feat(docker): containerize application and add TTS integration

haystack_rag/embedding.py

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+# embedding.py
+from haystack.components.embedders import OpenAITextEmbedder
+
+from haystack.utils import Secret
+
+# 从 config 导入新的变量名
+from config import (
+    OPENAI_EMBEDDING_MODEL,
+    OPENAI_API_KEY_FROM_CONFIG,  # 使用配置中的 Key
+    OPENAI_API_BASE_URL_CONFIG,  # 使用配置中的 Base URL
+    OPENAI_EMBEDDING_KEY,
+    OPENAI_EMBEDDING_BASE,
+    HUGGINGFACE_KEY,
+    HUGGINGFACE_EMBEDDING_MODEL,
+    OPENAI_EMBEDDING_DIM
+)
+
+
+def initialize_text_embedder() -> OpenAITextEmbedder:
+    """
+    Initializes the Haystack OpenAITextEmbedder component.
+    Reads API Key and Base URL directly from config.py.
+    """
+
+    # 检查从配置加载的 key 是否有效 (基础检查)
+    if not OPENAI_API_KEY_FROM_CONFIG or "YOUR_API_KEY" in OPENAI_API_KEY_FROM_CONFIG:
+        print("警告: OpenAI API Key 未在 config.py 中有效配置。")
+        # Consider raising an error here if the key is mandatory
+        # raise ValueError("OpenAI API Key not configured correctly in config.py")
+
+    print(f"Initializing OpenAI Text Embedder with model: {OPENAI_EMBEDDING_MODEL}")
+
+    # 使用配置中的 Base URL
+    if OPENAI_API_BASE_URL_CONFIG:
+        print(f"Using custom API base URL from config: {OPENAI_API_BASE_URL_CONFIG}")
+    else:
+        print("Using default OpenAI API base URL (None specified in config).")
+
+    text_embedder = OpenAITextEmbedder(
+        # 直接使用从 config.py 导入的 key 和 base_url
+        api_key=Secret.from_token(OPENAI_EMBEDDING_KEY),
+        api_base_url=OPENAI_EMBEDDING_BASE,
+        model=OPENAI_EMBEDDING_MODEL,
+        dimensions=OPENAI_EMBEDDING_DIM,
+    )
+    print("Text Embedder initialized.")
+    return text_embedder
+
+
+# __main__ 部分也需要调整以反映不依赖环境变量
+# Example usage
+if __name__ == "__main__":
+    embedder = initialize_text_embedder()
+    sample_text = "这是一个示例文本，用于测试嵌入功能。"
+    try:
+        result = embedder.run(text=sample_text)
+        embedding = result["embedding"]
+        print(f"Sample text: '{sample_text}'")
+        # print(f"Generated embedding (first 5 dims): {embedding[:5]}")
+        print(f"Generated embedding dimension: {len(embedding)}")
+        print(f"Tokens used: {result['meta']['usage']['total_tokens']}")
+    except Exception as e:
+        print(f"Error during huggingface API call: {e}")