FAISS Archives - 2 중 2 번째 페이지

[PYTHON/LANGCHAIN] Graph 클래스 : print_ascii 메소드를 사용해 그래프 인쇄하기

■ Graph 클래스의 print_ascii 메소드를 사용해 그래프를 인쇄하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                           import load_dotenv
from langchain_community.vectorstores import FAISS
from langchain_openai                 import OpenAIEmbeddings
from langchain_core.prompts           import ChatPromptTemplate
from langchain_openai                 import ChatOpenAI
from langchain_core.runnables         import RunnablePassthrough
from langchain_core.output_parsers    import StrOutputParser

load_dotenv()

faiss = FAISS.from_texts(["harrison worked at kensho"], embedding = OpenAIEmbeddings())

vectorStoreRetriever = faiss.as_retriever()

chatPromptTemplateString = """Answer the question based only on the following context :
{context}

Question : {question}
"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

chatOpenAI = ChatOpenAI()

runnableSequence = {"context" : vectorStoreRetriever, "question" : RunnablePassthrough()} | chatPromptTemplate | chatOpenAI | StrOutputParser()

graph = runnableSequence.get_graph()

graph.print_ascii()

"""
           +---------------------------------+
           | Parallel<context,question>Input |
           +---------------------------------+
                    **               **
                 ***                   ***
               **                         **
+----------------------+              +-------------+
| VectorStoreRetriever |              | Passthrough |
+----------------------+              +-------------+
                    **               **
                      ***         ***
                         **     **
           +----------------------------------+
           | Parallel<context,question>Output |
           +----------------------------------+
                             *
                             *
                             *
                  +--------------------+
                  | ChatPromptTemplate |
                  +--------------------+
                             *
                             *
                             *
                      +------------+
                      | ChatOpenAI |
                      +------------+
                             *
                             *
                             *
                   +-----------------+
                   | StrOutputParser |
                   +-----------------+
                             *
                             *
                             *
                +-----------------------+
                | StrOutputParserOutput |
                +-----------------------+
"""

from dotenv import load_dotenv

from langchain_community.vectorstores import FAISS

from langchain_openai import OpenAIEmbeddings

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import ChatOpenAI

from langchain_core.runnables import RunnablePassthrough

from langchain_core.output_parsers import StrOutputParser

load_dotenv()

faiss = FAISS.from_texts(["harrison worked at kensho"], embedding = OpenAIEmbeddings())

vectorStoreRetriever = faiss.as_retriever()

chatPromptTemplateString = """Answer the question based only on the following context :

{context}

Question : {question}

"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

chatOpenAI = ChatOpenAI()

runnableSequence = {"context" : vectorStoreRetriever, "question" : RunnablePassthrough()} | chatPromptTemplate | chatOpenAI | StrOutputParser()

graph = runnableSequence.get_graph()

graph.print_ascii()

"""

+---------------------------------+

| Parallel<context,question>Input |

+---------------------------------+

** **

*** ***

** **

+----------------------+ +-------------+

| VectorStoreRetriever | | Passthrough |

+----------------------+ +-------------+

** **

*** ***

** **

+----------------------------------+

| Parallel<context,question>Output |

+----------------------------------+

+--------------------+

| ChatPromptTemplate |

+--------------------+

+------------+

| ChatOpenAI |

+------------+

+-----------------+

| StrOutputParser |

+-----------------+

+-----------------------+

| StrOutputParserOutput |

+-----------------------+

"""

▶

[PYTHON/LANGCHAIN] RunnableSequence 클래스 : get_graph 메소드를 사용해 Graph 객체 구하기

■ RunnableSequence 클래스의 get_graph 메소드를 사용해 Graph 객체를 구하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                           import load_dotenv
from langchain_community.vectorstores import FAISS
from langchain_openai                 import OpenAIEmbeddings
from langchain_core.prompts           import ChatPromptTemplate
from langchain_openai                 import ChatOpenAI
from langchain_core.runnables         import RunnablePassthrough
from langchain_core.output_parsers    import StrOutputParser

load_dotenv()

faiss = FAISS.from_texts(["harrison worked at kensho"], embedding = OpenAIEmbeddings())

vectorStoreRetriever = faiss.as_retriever()

chatPromptTemplateString = """Answer the question based only on the following context :
{context}

Question : {question}
"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

chatOpenAI = ChatOpenAI()

runnableSequence = {"context" : vectorStoreRetriever, "question" : RunnablePassthrough()} | chatPromptTemplate | chatOpenAI | StrOutputParser()

graph = runnableSequence.get_graph()

from dotenv import load_dotenv

from langchain_community.vectorstores import FAISS

from langchain_openai import OpenAIEmbeddings

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import ChatOpenAI

from langchain_core.runnables import RunnablePassthrough

from langchain_core.output_parsers import StrOutputParser

load_dotenv()

faiss = FAISS.from_texts(["harrison worked at kensho"], embedding = OpenAIEmbeddings())

vectorStoreRetriever = faiss.as_retriever()

chatPromptTemplateString = """Answer the question based only on the following context :

{context}

Question : {question}

"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

chatOpenAI = ChatOpenAI()

runnableSequence = {"context" : vectorStoreRetriever, "question" : RunnablePassthrough()} | chatPromptTemplate | chatOpenAI | StrOutputParser()

graph = runnableSequence.get_graph()

[PYTHON/LANGCHAIN] RunnablePassthrough 클래스 : 검색기와 함께 사용자 질문을 채팅 프롬프트 템플리트에 전달하기

■ RunnablePassthrough 클래스를 사용해 검색기와 함께 사용자 질문을 채팅 프롬프트 템플리트에 전달하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.

[PYTHON/LANGCHAIN] RunnableParallel 클래스 : itemgetter 객체를 사용해 딕셔너리에서 데이터 추출하기

■ RunnableParallel 클래스에서 itemgetter 객체를 사용해 딕셔너리에서 데이터를 추출하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from operator import itemgetter

from dotenv                           import load_dotenv
from langchain_community.vectorstores import FAISS
from langchain_openai                 import OpenAIEmbeddings
from langchain_core.prompts           import ChatPromptTemplate
from langchain_openai                 import ChatOpenAI
from langchain_core.output_parsers    import StrOutputParser

load_dotenv()

faiss = FAISS.from_texts(["harrison worked at kensho"], embedding = OpenAIEmbeddings())

vectorStoreRetriever = faiss.as_retriever()

chatPromptTemplateString = """Answer the question based only on the following context :
{context}

Question : {question}

Answer in the following language : {language}
"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

chatOpenAI = ChatOpenAI()

chain = (
    # { ... } 부분은 RunnableParallel 클래스로 자동 변환된다.
    {"context" : itemgetter("question") | vectorStoreRetriever, "question" : itemgetter("question"), "language" : itemgetter("language")}
    | chatPromptTemplate
    | chatOpenAI
    | StrOutputParser()
)

responseString = chain.invoke({"question" : "where did harrison work", "language" : "korean"})

print(responseString)

"""
헤리슨은 켄쇼에서 일했습니다.
"""

from operator import itemgetter

from dotenv import load_dotenv

from langchain_community.vectorstores import FAISS

from langchain_openai import OpenAIEmbeddings

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import ChatOpenAI

from langchain_core.output_parsers import StrOutputParser

load_dotenv()

faiss = FAISS.from_texts(["harrison worked at kensho"], embedding = OpenAIEmbeddings())

vectorStoreRetriever = faiss.as_retriever()

chatPromptTemplateString = """Answer the question based only on the following context :

{context}

Question : {question}

Answer in the following language : {language}

"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

chatOpenAI = ChatOpenAI()

chain = (

# { ... } 부분은 RunnableParallel 클래스로 자동 변환된다.

{"context" : itemgetter("question") | vectorStoreRetriever, "question" : itemgetter("question"), "language" : itemgetter("language")}

| chatPromptTemplate

| chatOpenAI

| StrOutputParser()

)

responseString = chain.invoke({"question" : "where did harrison work", "language" : "korean"})

print(responseString)

"""

헤리슨은 켄쇼에서 일했습니다.

