**ValueError:** Expected IDs to be a non-empty list, got []

**Traceback:**
File "C:\Users\scite\Desktop\HAMBOTAI\HAMBotAI\HAMBotAI\homehambotai.py", line 96, in app
    db = Chroma.from_documents(texts, embeddings)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\scite\AppData\Roaming\Python\Python311\site-packages\langchain_community\vectorstores\chroma.py", line 771, in from_documents
    return cls.from_texts(
           ^^^^^^^^^^^^^^^
File "C:\Users\scite\AppData\Roaming\Python\Python311\site-packages\langchain_community\vectorstores\chroma.py", line 729, in from_texts
    chroma_collection.add_texts(
File "C:\Users\scite\AppData\Roaming\Python\Python311\site-packages\langchain_community\vectorstores\chroma.py", line 324, in add_texts
    self._collection.upsert(
File "C:\Users\scite\AppData\Roaming\Python\Python311\site-packages\chromadb\api\models\Collection.py", line 449, in upsert
    ) = self._validate_embedding_set(
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\scite\AppData\Roaming\Python\Python311\site-packages\chromadb\api\models\Collection.py", line 512, in _validate_embedding_set
    valid_ids = validate_ids(maybe_cast_one_to_many_ids(ids))
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\scite\AppData\Roaming\Python\Python311\site-packages\chromadb\api\types.py", line 228, in validate_ids
    raise ValueError(f"Expected IDs to be a non-empty list, got {ids}")

字符串

代码块：

if 'processed' in query_params:
    # Create a temporary text file
    with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as temp_file:
        temp_file.write(text)
        temp_file_path = temp_file.name

    # load document
    loader = TextLoader(temp_file_path)
    documents = loader.load()
    # split the documents into chunks
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    texts = text_splitter.split_documents(documents)
    # select which embeddings we want to use
    embeddings = OpenAIEmbeddings()
    # ids =[str(i) for i in range(1, len(texts) + 1)]
    # create the vectorestore to use as the index
    db = Chroma.from_documents(texts, embeddings)
    # expose this index in a retriever interface
    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 2})
    # create a chain to answer questions
    qa = ConversationalRetrievalChain.from_llm(OpenAI(), retriever)
    chat_history = []
    # query = "What's the Name of patient and doctor as mentioned in the data?"
    # result = qa({"question": query, "chat_history": chat_history})
    # st.write("Patient and Doctor name:", result['answer'])
    #
    # chat_history = [(query, result["answer"])]
    query = "Provide summary of medical and health related info from this data in points, every point should be in new line (Formatted in HTML)?"
    result = qa({"question": query, "chat_history": chat_history})
    toshow = result['answer']
    # chat_history = [(query, result["answer"])]
    # chat_history.append((query, result["answer"]))
    # print(chat_history)

    st.title("Data Fetched From Your Health & Medical Reports")
    components.html(
        f"""
        {toshow}
        """,
        height=250,
        scrolling=True,
    )

    if st.button('Continue to Questionarrie'):
        st.write('Loading')
    st.text("(OR)")
    if st.button('Chat with BotAI'):
        st.title("Chat with BotAI")

型
我成功地能够从llm得到我的问题的答案，但只要我点击下面的任何按钮，“继续浏览”/“与BotAI聊天”，它给出了如上所示的错误，但它不应该出现.我想确定什么是主要原因，我如何才能删除此错误.