on a small number of content publishers for a majority of our streaming hours, and if we fail to maintain these relationships, our business could be harmed.*Historically, a small number of content publishers have accounted for a significant portion of the hours streamed on our platform.In the three months ended March 31, 2023, the top three streaming services represented over 50% of all hours streamed in the period.If, for any reason, we cease distributing channels that have historically streamed a large percentage of the aggregate streaming hours on our platform, our streaming hours, our active accounts, or Roku streaming device sales may be adversely affected, and our business may be harmed.', metadata={'_additional': {'id': '2a92b2bb-02a0-4e15-8b64-d7e04078a205'}, 'chunk_type': 'text', 'chunk_years_mentioned': [2023], 'company_name': 'ROKU INC', 'company_sic_code_description': 'CABLE & OTHER PAY TELEVISION SERVICES', 'data_source': '10-Q', 'data_source_link': 'https://www.sec.gov/Archives/edgar/data/1428439/000142843923000017', 'data_source_publish_date': '2023-01-01T00:00:00Z', 'data_source_uid': '0001428439-23-000017', 'title': 'ROKU INC |  10-Q 2023-Q1 '})]Usage in a chain‚ÄãOPENAI_API_KEY = getpass()     ¬∑¬∑¬∑¬∑¬∑¬∑¬∑¬∑os.environ["OPENAI_API_KEY"] = OPENAI_API_KEYfrom langchain.chat_models import ChatOpenAIfrom langchain.chains import ConversationalRetrievalChainmodel = ChatOpenAI(model_name="gpt-3.5-turbo")qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)questions = [    "What were the biggest strategy changes and partnerships made by Roku in 2023?"    # "Where is Wex making the most money in 2023?",]chat_history = []for question in questions:    result = qa({"question": question, "chat_history": chat_history})    chat_history.append((question, result["answer"]))    print(f"-> **Question**: {question} \n")    print(f"**Answer**: {result['answer']} \n")    -> **Question**: What were the biggest strategy changes and partnerships made by
Data API built for RAG üïµÔ∏è We are curating the world's largest datasets as high-quality embeddings so your AI agents can retrieve context on the fly. Latest models, fast retrieval, and zero infra.
Data API built for RAG üïµÔ∏è We are curating the world's largest datasets as high-quality embeddings so your AI agents can retrieve context on the fly. Latest models, fast retrieval, and zero infra. ->: on a small number of content publishers for a majority of our streaming hours, and if we fail to maintain these relationships, our business could be harmed.*Historically, a small number of content publishers have accounted for a significant portion of the hours streamed on our platform.In the three months ended March 31, 2023, the top three streaming services represented over 50% of all hours streamed in the period.If, for any reason, we cease distributing channels that have historically streamed a large percentage of the aggregate streaming hours on our platform, our streaming hours, our active accounts, or Roku streaming device sales may be adversely affected, and our business may be harmed.', metadata={'_additional': {'id': '2a92b2bb-02a0-4e15-8b64-d7e04078a205'}, 'chunk_type': 'text', 'chunk_years_mentioned': [2023], 'company_name': 'ROKU INC', 'company_sic_code_description': 'CABLE & OTHER PAY TELEVISION SERVICES', 'data_source': '10-Q', 'data_source_link': 'https://www.sec.gov/Archives/edgar/data/1428439/000142843923000017', 'data_source_publish_date': '2023-01-01T00:00:00Z', 'data_source_uid': '0001428439-23-000017', 'title': 'ROKU INC |  10-Q 2023-Q1 '})]Usage in a chain‚ÄãOPENAI_API_KEY = getpass()     ¬∑¬∑¬∑¬∑¬∑¬∑¬∑¬∑os.environ["OPENAI_API_KEY"] = OPENAI_API_KEYfrom langchain.chat_models import ChatOpenAIfrom langchain.chains import ConversationalRetrievalChainmodel = ChatOpenAI(model_name="gpt-3.5-turbo")qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)questions = [    "What were the biggest strategy changes and partnerships made by Roku in 2023?"    # "Where is Wex making the most money in 2023?",]chat_history = []for question in questions:    result = qa({"question": question, "chat_history": chat_history})    chat_history.append((question, result["answer"]))    print(f"-> **Question**: {question} \n")    print(f"**Answer**: {result['answer']} \n")    -> **Question**: What were the biggest strategy changes and partnerships made by