"""

[PYTHON/LANGCHAIN] RunnableSequence 클래스 : astream_events 메소드에서 마지막 비스트리밍 단계 이후 비동기 스트리밍 이벤트 수신하기

■ RunnableSequence 클래스의 astream_events 메소드에서 마지막 비스트리밍 단계 이후 비동기 스트리밍 이벤트를 수신하는 방법을 보여준다. ▶ main.py


import asyncio
import os

from langchain_openai              import ChatOpenAI
from langchain_core.output_parsers import JsonOutputParser

async def main():
    os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

    chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

    jsonOutputParser = JsonOutputParser()

    def getCountryNameList(inputDictionary):
        """입력 스트림에서 작동하지 않고 스트리밍을 중단하는 함수이다."""
        if not isinstance(inputDictionary, dict):
            return ""
        if "countries" not in inputDictionary:
            return ""
        countryList = inputDictionary["countries"]
        if not isinstance(countryList, list):
            return ""
        countryNameList = [country.get("name") for country in countryList if isinstance(country, dict)]
        return countryNameList

    runnableSequence = chatOpenAI | jsonOutputParser | getCountryNameList

    requestString = """output a list of the countries france, spain and japan and their populations in JSON format. \
    Use a dict with an outer key of "countries" which contains a list of countries. \
    Each country should have the key `name` and `population`"""

    async for chunkList in runnableSequence.astream(requestString):
        print(chunkList, flush = True)

    eventCount = 0

    async for eventDictionary in runnableSequence.astream_events(requestString, version = "v2"):
        kind = eventDictionary["event"]

        if kind == "on_chat_model_stream":
            content = eventDictionary["data"]["chunk"].content
            print(f"Chat model chunk : {content}", flush = True)
        if kind == "on_parser_stream":
            text = eventDictionary["data"]["chunk"]
            print(f"Parser chunk     : {text}", flush = True)
        eventCount += 1
        if eventCount > 30:
            print("...")
            break

asyncio.run(main())

"""
Chat model chunk :
Chat model chunk : {

Parser chunk     : {}
Chat model chunk :
Chat model chunk :  "
Chat model chunk : countries
Chat model chunk : ":
Chat model chunk :  [

Parser chunk     : {'countries': []}
Chat model chunk :
Chat model chunk :  {

Parser chunk     : {'countries': [{}]}
Chat model chunk :
Chat model chunk :  "
Chat model chunk : name
Chat model chunk : ":
Chat model chunk :  "
Parser chunk     : {'countries': [{'name': ''}]}
Chat model chunk : France
Parser chunk     : {'countries': [{'name': 'France'}]}
Chat model chunk : ",

Chat model chunk :
Chat model chunk :  "
Chat model chunk : population
Chat model chunk : ":
Chat model chunk :
Chat model chunk : 652
...
"""

import asyncio

import os

from langchain_openai import ChatOpenAI

from langchain_core.output_parsers import JsonOutputParser

async def main():

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

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

jsonOutputParser = JsonOutputParser()

def getCountryNameList(inputDictionary):

"""입력 스트림에서 작동하지 않고 스트리밍을 중단하는 함수이다."""

if not isinstance(inputDictionary, dict):

return ""

if "countries" not in inputDictionary:

return ""

countryList = inputDictionary["countries"]

if not isinstance(countryList, list):

return ""

countryNameList = [country.get("name") for country in countryList if isinstance(country, dict)]

return countryNameList

runnableSequence = chatOpenAI | jsonOutputParser | getCountryNameList

requestString = """output a list of the countries france, spain and japan and their populations in JSON format. \

Use a dict with an outer key of "countries" which contains a list of countries. \

Each country should have the key `name` and `population`"""

async for chunkList in runnableSequence.astream(requestString):

print(chunkList, flush = True)

eventCount = 0

async for eventDictionary in runnableSequence.astream_events(requestString, version = "v2"):

kind = eventDictionary["event"]

if kind == "on_chat_model_stream":

content = eventDictionary["data"]["chunk"].content

print(f"Chat model chunk : {content}", flush = True)

if kind == "on_parser_stream":

text = eventDictionary["data"]["chunk"]

print(f"Parser chunk : {text}", flush = True)

eventCount += 1

if eventCount > 30:

print("...")

break

asyncio.run(main())

"""

Chat model chunk :

Chat model chunk : {

Parser chunk : {}

Chat model chunk :

Chat model chunk : "

Chat model chunk : countries

Chat model chunk : ":

Chat model chunk : [

Parser chunk : {'countries': []}

Chat model chunk :

Chat model chunk : {

Parser chunk : {'countries': [{}]}

Chat model chunk :

Chat model chunk : "

Chat model chunk : name

Chat model chunk : ":

Chat model chunk : "

Parser chunk : {'countries': [{'name': ''}]}

Chat model chunk : France

Parser chunk : {'countries': [{'name': 'France'}]}

Chat model chunk : ",

Chat model chunk :

Chat model chunk : "

Chat model chunk : population

Chat model chunk : ":

Chat model chunk :

Chat model chunk : 652

...

"""

▶ 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==0.2.5
langchain-community==0.2.5
langchain-core==0.2.8
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.79
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.34.0
orjson==3.10.5
packaging==24.1
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==8.4.1
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.2
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==0.2.5

langchain-community==0.2.5

langchain-core==0.2.8

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.79

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.34.0

orjson==3.10.5

packaging==24.1

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==8.4.1

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.9.4

※

[PYTHON/LANGCHAIN] RunnableSequence 클래스 : astream_events 메소드에서 include_tags 인자를 사용해 이벤트 필터링하기

■ RunnableSequence 클래스의 astream_events 메소드에서 include_tags 인자를 사용해 이벤트를 필터링하는 방법을 보여준다. ▶ main.py


import asyncio
import os

from langchain_core.prompts           import ChatPromptTemplate
from langchain_openai                 import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.runnables         import RunnablePassthrough
from langchain_openai                 import ChatOpenAI
from langchain_core.output_parsers    import StrOutputParser

async def main():
    os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

    chatPromptTemplateString = """Answer the question based only on the following context :
    {context}

    Question : {question}
    """

    chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

    faiss = FAISS.from_texts(
        ["harrison worked at kensho", "harrison likes spicy food"],
        embedding = OpenAIEmbeddings(),
    )

    vectorStoreRetriever = faiss.as_retriever()

    chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

    runnableSequence = (
        {
            "context"  : vectorStoreRetriever.with_config(run_name = "vectorStoreRetriever"),
            "question" : RunnablePassthrough()
        }
        | chatPromptTemplate
        | (chatOpenAI.with_config(run_name = "chatOpenAI") | StrOutputParser().with_config(run_name = "strOutputParser")).with_config({"tags" : ["chatOpenAI_strOutputParser"]})
    )

    eventCount = 0

    async for eventDictionary in runnableSequence.astream_events(
        "Where did harrison work? Write 3 made up sentences about this place.",
        include_tags = ["chatOpenAI_strOutputParser"],
        version = "v2"):
        print(eventDictionary)
        eventCount += 1
        if eventCount > 10:
            print("...")
            break

asyncio.run(main())

