I apologize for the confusion. Here's the revised Python script that demonstrates how to generate dense and sparse embeddings, and upsert them together to Pinecone in one call:
```python
import os
import json
from sentence_transformers import SentenceTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
import pinecone

# Load your transcripts dataset
def load_transcripts(file_path):
    with open(file_path, "r") as file:
        data = json.load(file)
    return data

# Get dense embeddings using a pre-trained Sentence Transformer model
def get_dense_embeddings(transcripts, model_name="paraphrase-distilroberta-base-v1"):
    model = SentenceTransformer(model_name)
    embeddings = model.encode(transcripts)
    return embeddings

# Get sparse embeddings using TfidfVectorizer
def get_sparse_embeddings(transcripts):
    vectorizer = TfidfVectorizer()
    embeddings = vectorizer.fit_transform(transcripts)
    return embeddings

# Initialize Pinecone
def init_pinecone(api_key):
    pinecone.deinit()
    pinecone.init(api_key=api_key)

# Create a Pinecone index
def create_pinecone_index(index_name):
    pinecone.deinit()
    pinecone.init(api_key="YOUR_API_KEY")
    pinecone.create_index(index_name=index_name, metric="cosine")

# Upsert dense and sparse embeddings to Pinecone index
def upsert_embeddings(index_name, ids, dense_embeddings, sparse_embeddings):
    with pinecone.Client(index_name=index_name) as client:
        for i, (dense_embedding, sparse_embedding) in enumerate(zip(dense_embeddings, sparse_embeddings)):
            sparse_values = {
                "indices": sparse_embedding.indices.tolist(),
                "values": sparse_embedding.data.tolist()
            }
            client.upsert(id=ids[i], values=dense_embedding, sparse_values=sparse_values)

# Perform semantic search
def search(index_name, query, dense_model, vectorizer, k=5):
    dense_query_embedding = dense_model.encode([query])
    sparse_query_embedding = vectorizer.transform([query])
    with pinecone.Client(index_name=index_name) as client:
        results = client.fetch(ids=client.query(queries=dense_query_embedding, sparse_queries=sparse_query_embedding, top_k=k))
    return results

# Main
if __name__ == "__main__":
    # Load transcripts
    transcripts = load_transcripts("path/to/transcripts.json")
    transcripts_text = [t["text"] for t in transcripts]

    # Generate dense embeddings
    dense_embeddings = get_dense_embeddings(transcripts_text)

    # Generate sparse embeddings
    vectorizer = TfidfVectorizer()
    sparse_embeddings = vectorizer.fit_transform(transcripts_text)

    # Initialize Pinecone
    init_pinecone("YOUR_API_KEY")

    # Create Pinecone index
    index_name = "podcast-transcripts"
    create_pinecone_index(index_name)

    # Upsert dense and sparse embeddings to Pinecone index
    ids = [str(i) for i in range(len(transcripts))]
    upsert_embeddings(index_name, ids, dense_embeddings, sparse_embeddings)

    # Load Sentence Transformer model for search
    search_model = SentenceTransformer("paraphrase-distilroberta-base-v1")

    # Example search
    query = "machine learning and artificial intelligence"
    top_k_results = search(index_name, query, search_model, vectorizer, k=5)

    for score, index in top_k_results.items():
        i = int(index)
        print(f"Episode: {transcripts[i]['episode_title']}\nTranscript: {transcripts_text[i]}\n")
```
Replace `"path/to/transcripts.json"` with the path to your transcripts dataset, and replace `"YOUR\_API
혼란을 드려 죄송합니다. 다음은 밀집 임베딩과 스파스 임베딩을 생성하고 이를 한 번의 호출로 Pinecone에 함께 업서트하는 방법을 보여주는 수정된 Python 스크립트입니다:
'''python
import os
import json
에서 문장_변환기에서 문장_변환기 가져오기
sklearn.feature_extraction.text에서 TfidfVectorizer를 가져옵니다.
import pinecone

# 성적 증명서 데이터 세트 로드
def load_transcripts(file_path):
    with open(file_path, "r") as file:
        data = json.load(file)
    반환 데이터

# 사전 학습된 문장 트랜스포머 모델을 사용하여 고밀도 임베딩 가져오기
def get_dense_embeddings(transcripts, model_name="paraphrase-distilroberta-base-v1"):
    모델 = 문장 트랜스포머(모델_이름)
    embeddings = model.encode(트랜스크립트)
    임베딩 반환

# TfidfVectorizer를 사용하여 스파스 임베딩 가져오기
def get_sparse_embeddings(transcripts):
    벡터라이저 = TfidfVectorizer()
    embeddings = 벡터라이저.fit_transform(트랜스크립트)
    임베딩 반환

# 파인콘 초기화
def init_pinecone(api_key):
    pinecone.deinit()
    pinecone.init(api_key=api_key)

# 파인콘 인덱스 생성
def create_pinecone_index(index_name):
    pinecone.deinit()
    pinecone.init(api_key="YOUR_API_KEY")
    pinecone.create_index(index_name=index_name, metric="코사인")

# 핀콘 인덱스에 조밀하고 희박한 임베딩을 업서트합니다.
def upsert_embeddings(index_name, ids, dense_embeddings, sparse_embeddings):
    with pinecone.Client(index_name=index_name) as client:
        for i, (dense_embedding, sparse_embedding) in enumerate(zip(dense_embedding, sparse_embedding)):
            sparse_values = {
                "인덱스": sparse_embedding.indices.tolist(),
                "values": sparse_embedding.data.tolist()
            }
            client.upsert(id=ids[i], values=dense_embedding, sparse_values=sparse_values)

# 시맨틱 검색 수행
def search(index_name, query, dense_model, vectorizer, k=5):
    dense_query_embedding = dense_model.encode([query])
    sparse_query_embedding = 벡터라이저.transform([쿼리])
    핀콘.클라이언트(index_name=index_name)를 클라이언트로 사용합니다:
        results = client.fetch(ids=client.query(queries=dense_query_embedding, sparse_queries=sparse_query_embedding, top_k=k))
    결과 반환

# 메인
if __name__ == "__main__":
    # 성적표 로드
    transcripts = load_transcripts("경로/to/transcripts.json")
    transcripts_text = [t["text"] for t in transcripts]

    # 고밀도 임베딩 생성
    dense_embeddings = get_dense_embeddings(transcripts_text)

    # 스파스 임베딩 생성하기
    벡터라이저 = TfidfVectorizer()
    sparse_embeddings = 벡터라이저.fit_transform(트랜스크립트_text)

    # 파인콘 초기화
    init_pinecone("YOUR_API_KEY")

    # Pinecone 인덱스 생성
    index_name = "팟캐스트-대본"
    create_pinecone_index(index_name)

    # 밀집된 임베딩과 드문드문 임베딩을 Pinecone 인덱스에 삽입합니다.
    ids = [str(i) for i in range(len(transcripts))]
    upsert_embeddings(index_name, ids, dense_embeddings, sparse_embeddings)

    # 검색을 위한 문장 트랜스포머 모델 로드
    search_model = SentenceTransformer("paraphrase-distilroberta-base-v1")

    # 검색 예제
    query = "기계 학습 및 인공 지능"
    top_k_results = search(index_name, query, search_model, vectorizer, k=5)

    for score, index in top_k_results.items():
        i = int(index)
        print(f"Episode: {transcripts[i]['episode_title']}\nTranscript: {transcripts_text[i]}\n")
```
"path/to/transcripts.json"`을 대본 데이터 세트의 경로로 바꾸고, `"YOUR\_API