From 8639b55d5248c4e17c7f1e2c51898edf8631f97d Mon Sep 17 00:00:00 2001 From: Li Yin Date: Sat, 18 May 2024 18:57:32 -0700 Subject: [PATCH] added google gen ai client, support gemini-1.0-pro and gemini-1.5-pro-latest --- components/api_client/__init__.py | 1 + components/api_client/google_client.py | 101 +++ components/api_client/transformers_client.py | 15 +- poetry.lock | 788 ++++++++++++++----- pyproject.toml | 1 + use_cases/simple_qa_anthropic.py | 33 + use_cases/simple_qa_google.py | 33 + use_cases/simple_qa_groq.py | 5 +- 8 files changed, 755 insertions(+), 222 deletions(-) create mode 100644 components/api_client/google_client.py create mode 100644 use_cases/simple_qa_anthropic.py create mode 100644 use_cases/simple_qa_google.py diff --git a/components/api_client/__init__.py b/components/api_client/__init__.py index 5f7512d0..eb30d0dc 100644 --- a/components/api_client/__init__.py +++ b/components/api_client/__init__.py @@ -2,3 +2,4 @@ from .groq_client import * from .anthropic_client import * from .transformers_client import * +from .google_client import * diff --git a/components/api_client/google_client.py b/components/api_client/google_client.py new file mode 100644 index 00000000..7ae8a9b4 --- /dev/null +++ b/components/api_client/google_client.py @@ -0,0 +1,101 @@ +""" +This demonstrates how to wrap the OpenAI API client to fit into LightRAG APIClient +""" + +import os +from core.api_client import APIClient +from typing import Any, Dict, Sequence, Union, Optional, List +from core.data_classes import ModelType +import backoff + +try: + import google.generativeai as genai + from google.api_core.exceptions import ( + InternalServerError, + BadRequest, + GoogleAPICallError, + ) + from google.generativeai.types import GenerateContentResponse +except ImportError: + raise ImportError("Please install google-generativeai to use GoogleGenAIClient") + + +class GoogleGenAIClient(APIClient): + def __init__(self): + super().__init__() + self.sync_client = self._init_sync_client() + self.async_client = None # only initialize if the async call is called + self.tested_llm_models = ["gemini-1.0-pro", "gemini-1.5-pro-latest"] + + def _init_sync_client(self): + api_key = os.getenv("GOOGLE_API_KEY") + if not api_key: + raise ValueError("Environment variable GOOGLE_API_KEY must be set") + genai.configure(api_key=api_key) + return genai + + # def _init_async_client(self): + # api_key = os.getenv("GOOGLE_API_KEY") + # if not api_key: + # raise ValueError("Environment variable GOOGLE_API_KEY must be set") + # return AsyncOpenAI() + + def parse_chat_completion(self, completion: GenerateContentResponse) -> str: + """ + Parse the completion to a structure your sytem standarizes. (here is str) + # TODO: standardize the completion + """ + print(f"completion: {completion}") + return completion.text + # result["candidates"][0]["contents"]["parts"][0]["text"] + + def convert_input_to_api_kwargs( + self, + input: Union[str, Sequence], + system_input: Optional[Union[str]] = None, + combined_model_kwargs: Dict = {}, + model_type: ModelType = ModelType.UNDEFINED, + ) -> Dict: + r""" + Specify the API input type and output api_kwargs that will be used in _call and _acall methods. + Convert the Component's standard input, and system_input(chat model) and model_kwargs into API-specific format + """ + final_model_kwargs = combined_model_kwargs.copy() + if model_type == ModelType.EMBEDDER: + if isinstance(input, str): + input = [input] + # convert input to input + assert isinstance(input, Sequence), "input must be a sequence of text" + final_model_kwargs["input"] = input + elif model_type == ModelType.LLM: + prompt: str = f"{system_input}\n\nUser query: {input}\n You:" + + final_model_kwargs["prompt"] = prompt + else: + raise ValueError(f"model_type {model_type} is not supported") + return final_model_kwargs + + @backoff.on_exception( + backoff.expo, + ( + InternalServerError, + BadRequest, + GoogleAPICallError, + ), + max_time=5, + ) + def call(self, api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED): + """ + kwargs is the combined input and model_kwargs + """ + + if model_type == ModelType.LLM: + # remove model from api_kwargs + model = api_kwargs.pop("model") + prompt = api_kwargs.pop("prompt") + + config = genai.GenerationConfig(**api_kwargs) + llm = genai.GenerativeModel(model_name=model, generation_config=config) + return llm.generate_content(contents=prompt) + else: + raise ValueError(f"model_type {model_type} is not supported") diff --git a/components/api_client/transformers_client.py b/components/api_client/transformers_client.py index e7781105..9b464c80 100644 --- a/components/api_client/transformers_client.py +++ b/components/api_client/transformers_client.py @@ -95,10 +95,10 @@ def __init__(self) -> None: def _init_sync_client(self): return TransformerEmbedder() - def _call(self, kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED): - return self.sync_client(**kwargs) + def call(self, api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED): + return self.sync_client(**api_kwargs) - def _combine_input_and_model_kwargs( + def convert_input_to_api_kwargs( self, input: Any, combined_model_kwargs: dict = {}, @@ -131,9 +131,16 @@ def test_transformer_client(): "model": "thenlper/gte-base", "mock": False, } + api_kwargs = transformer_client.convert_input_to_api_kwargs( + input="Hello world", + combined_model_kwargs=kwargs, + model_type=ModelType.EMBEDDER, + ) + print(api_kwargs) output = transformer_client.call( - input="Hello world", model_type=ModelType.EMBEDDER, model_kwargs=kwargs + api_kwargs=api_kwargs, model_type=ModelType.EMBEDDER ) + print(transformer_client) print(output) diff --git a/poetry.lock b/poetry.lock index f49dbaf8..d40ae9ce 100644 --- a/poetry.lock +++ b/poetry.lock @@ -110,24 +110,16 @@ files = [ frozenlist = ">=1.1.0" [[package]] -name = "alembic" -version = "1.13.1" -description = "A database migration tool for SQLAlchemy." +name = "alabaster" +version = "0.7.16" +description = "A light, configurable Sphinx theme" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" files = [ - {file = "alembic-1.13.1-py3-none-any.whl", hash = "sha256:2edcc97bed0bd3272611ce3a98d98279e9c209e7186e43e75bbb1b2bdfdbcc43"}, - {file = "alembic-1.13.1.tar.gz", hash = "sha256:4932c8558bf68f2ee92b9bbcb8218671c627064d5b08939437af6d77dc05e595"}, + {file = "alabaster-0.7.16-py3-none-any.whl", hash = "sha256:b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92"}, + {file = "alabaster-0.7.16.tar.gz", hash = "sha256:75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65"}, ] -[package.dependencies] -Mako = "*" -SQLAlchemy = ">=1.3.0" -typing-extensions = ">=4" - -[package.extras] -tz = ["backports.zoneinfo"] - [[package]] name = "annotated-types" version = "0.6.0" @@ -220,6 +212,20 @@ tests = ["attrs[tests-no-zope]", "zope-interface"] tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"] tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"] +[[package]] +name = "babel" +version = "2.15.0" +description = "Internationalization utilities" +optional = false +python-versions = ">=3.8" +files = [ + {file = "Babel-2.15.0-py3-none-any.whl", hash = "sha256:08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb"}, + {file = "babel-2.15.0.tar.gz", hash = "sha256:8daf0e265d05768bc6c7a314cf1321e9a123afc328cc635c18622a2f30a04413"}, +] + +[package.extras] +dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"] + [[package]] name = "backoff" version = "2.2.1" @@ -263,6 +269,17 @@ files = [ {file = "boilerpy3-1.0.7.tar.gz", hash = "sha256:a9fede212f80a36dbc7d4f93e35d8636911cb6b37085a3230557d16ad0f076c8"}, ] +[[package]] +name = "cachetools" +version = "5.3.3" +description = "Extensible memoizing collections and decorators" +optional = false +python-versions = ">=3.7" +files = [ + {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"}, + {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"}, +] + [[package]] name = "certifi" version = "2024.2.2" @@ -409,23 +426,6 @@ files = [ {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] -[[package]] -name = "colorlog" -version = "6.8.2" -description = "Add colours to the output of Python's logging module." -optional = false -python-versions = ">=3.6" -files = [ - {file = "colorlog-6.8.2-py3-none-any.whl", hash = "sha256:4dcbb62368e2800cb3c5abd348da7e53f6c362dda502ec27c560b2e58a66bd33"}, - {file = "colorlog-6.8.2.tar.gz", hash = "sha256:3e3e079a41feb5a1b64f978b5ea4f46040a94f11f0e8bbb8261e3dbbeca64d44"}, -] - -[package.dependencies] -colorama = {version = "*", markers = "sys_platform == \"win32\""} - -[package.extras] -development = ["black", "flake8", "mypy", "pytest", "types-colorama"] - [[package]] name = "contourpy" version = "1.2.1" @@ -521,25 +521,26 @@ typing-inspect = ">=0.4.0,<1" [[package]] name = "datasets" -version = "2.14.7" +version = "2.19.1" description = "HuggingFace community-driven open-source library of datasets" optional = false python-versions = ">=3.8.0" files = [ - {file = "datasets-2.14.7-py3-none-any.whl", hash = "sha256:1a64041a7da4f4130f736fc371c1f528b8ddd208cebe156400f65719bdbba79d"}, - {file = "datasets-2.14.7.tar.gz", hash = "sha256:394cf9b4ec0694b25945977b16ad5d18d5c15fb0e94141713eb8ead7452caf9e"}, + {file = "datasets-2.19.1-py3-none-any.whl", hash = "sha256:f7a78d15896f45004ccac1c298f3c7121f92f91f6f2bfbd4e4f210f827e6e411"}, + {file = "datasets-2.19.1.tar.gz", hash = "sha256:0df9ef6c5e9138cdb996a07385220109ff203c204245578b69cca905eb151d3a"}, ] [package.dependencies] aiohttp = "*" -dill = ">=0.3.0,<0.3.8" -fsspec = {version = ">=2023.1.0,<=2023.10.0", extras = ["http"]} -huggingface-hub = ">=0.14.0,<1.0.0" +dill = ">=0.3.0,<0.3.9" +filelock = "*" +fsspec = {version = ">=2023.1.0,<=2024.3.1", extras = ["http"]} +huggingface-hub = ">=0.21.2" multiprocess = "*" numpy = ">=1.17" packaging = "*" pandas = "*" -pyarrow = ">=8.0.0" +pyarrow = ">=12.0.0" pyarrow-hotfix = "*" pyyaml = ">=5.1" requests = ">=2.19.0" @@ -547,18 +548,18 @@ tqdm = ">=4.62.1" xxhash = "*" [package.extras] -apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"] +apache-beam = ["apache-beam (>=2.26.0)"] audio = ["librosa", "soundfile (>=0.12.1)"] benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"] -dev = ["Pillow (>=6.2.1)", "absl-py", 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"weaviate-client (>=4.5.4,<4.6.0)"] - [[package]] name = "executing" version = "2.0.1" @@ -935,6 +907,144 @@ smb = ["smbprotocol"] ssh = ["paramiko"] tqdm = ["tqdm"] +[[package]] +name = "google-ai-generativelanguage" +version = "0.6.4" +description = "Google Ai Generativelanguage API client library" +optional = false +python-versions = ">=3.7" +files = [ + {file = "google-ai-generativelanguage-0.6.4.tar.gz", hash = "sha256:1750848c12af96cb24ae1c3dd05e4bfe24867dc4577009ed03e1042d8421e874"}, + {file = "google_ai_generativelanguage-0.6.4-py3-none-any.whl", hash = "sha256:730e471aa549797118fb1c88421ba1957741433ada575cf5dd08d3aebf903ab1"}, +] + +[package.dependencies] +google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]} +google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev" +proto-plus = ">=1.22.3,<2.0.0dev" +protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || 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llama-index-llms-replicate = "^0.1.3" anthropic = "^0.26.0" +google-generativeai = "^0.5.4" [tool.ruff] diff --git a/use_cases/simple_qa_anthropic.py b/use_cases/simple_qa_anthropic.py new file mode 100644 index 00000000..6a683cc3 --- /dev/null +++ b/use_cases/simple_qa_anthropic.py @@ -0,0 +1,33 @@ +""" +We just need to very basic generator that can be used to generate text from a prompt. +""" + +from core.generator import Generator +from core.component import Component + +from components.api_client import AnthropicAPIClient + +import utils.setup_env + + +class SimpleQA(Component): + def __init__(self): + super().__init__() + self.generator = Generator( + model_client=AnthropicAPIClient, + model_kwargs={"model": "claude-3-opus-20240229", "max_tokens": 1000}, + preset_prompt_kwargs={ + "task_desc_str": "You are a helpful assistant and with a great sense of humor." + }, + ) + + def call(self, query: str) -> str: + return self.generator.call(input=query) + + +if __name__ == "__main__": + simple_qa = SimpleQA() + print(simple_qa) + print("show the system prompt") + simple_qa.generator.print_prompt() + print(simple_qa.call("What is the capital of France?")) diff --git a/use_cases/simple_qa_google.py b/use_cases/simple_qa_google.py new file mode 100644 index 00000000..06759617 --- /dev/null +++ b/use_cases/simple_qa_google.py @@ -0,0 +1,33 @@ +""" +We just need to very basic generator that can be used to generate text from a prompt. +""" + +from core.generator import Generator +from core.component import Component + +from components.api_client import GoogleGenAIClient + +import utils.setup_env + + +class SimpleQA(Component): + def __init__(self): + super().__init__() + self.generator = Generator( + model_client=GoogleGenAIClient, + model_kwargs={"model": "gemini-1.5-pro-latest"}, + preset_prompt_kwargs={ + "task_desc_str": "You are a helpful assistant and with a great sense of humor." + }, + ) + + def call(self, query: str) -> str: + return self.generator.call(input=query) + + +if __name__ == "__main__": + simple_qa = SimpleQA() + print(simple_qa) + print("show the system prompt") + simple_qa.generator.print_prompt() + print(simple_qa.call("What is the capital of France?")) diff --git a/use_cases/simple_qa_groq.py b/use_cases/simple_qa_groq.py index 3ce4b99f..7e1e706c 100644 --- a/use_cases/simple_qa_groq.py +++ b/use_cases/simple_qa_groq.py @@ -6,7 +6,6 @@ from core.component import Component from components.api_client import GroqAPIClient -from components.api_client import AnthropicAPIClient import utils.setup_env @@ -15,8 +14,8 @@ class SimpleQA(Component): def __init__(self): super().__init__() self.generator = Generator( - model_client=AnthropicAPIClient, - model_kwargs={"model": "claude-3-opus-20240229", "max_tokens": 1000}, + model_client=GroqAPIClient, + model_kwargs={"model": "llama3-8b-8192"}, preset_prompt_kwargs={ "task_desc_str": "You are a helpful assistant and with a great sense of humor." },