"""
{'event': 'on_chain_start', 'data': {'input': 'Where did harrison work? Write 3 made up sentences about this place.'}, 'name': 'RunnableSequence', 'tags': ['seq:step:3', 'chatOpenAI_strOutputParser'], 'run_id': '8933aa37-93c0-4c63-ae77-1c568c2f5a7a', 'metadata': {}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2']}
{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[HumanMessage(content="Answer the question based only on the following context :\n    [Document(page_content='harrison worked at kensho'), Document(page_content='harrison likes spicy food')]\n\n    Question : Where did harrison work? Write 3 made up sentences about this place.\n    ")]]}}, 'name': 'chatOpenAI', 'tags': ['seq:step:1', 'chatOpenAI_strOutputParser'], 'run_id': '35559cbb-163c-46cf-b278-fbda9d987add', 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', id='run-35559cbb-163c-46cf-b278-fbda9d987add')}, 'run_id': '35559cbb-163c-46cf-b278-fbda9d987add', 'name': 'chatOpenAI', 'tags': ['seq:step:1', 'chatOpenAI_strOutputParser'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}
{'event': 'on_parser_start', 'data': {}, 'name': 'strOutputParser', 'tags': ['seq:step:2', 'chatOpenAI_strOutputParser'], 'run_id': '7c5ca72a-9681-49a0-b33d-23dc26a1e245', 'metadata': {}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}
{'event': 'on_parser_stream', 'run_id': '7c5ca72a-9681-49a0-b33d-23dc26a1e245', 'name': 'strOutputParser', 'tags': ['seq:step:2', 'chatOpenAI_strOutputParser'], 'metadata': {}, 'data': {'chunk': ''}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}
{'event': 'on_chain_stream', 'run_id': '8933aa37-93c0-4c63-ae77-1c568c2f5a7a', 'name': 'RunnableSequence', 'tags': ['seq:step:3', 'chatOpenAI_strOutputParser'], 'metadata': {}, 'data': {'chunk': ''}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='H', id='run-35559cbb-163c-46cf-b278-fbda9d987add')}, 'run_id': '35559cbb-163c-46cf-b278-fbda9d987add', 'name': 'chatOpenAI', 'tags': ['seq:step:1', 'chatOpenAI_strOutputParser'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}
{'event': 'on_parser_stream', 'run_id': '7c5ca72a-9681-49a0-b33d-23dc26a1e245', 'name': 'strOutputParser', 'tags': ['seq:step:2', 'chatOpenAI_strOutputParser'], 'metadata': {}, 'data': {'chunk': 'H'}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}
{'event': 'on_chain_stream', 'run_id': '8933aa37-93c0-4c63-ae77-1c568c2f5a7a', 'name': 'RunnableSequence', 'tags': ['seq:step:3', 'chatOpenAI_strOutputParser'], 'metadata': {}, 'data': {'chunk': 'H'}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='arrison', id='run-35559cbb-163c-46cf-b278-fbda9d987add')}, 'run_id': '35559cbb-163c-46cf-b278-fbda9d987add', 'name': 'chatOpenAI', 'tags': ['seq:step:1', 'chatOpenAI_strOutputParser'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}
{'event': 'on_parser_stream', 'run_id': '7c5ca72a-9681-49a0-b33d-23dc26a1e245', 'name': 'strOutputParser', 'tags': ['seq:step:2', 'chatOpenAI_strOutputParser'], 'metadata': {}, 'data': {'chunk': 'arrison'}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}
...
"""

import asyncio

import os

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import OpenAIEmbeddings

from langchain_community.vectorstores import FAISS

from langchain_core.runnables import RunnablePassthrough

from langchain_openai import ChatOpenAI

from langchain_core.output_parsers import StrOutputParser

async def main():

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

chatPromptTemplateString = """Answer the question based only on the following context :

{context}

Question : {question}

"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

faiss = FAISS.from_texts(

["harrison worked at kensho", "harrison likes spicy food"],

embedding = OpenAIEmbeddings(),

)

vectorStoreRetriever = faiss.as_retriever()

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

runnableSequence = (

{

"context" : vectorStoreRetriever.with_config(run_name = "vectorStoreRetriever"),

"question" : RunnablePassthrough()

}

| chatPromptTemplate

| (chatOpenAI.with_config(run_name = "chatOpenAI") | StrOutputParser().with_config(run_name = "strOutputParser")).with_config({"tags" : ["chatOpenAI_strOutputParser"]})

)

eventCount = 0

async for eventDictionary in runnableSequence.astream_events(

"Where did harrison work? Write 3 made up sentences about this place.",

include_tags = ["chatOpenAI_strOutputParser"],

version = "v2"):

print(eventDictionary)

eventCount += 1

if eventCount > 10:

print("...")

break

asyncio.run(main())

"""

{'event': 'on_chain_start', 'data': {'input': 'Where did harrison work? Write 3 made up sentences about this place.'}, 'name': 'RunnableSequence', 'tags': ['seq:step:3', 'chatOpenAI_strOutputParser'], 'run_id': '8933aa37-93c0-4c63-ae77-1c568c2f5a7a', 'metadata': {}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2']}

{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[HumanMessage(content="Answer the question based only on the following context :\n [Document(page_content='harrison worked at kensho'), Document(page_content='harrison likes spicy food')]\n\n Question : Where did harrison work? Write 3 made up sentences about this place.\n ")]]}}, 'name': 'chatOpenAI', 'tags': ['seq:step:1', 'chatOpenAI_strOutputParser'], 'run_id': '35559cbb-163c-46cf-b278-fbda9d987add', 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', id='run-35559cbb-163c-46cf-b278-fbda9d987add')}, 'run_id': '35559cbb-163c-46cf-b278-fbda9d987add', 'name': 'chatOpenAI', 'tags': ['seq:step:1', 'chatOpenAI_strOutputParser'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}

{'event': 'on_parser_start', 'data': {}, 'name': 'strOutputParser', 'tags': ['seq:step:2', 'chatOpenAI_strOutputParser'], 'run_id': '7c5ca72a-9681-49a0-b33d-23dc26a1e245', 'metadata': {}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}

{'event': 'on_parser_stream', 'run_id': '7c5ca72a-9681-49a0-b33d-23dc26a1e245', 'name': 'strOutputParser', 'tags': ['seq:step:2', 'chatOpenAI_strOutputParser'], 'metadata': {}, 'data': {'chunk': ''}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}

{'event': 'on_chain_stream', 'run_id': '8933aa37-93c0-4c63-ae77-1c568c2f5a7a', 'name': 'RunnableSequence', 'tags': ['seq:step:3', 'chatOpenAI_strOutputParser'], 'metadata': {}, 'data': {'chunk': ''}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='H', id='run-35559cbb-163c-46cf-b278-fbda9d987add')}, 'run_id': '35559cbb-163c-46cf-b278-fbda9d987add', 'name': 'chatOpenAI', 'tags': ['seq:step:1', 'chatOpenAI_strOutputParser'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}

{'event': 'on_parser_stream', 'run_id': '7c5ca72a-9681-49a0-b33d-23dc26a1e245', 'name': 'strOutputParser', 'tags': ['seq:step:2', 'chatOpenAI_strOutputParser'], 'metadata': {}, 'data': {'chunk': 'H'}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}

{'event': 'on_chain_stream', 'run_id': '8933aa37-93c0-4c63-ae77-1c568c2f5a7a', 'name': 'RunnableSequence', 'tags': ['seq:step:3', 'chatOpenAI_strOutputParser'], 'metadata': {}, 'data': {'chunk': 'H'}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='arrison', id='run-35559cbb-163c-46cf-b278-fbda9d987add')}, 'run_id': '35559cbb-163c-46cf-b278-fbda9d987add', 'name': 'chatOpenAI', 'tags': ['seq:step:1', 'chatOpenAI_strOutputParser'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}

{'event': 'on_parser_stream', 'run_id': '7c5ca72a-9681-49a0-b33d-23dc26a1e245', 'name': 'strOutputParser', 'tags': ['seq:step:2', 'chatOpenAI_strOutputParser'], 'metadata': {}, 'data': {'chunk': 'arrison'}, 'parent_ids': ['bf69804f-1597-4a2a-9ad8-faaff7c767c2', '8933aa37-93c0-4c63-ae77-1c568c2f5a7a']}

...

