LangChain x Flyte

langchain_flyte_pipeline.py

import json
import os
from functools import partial

import flytekit
from flytekit import ImageSpec, Resources, Secret, map_task, task, workflow

embed_image = ImageSpec(
    name="langchain-flyte-vectordb",
    packages=[
        "langchain",
        "pinecone-client",
        "huggingface_hub",
        "sentence_transformers",
        "yt_dlp",
        "pydub",
        "openai",
    ],
    apt_packages=["ffmpeg"],
    registry="ghcr.io/samhita-alla",
    base_image="ghcr.io/flyteorg/flytekit:py3.11-1.7.0",
)

query_image = ImageSpec(
    name="langchain-flyte-query",
    packages=[
        "langchain",
        "pinecone-client",
        "huggingface_hub",
        "sentence_transformers",
        "openai",
        "spacy",
        "textstat",
        "https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0.tar.gz",
    ],
    registry="ghcr.io/samhita-alla",
    base_image="ghcr.io/flyteorg/flytekit:py3.11-1.7.0",
)

SECRET_GROUP = "arn:aws:secretsmanager:us-east-2:356633062068:secret"
SECRET_KEY = "flyte_langchain-YtD8OW"


@task(
    cache=True,
    cache_version="1.0",
    secret_requests=[
        Secret(
            group=SECRET_GROUP,
            key=SECRET_KEY,
            mount_requirement=Secret.MountType.FILE,
        ),
    ],
    container_image=embed_image,
    requests=Resources(mem="5Gi"),
)
def embed_and_store(url: str, index_name: str) -> str:
    import openai
    import pinecone
    from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
    from langchain.document_loaders.generic import GenericLoader
    from langchain.document_loaders.parsers import OpenAIWhisperParser
    from langchain.embeddings import HuggingFaceEmbeddings
    from langchain.text_splitter import RecursiveCharacterTextSplitter
    from langchain.vectorstores import Pinecone

    pinecone.init(
        api_key=json.loads(
            flytekit.current_context().secrets.get(SECRET_GROUP, SECRET_KEY)
        )["pinecone_api_key"],
        environment=json.loads(
            flytekit.current_context().secrets.get(SECRET_GROUP, SECRET_KEY)
        )["pinecone_environment"],
    )
    openai.api_key = json.loads(
        flytekit.current_context().secrets.get(SECRET_GROUP, SECRET_KEY)
    )["openai_api_key"]

    # Directory to save audio files
    save_dir = os.path.join(flytekit.current_context().working_directory, "youtube")

    # Transcribe the videos to text
    loader = GenericLoader(YoutubeAudioLoader([url], save_dir), OpenAIWhisperParser())
    docs = loader.load()

    combined_docs = [doc.page_content for doc in docs]
    text = " ".join(combined_docs)

    # Split them
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)
    splits = text_splitter.split_text(text)

    huggingface_embeddings = HuggingFaceEmbeddings(
        cache_folder=os.path.join(
            flytekit.current_context().working_directory, "embeddings-cache-folder"
        )
    )

    Pinecone.from_texts(
        texts=splits, embedding=huggingface_embeddings, index_name=index_name
    )
    return f"{url} data is stored in the vectordb."


@task(
    disable_deck=False,
    secret_requests=[
        Secret(
            group=SECRET_GROUP,
            key=SECRET_KEY,
            mount_requirement=Secret.MountType.FILE,
        ),
    ],
    container_image=query_image,
    requests=Resources(mem="5Gi"),
)
def query_vectordb(index_name: str, query: str) -> str:
    import pinecone
    from langchain.callbacks import FlyteCallbackHandler
    from langchain.chains import RetrievalQA
    from langchain.chat_models import ChatOpenAI
    from langchain.embeddings import HuggingFaceEmbeddings
    from langchain.vectorstores import Pinecone

    pinecone.init(
        api_key=json.loads(
            flytekit.current_context().secrets.get(SECRET_GROUP, SECRET_KEY)
        )["pinecone_api_key"],
        environment=json.loads(
            flytekit.current_context().secrets.get(SECRET_GROUP, SECRET_KEY)
        )["pinecone_environment"],
    )

    huggingface_embeddings = HuggingFaceEmbeddings(
        cache_folder=os.path.join(
            flytekit.current_context().working_directory, "embeddings-cache-folder"
        )
    )

    vectordb = Pinecone.from_existing_index(index_name, huggingface_embeddings)
    retriever = vectordb.as_retriever(search_type="similarity", search_kwargs={"k": 2})

    qa_chain = RetrievalQA.from_chain_type(
        llm=ChatOpenAI(
            model_name="gpt-3.5-turbo",
            callbacks=[FlyteCallbackHandler()],
            temperature=0,
            openai_api_key=json.loads(
                flytekit.current_context().secrets.get(SECRET_GROUP, SECRET_KEY)
            )["openai_api_key"],
        ),
        chain_type="stuff",
        retriever=retriever,
    )

    result = qa_chain.run(query)
    return result


@workflow
def flyte_youtube_embed_wf(
    index_name: str = "flyte-youtube-data",
    urls: list[str] = [
        "https://youtu.be/CNmO1q3MamM",
        "https://youtu.be/8rLj_YVOpzE",
        "https://youtu.be/sGqS8PFQz6c",
        "https://youtu.be/1668vZczslw",
        "https://youtu.be/NrFOXQKrREA",
        "https://youtu.be/4ktHNeT8kq4",
        "https://youtu.be/gMyTz8gKWVc",
    ],
) -> list[str]:
    partial_embed_and_store = partial(embed_and_store, index_name=index_name)
    return map_task(partial_embed_and_store)(url=urls)