base.py | 878 +---------- .../integrations/llms/dashscope/utils.py | 30 - src/pai_rag/integrations/llms/paieas/base.py | 623 -------- .../integrations/llms/paieas/base_old.py | 249 ---- .../modules/llm/function_calling_llm.py | 1 - src/pai_rag/modules/llm/llm_module.py | 7 +- src/pai_rag/modules/tool/utils.py | 53 +- 19 files changed, 750 insertions(+), 2465 deletions(-) delete mode 100644 src/pai_rag/integrations/llms/dashscope/utils.py delete mode 100644 src/pai_rag/integrations/llms/paieas/base.py delete mode 100644 src/pai_rag/integrations/llms/paieas/base_old.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 0e57c3e8..b85ae7fb 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -19,12 +19,12 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Set up Python 3.10 + - name: Set up Python 3.11 # This is the version of the action for setting up Python, not the Python version. uses: actions/setup-python@v5 with: # Semantic version range syntax or exact version of a Python version - python-version: "3.10" + python-version: "3.11" # Optional - x64 or x86 architecture, defaults to x64 architecture: "x64" diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index a486c92a..4168e39a 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -19,12 +19,12 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Set up Python 3.10 + - name: Set up Python 3.11 # This is the version of the action for setting up Python, not the Python version. uses: actions/setup-python@v5 with: # Semantic version range syntax or exact version of a Python version - python-version: "3.10" + python-version: "3.11" # Optional - x64 or x86 architecture, defaults to x64 architecture: "x64" diff --git a/.github/workflows/main_gpu.yml b/.github/workflows/main_gpu.yml index 6694d1d2..e58df475 100644 --- a/.github/workflows/main_gpu.yml +++ b/.github/workflows/main_gpu.yml @@ -19,12 +19,12 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Set up Python 3.10 + - name: Set up Python 3.11 # This is the version of the action for setting up Python, not the Python version. uses: actions/setup-python@v5 with: # Semantic version range syntax or exact version of a Python version - python-version: "3.10" + python-version: "3.11" # Optional - x64 or x86 architecture, defaults to x64 architecture: "x64" diff --git a/Dockerfile b/Dockerfile index f86927c7..54607ce5 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.10-slim AS builder +FROM python:3.11-slim AS builder RUN pip3 install poetry @@ -12,7 +12,7 @@ COPY . . RUN poetry install && rm -rf $POETRY_CACHE_DIR -FROM python:3.10-slim AS prod +FROM python:3.11-slim AS prod RUN rm -rf /etc/localtime && ln -s /usr/share/zoneinfo/Asia/Harbin /etc/localtime diff --git a/Dockerfile_gpu b/Dockerfile_gpu index 7388fa4b..e3e6a081 100644 --- a/Dockerfile_gpu +++ b/Dockerfile_gpu @@ -1,4 +1,4 @@ -FROM python:3.10-slim AS builder +FROM python:3.11-slim AS builder RUN pip3 install poetry @@ -14,7 +14,7 @@ RUN mv pyproject_gpu.toml pyproject.toml \ RUN poetry install && rm -rf $POETRY_CACHE_DIR -FROM python:3.10-slim AS prod +FROM python:3.11-slim AS prod RUN rm -rf /etc/localtime && ln -s /usr/share/zoneinfo/Asia/Harbin /etc/localtime diff --git a/Dockerfile_ui b/Dockerfile_ui index a2509356..ca07c25e 100644 --- a/Dockerfile_ui +++ b/Dockerfile_ui @@ -1,4 +1,4 @@ -FROM python:3.10-slim AS builder +FROM python:3.11-slim AS builder RUN pip3 install poetry @@ -12,7 +12,7 @@ COPY . . RUN poetry install && rm -rf $POETRY_CACHE_DIR -FROM python:3.10-slim AS prod +FROM python:3.11-slim AS prod RUN rm -rf /etc/localtime && ln -s /usr/share/zoneinfo/Asia/Harbin /etc/localtime diff --git a/README.md b/README.md index c60e6246..d6fa4672 100644 --- a/README.md +++ b/README.md @@ -48,10 +48,10 @@ PAI-RAG is an easy-to-use opensource framework for modular RAG (Retrieval-Augmen 2. Development Environment Settings - This project uses poetry for management. To ensure environmental consistency and avoid problems caused by Python version differences, we specify Python version 3.10. + This project uses poetry for management. To ensure environmental consistency and avoid problems caused by Python version differences, we specify Python version 3.11. ```bash - conda create -n rag_env python==3.10 + conda create -n rag_env python==3.11 conda activate rag_env ``` diff --git a/README_zh.md b/README_zh.md index 12527169..db37aa22 100644 --- a/README_zh.md +++ b/README_zh.md @@ -43,10 +43,10 @@ PAI-RAG 是一个易于使用的模块化 RAG（检索增强生成）开源框 2. 配置开发环境 - 本项目使用poetry进行管理，若在本地环境下使用，建议在安装环境之前先创建一个空环境。为了确保环境一致性并避免因Python版本差异造成的问题，我们指定Python版本为3.10。 + 本项目使用poetry进行管理，若在本地环境下使用，建议在安装环境之前先创建一个空环境。为了确保环境一致性并避免因Python版本差异造成的问题，我们指定Python版本为3.11。 ```bash - conda create -n rag_env python==3.10 + conda create -n rag_env python==3.11 conda activate rag_env ``` diff --git a/poetry.lock b/poetry.lock index 2639d061..41a9c73a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -44,104 +44,103 @@ files = [ [[package]] name = "aiohappyeyeballs" -version = "2.3.2" -description = "Happy Eyeballs" +version = "2.3.4" +description = "Happy Eyeballs for 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100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,14 +10,14 @@ authors = [] readme = "README.md" [tool.poetry.dependencies] -python = ">=3.10.0,<3.12" +python = ">=3.11.0,<3.12" fastapi = "^0.110.1" uvicorn = "^0.29.0" -llama-index-core = ">=0.10.29,<=0.10.39" +llama-index-core = "0.10.57" llama-index-embeddings-openai = "^0.1.7" llama-index-embeddings-azure-openai = "^0.1.7" llama-index-embeddings-dashscope = "^0.1.3" -llama-index-llms-openai = "^0.1.15" +llama-index-llms-openai = "^0.1.27" llama-index-llms-azure-openai = "^0.1.6" llama-index-llms-dashscope = "^0.1.2" llama-index-readers-database = "^0.1.3" @@ -42,7 +42,7 @@ flagembedding = "^1.2.10" sentencepiece = "^0.2.0" oss2 = "^2.18.5" asgi-correlation-id = "^4.3.1" -openinference-instrumentation-llama-index = "1.3.0" +openinference-instrumentation-llama-index = "^1.3.0" torch = [ {version = "2.3.0+cpu", source = "pytorch_cpu", platform = "linux"}, {version = "2.3.0+cpu", source = "pytorch_cpu", platform = "win32"}, @@ -63,10 +63,10 @@ llama-parse = "0.4.2" pypdf2 = "^3.0.1" pdfplumber = "^0.11.0" pdfminer-six = "^20231228" -openinference-semantic-conventions = "0.1.6" +openinference-semantic-conventions = "^0.1.9" llama-index-tools-google = "^0.1.5" llama-index-tools-duckduckgo = "^0.1.1" -openinference-instrumentation = "^0.1.7" +openinference-instrumentation = "^0.1.12" llama-index-llms-huggingface = "^0.2.0" pytest-asyncio = "^0.23.7" pytest-cov = "^5.0.0" @@ -83,6 +83,7 @@ llama-index-vector-stores-alibabacloud-opensearch = "^0.1.0" asyncpg = "^0.29.0" pgvector = "^0.3.2" pre-commit = "^3.8.0" +llama-index-llms-paieas = "^0.1.0" [tool.poetry.scripts] pai_rag = "pai_rag.main:main" diff --git a/pyproject_gpu.toml b/pyproject_gpu.toml index d0857059..7e5119c0 100644 --- a/pyproject_gpu.toml +++ b/pyproject_gpu.toml @@ -10,14 +10,14 @@ authors = [] readme = "README.md" [tool.poetry.dependencies] -python = ">=3.10.0,<3.12" +python = ">=3.11.0,<3.12" fastapi = "^0.110.1" uvicorn = "^0.29.0" -llama-index-core = ">=0.10.29,<=0.10.39" +llama-index-core = "0.10.57" llama-index-embeddings-openai = "^0.1.7" llama-index-embeddings-azure-openai = "^0.1.7" llama-index-embeddings-dashscope = "^0.1.3" -llama-index-llms-openai = "^0.1.15" +llama-index-llms-openai = "^0.1.27" llama-index-llms-azure-openai = "^0.1.6" llama-index-llms-dashscope = "^0.1.2" llama-index-readers-database = "^0.1.3" @@ -42,7 +42,7 @@ flagembedding = "^1.2.10" sentencepiece = "^0.2.0" oss2 = "^2.18.5" asgi-correlation-id = "^4.3.1" -openinference-instrumentation-llama-index = "1.3.0" +openinference-instrumentation-llama-index = "^1.3.0" torch = "2.2.2" torchvision = "0.17.2" openpyxl = "^3.1.2" @@ -55,10 +55,10 @@ llama-parse = "0.4.2" pypdf2 = "^3.0.1" pdfplumber = "^0.11.0" pdfminer-six = "^20231228" -openinference-semantic-conventions = "0.1.6" +openinference-semantic-conventions = "^0.1.9" llama-index-tools-google = "^0.1.5" llama-index-tools-duckduckgo = "^0.1.1" -openinference-instrumentation = "0.1.7" +openinference-instrumentation = "^0.1.12" llama-index-llms-huggingface = "^0.2.0" pytest-asyncio = "^0.23.7" pytest-cov = "^5.0.0" @@ -75,6 +75,7 @@ llama-index-vector-stores-alibabacloud-opensearch = "^0.1.0" asyncpg = "^0.29.0" pgvector = "^0.3.2" pre-commit = "^3.8.0" +llama-index-llms-paieas = "^0.1.0" [tool.poetry.scripts] pai_rag = "pai_rag.main:main" diff --git a/src/pai_rag/config/settings_fc_demo.toml b/src/pai_rag/config/settings_fc_demo.toml index 46ac0160..96b96be8 100644 --- a/src/pai_rag/config/settings_fc_demo.toml +++ b/src/pai_rag/config/settings_fc_demo.toml @@ -58,6 +58,11 @@ type = "SimpleChatEngine" [rag.multi_modal_llm] source = "" +[rag.node_enhancement] +tree_depth = 3 +max_clusters = 52 +proba_threshold = 0.10 + [rag.node_parser] type = "Sentence" chunk_size = 500 diff --git a/src/pai_rag/integrations/llms/dashscope/fc_base.py b/src/pai_rag/integrations/llms/dashscope/fc_base.py index 39e56bbe..04f77d3d 100644 --- a/src/pai_rag/integrations/llms/dashscope/fc_base.py +++ b/src/pai_rag/integrations/llms/dashscope/fc_base.py @@ -1,77 +1,8 @@ -import functools -import json -from typing import ( - TYPE_CHECKING, - Any, - Awaitable, - Callable, - Dict, - List, - Optional, - Protocol, - Sequence, - Union, - cast, - get_args, - runtime_checkable, -) - -import httpx -import tiktoken -from llama_index.core.base.llms.generic_utils import ( - achat_to_completion_decorator, - acompletion_to_chat_decorator, - astream_chat_to_completion_decorator, - astream_completion_to_chat_decorator, - chat_to_completion_decorator, - completion_to_chat_decorator, - stream_chat_to_completion_decorator, - stream_completion_to_chat_decorator, -) -from llama_index.core.base.llms.types import ( - ChatMessage, - ChatResponse, - ChatResponseAsyncGen, - ChatResponseGen, - CompletionResponse, - CompletionResponseAsyncGen, - CompletionResponseGen, - LLMMetadata, - MessageRole, -) -from llama_index.core.bridge.pydantic import Field, PrivateAttr -from llama_index.core.callbacks import CallbackManager -from llama_index.core.constants import ( - DEFAULT_TEMPERATURE, -) -from llama_index.core.llms.callbacks import ( - llm_chat_callback, - llm_completion_callback, -) -from llama_index.core.llms.function_calling import FunctionCallingLLM -from llama_index.core.llms.llm import ToolSelection -from llama_index.core.types import BaseOutputParser, PydanticProgramMode -from llama_index.llms.openai.utils import ( - OpenAIToolCall, - create_retry_decorator, - from_openai_completion_logprobs, - from_openai_message, - from_openai_token_logprobs, - to_openai_message_dicts, -) - -from openai import AsyncOpenAI -from openai import OpenAI as SyncOpenAI -from openai.types.chat.chat_completion_chunk import ( - ChatCompletionChunk, - ChoiceDelta, - ChoiceDeltaToolCall, -) -from pai_rag.integrations.llms.dashscope.utils import resolve_dashscope_credentials - -if TYPE_CHECKING: - from llama_index.core.chat_engine.types import AgentChatResponse - from llama_index.core.tools.types import BaseTool +from typing import Any, Dict, Optional, Tuple +from llama_index.core.base.llms.generic_utils import get_from_param_or_env +import dashscope +from llama_index.core.base.llms.types import LLMMetadata +from llama_index.llms.openai_like import OpenAILike class DashScopeGenerationModels: @@ -168,137 +99,44 @@ class DashScopeGenerationModels: }, } +DEFAULT_DASHSCOPE_API_BASE = "https://dashscope.aliyuncs.com/compatible-mode/v1" -def llm_retry_decorator(f: Callable[[Any], Any]) -> Callable[[Any], Any]: - @functools.wraps(f) - def wrapper(self, *args: Any, **kwargs: Any) -> Any: - max_retries = getattr(self, "max_retries", 0) - if max_retries <= 0: - return f(self, *args, **kwargs) - - retry = create_retry_decorator( - max_retries=max_retries, - random_exponential=True, - stop_after_delay_seconds=60, - min_seconds=1, - max_seconds=20, - ) - return retry(f)(self, *args, **kwargs) - - return wrapper +def resolve_dashscope_credentials( + api_key: Optional[str] = None, + api_base: Optional[str] = None, +) -> Tuple[Optional[str], str, str]: + """ "Resolve OpenAI credentials. -@runtime_checkable -class Tokenizer(Protocol): - """Tokenizers support an encode function that returns a list of ints.""" - - def encode(self, text: str) -> List[int]: - ... + The order of precedence is: + 1. param + 2. env + 3. openai module + 4. default + """ + # resolve from param or env + api_key = get_from_param_or_env("api_key", api_key, "DASHSCOPE_API_KEY", "") + api_base = get_from_param_or_env("api_base", api_base, "DASHSCOPE_API_BASE", "") + # resolve from openai module or default + final_api_key = api_key or dashscope.api_key or "" + final_api_base = api_base or DEFAULT_DASHSCOPE_API_BASE -def force_single_tool_call(response: ChatResponse) -> None: - tool_calls = response.message.additional_kwargs.get("tool_calls", []) - if len(tool_calls) > 1: - response.message.additional_kwargs["tool_calls"] = [tool_calls[0]] + return final_api_key, str(final_api_base) -class MyFCDashScope(FunctionCallingLLM): +class MyFCDashScope(OpenAILike): """ - DashScope LLM. - - Args: - model: name of the DashScope model to use. - temperature: a float from 0 to 1 controlling randomness in generation; higher will lead to more creative, less deterministic responses. - max_tokens: the maximum number of tokens to generate. - additional_kwargs: Add additional parameters to OpenAI request body. - max_retries: How many times to retry the API call if it fails. - timeout: How long to wait, in seconds, for an API call before failing. - reuse_client: Reuse the DashScope client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - api_key: Your DashScope api key - api_base: The base URL of the API to call - callback_manager: the callback manager is used for observability. - default_headers: override the default headers for API requests. - http_client: pass in your own httpx.Client instance. - async_http_client: pass in your own httpx.AsyncClient instance. - + MyFCDashScope LLM is a thin wrapper around the OpenAILike model that makes it compatible + with Function Calling DashScope. """ - model: str = Field( - default=DashScopeGenerationModels.QWEN_MAX, - description="The OpenAI model to use.", - ) - temperature: float = Field( - default=DEFAULT_TEMPERATURE, - description="The temperature to use during generation.", - gte=0.0, - lte=1.0, - ) - max_tokens: Optional[int] = Field( - description="The maximum number of tokens to generate.", - gt=0, - ) - logprobs: Optional[bool] = Field( - description="Whether to return logprobs per token." - ) - top_logprobs: int = Field( - description="The number of top token log probs to return.", - default=0, - gte=0, - lte=20, - ) - additional_kwargs: Dict[str, Any] = Field( - default_factory=dict, description="Additional kwargs for the OpenAI API." - ) - max_retries: int = Field( - default=3, - description="The maximum number of API retries.", - gte=0, - ) - timeout: float = Field( - default=60.0, - description="The timeout, in seconds, for API requests.", - gte=0, - ) - default_headers: Dict[str, str] = Field( - default=None, description="The default headers for API requests." - ) - reuse_client: bool = Field( - default=True, - description=( - "Reuse the OpenAI client between requests. When doing anything with large " - "volumes of async API calls, setting this to false can improve stability." - ), - ) - - api_key: str = Field(default=None, description="The DashScope API key.") - api_base: str = Field(description="The base URL for DashScope API.") - - _client: Optional[SyncOpenAI] = PrivateAttr() - _aclient: Optional[AsyncOpenAI] = PrivateAttr() - _http_client: Optional[httpx.Client] = PrivateAttr() - _async_http_client: Optional[httpx.AsyncClient] = PrivateAttr() - def __init__( self, model: Optional[str] = DashScopeGenerationModels.QWEN_MAX, - temperature: float = DEFAULT_TEMPERATURE, - max_tokens: Optional[int] = None, additional_kwargs: Optional[Dict[str, Any]] = None, - max_retries: int = 3, - timeout: float = 60.0, - reuse_client: bool = True, api_key: Optional[str] = None, api_base: Optional[str] = None, - callback_manager: Optional[CallbackManager] = None, - default_headers: Optional[Dict[str, str]] = None, - http_client: Optional[httpx.Client] = None, - async_http_client: Optional[httpx.AsyncClient] = None, - # base class - system_prompt: Optional[str] = None, - messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, - completion_to_prompt: Optional[Callable[[str], str]] = None, - pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, - output_parser: Optional[BaseOutputParser] = None, **kwargs: Any, ) -> None: additional_kwargs = additional_kwargs or {} @@ -310,78 +148,17 @@ def __init__( super().