"""

▶ 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==0.2.5
langchain-community==0.2.5
langchain-core==0.2.8
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.79
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.34.0
orjson==3.10.5
packaging==24.1
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==8.4.1
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.2
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==0.2.5

langchain-community==0.2.5

langchain-core==0.2.8

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.79

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.34.0

orjson==3.10.5

packaging==24.1

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==8.4.1

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.9.4

※ pip install langchain

[PYTHON/LANGCHAIN] RunnableSequence 클래스 : astream_events 메소드에서 include_types 인자를 사용해 이벤트 필터링하기

■ RunnableSequence 클래스의 astream_events 메소드에서 include_types 인자를 사용해 이벤트를 필터링하는 방법을 보여준다. ▶ main.py


import asyncio
import os

from langchain_core.prompts           import ChatPromptTemplate
from langchain_openai                 import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.runnables         import RunnablePassthrough
from langchain_openai                 import ChatOpenAI
from langchain_core.output_parsers    import StrOutputParser

async def main():
    os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

    chatPromptTemplateString = """Answer the question based only on the following context :
    {context}

    Question : {question}
    """

    chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

    faiss = FAISS.from_texts(
        ["harrison worked at kensho", "harrison likes spicy food"],
        embedding = OpenAIEmbeddings(),
    )

    vectorStoreRetriever = faiss.as_retriever()

    chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

    runnableSequence = (
        {
            "context"  : vectorStoreRetriever.with_config(run_name = "vectorStoreRetriever"),
            "question" : RunnablePassthrough()
        }
        | chatPromptTemplate
        | chatOpenAI.with_config(run_name = "chatOpenAI")
        | StrOutputParser().with_config(run_name = "strOutputParser")
    )

    eventCount = 0

    async for eventDictionary in runnableSequence.astream_events(
        "Where did harrison work? Write 3 made up sentences about this place.",
        include_types = ["chat_model"],
        version = "v2"):
        print(eventDictionary)
        eventCount += 1
        if eventCount > 10:
            print("...")
            break

asyncio.run(main())

"""
{'event': 'on_chat_model_start', 'data': {'input': 'Where did harrison work? Write 3 made up sentences about this place.'}, 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='H', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='arrison', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' worked', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' at', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' Kens', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='ho', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=',', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' a', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' bustling', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}
...
"""

import asyncio

import os

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import OpenAIEmbeddings

from langchain_community.vectorstores import FAISS

from langchain_core.runnables import RunnablePassthrough

from langchain_openai import ChatOpenAI

from langchain_core.output_parsers import StrOutputParser

async def main():

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

chatPromptTemplateString = """Answer the question based only on the following context :

{context}

Question : {question}

"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

faiss = FAISS.from_texts(

["harrison worked at kensho", "harrison likes spicy food"],

embedding = OpenAIEmbeddings(),

)

vectorStoreRetriever = faiss.as_retriever()

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

runnableSequence = (

{

"context" : vectorStoreRetriever.with_config(run_name = "vectorStoreRetriever"),

"question" : RunnablePassthrough()

}

| chatPromptTemplate

| chatOpenAI.with_config(run_name = "chatOpenAI")

| StrOutputParser().with_config(run_name = "strOutputParser")

)

eventCount = 0

async for eventDictionary in runnableSequence.astream_events(

"Where did harrison work? Write 3 made up sentences about this place.",

include_types = ["chat_model"],

version = "v2"):

print(eventDictionary)

eventCount += 1

if eventCount > 10:

print("...")

break

asyncio.run(main())

"""

{'event': 'on_chat_model_start', 'data': {'input': 'Where did harrison work? Write 3 made up sentences about this place.'}, 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='H', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='arrison', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' worked', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' at', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' Kens', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='ho', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=',', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' a', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' bustling', id='run-ba5d2b4e-a267-407a-b028-c42ccbcb9d63')}, 'run_id': 'ba5d2b4e-a267-407a-b028-c42ccbcb9d63', 'name': 'chatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['be3b9431-47a9-40fd-ac10-4c6dcc29a0d4']}

...

"""

▶ 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==0.2.5
langchain-community==0.2.5
langchain-core==0.2.8
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.79
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.34.0
orjson==3.10.5
packaging==24.1
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==8.4.1
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.2
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==0.2.5

langchain-community==0.2.5

langchain-core==0.2.8

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.79

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.34.0

orjson==3.10.5

packaging==24.1

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==8.4.1

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.9.4

※ pip install langchain

[PYTHON/LANGCHAIN] RunnableSequence 클래스 : astream_events 메소드에서 include_names 인자를 사용해 이벤트 필터링하기

■ RunnableSequence 클래스의 astream_events 메소드에서 include_names 인자를 사용해 이벤트를 필터링하는 방법을 보여준다. ▶ main.py


import asyncio
import os

from langchain_core.prompts           import ChatPromptTemplate
from langchain_openai                 import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.runnables         import RunnablePassthrough
from langchain_openai                 import ChatOpenAI
from langchain_core.output_parsers    import StrOutputParser

async def main():
    os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

    chatPromptTemplateString = """Answer the question based only on the following context :
    {context}

    Question : {question}
    """

    chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

    faiss = FAISS.from_texts(
        ["harrison worked at kensho", "harrison likes spicy food"],
        embedding = OpenAIEmbeddings(),
    )

    vectorStoreRetriever = faiss.as_retriever()

    chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

    runnableSequence = (
        {
            "context"  : vectorStoreRetriever.with_config(run_name = "vectorStoreRetriever"),
            "question" : RunnablePassthrough()
        }
        | chatPromptTemplate
        | chatOpenAI.with_config(run_name = "chatOpenAI")
        | StrOutputParser().with_config(run_name = "strOutputParser")
    )

    eventCount = 0

    async for eventDictionary in runnableSequence.astream_events(
        "Where did harrison work? Write 3 made up sentences about this place.",
        include_names = ["strOutputParser"],
        version = "v2"):
        print(eventDictionary)
        eventCount += 1
        if eventCount > 10:
            print("...")
            break

asyncio.run(main())

"""
{'event': 'on_parser_start', 'data': {'input': 'Where did harrison work? Write 3 made up sentences about this place.'}, 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'metadata': {}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}
{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ''}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}
{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': 'H'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}
{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': 'arrison'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}
{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ' worked'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}
{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ' at'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}
{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ' a'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}
{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ' popular'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}
{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ' restaurant'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}
{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ' called'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}
...
"""

import asyncio

import os

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import OpenAIEmbeddings

from langchain_community.vectorstores import FAISS

from langchain_core.runnables import RunnablePassthrough

from langchain_openai import ChatOpenAI

from langchain_core.output_parsers import StrOutputParser

async def main():

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

chatPromptTemplateString = """Answer the question based only on the following context :

{context}

Question : {question}

"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

faiss = FAISS.from_texts(

["harrison worked at kensho", "harrison likes spicy food"],

embedding = OpenAIEmbeddings(),

)

vectorStoreRetriever = faiss.as_retriever()

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

runnableSequence = (

{

"context" : vectorStoreRetriever.with_config(run_name = "vectorStoreRetriever"),

"question" : RunnablePassthrough()

}

| chatPromptTemplate

| chatOpenAI.with_config(run_name = "chatOpenAI")

| StrOutputParser().with_config(run_name = "strOutputParser")

)

eventCount = 0

async for eventDictionary in runnableSequence.astream_events(

"Where did harrison work? Write 3 made up sentences about this place.",

include_names = ["strOutputParser"],

version = "v2"):

print(eventDictionary)

eventCount += 1

if eventCount > 10:

print("...")

break

asyncio.run(main())

"""

{'event': 'on_parser_start', 'data': {'input': 'Where did harrison work? Write 3 made up sentences about this place.'}, 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'metadata': {}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}

{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ''}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}

{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': 'H'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}

{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': 'arrison'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}

{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ' worked'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}

{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ' at'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}

{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ' a'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}

{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ' popular'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}

{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ' restaurant'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}

{'event': 'on_parser_stream', 'run_id': '99be2832-6fb2-4482-af73-93bb583db3df', 'name': 'strOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ' called'}, 'parent_ids': ['735f3142-4099-43ca-bd0e-c141bd5ab020']}

...

