From 5c662d428689c0ed35424f3fd661f54ab7379ba2 Mon Sep 17 00:00:00 2001 From: Yoonjin Hwang <21603682@hankooktech.com> Date: Mon, 21 Apr 2025 20:25:12 +0900 Subject: [PATCH] add multi-llm provider --- .env.example | 66 + llm_utils/llm_factory.py | 182 +- poetry.lock | 5945 ++++++++++++++++++++++++++++++++++++++ pyproject.toml | 37 + 4 files changed, 6216 insertions(+), 14 deletions(-) create mode 100644 .env.example create mode 100644 poetry.lock diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..7403b34 --- /dev/null +++ b/.env.example @@ -0,0 +1,66 @@ +############################################### +############## LLM API SELECTION ############## +############################################### + +# LLM_PROVIDER=openai +# OPEN_AI_LLM_KEY= +# OPEN_AI_LLM_MODEL=gpt-4o + +# LLM_PROVIDER=gemini +# GEMINI_API_KEY= +# GEMINI_LLM_MODEL=gemini-2.0-flash-lite + +# LLM_PROVIDER=azure +# AZURE_OPENAI_LLM_ENDPOINT= +# AZURE_OPENAI_LLM_KEY= +# AZURE_OPENAI_LLM_MODEL= +# AZURE_OPENAI_LLM_API_VERSION= + +# LLM_PROVIDER=ollama +# OLLAMA_LLM_BASE_URL= +# OLLAMA_LLM_MODEL= + +# LLM_PROVIDER=huggingface +# HUGGING_FACE_LLM_REPO_ID= +# HUGGING_FACE_LLM_ENDPOINT= +# HUGGING_FACE_LLM_API_TOKEN= + +# LLM_PROVIDER=bedrock +# AWS_BEDROCK_LLM_ACCESS_KEY_ID= +# AWS_BEDROCK_LLM_SECRET_ACCESS_KEY= +# AWS_BEDROCK_LLM_REGION=us-west-2 +# AWS_BEDROCK_LLM_ENDPOINT_URL=https://bedrock.us-west-2.amazonaws.com +# AWS_BEDROCK_LLM_MODEL=anthropic.claude-3-5-sonnet-20241022-v2:0\ + +############################################### +########### Embedding API SElECTION ########### +############################################### +# Only used if you are using an LLM that does not natively support embedding (openai or Azure) +# EMBEDDING_ENGINE='openai' +# OPEN_AI_KEY=sk-xxxx +# EMBEDDING_MODEL_PREF='text-embedding-ada-002' + +# EMBEDDING_ENGINE='azure' +# AZURE_OPENAI_ENDPOINT= +# AZURE_OPENAI_KEY= +# EMBEDDING_MODEL_PREF='my-embedder-model' # This is the "deployment" on Azure you want to use for embeddings. Not the base model. Valid base model is text-embedding-ada-002 + +# EMBEDDING_ENGINE='ollama' +# EMBEDDING_BASE_PATH='http://host.docker.internal:11434' +# EMBEDDING_MODEL_PREF='nomic-embed-text:latest' +# EMBEDDING_MODEL_MAX_CHUNK_LENGTH=8192 + +# EMBEDDING_ENGINE='bedrock' +# AWS_BEDROCK_EMBEDDING_ACCESS_KEY_ID= +# AWS_BEDROCK_EMBEDDING_ACCESS_KEY= +# AWS_BEDROCK_EMBEDDING_REGION=us-west-2 +# AWS_BEDROCK_EMBEDDING_MODEL_PREF=amazon.embedding-embedding-ada-002:0 + +# EMBEDDING_ENGINE='gemini' +# GEMINI_EMBEDDING_API_KEY= +# EMBEDDING_MODEL_PREF='text-embedding-004' + +# EMBEDDING_ENGINE='huggingface' +# HUGGING_FACE_EMBEDDING_REPO_ID= +# HUGGING_FACE_EMBEDDING_MODEL= +# HUGGING_FACE_EMBEDDING_API_TOKEN= diff --git a/llm_utils/llm_factory.py b/llm_utils/llm_factory.py index 0ed42ee..bdb4d64 100644 --- a/llm_utils/llm_factory.py +++ b/llm_utils/llm_factory.py @@ -1,25 +1,179 @@ # llm_factory.py +import os from typing import Optional +from dotenv import load_dotenv from langchain.llms.base import BaseLanguageModel -from langchain_openai import ChatOpenAI +from langchain_aws import ChatBedrockConverse, BedrockEmbeddings +from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings +from langchain_huggingface import ( + ChatHuggingFace, + HuggingFaceEndpoint, + HuggingFaceEndpointEmbeddings, +) +from langchain_ollama import ChatOllama, OllamaEmbeddings +from langchain_openai import ( + AzureOpenAIEmbeddings, + ChatOpenAI, + AzureChatOpenAI, + OpenAIEmbeddings, +) +from langchain_community.llms.bedrock import Bedrock +# .env 파일 로딩 +load_dotenv() -def get_llm( - model_type: str, - model_name: Optional[str] = None, - openai_api_key: Optional[str] = None, - **kwargs, -) -> BaseLanguageModel: + +def get_llm() -> BaseLanguageModel: """ - 주어진 model_type과 model_name 등에 따라 적절한 LLM 객체를 생성/반환한다. + return chat model interface """ - if model_type == "openai": - return ChatOpenAI( - model=model_name, - api_key=openai_api_key, - **kwargs, + provider = os.getenv("LLM_PROVIDER") + + if provider is None: + raise ValueError("LLM_PROVIDER environment variable is not set.") + + if provider == "openai": + return get_llm_openai() + + elif provider == "azure": + return get_llm_azure() + + elif provider == "bedrock": + return get_llm_bedrock() + + elif provider == "gemini": + return get_llm_gemini() + + elif provider == "ollama": + return get_llm_ollama() + + elif provider == "huggingface": + return get_llm_huggingface() + + else: + raise ValueError(f"Invalid LLM API Provider: {provider}") + + +def get_llm_openai() -> BaseLanguageModel: + return ChatOpenAI( + model=os.getenv("OPEN_MODEL_PREF", "gpt-4o"), + api_key=os.getenv("OPEN_AI_KEY"), + ) + + +def get_llm_azure() -> BaseLanguageModel: + return AzureChatOpenAI( + api_key=os.getenv("AZURE_OPENAI_LLM_KEY"), + azure_endpoint=os.getenv("AZURE_OPENAI_LLM_ENDPOINT"), + azure_deployment=os.getenv("AZURE_OPENAI_LLM_MODEL"), # Deployment name + api_version=os.getenv("AZURE_OPENAI_LLM_API_VERSION", "2023-07-01-preview"), + ) + + +def get_llm_bedrock() -> BaseLanguageModel: + return ChatBedrockConverse( + model=os.getenv("AWS_BEDROCK_LLM_MODEL"), + aws_access_key_id=os.getenv("AWS_BEDROCK_LLM_ACCESS_KEY_ID"), + aws_secret_access_key=os.getenv("AWS_BEDROCK_LLM_SECRET_ACCESS_KEY"), + region_name=os.getenv("AWS_BEDROCK_LLM_REGION", "us-east-1"), + ) + + +def get_llm_gemini() -> BaseLanguageModel: + return ChatGoogleGenerativeAI(model=os.getenv("GEMINI_LLM_MODEL")) + + +def get_llm_ollama() -> BaseLanguageModel: + base_url = os.getenv("OLLAMA_LLM_BASE_URL") + if base_url: + return ChatOllama(base_url=base_url, model=os.getenv("OLLAMA_LLM_MODEL")) + else: + return ChatOllama(model=os.getenv("OLLAMA_LLM_MODEL")) + + +def get_llm_huggingface() -> BaseLanguageModel: + return ChatHuggingFace( + llm=HuggingFaceEndpoint( + model=os.getenv("HUGGING_FACE_LLM_MODEL"), + repo_id=os.getenv("HUGGING_FACE_LLM_REPO_ID"), + task="text-generation", + endpoint_url=os.getenv("HUGGING_FACE_LLM_ENDPOINT"), + huggingfacehub_api_token=os.getenv("HUGGING_FACE_LLM_API_TOKEN"), ) + ) + + +def get_embeddings() -> Optional[BaseLanguageModel]: + """ + return embedding model interface + """ + provider = os.getenv("EMBEDDING_PROVIDER") + + if provider is None: + raise ValueError("EMBEDDING_PROVIDER environment variable is not set.") + + if provider == "openai": + return get_embeddings_openai() + + elif provider == "bedrock": + return get_embeddings_bedrock() + + elif provider == "azure": + return get_embeddings_azure() + + elif provider == "gemini": + return