Langhcain QA notebook code throws errors locally

Short Course Q&A LangChain for LLM Application Development
1 
{
	"name": "ValidationError",
	"message": "2 validation errors for DocArrayDoc
text
  Field required [type=missing, input_value={'embedding': [-0.0021856... -0.020489846465678658]}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.6/v/missing
metadata
  Field required [type=missing, input_value={'embedding': [-0.0021856... -0.020489846465678658]}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.6/v/missing",
	"stack": "---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
Cell In[28], line 1
----> 1 docs = db.similarity_search(query)

File ~/miniconda3/envs/deeplearning-ng/lib/python3.10/site-packages/langchain_community/vectorstores/docarray/base.py:127, in DocArrayIndex.similarity_search(self, query, k, **kwargs)
    115 def similarity_search(
    116     self, query: str, k: int = 4, **kwargs: Any
    117 ) -> List[Document]:
    118     \"\"\"Return docs most similar to query.
    119 
    120     Args:
   (...)
    125         List of Documents most similar to the query.
    126     \"\"\"
--> 127     results = self.similarity_search_with_score(query, k=k, **kwargs)
    128     return [doc for doc, _ in results]

File ~/miniconda3/envs/deeplearning-ng/lib/python3.10/site-packages/langchain_community/vectorstores/docarray/base.py:106, in DocArrayIndex.similarity_search_with_score(self, query, k, **kwargs)
     94 \"\"\"Return docs most similar to query.
     95 
     96 Args:
   (...)
    103     Lower score represents more similarity.
    104 \"\"\"
    105 query_embedding = self.embedding.embed_query(query)
--> 106 query_doc = self.doc_cls(embedding=query_embedding)  # type: ignore
    107 docs, scores = self.doc_index.find(query_doc, search_field=\"embedding\", limit=k)
    109 result = [
    110     (Document(page_content=doc.text, metadata=doc.metadata), score)
    111     for doc, score in zip(docs, scores)
    112 ]

File ~/miniconda3/envs/deeplearning-ng/lib/python3.10/site-packages/pydantic/main.py:171, in BaseModel.__init__(self, **data)
    169 # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    170 __tracebackhide__ = True
--> 171 self.__pydantic_validator__.validate_python(data, self_instance=self)

ValidationError: 2 validation errors for DocArrayDoc
text
  Field required [type=missing, input_value={'embedding': [-0.0021856... -0.020489846465678658]}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.6/v/missing
metadata
  Field required [type=missing, input_value={'embedding': [-0.0021856... -0.020489846465678658]}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.6/v/missing"
}```
on this cell:

docs = db.similarity_search(query)

2

Anyone else with this issue, install lower version of Pydantic as seen on ValidationError: 2 validation errors for DocArrayDoc returned when try to execute the RAG Search Example · Issue #14585 · langchain-ai/langchain · GitHub