"""

▶ 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==0.2.5
langchain-community==0.2.5
langchain-core==0.2.8
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.79
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.34.0
orjson==3.10.5
packaging==24.1
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==8.4.1
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.2
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==0.2.5

langchain-community==0.2.5

langchain-core==0.2.8

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.79

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.34.0

orjson==3.10.5

packaging==24.1

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==8.4.1

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.9.4

※ pip install langchain

[PYTHON/LANGCHAIN] RunnableSequence 클래스 : astream_events 메소드를 사용해 비동기 스트리밍 이벤트 수신시 이벤트 종류에 따라 처리하기

■ RunnableSequence 클래스의 astream_events 메소드를 사용해 비동기 스트리밍 이벤트 수신시 이벤트 종류에 따라 처리하는 방법을 보여준다. ▶ main.py


import asyncio
import os

from langchain_core.prompts           import ChatPromptTemplate
from langchain_openai                 import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.runnables         import RunnablePassthrough
from langchain_openai                 import ChatOpenAI
from langchain_core.output_parsers    import StrOutputParser

async def main():
    os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

    chatPromptTemplateString = """Answer the question based only on the following context :
    {context}

    Question : {question}
    """

    chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

    faiss = FAISS.from_texts(
        ["harrison worked at kensho", "harrison likes spicy food"],
        embedding = OpenAIEmbeddings(),
    )

    vectorStoreRetriever = faiss.as_retriever()

    chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

    runnableSequence = (
        {
            "context"  : vectorStoreRetriever.with_config(run_name = "vectorStoreRetriever"),
            "question" : RunnablePassthrough()
        }
        | chatPromptTemplate
        | chatOpenAI
        | StrOutputParser()
    )

    eventCount = 0

    async for eventDictionary in runnableSequence.astream_events("Where did harrison work? Write 3 made up sentences about this place.", version = "v2"):
        kind = eventDictionary["event"]
        if kind == "on_chat_model_stream":
            content = eventDictionary["data"]["chunk"].content
            print(f"Chat model chunk : {content}", flush = True)
        if kind == "on_parser_stream":
            text = eventDictionary["data"]["chunk"]
            print(f"Parser chunk     : {text}", flush = True)
        eventCount += 1
        if eventCount > 30:
            print("...")
            break

asyncio.run(main())

"""
Chat model chunk :
Parser chunk     :
Chat model chunk : H
Parser chunk     : H
Chat model chunk : arrison
Parser chunk     : arrison
Chat model chunk :  worked
Parser chunk     :  worked
Chat model chunk :  at
Parser chunk     :  at
Chat model chunk :  a
Parser chunk     :  a
...
"""

import asyncio

import os

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import OpenAIEmbeddings

from langchain_community.vectorstores import FAISS

from langchain_core.runnables import RunnablePassthrough

from langchain_openai import ChatOpenAI

from langchain_core.output_parsers import StrOutputParser

async def main():

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

chatPromptTemplateString = """Answer the question based only on the following context :

{context}

Question : {question}

"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

faiss = FAISS.from_texts(

["harrison worked at kensho", "harrison likes spicy food"],

embedding = OpenAIEmbeddings(),

)

vectorStoreRetriever = faiss.as_retriever()

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

runnableSequence = (

{

"context" : vectorStoreRetriever.with_config(run_name = "vectorStoreRetriever"),

"question" : RunnablePassthrough()

}

| chatPromptTemplate

| chatOpenAI

| StrOutputParser()

)

eventCount = 0

async for eventDictionary in runnableSequence.astream_events("Where did harrison work? Write 3 made up sentences about this place.", version = "v2"):

kind = eventDictionary["event"]

if kind == "on_chat_model_stream":

content = eventDictionary["data"]["chunk"].content

print(f"Chat model chunk : {content}", flush = True)

if kind == "on_parser_stream":

text = eventDictionary["data"]["chunk"]

print(f"Parser chunk : {text}", flush = True)

eventCount += 1

if eventCount > 30:

print("...")

break

asyncio.run(main())

"""

Chat model chunk :

Parser chunk :

Chat model chunk : H

Parser chunk : H

Chat model chunk : arrison

Parser chunk : arrison

Chat model chunk : worked

Parser chunk : worked

Chat model chunk : at

Parser chunk : at

Chat model chunk : a

Parser chunk : a

...

"""

▶ 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==0.2.5
langchain-community==0.2.5
langchain-core==0.2.8
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.79
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.34.0
orjson==3.10.5
packaging==24.1
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==8.4.1
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.2
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==0.2.5

langchain-community==0.2.5

langchain-core==0.2.8

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.79

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.34.0

orjson==3.10.5

packaging==24.1

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==8.4.1

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.9.4

[PYTHON/LANGCHAIN] RunnableSequence 클래스 : astream_events 메소드를 사용해 비동기 스트리밍 이벤트 수신하기

■ RunnableSequence 클래스의 astream_events 메소드를 사용해 비동기 스트리밍 이벤트를 수신하는 방법을 보여준다. ▶ main.py


import asyncio
import os

from langchain_core.prompts           import ChatPromptTemplate
from langchain_openai                 import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.runnables         import RunnablePassthrough
from langchain_openai                 import ChatOpenAI
from langchain_core.output_parsers    import StrOutputParser

async def main():
    os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

    chatPromptTemplateString = """Answer the question based only on the following context :
    {context}

    Question : {question}
    """

    chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

    faiss = FAISS.from_texts(
        ["harrison worked at kensho", "harrison likes spicy food"],
        embedding = OpenAIEmbeddings(),
    )

    vectorStoreRetriever = faiss.as_retriever()

    chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

    runnableSequence = (
        {
            "context"  : vectorStoreRetriever.with_config(run_name = "vectorStoreRetriever"),
            "question" : RunnablePassthrough()
        }
        | chatPromptTemplate
        | chatOpenAI
        | StrOutputParser()
    )

    async for eventDictionary in runnableSequence.astream_events("Where did harrison work? Write 3 made up sentences about this place.", version = "v2"):
        print(eventDictionary)

asyncio.run(main())

