[PYTHON/LANGCHAIN] FAISS 클래스 : as_retriever 메소드에서 search_kwargs 인자를 사용해 벡터 스토어 검색기 구하기

■ FAISS 클래스의 as_retriever 메소드에서 search_kwargs 인자를 사용해 벡터 스토어 검색기를 구하는 방법을 보여준다.

※ 검색기가 반환하는 문서 수를 제한할 수도 있다.
※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.

▶ main.py


import ast
import os
import re

from langchain_community.utilities    import SQLDatabase
from langchain_community.vectorstores import FAISS
from langchain_openai                 import OpenAIEmbeddings

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

# SQLITE 데이터베이스를 초기화한다.
sqlDatabase = SQLDatabase.from_uri("sqlite:///chinook.db")

# SQLite 데이터를 가져오는 함수를 정의한다.
def getSQLiteData(sqlDatabase, sql):
    resultString    = sqlDatabase.run(sql)
    resultTupleList = ast.literal_eval(resultString)
    resultList1     = [element for resultTuple in resultTupleList for element in resultTuple if element]
    resultList2     = [re.sub(r"\b\d+\b", "", result).strip() for result in resultList1]
    return list(set(resultList2))

# 아티스트명, 앨범 타이틀 리스트를 구한다.
artistNameList = getSQLiteData(sqlDatabase, "SELECT Name  FROM artists")
albumTitleList = getSQLiteData(sqlDatabase, "SELECT Title FROM albums" )

# FAISS 벡터 스토어를 생성한다:
faiss = FAISS.from_texts(artistNameList + albumTitleList, OpenAIEmbeddings())

# 벡터 스토어 검색기를 설정한다.
vectorStoreRetriever = faiss.as_retriever(search_kwargs = {"k" : 5})

import ast

import os

import re

from langchain_community.utilities import SQLDatabase

from langchain_community.vectorstores import FAISS

from langchain_openai import OpenAIEmbeddings

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

# SQLITE 데이터베이스를 초기화한다.

sqlDatabase = SQLDatabase.from_uri("sqlite:///chinook.db")

# SQLite 데이터를 가져오는 함수를 정의한다.

def getSQLiteData(sqlDatabase, sql):

resultString = sqlDatabase.run(sql)

resultTupleList = ast.literal_eval(resultString)

resultList1 = [element for resultTuple in resultTupleList for element in resultTuple if element]

resultList2 = [re.sub(r"\b\d+\b", "", result).strip() for result in resultList1]

return list(set(resultList2))

# 아티스트명, 앨범 타이틀 리스트를 구한다.

artistNameList = getSQLiteData(sqlDatabase, "SELECT Name FROM artists")

albumTitleList = getSQLiteData(sqlDatabase, "SELECT Title FROM albums" )

# FAISS 벡터 스토어를 생성한다:

faiss = FAISS.from_texts(artistNameList + albumTitleList, OpenAIEmbeddings())

# 벡터 스토어 검색기를 설정한다.

vectorStoreRetriever = faiss.as_retriever(search_kwargs = {"k" : 5})

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
dataclasses-json==0.6.7
distro==1.9.0
exceptiongroup==1.2.1
faiss-gpu==1.7.2
frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.77
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.33.0
orjson==3.10.4
packaging==23.2
pydantic==2.7.4
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.30
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

dataclasses-json==0.6.7

distro==1.9.0

exceptiongroup==1.2.1

faiss-gpu==1.7.2

frozenlist==1.4.1

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.77

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.33.0

orjson==3.10.4

packaging==23.2

pydantic==2.7.4

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.30

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install langchain langchain-community langchain-openai faiss-gpu 명령을 실행했다.

chinook.zip