Missing metadata key in results?

Good afternoon from the Philippines. I’m pretty new to Pinecone. When i run this code it seems that the metadata, ‘text’ is missing

# Embed and index dataset
batch_size = 100

for i in tqdm(range(0, len(data), batch_size)):
    i_end = min(len(data), i+batch_size)
    batch = data.iloc[i:i_end]
    ids = [f"{x['doi']}-{x['chunk-id']}" for _, x in batch.iterrows()]
    texts = [x['chunk'] for _, x in batch.iterrows()]
    embeds = embed_model.embed_documents(texts)
    metadata = [{'text': x['chunk'], 'source': x['source'], 'title': x['title']} for _, x in batch.iterrows()]
    index.upsert(vectors=zip(ids, embeds, metadata))

text_field = "text"

# Initialize vector store
vectorstore = PineconeVectorStore(index, embed_model, text_field)

# Define function to augment prompt with context
def augment_prompt(query: str):
    results = vectorstore.similarity_search(query, k=3)
    for result in results:
        print("Available keys in metadata:", result.metadata)
    source_knowledge = "\n".join([x.metadata.get('text', 'No text available in metadata') for x in results])
    augmented_prompt = f"""Using the contexts below, answer the query.

    Contexts:
    {source_knowledge}

    Query: {query}"""
    return augmented_prompt

It returns these results
Available keys in metadata: dict_keys([‘source’, ‘title’])
Available keys in metadata: dict_keys([‘source’, ‘title’])
Available keys in metadata: dict_keys([‘source’, ‘title’])

In my vector db, i’ve already upserted the text, source, and title with no issue. It’s just when i retrieve the metadata that there happens to be an issue. I’m trying to build a chatbot if it’s any relevant.