"""
{'event': 'on_chain_start', 'data': {'input': 'Where did harrison work? Write 3 made up sentences about this place.'}, 'name': 'RunnableSequence', 'tags': [], 'run_id': 'd897f470-051b-41be-953a-832c5cc06611', 'metadata': {}, 'parent_ids': []}
{'event': 'on_chain_start', 'data': {}, 'name': 'RunnableParallel<context,question>', 'tags': ['seq:step:1'], 'run_id': '98a359fb-0a7b-455e-901a-819e5db32dba', 'metadata': {}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}
{'event': 'on_retriever_start', 'data': {'input': {'query': 'Where did harrison work? Write 3 made up sentences about this place.'}}, 'name': 'Docs', 'tags': ['map:key:context', 'FAISS', 'OpenAIEmbeddings'], 'run_id': '892860bc-24a2-41d2-adc8-1572931c2c8c', 'metadata': {}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611', '98a359fb-0a7b-455e-901a-819e5db32dba']}
{'event': 'on_chain_start', 'data': {}, 'name': 'RunnablePassthrough', 'tags': ['map:key:question'], 'run_id': '5343e2d7-1d30-44c8-b599-bf3a11170ffc', 'metadata': {}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611', '98a359fb-0a7b-455e-901a-819e5db32dba']}
{'event': 'on_chain_stream', 'run_id': '5343e2d7-1d30-44c8-b599-bf3a11170ffc', 'name': 'RunnablePassthrough', 'tags': ['map:key:question'], 'metadata': {}, 'data': {'chunk': 'Where did harrison work? Write 3 made up sentences about this place.'}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611', '98a359fb-0a7b-455e-901a-819e5db32dba']}
{'event': 'on_chain_stream', 'run_id': '98a359fb-0a7b-455e-901a-819e5db32dba', 'name': 'RunnableParallel<context,question>', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': {'question': 'Where did harrison work? Write 3 made up sentences about this place.'}}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}
{'event': 'on_chain_end', 'data': {'output': 'Where did harrison work? Write 3 made up sentences about this place.', 'input': 'Where did harrison work? Write 3 made up sentences about this place.'}, 'run_id': '5343e2d7-1d30-44c8-b599-bf3a11170ffc', 'name': 'RunnablePassthrough', 'tags': ['map:key:question'], 'metadata': {}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611', '98a359fb-0a7b-455e-901a-819e5db32dba']}
{'event': 'on_retriever_end', 'data': {'output': [Document(page_content='harrison worked at kensho'), Document(page_content='harrison likes spicy food')], 'input': {'query': 'Where did harrison work? Write 3 made up sentences about this place.'}}, 'run_id': '892860bc-24a2-41d2-adc8-1572931c2c8c', 'name': 'Docs', 'tags': ['map:key:context', 'FAISS', 'OpenAIEmbeddings'], 'metadata': {}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611', '98a359fb-0a7b-455e-901a-819e5db32dba']}
{'event': 'on_chain_stream', 'run_id': '98a359fb-0a7b-455e-901a-819e5db32dba', 'name': 'RunnableParallel<context,question>', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': {'context': [Document(page_content='harrison worked at kensho'), Document(page_content='harrison likes spicy food')]}}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}

...

{'event': 'on_chain_stream', 'run_id': 'd897f470-051b-41be-953a-832c5cc06611', 'name': 'RunnableSequence', 'tags': [], 'metadata': {}, 'data': {'chunk': ' customers'}, 'parent_ids': []}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='.', id='run-8fecc191-56e5-4860-b876-6da901a96e11')}, 'run_id': '8fecc191-56e5-4860-b876-6da901a96e11', 'name': 'ChatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}
{'event': 'on_parser_stream', 'run_id': '6959f904-9b48-42d4-8859-d9c1ae0c0b2e', 'name': 'StrOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': '.'}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}
{'event': 'on_chain_stream', 'run_id': 'd897f470-051b-41be-953a-832c5cc06611', 'name': 'RunnableSequence', 'tags': [], 'metadata': {}, 'data': {'chunk': '.'}, 'parent_ids': []}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', response_metadata={'finish_reason': 'stop'}, id='run-8fecc191-56e5-4860-b876-6da901a96e11')}, 'run_id': '8fecc191-56e5-4860-b876-6da901a96e11', 'name': 'ChatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}
{'event': 'on_parser_stream', 'run_id': '6959f904-9b48-42d4-8859-d9c1ae0c0b2e', 'name': 'StrOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ''}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}
{'event': 'on_chain_stream', 'run_id': 'd897f470-051b-41be-953a-832c5cc06611', 'name': 'RunnableSequence', 'tags': [], 'metadata': {}, 'data': {'chunk': ''}, 'parent_ids': []}
{'event': 'on_chat_model_end', 'data': {'output': AIMessageChunk(content='Harrison worked at Kensho, a trendy restaurant known for its fusion cuisine. The atmosphere at Kensho is lively and welcoming, with a modern decor that attracts a hip crowd. The menu at Kensho features a variety of dishes from around the world, all prepared with a spicy twist to cater to the adventurous palate of its customers.', response_metadata={'finish_reason': 'stop'}, id='run-8fecc191-56e5-4860-b876-6da901a96e11'), 'input': {'messages': [[HumanMessage(content="Answer the question based only on the following context :\n    [Document(page_content='harrison worked at kensho'), Document(page_content='harrison likes spicy food')]\n\n    Question : Where did harrison work? Write 3 made up sentences about this place.\n    ")]]}}, 'run_id': '8fecc191-56e5-4860-b876-6da901a96e11', 'name': 'ChatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}
{'event': 'on_parser_end', 'data': {'output': 'Harrison worked at Kensho, a trendy restaurant known for its fusion cuisine. The atmosphere at Kensho is lively and welcoming, with a modern decor that attracts a hip crowd. The menu at Kensho features a variety of dishes from around the world, all prepared with a spicy twist to cater to the adventurous palate of its customers.', 'input': AIMessageChunk(content='Harrison worked at Kensho, a trendy restaurant known for its fusion cuisine. The atmosphere at Kensho is lively and welcoming, with a modern decor that attracts a hip crowd. The menu at Kensho features a variety of dishes from around the world, all prepared with a spicy twist to cater to the adventurous palate of its customers.', response_metadata={'finish_reason': 'stop'}, id='run-8fecc191-56e5-4860-b876-6da901a96e11')}, 'run_id': '6959f904-9b48-42d4-8859-d9c1ae0c0b2e', 'name': 'StrOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}
{'event': 'on_chain_end', 'data': {'output': 'Harrison worked at Kensho, a trendy restaurant known for its fusion cuisine. The atmosphere at Kensho is lively and welcoming, with a modern decor that attracts a hip crowd. The menu at Kensho features a variety of dishes from around the world, all prepared with a spicy twist to cater to the adventurous palate of its customers.'}, 'run_id': 'd897f470-051b-41be-953a-832c5cc06611', 'name': 'RunnableSequence', 'tags': [], 'metadata': {}, 'parent_ids': []}
"""

import asyncio

import os

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import OpenAIEmbeddings

from langchain_community.vectorstores import FAISS

from langchain_core.runnables import RunnablePassthrough

from langchain_openai import ChatOpenAI

from langchain_core.output_parsers import StrOutputParser

async def main():

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

chatPromptTemplateString = """Answer the question based only on the following context :

{context}

Question : {question}

"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

faiss = FAISS.from_texts(

["harrison worked at kensho", "harrison likes spicy food"],

embedding = OpenAIEmbeddings(),

)

vectorStoreRetriever = faiss.as_retriever()

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

runnableSequence = (

{

"context" : vectorStoreRetriever.with_config(run_name = "vectorStoreRetriever"),

"question" : RunnablePassthrough()

}

| chatPromptTemplate

| chatOpenAI

| StrOutputParser()

)

async for eventDictionary in runnableSequence.astream_events("Where did harrison work? Write 3 made up sentences about this place.", version = "v2"):

print(eventDictionary)

asyncio.run(main())

"""

{'event': 'on_chain_start', 'data': {'input': 'Where did harrison work? Write 3 made up sentences about this place.'}, 'name': 'RunnableSequence', 'tags': [], 'run_id': 'd897f470-051b-41be-953a-832c5cc06611', 'metadata': {}, 'parent_ids': []}