__init__( model=model, - temperature=temperature, - max_tokens=max_tokens, - additional_kwargs=additional_kwargs, - max_retries=max_retries, - callback_manager=callback_manager, api_key=api_key, api_base=api_base, - timeout=timeout, - reuse_client=reuse_client, - default_headers=default_headers, - system_prompt=system_prompt, - messages_to_prompt=messages_to_prompt, - completion_to_prompt=completion_to_prompt, - pydantic_program_mode=pydantic_program_mode, - output_parser=output_parser, **kwargs, ) - self._client = None - self._aclient = None - self._http_client = http_client - self._async_http_client = async_http_client - - def _get_client(self) -> SyncOpenAI: - if not self.reuse_client: - return SyncOpenAI(**self._get_credential_kwargs()) - - if self._client is None: - self._client = SyncOpenAI(**self._get_credential_kwargs()) - return self._client - - def _get_aclient(self) -> AsyncOpenAI: - if not self.reuse_client: - return AsyncOpenAI(**self._get_credential_kwargs(is_async=True)) - - if self._aclient is None: - self._aclient = AsyncOpenAI(**self._get_credential_kwargs(is_async=True)) - return self._aclient - - def _get_model_name(self) -> str: - model_name = self.model - if "ft-" in model_name: # legacy fine-tuning - model_name = model_name.split(":")[0] - elif model_name.startswith("ft:"): - model_name = model_name.split(":")[1] - return model_name - @classmethod def class_name(cls) -> str: return "fc_dashscope_llm" - @property - def _tokenizer(self) -> Optional[Tokenizer]: - """ - Get a tokenizer for this model, or None if a tokenizing method is unknown. - - OpenAI can do this using the tiktoken package, subclasses may not have - this convenience. - """ - return tiktoken.encoding_for_model(self._get_model_name()) - @property def metadata(self) -> LLMMetadata: - # return LLMMetadata( - # context_window=openai_modelname_to_contextsize(self._get_model_name()), - # num_output=self.max_tokens or -1, - # is_chat_model=is_chat_model(model=self._get_model_name()), - # is_function_calling_model=is_function_calling_model( - # model=self._get_model_name() - # ), - # model_name=self.model, - # ) DASHSCOPE_MODEL_META[self.model]["num_output"] = ( self.max_tokens or DASHSCOPE_MODEL_META[self.model]["num_output"] ) @@ -389,604 +166,3 @@ def metadata(self) -> LLMMetadata: model_name=self.model, **DASHSCOPE_MODEL_META[self.model], ) - - @llm_chat_callback() - def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: - if self._use_chat_completions(kwargs): - chat_fn = self._chat - else: - chat_fn = completion_to_chat_decorator(self._complete) - return chat_fn(messages, **kwargs) - - @llm_chat_callback() - def stream_chat( - self, messages: Sequence[ChatMessage], **kwargs: Any - ) -> ChatResponseGen: - if self._use_chat_completions(kwargs): - stream_chat_fn = self._stream_chat - else: - stream_chat_fn = stream_completion_to_chat_decorator(self._stream_complete) - return stream_chat_fn(messages, **kwargs) - - @llm_completion_callback() - def complete( - self, prompt: str, formatted: bool = False, **kwargs: Any - ) -> CompletionResponse: - if self._use_chat_completions(kwargs): - complete_fn = chat_to_completion_decorator(self._chat) - else: - complete_fn = self._complete - return complete_fn(prompt, **kwargs) - - @llm_completion_callback() - def stream_complete( - self, prompt: str, formatted: bool = False, **kwargs: Any - ) -> CompletionResponseGen: - if self._use_chat_completions(kwargs): - stream_complete_fn = stream_chat_to_completion_decorator(self._stream_chat) - else: - stream_complete_fn = self._stream_complete - return stream_complete_fn(prompt, **kwargs) - - def _use_chat_completions(self, kwargs: Dict[str, Any]) -> bool: - if "use_chat_completions" in kwargs: - return kwargs["use_chat_completions"] - return self.metadata.is_chat_model - - def _get_credential_kwargs(self, is_async: bool = False) -> Dict[str, Any]: - return { - "api_key": self.api_key, - "base_url": self.api_base, - "max_retries": self.max_retries, - "timeout": self.timeout, - "default_headers": self.default_headers, - "http_client": self._async_http_client if is_async else self._http_client, - } - - def _get_model_kwargs(self, **kwargs: Any) -> Dict[str, Any]: - base_kwargs = {"model": self.model, "temperature": self.temperature, **kwargs} - if self.max_tokens is not None: - # If max_tokens is None, don't include in the payload: - # https://platform.openai.com/docs/api-reference/chat - # https://platform.openai.com/docs/api-reference/completions - base_kwargs["max_tokens"] = self.max_tokens - if self.logprobs is not None and self.logprobs is True: - if self.metadata.is_chat_model: - base_kwargs["logprobs"] = self.logprobs - base_kwargs["top_logprobs"] = self.top_logprobs - else: - base_kwargs["logprobs"] = self.top_logprobs # int in this case - return {**base_kwargs, **self.additional_kwargs} - - @llm_retry_decorator - def _chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: - client = self._get_client() - message_dicts = to_openai_message_dicts(messages) - - if self.reuse_client: - response = client.chat.completions.create( - messages=message_dicts, - stream=False, - **self._get_model_kwargs(**kwargs), - ) - else: - with client: - response = client.chat.completions.create( - messages=message_dicts, - stream=False, - **self._get_model_kwargs(**kwargs), - ) - - openai_message = response.choices[0].message - message = from_openai_message(openai_message) - openai_token_logprobs = response.choices[0].logprobs - logprobs = None - if openai_token_logprobs and openai_token_logprobs.content: - logprobs = from_openai_token_logprobs(openai_token_logprobs.content) - - return ChatResponse( - message=message, - raw=response, - logprobs=logprobs, - additional_kwargs=self._get_response_token_counts(response), - ) - - def _update_tool_calls( - self, - tool_calls: List[ChoiceDeltaToolCall], - tool_calls_delta: Optional[List[ChoiceDeltaToolCall]], - ) -> List[ChoiceDeltaToolCall]: - """ - Use the tool_calls_delta objects received from openai stream chunks - to update the running tool_calls object. - - Args: - tool_calls (List[ChoiceDeltaToolCall]): the list of tool calls - tool_calls_delta (ChoiceDeltaToolCall): the delta to update tool_calls - - Returns: - List[ChoiceDeltaToolCall]: the updated tool calls - """ - # openai provides chunks consisting of tool_call deltas one tool at a time - if tool_calls_delta is None: - return tool_calls - - tc_delta = tool_calls_delta[0] - - if len(tool_calls) == 0: - tool_calls.append(tc_delta) - else: - # we need to either update latest tool_call or start a - # new tool_call (i.e., multiple tools in this turn) and - # accumulate that new tool_call with future delta chunks - t = tool_calls[-1] - if t.index != tc_delta.index: - # the start of a new tool call, so append to our running tool_calls list - tool_calls.append(tc_delta) - else: - # not the start of a new tool call, so update last item of tool_calls - - # validations to get passed by mypy - assert t.function is not None - assert tc_delta.function is not None - assert t.function.arguments is not None - assert t.function.name is not None - assert t.id is not None - - t.function.arguments += tc_delta.function.arguments or "" - t.function.name += tc_delta.function.name or "" - t.id += tc_delta.id or "" - return tool_calls - - @llm_retry_decorator - def _stream_chat( - self, messages: Sequence[ChatMessage], **kwargs: Any - ) -> ChatResponseGen: - client = self._get_client() - message_dicts = to_openai_message_dicts(messages) - - def gen() -> ChatResponseGen: - content = "" - tool_calls: List[ChoiceDeltaToolCall] = [] - - is_function = False - for response in client.chat.completions.create( - messages=message_dicts, - stream=True, - **self._get_model_kwargs(**kwargs), - ): - response = cast(ChatCompletionChunk, response) - if len(response.choices) > 0: - delta = response.choices[0].delta - else: - delta = ChoiceDelta() - - # check if this chunk is the start of a function call - if delta.tool_calls: - is_function = True - - # update using deltas - role = delta.role or MessageRole.ASSISTANT - content_delta = delta.content or "" - content += content_delta - - additional_kwargs = {} - if is_function: - tool_calls = self._update_tool_calls(tool_calls, delta.tool_calls) - additional_kwargs["tool_calls"] = tool_calls - - yield ChatResponse( - message=ChatMessage( - role=role, - content=content, - additional_kwargs=additional_kwargs, - ), - delta=content_delta, - raw=response, - additional_kwargs=self._get_response_token_counts(response), - ) - - return gen() - - @llm_retry_decorator - def _complete(self, prompt: str, **kwargs: Any) -> CompletionResponse: - client = self._get_client() - all_kwargs = self._get_model_kwargs(**kwargs) - self._update_max_tokens(all_kwargs, prompt) - - if self.reuse_client: - response = client.completions.create( - prompt=prompt, - stream=False, - **all_kwargs, - ) - else: - with client: - response = client.completions.create( - prompt=prompt, - stream=False, - **all_kwargs, - ) - text = response.choices[0].text - - openai_completion_logprobs = response.choices[0].logprobs - logprobs = None - if openai_completion_logprobs: - logprobs = from_openai_completion_logprobs(openai_completion_logprobs) - - return CompletionResponse( - text=text, - raw=response, - logprobs=logprobs, - additional_kwargs=self._get_response_token_counts(response), - ) - - @llm_retry_decorator - def _stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen: - client = self._get_client() - all_kwargs = self._get_model_kwargs(**kwargs) - self._update_max_tokens(all_kwargs, prompt) - - def gen() -> CompletionResponseGen: - text = "" - for response in client.completions.create( - prompt=prompt, - stream=True, - **all_kwargs, - ): - if len(response.choices) > 0: - delta = response.choices[0].text - if delta is None: - delta = "" - else: - delta = "" - text += delta - yield CompletionResponse( - delta=delta, - text=text, - raw=response, - additional_kwargs=self._get_response_token_counts(response), - ) - - return gen() - - def _update_max_tokens(self, all_kwargs: Dict[str, Any], prompt: str) -> None: - """Infer max_tokens for the payload, if possible.""" - if self.max_tokens is not None or self._tokenizer is None: - return - # NOTE: non-chat completion endpoint requires max_tokens to be set - num_tokens = len(self._tokenizer.encode(prompt)) - max_tokens = self.metadata.context_window - num_tokens - if max_tokens <= 0: - raise ValueError( - f"The prompt has {num_tokens} tokens, which is too long for" - " the model. Please use a prompt that fits within" - f" {self.metadata.context_window} tokens." - ) - all_kwargs["max_tokens"] = max_tokens - - def _get_response_token_counts(self, raw_response: Any) -> dict: - """Get the token usage reported by the response.""" - if not isinstance(raw_response, dict): - return {} - - usage = raw_response.get("usage", {}) - # NOTE: other model providers that use the OpenAI client may not report usage - if usage is None: - return {} - - return { - "prompt_tokens": usage.get("prompt_tokens", 0), - "completion_tokens": usage.get("completion_tokens", 0), - "total_tokens": usage.get("total_tokens", 0), - } - - # ===== Async Endpoints ===== - @llm_chat_callback() - async def achat( - self, - messages: Sequence[ChatMessage], - **kwargs: Any, - ) -> ChatResponse: - achat_fn: Callable[..., Awaitable[ChatResponse]] - if self._use_chat_completions(kwargs): - achat_fn = self._achat - else: - achat_fn = acompletion_to_chat_decorator(self._acomplete) - return await achat_fn(messages, **kwargs) - - @llm_chat_callback() - async def astream_chat( - self, - messages: Sequence[ChatMessage], - **kwargs: Any, - ) -> ChatResponseAsyncGen: - astream_chat_fn: Callable[..., Awaitable[ChatResponseAsyncGen]] - if self._use_chat_completions(kwargs): - astream_chat_fn = self._astream_chat - else: - astream_chat_fn = astream_completion_to_chat_decorator( - self._astream_complete - ) - return await astream_chat_fn(messages, **kwargs) - - @llm_completion_callback() - async def acomplete( - self, prompt: str, formatted: bool = False, **kwargs: Any - ) -> CompletionResponse: - if self._use_chat_completions(kwargs): - acomplete_fn = achat_to_completion_decorator(self._achat) - else: - acomplete_fn = self._acomplete - return await acomplete_fn(prompt, **kwargs) - - @llm_completion_callback() - async def astream_complete( - self, prompt: str, formatted: bool = False, **kwargs: Any - ) -> CompletionResponseAsyncGen: - if self._use_chat_completions(kwargs): - astream_complete_fn = astream_chat_to_completion_decorator( - self._astream_chat - ) - else: - astream_complete_fn = self._astream_complete - return await astream_complete_fn(prompt, **kwargs) - - @llm_retry_decorator - async def _achat( - self, messages: Sequence[ChatMessage], **kwargs: Any - ) -> ChatResponse: - aclient = self._get_aclient() - message_dicts = to_openai_message_dicts(messages) - - if self.reuse_client: - response = await aclient.chat.completions.create( - messages=message_dicts, stream=False, **self._get_model_kwargs(**kwargs) - ) - else: - async with aclient: - response = await aclient.chat.completions.create( - messages=message_dicts, - stream=False, - **self._get_model_kwargs(**kwargs), - ) - - openai_message = response.choices[0].message - message = from_openai_message(openai_message) - openai_token_logprobs = response.choices[0].logprobs - logprobs = None - if openai_token_logprobs and openai_token_logprobs.content: - logprobs = from_openai_token_logprobs(openai_token_logprobs.content) - - return ChatResponse( - message=message, - raw=response, - logprobs=logprobs, - additional_kwargs=self._get_response_token_counts(response), - ) - - @llm_retry_decorator - async def _astream_chat( - self, messages: Sequence[ChatMessage], **kwargs: Any - ) -> ChatResponseAsyncGen: - aclient = self._get_aclient() - message_dicts = to_openai_message_dicts(messages) - - async def gen() -> ChatResponseAsyncGen: - content = "" - tool_calls: List[ChoiceDeltaToolCall] = [] - - is_function = False - first_chat_chunk = True - async for response in await aclient.chat.completions.create( - messages=message_dicts, - stream=True, - **self._get_model_kwargs(**kwargs), - ): - response = cast(ChatCompletionChunk, response) - if len(response.choices) > 0: - # check if the first chunk has neither content nor tool_calls - # this happens when 1106 models end up calling multiple tools - if ( - first_chat_chunk - and response.choices[0].delta.content is None - and response.choices[0].delta.tool_calls is None - ): - first_chat_chunk = False - continue - delta = response.choices[0].delta - else: - delta = ChoiceDelta() - first_chat_chunk = False - - # check if this chunk is the start of a function call - if delta.tool_calls: - is_function = True - - # update using deltas - role = delta.role or MessageRole.ASSISTANT - content_delta = delta.content or "" - content += content_delta - - additional_kwargs = {} - if is_function: - tool_calls = self._update_tool_calls(tool_calls, delta.tool_calls) - additional_kwargs["tool_calls"] = tool_calls - - yield ChatResponse( - message=ChatMessage( - role=role, - content=content, - additional_kwargs=additional_kwargs, - ), - delta=content_delta, - raw=response, - additional_kwargs=self._get_response_token_counts(response), - ) - - return gen() - - @llm_retry_decorator - async def _acomplete(self, prompt: str, **kwargs: Any) -> CompletionResponse: - aclient = self._get_aclient() - all_kwargs = self._get_model_kwargs(**kwargs) - self._update_max_tokens(all_kwargs, prompt) - - if self.reuse_client: - response = await aclient.completions.create( - prompt=prompt, - stream=False, - **all_kwargs, - ) - else: - async with aclient: - response = await aclient.completions.create( - prompt=prompt, - stream=False, - **all_kwargs, - ) - - text = response.choices[0].text - openai_completion_logprobs = response.choices[0].logprobs - logprobs = None - if openai_completion_logprobs: - logprobs = from_openai_completion_logprobs(openai_completion_logprobs) - - return CompletionResponse( - text=text, - raw=response, - logprobs=logprobs, - additional_kwargs=self._get_response_token_counts(response), - ) - - @llm_retry_decorator - async def _astream_complete( - self, prompt: str, **kwargs: Any - ) -> CompletionResponseAsyncGen: - aclient = self._get_aclient() - all_kwargs = self._get_model_kwargs(**kwargs) - self._update_max_tokens(all_kwargs, prompt) - - async def gen() -> CompletionResponseAsyncGen: - text = "" - async for response in await aclient.completions.create( - prompt=prompt, - stream=True, - **all_kwargs, - ): - if len(response.choices) > 0: - delta = response.choices[0].text - if delta is None: - delta = "" - else: - delta = "" - text += delta - yield CompletionResponse( - delta=delta, - text=text, - raw=response, - additional_kwargs=self._get_response_token_counts(response), - ) - - return gen() - - def chat_with_tools( - self, - tools: List["BaseTool"], - user_msg: Optional[Union[str, ChatMessage]] = None, - chat_history: Optional[List[ChatMessage]] = None, - verbose: bool = False, - allow_parallel_tool_calls: bool = False, - tool_choice: Union[str, dict] = "auto", - **kwargs: Any, - ) -> ChatResponse: - """Predict and call the tool.""" - from llama_index.agent.openai.utils import resolve_tool_choice - - # misralai uses the same openai tool format - tool_specs = [tool.metadata.to_openai_tool() for tool in tools] - - if isinstance(user_msg, str): - user_msg = ChatMessage(role=MessageRole.USER, content=user_msg) - - messages = chat_history or [] - if user_msg: - messages.append(user_msg) - - response = self.chat( - messages, - tools=tool_specs or None, - tool_choice=resolve_tool_choice(tool_choice) if tool_specs else None, - **kwargs, - ) - if not allow_parallel_tool_calls: - force_single_tool_call(response) - return response - - async def achat_with_tools( - self, - tools: List["BaseTool"], - user_msg: Optional[Union[str, ChatMessage]] = None, - chat_history: Optional[List[ChatMessage]] = None, - verbose: bool = False, - allow_parallel_tool_calls: bool = False, - tool_choice: Union[str, dict] = "auto", - **kwargs: Any, - ) -> ChatResponse: - """Predict and call the tool.""" - from llama_index.agent.openai.utils import resolve_tool_choice - - # misralai uses the same openai tool format - tool_specs = [tool.metadata.to_openai_tool() for tool in tools] - - if isinstance(user_msg, str): - user_msg = ChatMessage(role=MessageRole.USER, content=user_msg) - - messages = chat_history or [] - if user_msg: - messages.append(user_msg) - - response = await self.achat( - messages, - tools=tool_specs or None, - tool_choice=resolve_tool_choice(tool_choice) if tool_specs else None, - **kwargs, - ) - if not allow_parallel_tool_calls: - force_single_tool_call(response) - return response - - def get_tool_calls_from_response( - self, - response: "AgentChatResponse", - error_on_no_tool_call: bool = True, - **kwargs: Any, - ) -> List[ToolSelection]: - """Predict and call the tool.""" - tool_calls = response.message.additional_kwargs.get("tool_calls", []) - - if len(tool_calls) < 1: - if error_on_no_tool_call: - raise ValueError( - f"Expected at least one tool call, but got {len(tool_calls)} tool calls." - ) - else: - return [] - - tool_selections = [] - for tool_call in tool_calls: - if not isinstance(tool_call, get_args(OpenAIToolCall)): - raise ValueError("Invalid tool_call object") - if tool_call.type != "function": - raise ValueError("Invalid tool type. Unsupported by OpenAI") - argument_dict = json.loads(tool_call.function.arguments) - - tool_selections.append( - ToolSelection( - tool_id=tool_call.id, - tool_name=tool_call.function.name, - tool_kwargs=argument_dict, - ) - ) - - return tool_selections diff --git a/src/pai_rag/integrations/llms/dashscope/utils.py b/src/pai_rag/integrations/llms/dashscope/utils.py deleted file mode 100644 index 45aa655e..00000000 --- a/src/pai_rag/integrations/llms/dashscope/utils.py +++ /dev/null @@ -1,30 +0,0 @@ -from typing import Optional, Tuple -import dashscope.version -from llama_index.core.base.llms.generic_utils import get_from_param_or_env -import dashscope - -DEFAULT_DASHSCOPE_API_BASE = "https://dashscope.aliyuncs.com/compatible-mode/v1" -DEFAULT_DASHSCOPE_API_VERSION = "" - - -def resolve_dashscope_credentials( - api_key: Optional[str] = None, - api_base: Optional[str] = None, -) -> Tuple[Optional[str], str, str]: - """ "Resolve OpenAI credentials. - - The order of precedence is: - 1. param - 2. env - 3. openai module - 4. default - """ - # resolve from param or env - api_key = get_from_param_or_env("api_key", api_key, "DASHSCOPE_API_KEY", "") - api_base = get_from_param_or_env("api_base", api_base, "DASHSCOPE_API_BASE", "") - - # resolve from openai module or default - final_api_key = api_key or dashscope.api_key or "" - final_api_base = api_base or DEFAULT_DASHSCOPE_API_BASE - - return final_api_key, str(final_api_base) diff --git a/src/pai_rag/integrations/llms/paieas/base.py b/src/pai_rag/integrations/llms/paieas/base.py deleted file mode 100644 index 3f2efc5c..00000000 --- a/src/pai_rag/integrations/llms/paieas/base.py +++ /dev/null @@ -1,623 +0,0 @@ -import functools -from typing import ( - Any, - Awaitable, - Callable, - Dict, - Optional, - Sequence, - cast, -) - -import httpx -from llama_index.core.base.llms.generic_utils import ( - achat_to_completion_decorator, - acompletion_to_chat_decorator, - astream_chat_to_completion_decorator, - astream_completion_to_chat_decorator, - chat_to_completion_decorator, - completion_to_chat_decorator, - stream_chat_to_completion_decorator, - stream_completion_to_chat_decorator, -) -from llama_index.core.base.llms.types import ( - ChatMessage, - ChatResponse, - ChatResponseAsyncGen, - ChatResponseGen, - CompletionResponse, - CompletionResponseAsyncGen, - CompletionResponseGen, - LLMMetadata, - MessageRole, -) -from llama_index.core.bridge.pydantic import Field, PrivateAttr -from llama_index.core.callbacks import CallbackManager -from llama_index.core.constants import ( - DEFAULT_TEMPERATURE, -) -from llama_index.core.llms.callbacks import ( - llm_chat_callback, - llm_completion_callback, -) -from llama_index.core.llms.function_calling import FunctionCallingLLM -from llama_index.core.types import BaseOutputParser, PydanticProgramMode -from llama_index.llms.openai.utils import ( - create_retry_decorator, - from_openai_completion_logprobs, - from_openai_message, - from_openai_token_logprobs, - resolve_openai_credentials, - to_openai_message_dicts, -) - -from openai import AsyncOpenAI, AzureOpenAI -from openai import OpenAI as SyncOpenAI -from openai.types.chat.chat_completion_chunk import ( - ChatCompletionChunk, - ChoiceDelta, -) - - -def llm_retry_decorator(f: Callable[[Any], Any]) -> Callable[[Any], Any]: - @functools.wraps(f) - def wrapper(self, *args: Any, **kwargs: Any) -> Any: - max_retries = getattr(self, "max_retries", 0) - if max_retries <= 0: - return f(self, *args, **kwargs) - - retry = create_retry_decorator( - max_retries=max_retries, - random_exponential=True, - stop_after_delay_seconds=60, - min_seconds=1, - max_seconds=20, - ) - return retry(f)(self, *args, **kwargs) - - return wrapper - - -class PaiEAS(FunctionCallingLLM): - """ - OpenAI LLM. - - Args: - temperature: a float from 0 to 1 controlling randomness in generation; higher will lead to more creative, less deterministic responses. - max_tokens: the maximum number of tokens to generate. - additional_kwargs: Optional[Dict[str, Any]] = None, - max_retries: How many times to retry the API call if it fails. - timeout: How long to wait, in seconds, for an API call before failing. - reuse_client: Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - api_key: Your OpenAI api key - api_base: The base