통합 LLM 팩토리 라이브러리 - 다양한 LLM 제공자를 통합된 인터페이스로 사용
Project description
LLM Factory
통합 LLM 팩토리 라이브러리 - 다양한 LLM 제공자를 통합된 인터페이스로 사용할 수 있게 해주는 Python 라이브러리입니다.
주요 기능
- 다중 LLM 제공자 지원: OpenAI, Azure OpenAI, Anthropic, Ollama
설치
requirements.txt에 추가:
process-gpt-llm-factory==1.0.0
직접 설치
pip install process-gpt-llm-factory
빠른 시작
1. 환경변수 설정
.env 파일에 다음 환경변수들을 설정하세요:
# LLM Provider 설정 (설정하지 않는 경우 openai 기본값 사용)
LLM_PROVIDER=openai # openai, azure, anthropic, ollama 중 선택
# OpenAI 설정
OPENAI_API_KEY=your_openai_api_key_here
OPENAI_MODEL=gpt-4o
# 아래 설정은 openai 외 사용시에만 필요
# Azure OpenAI 설정
AZURE_API_KEY=your_azure_api_key_here
AZURE_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_DEPLOYMENT=your_deployment_name
AZURE_MODEL=gpt-4o
AZURE_API_VERSION=2024-06-01-preview
# Anthropic 설정
ANTHROPIC_API_KEY=your_anthropic_api_key_here
ANTHROPIC_MODEL=claude-3-sonnet-20240229
# Ollama 설정
OLLAMA_MODEL=llama3
OLLAMA_BASE_URL=http://localhost:11434
2. 기본 사용법
from llm_factory import create_llm, create_embedding
# 환경변수에 설정된 제공자와 모델 사용
llm = create_llm()
# 특정 제공자 지정
llm = create_llm(provider="openai")
# 특정 모델과 옵션 지정
llm = create_llm(
model="gpt-4o",
temperature=0.7,
streaming=True
)
## API 참조
### LLM 생성 함수
```python
from llm_factory import (
create_llm,
create_openai_llm,
create_azure_llm,
create_anthropic_llm,
create_ollama_llm
)
# 기본 LLM 생성
llm = create_llm()
# 특정 제공자 LLM 생성
openai_llm = create_openai_llm(model="gpt-4o", temperature=0.5)
azure_llm = create_azure_llm(model="gpt-4o")
anthropic_llm = create_anthropic_llm(model="claude-3-sonnet-20240229")
ollama_llm = create_ollama_llm(model="llama3")
Embedding 생성 함수
from llm_factory import (
create_embedding,
create_openai_embedding,
create_azure_embedding
)
# 기본 Embedding 생성 (환경변수 기반)
embedding = create_embedding()
# 특정 제공자 Embedding 생성
openai_embedding = create_openai_embedding(model="text-embedding-3-large")
azure_embedding = create_azure_embedding(model="text-embedding-3-large")
마이그레이션 가이드
기존 코드에서 직접 LLM을 생성하던 방식을 LLM Factory로 변경하는 방법:
Before (기존 방식)
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o", streaming=True, temperature=0)
After (LLM Factory 사용)
from llm_factory import create_llm
model = create_llm(temperature=0)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file process_gpt_llm_factory-1.0.1.tar.gz.
File metadata
- Download URL: process_gpt_llm_factory-1.0.1.tar.gz
- Upload date:
- Size: 6.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
ae2a5bb4aca2b09b77848da7a69b150af611a42c02c2b67047dc7f81582ab597
|
|
| MD5 |
907d5958d6f0401c3828c1dbf8c429ee
|
|
| BLAKE2b-256 |
eb31583cfc3f0f1459f520ca7b38ce1534e79d8cc84407d336463ab4fc925cb0
|
File details
Details for the file process_gpt_llm_factory-1.0.1-py3-none-any.whl.
File metadata
- Download URL: process_gpt_llm_factory-1.0.1-py3-none-any.whl
- Upload date:
- Size: 6.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
00cdcf7fa58e10f95e4521ed82af8453e0b7939d6c0f0f9b32e3f11ca847da83
|
|
| MD5 |
6d24086a217653a07c5725055890d6fd
|
|
| BLAKE2b-256 |
307d8e07956b7ef8e2f6823670c81dfb43577515739373064c38c109d645edcf
|