{'event': 'on_chain_start', 'data': {}, 'name': 'RunnableParallel<context,question>', 'tags': ['seq:step:1'], 'run_id': '98a359fb-0a7b-455e-901a-819e5db32dba', 'metadata': {}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}

{'event': 'on_retriever_start', 'data': {'input': {'query': 'Where did harrison work? Write 3 made up sentences about this place.'}}, 'name': 'Docs', 'tags': ['map:key:context', 'FAISS', 'OpenAIEmbeddings'], 'run_id': '892860bc-24a2-41d2-adc8-1572931c2c8c', 'metadata': {}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611', '98a359fb-0a7b-455e-901a-819e5db32dba']}

{'event': 'on_chain_start', 'data': {}, 'name': 'RunnablePassthrough', 'tags': ['map:key:question'], 'run_id': '5343e2d7-1d30-44c8-b599-bf3a11170ffc', 'metadata': {}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611', '98a359fb-0a7b-455e-901a-819e5db32dba']}

{'event': 'on_chain_stream', 'run_id': '5343e2d7-1d30-44c8-b599-bf3a11170ffc', 'name': 'RunnablePassthrough', 'tags': ['map:key:question'], 'metadata': {}, 'data': {'chunk': 'Where did harrison work? Write 3 made up sentences about this place.'}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611', '98a359fb-0a7b-455e-901a-819e5db32dba']}

{'event': 'on_chain_stream', 'run_id': '98a359fb-0a7b-455e-901a-819e5db32dba', 'name': 'RunnableParallel<context,question>', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': {'question': 'Where did harrison work? Write 3 made up sentences about this place.'}}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}

{'event': 'on_chain_end', 'data': {'output': 'Where did harrison work? Write 3 made up sentences about this place.', 'input': 'Where did harrison work? Write 3 made up sentences about this place.'}, 'run_id': '5343e2d7-1d30-44c8-b599-bf3a11170ffc', 'name': 'RunnablePassthrough', 'tags': ['map:key:question'], 'metadata': {}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611', '98a359fb-0a7b-455e-901a-819e5db32dba']}

{'event': 'on_retriever_end', 'data': {'output': [Document(page_content='harrison worked at kensho'), Document(page_content='harrison likes spicy food')], 'input': {'query': 'Where did harrison work? Write 3 made up sentences about this place.'}}, 'run_id': '892860bc-24a2-41d2-adc8-1572931c2c8c', 'name': 'Docs', 'tags': ['map:key:context', 'FAISS', 'OpenAIEmbeddings'], 'metadata': {}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611', '98a359fb-0a7b-455e-901a-819e5db32dba']}

{'event': 'on_chain_stream', 'run_id': '98a359fb-0a7b-455e-901a-819e5db32dba', 'name': 'RunnableParallel<context,question>', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': {'context': [Document(page_content='harrison worked at kensho'), Document(page_content='harrison likes spicy food')]}}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}

...

{'event': 'on_chain_stream', 'run_id': 'd897f470-051b-41be-953a-832c5cc06611', 'name': 'RunnableSequence', 'tags': [], 'metadata': {}, 'data': {'chunk': ' customers'}, 'parent_ids': []}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='.', id='run-8fecc191-56e5-4860-b876-6da901a96e11')}, 'run_id': '8fecc191-56e5-4860-b876-6da901a96e11', 'name': 'ChatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}

{'event': 'on_parser_stream', 'run_id': '6959f904-9b48-42d4-8859-d9c1ae0c0b2e', 'name': 'StrOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': '.'}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}

{'event': 'on_chain_stream', 'run_id': 'd897f470-051b-41be-953a-832c5cc06611', 'name': 'RunnableSequence', 'tags': [], 'metadata': {}, 'data': {'chunk': '.'}, 'parent_ids': []}

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', response_metadata={'finish_reason': 'stop'}, id='run-8fecc191-56e5-4860-b876-6da901a96e11')}, 'run_id': '8fecc191-56e5-4860-b876-6da901a96e11', 'name': 'ChatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}

{'event': 'on_parser_stream', 'run_id': '6959f904-9b48-42d4-8859-d9c1ae0c0b2e', 'name': 'StrOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'data': {'chunk': ''}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}

{'event': 'on_chain_stream', 'run_id': 'd897f470-051b-41be-953a-832c5cc06611', 'name': 'RunnableSequence', 'tags': [], 'metadata': {}, 'data': {'chunk': ''}, 'parent_ids': []}

{'event': 'on_chat_model_end', 'data': {'output': AIMessageChunk(content='Harrison worked at Kensho, a trendy restaurant known for its fusion cuisine. The atmosphere at Kensho is lively and welcoming, with a modern decor that attracts a hip crowd. The menu at Kensho features a variety of dishes from around the world, all prepared with a spicy twist to cater to the adventurous palate of its customers.', response_metadata={'finish_reason': 'stop'}, id='run-8fecc191-56e5-4860-b876-6da901a96e11'), 'input': {'messages': [[HumanMessage(content="Answer the question based only on the following context :\n [Document(page_content='harrison worked at kensho'), Document(page_content='harrison likes spicy food')]\n\n Question : Where did harrison work? Write 3 made up sentences about this place.\n ")]]}}, 'run_id': '8fecc191-56e5-4860-b876-6da901a96e11', 'name': 'ChatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_model_type': 'chat', 'ls_temperature': 0.7}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}

{'event': 'on_parser_end', 'data': {'output': 'Harrison worked at Kensho, a trendy restaurant known for its fusion cuisine. The atmosphere at Kensho is lively and welcoming, with a modern decor that attracts a hip crowd. The menu at Kensho features a variety of dishes from around the world, all prepared with a spicy twist to cater to the adventurous palate of its customers.', 'input': AIMessageChunk(content='Harrison worked at Kensho, a trendy restaurant known for its fusion cuisine. The atmosphere at Kensho is lively and welcoming, with a modern decor that attracts a hip crowd. The menu at Kensho features a variety of dishes from around the world, all prepared with a spicy twist to cater to the adventurous palate of its customers.', response_metadata={'finish_reason': 'stop'}, id='run-8fecc191-56e5-4860-b876-6da901a96e11')}, 'run_id': '6959f904-9b48-42d4-8859-d9c1ae0c0b2e', 'name': 'StrOutputParser', 'tags': ['seq:step:4'], 'metadata': {}, 'parent_ids': ['d897f470-051b-41be-953a-832c5cc06611']}

{'event': 'on_chain_end', 'data': {'output': 'Harrison worked at Kensho, a trendy restaurant known for its fusion cuisine. The atmosphere at Kensho is lively and welcoming, with a modern decor that attracts a hip crowd. The menu at Kensho features a variety of dishes from around the world, all prepared with a spicy twist to cater to the adventurous palate of its customers.'}, 'run_id': 'd897f470-051b-41be-953a-832c5cc06611', 'name': 'RunnableSequence', 'tags': [], 'metadata': {}, 'parent_ids': []}

"""

▶ 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==0.2.5
langchain-community==0.2.5
langchain-core==0.2.8
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.79
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.34.0
orjson==3.10.5
packaging==24.1
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==8.4.1
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.2
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==0.2.5

langchain-community==0.2.5

langchain-core==0.2.8

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.79

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.34.0

orjson==3.10.5

packaging==24.1

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==8.4.1

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.9.4

※ pip install langchain

[PYTHON/LANGCHAIN] create_retriever_tool 함수 : FAISS 벡터 스토어 검색 도구 만들기 1

■ create_retriever_tool 함수를 사용해 FAISS 벡터 스토어 검색 도구를 만드는 방법을 보여준다. ▶ 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
from langchain.agents.agent_toolkits  import create_retriever_tool