URL of the API to call - api_version: the version of the API to call - callback_manager: the callback manager is used for observability. - default_headers: override the default headers for API requests. - http_client: pass in your own httpx.Client instance. - async_http_client: pass in your own httpx.AsyncClient instance. - - Examples: - `pip install llama-index-llms-openai` - - ```python - import os - import openai - - os.environ["OPENAI_API_KEY"] = "sk-..." - openai.api_key = os.environ["OPENAI_API_KEY"] - - from llama_index.llms.openai import OpenAI - - llm = OpenAI(model="gpt-3.5-turbo") - - stream = llm.stream("Hi, write a short story") - - for r in stream: - print(r.delta, end="") - ``` - """ - - temperature: float = Field( - default=DEFAULT_TEMPERATURE, - description="The temperature to use during generation.", - gte=0.0, - lte=1.0, - ) - max_tokens: Optional[int] = Field( - description="The maximum number of tokens to generate.", - gt=0, - ) - logprobs: Optional[bool] = Field( - description="Whether to return logprobs per token." - ) - top_logprobs: int = Field( - description="The number of top token log probs to return.", - default=0, - gte=0, - lte=20, - ) - additional_kwargs: Dict[str, Any] = Field( - default_factory=dict, description="Additional kwargs for the OpenAI API." - ) - max_retries: int = Field( - default=3, - description="The maximum number of API retries.", - gte=0, - ) - timeout: float = Field( - default=60.0, - description="The timeout, in seconds, for API requests.", - gte=0, - ) - default_headers: Dict[str, str] = Field( - default=None, description="The default headers for API requests." - ) - reuse_client: bool = Field( - default=True, - description=( - "Reuse the OpenAI client between requests. When doing anything with large " - "volumes of async API calls, setting this to false can improve stability." - ), - ) - - api_key: str = Field(default=None, description="The OpenAI API key.") - api_base: str = Field(description="The base URL for OpenAI API.") - api_version: str = Field(description="The API version for OpenAI API.") - - _client: Optional[SyncOpenAI] = PrivateAttr() - _aclient: Optional[AsyncOpenAI] = PrivateAttr() - _http_client: Optional[httpx.Client] = PrivateAttr() - _async_http_client: Optional[httpx.AsyncClient] = PrivateAttr() - - def __init__( - self, - temperature: float = DEFAULT_TEMPERATURE, - max_tokens: Optional[int] = None, - additional_kwargs: Optional[Dict[str, Any]] = None, - max_retries: int = 3, - timeout: float = 60.0, - reuse_client: bool = True, - api_key: Optional[str] = None, - api_base: Optional[str] = None, - api_version: Optional[str] = None, - callback_manager: Optional[CallbackManager] = None, - default_headers: Optional[Dict[str, str]] = None, - http_client: Optional[httpx.Client] = None, - async_http_client: Optional[httpx.AsyncClient] = None, - # base class - system_prompt: Optional[str] = None, - messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, - completion_to_prompt: Optional[Callable[[str], str]] = None, - pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, - output_parser: Optional[BaseOutputParser] = None, - **kwargs: Any, - ) -> None: - additional_kwargs = additional_kwargs or {} - - api_key, api_base, api_version = resolve_openai_credentials( - api_key=api_key, - api_base=api_base, - api_version=api_version, - ) - - super().__init__( - temperature=temperature, - max_tokens=max_tokens, - additional_kwargs=additional_kwargs, - max_retries=max_retries, - callback_manager=callback_manager, - api_key=api_key, - api_version=api_version, - api_base=api_base, - timeout=timeout, - reuse_client=reuse_client, - default_headers=default_headers, - system_prompt=system_prompt, - messages_to_prompt=messages_to_prompt, - completion_to_prompt=completion_to_prompt, - pydantic_program_mode=pydantic_program_mode, - output_parser=output_parser, - **kwargs, - ) - - self._client = None - self._aclient = None - self._http_client = http_client - self._async_http_client = async_http_client - - def _get_client(self) -> SyncOpenAI: - if not self.reuse_client: - return SyncOpenAI(**self._get_credential_kwargs()) - - if self._client is None: - self._client = SyncOpenAI(**self._get_credential_kwargs()) - return self._client - - def _get_aclient(self) -> AsyncOpenAI: - if not self.reuse_client: - return AsyncOpenAI(**self._get_credential_kwargs(is_async=True)) - - if self._aclient is None: - self._aclient = AsyncOpenAI(**self._get_credential_kwargs(is_async=True)) - return self._aclient - - def _is_azure_client(self) -> bool: - return isinstance(self._get_client(), AzureOpenAI) - - @classmethod - def class_name(cls) -> str: - return "paieas_llm" - - @property - def metadata(self) -> LLMMetadata: - return LLMMetadata(model_name="pai-eas-custom-llm", is_chat_model=True) - - @llm_chat_callback() - def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: - if self._use_chat_completions(kwargs): - chat_fn = self._chat - else: - chat_fn = completion_to_chat_decorator(self._complete) - return chat_fn(messages, **kwargs) - - @llm_chat_callback() - def stream_chat( - self, messages: Sequence[ChatMessage], **kwargs: Any - ) -> ChatResponseGen: - if self._use_chat_completions(kwargs): - stream_chat_fn = self._stream_chat - else: - stream_chat_fn = stream_completion_to_chat_decorator(self._stream_complete) - return stream_chat_fn(messages, **kwargs) - - @llm_completion_callback() - def complete( - self, prompt: str, formatted: bool = False, **kwargs: Any - ) -> CompletionResponse: - if self._use_chat_completions(kwargs): - complete_fn = chat_to_completion_decorator(self._chat) - else: - complete_fn = self._complete - return complete_fn(prompt, **kwargs) - - @llm_completion_callback() - def stream_complete( - self, prompt: str, formatted: bool = False, **kwargs: Any - ) -> CompletionResponseGen: - if self._use_chat_completions(kwargs): - stream_complete_fn = stream_chat_to_completion_decorator(self._stream_chat) - else: - stream_complete_fn = self._stream_complete - return stream_complete_fn(prompt, **kwargs) - - def _use_chat_completions(self, kwargs: Dict[str, Any]) -> bool: - if "use_chat_completions" in kwargs: - return kwargs["use_chat_completions"] - return self.metadata.is_chat_model - - def _get_credential_kwargs(self, is_async: bool = False) -> Dict[str, Any]: - return { - "api_key": self.api_key, - "base_url": self.api_base, - "http_client": self._async_http_client if is_async else self._http_client, - } - - @llm_retry_decorator - def _chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: - client = self._get_client() - message_dicts = to_openai_message_dicts(messages) - if self.reuse_client: - response = client.chat.completions.create( - model="default", - messages=message_dicts, - stream=False, - ) - else: - with client: - response = client.chat.completions.create( - model="default", - messages=message_dicts, - stream=False, - ) - - openai_message = response.choices[0].message - message = from_openai_message(openai_message) - openai_token_logprobs = response.choices[0].logprobs - logprobs = None - if openai_token_logprobs and openai_token_logprobs.content: - logprobs = from_openai_token_logprobs(openai_token_logprobs.content) - - return ChatResponse( - message=message, - raw=response, - logprobs=logprobs, - ) - - @llm_retry_decorator - def _stream_chat( - self, messages: Sequence[ChatMessage], **kwargs: Any - ) -> ChatResponseGen: - client = self._get_client() - message_dicts = to_openai_message_dicts(messages) - - def gen() -> ChatResponseGen: - content = "" - - for response in client.chat.completions.create( - model="default", - messages=message_dicts, - stream=True, - ): - response = cast(ChatCompletionChunk, response) - if len(response.choices) > 0: - delta = response.choices[0].delta - else: - if self._is_azure_client(): - continue - else: - delta = ChoiceDelta() - - # update using deltas - role = delta.role or MessageRole.ASSISTANT - content_delta = delta.content or "" - content += content_delta - - yield ChatResponse( - message=ChatMessage( - role=role, - content=content, - ), - delta=content_delta, - raw=response, - ) - - return