get_embeddings_gemini() + + elif provider == "ollama": + return get_embeddings_ollama() else: - raise ValueError(f"지원하지 않는 model_type: {model_type}") + raise ValueError(f"Invalid Embedding API Provider: {provider}") + + +def get_embeddings_openai() -> BaseLanguageModel: + return OpenAIEmbeddings( + model=os.getenv("OPEN_AI_EMBEDDING_MODEL"), + openai_api_key=os.getenv("OPEN_AI_EMBEDDING_KEY"), + ) + + +def get_embeddings_azure() -> BaseLanguageModel: + return AzureOpenAIEmbeddings( + api_key=os.getenv("AZURE_OPENAI_EMBEDDING_KEY"), + azure_endpoint=os.getenv("AZURE_OPENAI_EMBEDDING_ENDPOINT"), + azure_deployment=os.getenv("AZURE_OPENAI_EMBEDDING_MODEL"), + api_version=os.getenv("AZURE_OPENAI_EMBEDDING_API_VERSION"), + ) + + +def get_embeddings_bedrock() -> BaseLanguageModel: + return BedrockEmbeddings( + model_id=os.getenv("AWS_BEDROCK_EMBEDDING_MODEL"), + aws_access_key_id=os.getenv("AWS_BEDROCK_EMBEDDING_ACCESS_KEY_ID"), + aws_secret_access_key=os.getenv("AWS_BEDROCK_EMBEDDING_SECRET_ACCESS_KEY"), + region_name=os.getenv("AWS_BEDROCK_EMBEDDING_REGION", "us-east-1"), + ) + + +def get_embeddings_gemini() -> BaseLanguageModel: + return GoogleGenerativeAIEmbeddings( + model=os.getenv("GEMINI_EMBEDDING_MODEL"), + api_key=os.getenv("GEMINI_EMBEDDING_KEY"), + ) + + +def get_embeddings_ollama() -> BaseLanguageModel: + return OllamaEmbeddings( + model=os.getenv("OLLAMA_EMBEDDING_MODEL"), + base_url=os.getenv("OLLAMA_EMBEDDING_BASE_URL"), + ) + + +def get_embeddings_huggingface() -> BaseLanguageModel: + return HuggingFaceEndpointEmbeddings( + model=os.getenv("HUGGING_FACE_EMBEDDING_MODEL"), + repo_id=os.getenv("HUGGING_FACE_EMBEDDING_REPO_ID"), + huggingfacehub_api_token=os.getenv("HUGGING_FACE_EMBEDDING_API_TOKEN"), + ) diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..27a4aac --- /dev/null +++ b/poetry.lock @@ -0,0 +1,5945 @@ +# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. + +[[package]] +name = "acryl-datahub" +version = "1.0.0.2" +description = "A CLI to work with DataHub metadata" +optional = false +python-versions = ">=3.8" +groups = ["main"] +markers = "python_version == \"3.11\" or python_version >= \"3.12\"" +files = [ + {file = "acryl_datahub-1.0.0.2-py3-none-any.whl", hash = "sha256:f6d019b871f9d24f371cd96ff9924412f58e80e03b4c37f5823a64e1e010ce32"}, + {file = "acryl_datahub-1.0.0.2.tar.gz", hash = "sha256:c3a6cd2a1b947b57bca80bf735d890dff3688a06319ca46e7b1f997783267c50"}, +] + +[package.dependencies] +aiohttp = "<4" +avro = ">=1.11.3,<1.13" +avro-gen3 = "0.7.16" +cached_property = "*" +click = ">=7.1.2" +click-default-group = "*" +click-spinner = "*" +Deprecated = "*" +docker = "*" +expandvars = ">=0.6.5" +humanfriendly = "*" +ijson = "*" +jsonref = "*" +jsonschema = "*" +mixpanel = ">=4.9.0" +packaging = "*" +progressbar2 = "*" +psutil = ">=5.8.0" +pydantic = ">=1.10.0,<1.10.3 || >1.10.3" +python-dateutil = ">=2.8.0" +PyYAML = "*" +requests_file = "*" +"ruamel.yaml" = "*" +sentry-sdk = "*" +tabulate = "*" +toml = ">=0.10.0" +typing_extensions = ">=4.5.0" +typing-inspect = "*" + +[package.extras] +abs = ["Deprecated", "PyYAML", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "azure-core (==1.29.4)", "azure-identity (>=1.17.1)", 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