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())

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

# FAISS 벡터 스토어 검색기 도구를 생성한다.
faissVectorStoreRetrieverTool = create_retriever_tool(
    faissVectorStoreRetriever,
    name        = "search_proper_nouns",
    description = "Use to look up values to filter on. Input is an approximate spelling of the proper noun, output is valid proper nouns. Use the noun most similar to the search."
)

# FAISS 벡터 스토어 검색기 도구를 실행한다.
resultString = faissVectorStoreRetrieverTool.invoke("Alice Chains")

print(resultString)

"""
Alice In Chains

Alanis Morissette

Pearl Jam

Pearl Jam

Audioslave
"""

import ast

import os

import re

from langchain_community.utilities import SQLDatabase

from langchain_community.vectorstores import FAISS

from langchain_openai import OpenAIEmbeddings

from langchain.agents.agent_toolkits import create_retriever_tool

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())

# FAISS 벡터 스토어 검색기를 설정한다.

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

# FAISS 벡터 스토어 검색기 도구를 생성한다.

faissVectorStoreRetrieverTool = create_retriever_tool(

faissVectorStoreRetriever,

name = "search_proper_nouns",

description = "Use to look up values to filter on. Input is an approximate spelling of the proper noun, output is valid proper nouns. Use the noun most similar to the search."

)

# FAISS 벡터 스토어 검색기 도구를 실행한다.

resultString = faissVectorStoreRetrieverTool.invoke("Alice Chains")

print(resultString)

"""

Alice In Chains

Alanis Morissette

Pearl Jam

Audioslave

"""

▶ 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

[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.

[PYTHON/LANGCHAIN] from_texts 함수 : FAISS 벡터 스토어 생성하기

■ from_texts 함수를 사용해 FAISS 벡터 스토어를 생성하는 방법을 보여준다. ▶ 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())

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())

▶ 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

[PYTHON/COMMON] llama-index-vector-stores-faiss 패키지 설치하기

■ llama-index-vector-stores-faiss 패키지를 설치하는 방법을 보여준다. 1. 명령 프롬프트를 실행한다. 2. 명령 프롬프트에서 아래 명령을 실행한다. ▶ 실행 명령


pip install llama-index-vector-stores-faiss

pip install llama-index-vector-stores-faiss

[PYTHON/OPENAI] OpenAI 클래스 : embeddings 변수를 사용해 유사도 검색하기 (FAISS 벡터 데이터베이스 사용)

■ OpenAI 클래스의 embeddings 변수를 사용해 유사도를 검색하는 방법을 보여준다. (FAISS 벡터 데이터베이스 사용) ▶ 예제 코드 (PY)


import os
import numpy

import faiss

from openai import OpenAI

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

openAI = OpenAI()

# 입력 텍스트의 텍스트 임베딩을 생성한다.
inputText = "오늘은 비가 오지 않아서 다행이다."

response = openAI.embeddings.create(
    input = inputText,
    model = "text-embedding-ada-002"
)

inputEmbeddingList    = [embedding.embedding for embedding in response.data] # inputEmbeddingList의 항목은 List 타입이다.
inputEmbeddingNDArray = numpy.array(inputEmbeddingList).astype("float32")    # 2차원 배열 : (1, 1536)

# 대상 텍스트의 텍스트 임베딩을 생성한다.
targetTextList = [
    "좋아하는 음식은 무엇인가요?",
    "어디에 살고 계신가요?",
    "아침 전철은 혼잡하네요.",
    "오늘은 날씨가 좋네요.",
    "요즘 경기가 좋지 않네요."
]

response = openAI.embeddings.create(
    input = targetTextList,
    model = "text-embedding-ada-002"
)

targetEmbeddingList    = [embedding.embedding for embedding in response.data] # targetEmbeddingList의 항목은 List 타입이다.
targetEmbeddingNDArray = numpy.array(targetEmbeddingList).astype("float32")   # 2차원 배열 : (5, 1536)

# Faiss의 인덱스를 생성한다.
indexFlatL2 = faiss.IndexFlatL2(targetEmbeddingNDArray.shape[1])

# 대상 텍스트의 임베딩 2차원 배열값을 인덱스에 추가한다.
indexFlatL2.add(targetEmbeddingNDArray)

# 유사도 검사를 수행한다.
distanceList, indexList = indexFlatL2.search(inputEmbeddingNDArray, 1)

print(distanceList)
print(indexList)
print(targetTextList[indexList[0][0]])

"""
[[0.28370145]]
[[3]]
오늘은 날씨가 좋네요.
"""

import os

import numpy

import faiss

from openai import OpenAI

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

openAI = OpenAI()

# 입력 텍스트의 텍스트 임베딩을 생성한다.

inputText = "오늘은 비가 오지 않아서 다행이다."

response = openAI.embeddings.create(

input = inputText,

model = "text-embedding-ada-002"

)

inputEmbeddingList = [embedding.embedding for embedding in response.data] # inputEmbeddingList의 항목은 List 타입이다.

inputEmbeddingNDArray = numpy.array(inputEmbeddingList).astype("float32") # 2차원 배열 : (1, 1536)

# 대상 텍스트의 텍스트 임베딩을 생성한다.

targetTextList = [

"좋아하는 음식은 무엇인가요?",

"어디에 살고 계신가요?",

"아침 전철은 혼잡하네요.",

"오늘은 날씨가 좋네요.",

"요즘 경기가 좋지 않네요."

]

response = openAI.embeddings.create(

input = targetTextList,

model = "text-embedding-ada-002"

)

targetEmbeddingList = [embedding.embedding for embedding in response.data] # targetEmbeddingList의 항목은 List 타입이다.

targetEmbeddingNDArray = numpy.array(targetEmbeddingList).astype("float32") # 2차원 배열 : (5, 1536)

# Faiss의 인덱스를 생성한다.

indexFlatL2 = faiss.IndexFlatL2(targetEmbeddingNDArray.shape[1])

# 대상 텍스트의 임베딩 2차원 배열값을 인덱스에 추가한다.

indexFlatL2.add(targetEmbeddingNDArray)

# 유사도 검사를 수행한다.

distanceList, indexList = indexFlatL2.search(inputEmbeddingNDArray, 1)

print(distanceList)

print(indexList)

print(targetTextList[indexList[0][0]])

"""

[[0.28370145]]

[[3]]

오늘은 날씨가 좋네요.

"""

▶ requirements.txt


annotated-types==0.7.0
anyio==4.4.0
certifi==2024.6.2
distro==1.9.0
exceptiongroup==1.2.1
faiss-gpu==1.7.2
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
numpy==1.26.4
openai==1.31.1
packaging==24.0
pydantic==2.7.3
pydantic_core==2.18.4
sniffio==1.3.1
tqdm==4.66.4
typing_extensions==4.12.1

annotated-types==0.7.0

anyio==4.4.0

certifi==2024.6.2

distro==1.9.0

exceptiongroup==1.2.1

faiss-gpu==1.7.2

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

numpy==1.26.4

openai==1.31.1

packaging==24.0

pydantic==2.7.3

pydantic_core==2.18.4

sniffio==1.3.1

tqdm==4.66.4

typing_extensions==4.12.1

[PYTHON/COMMON] faiss 패키지 설치하기

■ faiss 패키지를 설치하는 방법을 보여준다. 1. 명령 프롬프트를 실행한다. 2. 명령 프롬프트에서 아래 명령을 실행한다. ▶ 실행 명령


pip install faiss-gpu

또는

pip install faiss-cpu

pip install faiss-gpu

또는

pip install faiss-cpu