gen() - - @llm_retry_decorator - def _complete(self, prompt: str, **kwargs: Any) -> CompletionResponse: - client = self._get_client() - - if self.reuse_client: - response = client.completions.create( - model="default", - prompt=prompt, - stream=False, - ) - else: - with client: - response = client.completions.create( - model="default", - prompt=prompt, - stream=False, - ) - text = response.choices[0].text - - openai_completion_logprobs = response.choices[0].logprobs - logprobs = None - if openai_completion_logprobs: - logprobs = from_openai_completion_logprobs(openai_completion_logprobs) - - return CompletionResponse( - text=text, - raw=response, - logprobs=logprobs, - ) - - @llm_retry_decorator - def _stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen: - client = self._get_client() - - def gen() -> CompletionResponseGen: - text = "" - for response in client.completions.create( - model="default", - prompt=prompt, - stream=True, - ): - if len(response.choices) > 0: - delta = response.choices[0].text - if delta is None: - delta = "" - else: - delta = "" - text += delta - yield CompletionResponse( - delta=delta, - text=text, - raw=response, - ) - - return gen() - - # ===== Async Endpoints ===== - @llm_chat_callback() - async def achat( - self, - messages: Sequence[ChatMessage], - **kwargs: Any, - ) -> ChatResponse: - achat_fn: Callable[..., Awaitable[ChatResponse]] - if self._use_chat_completions(kwargs): - achat_fn = self._achat - else: - achat_fn = acompletion_to_chat_decorator(self._acomplete) - return await achat_fn(messages, **kwargs) - - @llm_chat_callback() - async def astream_chat( - self, - messages: Sequence[ChatMessage], - **kwargs: Any, - ) -> ChatResponseAsyncGen: - astream_chat_fn: Callable[..., Awaitable[ChatResponseAsyncGen]] - if self._use_chat_completions(kwargs): - astream_chat_fn = self._astream_chat - else: - astream_chat_fn = astream_completion_to_chat_decorator( - self._astream_complete - ) - return await astream_chat_fn(messages, **kwargs) - - @llm_completion_callback() - async def acomplete( - self, prompt: str, formatted: bool = False, **kwargs: Any - ) -> CompletionResponse: - if self._use_chat_completions(kwargs): - acomplete_fn = achat_to_completion_decorator(self._achat) - else: - acomplete_fn = self._acomplete - return await acomplete_fn(prompt, **kwargs) - - @llm_completion_callback() - async def astream_complete( - self, prompt: str, formatted: bool = False, **kwargs: Any - ) -> CompletionResponseAsyncGen: - if self._use_chat_completions(kwargs): - astream_complete_fn = astream_chat_to_completion_decorator( - self._astream_chat - ) - else: - astream_complete_fn = self._astream_complete - return await astream_complete_fn(prompt, **kwargs) - - @llm_retry_decorator - async def _achat( - self, messages: Sequence[ChatMessage], **kwargs: Any - ) -> ChatResponse: - aclient = self._get_aclient() - message_dicts = to_openai_message_dicts(messages) - - if self.reuse_client: - response = await aclient.chat.completions.create( - model="default", messages=message_dicts, stream=False - ) - else: - async with aclient: - response = await aclient.chat.completions.create( - model="default", - messages=message_dicts, - stream=False, - ) - - openai_message = response.choices[0].message - message = from_openai_message(openai_message) - openai_token_logprobs = response.choices[0].logprobs - logprobs = None - if openai_token_logprobs and openai_token_logprobs.content: - logprobs = from_openai_token_logprobs(openai_token_logprobs.content) - - return ChatResponse( - message=message, - raw=response, - logprobs=logprobs, - ) - - @llm_retry_decorator - async def _astream_chat( - self, messages: Sequence[ChatMessage], **kwargs: Any - ) -> ChatResponseAsyncGen: - aclient = self._get_aclient() - message_dicts = to_openai_message_dicts(messages) - - async def gen() -> ChatResponseAsyncGen: - content = "" - - first_chat_chunk = True - async for response in await aclient.chat.completions.create( - model="default", - messages=message_dicts, - stream=True, - ): - response = cast(ChatCompletionChunk, response) - if len(response.choices) > 0: - # check if the first chunk has neither content nor tool_calls - # this happens when 1106 models end up calling multiple tools - if ( - first_chat_chunk - and response.choices[0].delta.content is None - and response.choices[0].delta.tool_calls is None - ): - first_chat_chunk = False - continue - delta = response.choices[0].delta - else: - if self._is_azure_client(): - continue - else: - delta = ChoiceDelta() - first_chat_chunk = False - - # update using deltas - role = delta.role or MessageRole.ASSISTANT - content_delta = delta.content or "" - content += content_delta - - yield ChatResponse( - message=ChatMessage( - role=role, - content=content, - ), - delta=content_delta, - raw=response, - ) - - return gen() - - @llm_retry_decorator - async def _acomplete(self, prompt: str, **kwargs: Any) -> CompletionResponse: - aclient = self._get_aclient() - - if self.reuse_client: - response = await aclient.completions.create( - model="default", - prompt=prompt, - stream=False, - ) - else: - async with aclient: - response = await aclient.completions.create( - model="default", - prompt=prompt, - stream=False, - ) - - text = response.choices[0].text - openai_completion_logprobs = response.choices[0].logprobs - logprobs = None - if openai_completion_logprobs: - logprobs = from_openai_completion_logprobs(openai_completion_logprobs) - - return CompletionResponse( - text=text, - raw=response, - logprobs=logprobs, - ) - - @llm_retry_decorator - async def _astream_complete( - self, prompt: str, **kwargs: Any - ) -> CompletionResponseAsyncGen: - aclient = self._get_aclient() - - async def gen() -> CompletionResponseAsyncGen: - text = "" - async for response in await aclient.completions.create( - model="default", - prompt=prompt, - stream=True, - ): - if len(response.choices) > 0: - delta = response.choices[0].text - if delta is None: - delta = "" - else: - delta = "" - text += delta - yield CompletionResponse( - delta=delta, - text=text, - raw=response, - ) - - return gen() diff --git a/src/pai_rag/integrations/llms/paieas/base_old.py b/src/pai_rag/integrations/llms/paieas/base_old.py deleted file mode 100644 index b667cc8a..00000000 --- a/src/pai_rag/integrations/llms/paieas/base_old.py +++ /dev/null @@ -1,249 +0,0 @@ -from llama_index.core.base.llms.types import ( - ChatMessage, - ChatResponse, - ChatResponseGen, - CompletionResponse, - CompletionResponseGen, - LLMMetadata, -) -from llama_index.core.llms.callbacks import ( - llm_chat_callback, - llm_completion_callback, -) -from llama_index.core.llms.custom import CustomLLM -from llama_index.core.base.llms.generic_utils import ( - completion_to_chat_decorator, - stream_completion_to_chat_decorator, - acompletion_to_chat_decorator, -) -from typing import Any, Dict, Optional, Sequence -from llama_index.core.bridge.pydantic import Field -import json -import requests -import httpx - -DEFAULT_EAS_MODEL_NAME = "pai-eas-custom-llm" -DEFAULT_EAS_MAX_NEW_TOKENS = 512 - - -class PaiEAS(CustomLLM): - """PaiEas LLM.""" - - model_name: str = Field( - default=DEFAULT_EAS_MODEL_NAME, - description="The DashScope model to use.", - ) - endpoint: str = Field(default=None, description="The PAI EAS endpoint.") - token: str = Field(default=None, description="The PAI EAS token.") - max_new_tokens: int = Field( - default=DEFAULT_EAS_MAX_NEW_TOKENS, description="Max new tokens." - ) - temperature: Optional[float] = Field( - description="The temperature to use during generation.", - default=0.1, - gte=0.0, - lte=2.0, - ) - top_p: float = Field( - default=0.8, description="Sample probability threshold when generate." - ) - top_k: int = Field(default=30, description="Sample counter when generate.") - top_k: int = Field(default=30, description="Sample counter when generate.") - version: str = Field(default="2.0", description="PAI EAS endpoint version.") - - """ - Llm model deployed in Aliyun PAI EAS. - """ - - def __init__( - self, - endpoint: str = "", - token: str = "", - model_name: str = "", - max_new_tokens: int = DEFAULT_EAS_MAX_NEW_TOKENS, - temperature: float = 0.1, - top_p: float = 0.8, - top_k: int = 30, - version: str = "2.0", - **kwargs: Any, - ): - super().