根據提供的資源數據，2017年大數據開發職位的招聘人數大幅增加。具體來說，2016年大數據開發的招聘人數為5,667人，而2017年則增加到41,831人。因此，2017年大數據開發職位比2016年增加了36,164人 (41,831 - 5,667 = 36,164)。

os.environ["LLAMA_CLOUD_API_KEY"] = "xxx"

parser = LlamaParse(
        result_type="json",
        use_vendor_multimodal_model=True,
        vendor_multimodal_model_name="gemini-2.0-flash-001",
        language="ch_sim"
    )
md_result = parser.get_json_result(file_path)
doc_id = md_result[0]["job_id"]
pages = md_result[0]["pages"]
# 下載圖片到本地
parser.get_images(md_result, download_path="data_images")

dataset = []
docs = []
base64_map = {}
for page in pages:
    md = page["md"]
    page_number = page["page"]

    # 查找并上傳對應頁碼的圖片
    local_image_path = find_image_by_page(
        "data_images", doc_id, page_number)
    with open(local_image_path, "rb") as image_file:
        image_base64 = base64.b64encode(image_file.read()).decode('utf-8')

    # 添加到dataset
    dataset.append({
        'content': md,
        'image_base64': image_base64,
        'page_number': page_number
    })
    docs.append(
        Document(
            page_cnotallow=md,
            metadata={
                "page_number": page_number,
            })
    )
    base64_map[page_number] = image_base64
    
vectorstore = Milvus.from_documents(
    documents=docs,
    embedding=embeddings,
    connection_args={"host": "127.0.0.1", "port": "19530"},
    drop_old=True,  # Drop the old Milvus collection if it exists
    collection_name="collection_ppt",
)

dat = vectorstore.similarity_search(query=question, k=5)

image_base64_list = []
chunk = []
for doc in dat:
    page_number = doc.metadata["page_number"]
    print(page_number)
    image_base64 = base64_map.get(page_number)
    if image_base64:
        image_base64_list.append(image_base64)
        chunk.append(doc.page_content)
        
openai_api_key = os.environ["OPENAI_API_KEY"]  # 替換為你的 OpenAI API Key
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {openai_api_key}"
}

# 構建 messages 內容
messages_content = [
    {"type": "text", "text": """The following is the Markdown text and image information parsed in the slide. Markdown text has attempted to convert the relevant charts into tables. Give priority to using picture information to answer questions. Use Markdown text information only when the image cannot be understood.
 
Here is the context:
---------
{context}
---------

Question: {question}
""".format(cnotallow="\n".join(chunk), questinotallow=question)}
]

if image_base64_list:
    # 添加所有檢索到的圖片
    for img_base64 in image_base64_list:
        messages_content.append({
            "type": "image_url",
            "image_url": {
                "url": f"data:image/jpeg;base64,{img_base64}"
            }
        })

payload = {
    "model": "gpt-4.1",
    "messages": [
        {
            "role": "user",
            "content": messages_content
        }
    ],
    "temperature": 0.1,
    "max_tokens": 1000# 根據需要調整
}

# 發送請求到 OpenAI API
try:
    response = requests.post(
        "https://api.openai-proxy.com/v1/chat/completions",
        headers=headers,
        jsnotallow=payload
    )
    response.raise_for_status()  # 檢查請求是否成功

    # 解析并打印結果
    result = response.json()
    print("OpenAI 分析結果:")
    print(result["choices"][0]["message"]["content"])

except requests.exceptions.RequestException as e:
    print(f"請求 OpenAI API 失敗: {e}")
    if hasattr(e, 'response') and e.response:
        print(f"錯誤詳情: {e.response.text}")