如何在LangChain的FAISS向量库中获取文档的Embedding？

我通过LangChain Python库构建向量存储，实现基于用户查询的相关文档检索，当前需要获取向量存储中doc1和doc2的Embedding，示例代码如下：

import pprint
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.docstore.document import Document

model = "sentence-transformers/multi-qa-MiniLM-L6-cos-v1"
embeddings = HuggingFaceEmbeddings(model_name = model)

def main():
    doc1 = Document(page_content="天空是蓝色的。",    metadata={"document_id": "10"})
    doc2 = Document(page_content="森林是绿色的。", metadata={"document_id": "62"})
    docs = []
    docs.append(doc1)
    docs.append(doc2)

    for doc in docs:
        doc.metadata['summary'] = '你好'

    pprint.pprint(docs)
    db = FAISS.from_documents(docs, embeddings)
    db.save_local("faiss_index")
    new_db = FAISS.load_local("faiss_index", embeddings)

    query = "天空是什么颜色的？"
    docs = new_db.similarity_search_with_score(query)
    print('检索到的文档:', docs)
    print('最相关文档的元数据:', docs[0][0].metadata)

if __name__ == '__main__':
    main()

该代码基于Python 3.11测试，执行以下命令安装所需依赖包：

pip install langchain==0.1.1 langchain_openai==0.0.2.post1 sentence-transformers==2.2.2 langchain_community==0.0.13 faiss-cpu==1.7.4

内容的提问来源于stack exchange，提问作者Franck Dernoncourt