__init__( - endpoint=endpoint, - token=token, - model_name=model_name, - max_new_tokens=max_new_tokens, - temperature=temperature, - top_p=top_p, - top_k=top_k, - version=version, - kwargs=kwargs, - ) - - @classmethod - def class_name(cls) -> str: - return "PaiEAS" - - @property - def metadata(self) -> LLMMetadata: - return LLMMetadata(model_name=self.model_name) - - def _default_params(self): - params = { - "temperature": self.temperature, - "top_p": self.top_p, - "top_k": self.top_k, - "max_new_tokens": self.max_new_tokens, - } - return params - - @llm_completion_callback() - def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse: - params = self._default_params() - params.update(kwargs) - response = self._call_eas(prompt, params=params) - text = self._process_eas_response(response) - return CompletionResponse(text=text) - - @llm_completion_callback() - async def acomplete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen: - params = self._default_params() - params.update(kwargs) - response = await self._call_eas_async(prompt, params=params) - text = self._process_eas_response(response) - return CompletionResponse(text=text) - - @llm_chat_callback() - def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: - chat_fn = completion_to_chat_decorator(self.complete) - return chat_fn(messages, **kwargs) - - @llm_chat_callback() - async def achat( - self, messages: Sequence[ChatMessage], **kwargs: Any - ) -> ChatResponse: - chat_fn = acompletion_to_chat_decorator(self.acomplete) - return await chat_fn(messages, **kwargs) - - @llm_completion_callback() - def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen: - return self._stream(prompt=prompt, kwargs=kwargs) - - @llm_chat_callback() - def stream_chat( - self, messages: Sequence[ChatMessage], **kwargs: Any - ) -> ChatResponseGen: - stream_chat_fn = stream_completion_to_chat_decorator(self.stream_complete) - return stream_chat_fn(messages, **kwargs) - - def _process_eas_response(self, response: Any) -> str: - if self.version == "1.0": - text = response - else: - text = response["response"] - return text - - def _call_eas(self, prompt: str = "", params: Dict = {}) -> Any: - """Generate text from the eas service.""" - headers = { - "Content-Type": "application/json", - "Authorization": f"{self.token}", - } - if self.version == "1.0": - body = { - "input_ids": f"{prompt}", - } - else: - body = { - "prompt": f"{prompt}", - } - - # add params to body - for key, value in params.items(): - body[key] = value - - # make request - response = requests.post(self.endpoint, headers=headers, json=body) - - if response.status_code != 200: - raise Exception( - f"Request failed with status code {response.status_code}" - f" and message {response.text}" - ) - - try: - return json.loads(response.text) - except Exception as e: - raise e - - async def _call_eas_async(self, prompt: str = "", params: Dict = {}) -> Any: - """Generate text from the eas service.""" - headers = { - "Content-Type": "application/json", - "Authorization": f"{self.token}", - } - if self.version == "1.0": - body = { - "input_ids": f"{prompt}", - } - else: - body = { - "prompt": f"{prompt}", - } - - # add params to body - for key, value in params.items(): - body[key] = value - - # make request - async with httpx.AsyncClient() as http_client: - response = await http_client.post( - url=self.endpoint, headers=headers, json=body, timeout=60 - ) - - if response.status_code != 200: - raise Exception( - f"Request failed with status code {response.status_code}" - f" and message {response.text}" - ) - - try: - return json.loads(response.text) - except Exception as e: - raise e - - def _stream( - self, - prompt: str, - **kwargs: Any, - ) -> CompletionResponseGen: - params = self._default_params() - headers = {"User-Agent": "PAI Rag Client", "Authorization": f"{self.token}"} - - if self.version == "1.0": - pload = {"input_ids": prompt, **params} - response = requests.post( - self.endpoint, headers=headers, json=pload, stream=True - ) - - res = CompletionResponse(text=response.text) - - # yield text, if any - yield res - else: - pload = {"prompt": prompt, "use_stream_chat": "True", **params} - - response = requests.post( - self.endpoint, headers=headers, json=pload, stream=True - ) - - for chunk in response.iter_lines( - chunk_size=8192, decode_unicode=False, delimiter=b"\0" - ): - if chunk: - data = json.loads(chunk.decode("utf-8")) - text = data["response"] - - # yield text, if any - if text: - res = CompletionResponse(text=text) - yield res diff --git a/src/pai_rag/modules/llm/function_calling_llm.py b/src/pai_rag/modules/llm/function_calling_llm.py index a8c2ba94..c990058d 100644 --- a/src/pai_rag/modules/llm/function_calling_llm.py +++ b/src/pai_rag/modules/llm/function_calling_llm.py @@ -14,7 +14,6 @@ def get_dependencies() -> List[str]: def _create_new_instance(self, new_params: Dict[str, Any]): llm_config = new_params[MODULE_PARAM_CONFIG] - print("llm_config", llm_config) if llm_config and llm_config.source.lower() == "dashscope": model_name = llm_config.get("name", "qwen2-7b-instruct") logger.info( diff --git a/src/pai_rag/modules/llm/llm_module.py b/src/pai_rag/modules/llm/llm_module.py index 49bd90d3..f6352854 100644 --- a/src/pai_rag/modules/llm/llm_module.py +++ b/src/pai_rag/modules/llm/llm_module.py @@ -3,10 +3,9 @@ from llama_index.core import Settings from llama_index.llms.openai import OpenAI from llama_index.llms.azure_openai import AzureOpenAI +from llama_index.llms.paieas import PaiEas -# from llama_index.llms.dashscope import DashScope from pai_rag.integrations.llms.dashscope.base import MyDashScope -from pai_rag.integrations.llms.paieas.base import PaiEAS from pai_rag.modules.base.configurable_module import ConfigurableModule from pai_rag.modules.base.module_constants import MODULE_PARAM_CONFIG @@ -76,9 +75,7 @@ def _create_new_instance(self, new_params: Dict[str, Any]): token = {token} """ ) - from urllib.parse import urljoin - - llm = PaiEAS(api_key=token, api_base=urljoin(endpoint, "v1")) + llm = PaiEas(api_key=token, api_base=endpoint) else: raise ValueError(f"Unknown LLM source: '{config['llm']['source']}'") diff --git a/src/pai_rag/modules/tool/utils.py b/src/pai_rag/modules/tool/utils.py index bebedbd4..43fc616b 100644 --- a/src/pai_rag/modules/tool/utils.py +++ b/src/pai_rag/modules/tool/utils.py @@ -109,28 +109,31 @@ def get_place_weather(city: str) -> str: def get_customized_tools(config): func_path = config["func_path"] sys.path.append(func_path) - module = __import__("custom_functions") - tools = [] - # 加载JSON文件 - with open(os.path.join(func_path, "custom_functions.json"), "r") as file: - custom_tools = json.load(file) - - for c_tool in custom_tools: - fn_name = c_tool["function"]["name"] - if hasattr(module, fn_name): - func = getattr(module, fn_name) - fn_schema = create_tool_fn_schema( - fn_name, c_tool["function"]["parameters"] - ) - tool = FunctionTool.from_defaults( - fn=func, - name=fn_name, - fn_schema=fn_schema, - description=c_tool["function"]["description"], - ) - tools.append(tool) - else: - raise ValueError( - f"Function {fn_name} has not been defined in the custom_functions.py, please define it." - ) - return tools + try: + module = __import__("custom_functions") + tools = [] + # 加载JSON文件 + with open(os.path.join(func_path, "custom_functions.json"), "r") as file: + custom_tools = json.load(file) + + for c_tool in custom_tools: + fn_name = c_tool["function"]["name"] + if hasattr(module, fn_name): + func = getattr(module, fn_name) + fn_schema = create_tool_fn_schema( + fn_name, c_tool["function"]["parameters"] + ) + tool = FunctionTool.from_defaults( + fn=func, + name=fn_name, + fn_schema=fn_schema, + description=c_tool["function"]["description"], + ) + tools.append(tool) + else: + raise ValueError( + f"Function {fn_name} has not been defined in the custom_functions.py, please define it." + ) + return tools + except Exception: + return []