错误 AttributeError: 'PineconeVectorStore' object has no attribute 'service_context' 的发生是因为 VectorIndexAutoRetriever 类试图访问 service_context 属性，而该属性在 PineconeVectorStore 示例中不存在。
要解决这个问题，你需要确保 VectorIndexAutoRetriever 类中 service_context 设置正确。以下是修复后的 VectorIndexAutoRetriever 类代码片段：

class `VectorIndexAutoRetriever`:
    

def init(
self,
index: VectorStoreIndex,
vector_store_info: VectorStoreInfo,
llm: Optional[LLM] = None,
prompt_template_str: Optional[str] = None,
max_top_k: int = 10,
similarity_top_k: int = DEFAULT_SIMILARITY_TOP_K,
empty_query_top_k: Optional[int] = 10,
vector_store_query_mode: VectorStoreQueryMode = VectorStoreQueryMode.DEFAULT,
default_empty_query_vector: Optional[List[float]] = None,
callback_manager: Optional[CallbackManager] = None,
verbose: bool = False,
extra_filters: Optional[MetadataFilters] = None,
object_map: Optional[dict] = None,
objects: Optional[List[IndexNode]] = None,
service_context: Optional[ServiceContext] = None,
**kwargs: Any,
) -> None:
self._index = index
self._vector_store_info = vector_store_info
self._default_empty_query_vector = default_empty_query_vector

    service_context = service_context or self._index.service_context
    self._llm = llm or llm_from_settings_or_context(self._index.settings, service_context)
    callback_manager = (
        callback_manager
        or callback_manager_from_settings_or_context(self._index.settings, service_context)
    )

    prompt_template_str = (
        prompt_template_str or DEFAULT_VECTOR_STORE_QUERY_PROMPT_TMPL
    )
    self._output_parser = VectorStoreQueryOutputParser()
    self._prompt = PromptTemplate(template=prompt_template_str)
    self._max_top_k = max_top_k
    self._similarity_top_k = similarity_top_k
    self._empty_query_top_k = empty_query_top_k
    self._vector_store_query_mode = vector_store_query_mode
    if extra_filters is not None and extra_filters.condition == FilterCondition.OR:
        raise ValueError("extra_filters cannot be OR condition")
    self._extra_filters = extra_filters or MetadataFilters(filters=[])
    self._kwargs = kwargs
    super().__init__(
        callback_manager=callback_manager,
        object_map=object_map or self._index._object_map,
        objects=objects,
        verbose=verbose,
    )

这个更改确保了使用的 Settings 是特定于 index 示例的，这应该解决了问题。
此外，确保你的 PineconeVectorStore 类正确初始化，并包含所有必要的属性和方法。以下是 PineconeVectorStore 类所需的属性和方法的简要概述：

属性：

• stores_text: bool = True
• flat_metadata: bool = False
• api_key: Optional[str]
• index_name: Optional[str]
• environment: Optional[str]
• namespace: Optional[str]
• insert_kwargs: Optional[Dict]
• add_sparse_vector: bool
• text_key: str
• batch_size: int
• remove_text_from_metadata: bool
• _pinecone_index: Any = PrivateAttr()
• _tokenizer: Optional[Callable] = PrivateAttr()

方法：

• __init__(self, pinecone_index: Optional[Any] = None, api_key: Optional[str] = None, index_name: Optional[str] = None, environment: Optional[str] = None, namespace: Optional[str] = None, insert_kwargs: Optional[Dict] = None, add_sparse_vector: bool = False, tokenizer: Optional[Callable] = None, text_key: str = DEFAULT_TEXT_KEY, batch_size: int = DEFAULT_BATCH_SIZE, remove_text_from_metadata: bool = False, default_empty_query_vector: Optional[List[float]] = None, **kwargs: Any) -> None
• @classmethod _initialize_pinecone_client(cls, api_key: Optional[str], index_name: Optional[str], environment: Optional[str], **kwargs: Any) -> Any
• @classmethod from_params(cls, api_key: Optional[str] = None, index_name: Optional[str] = None, environment: Optional[str] = None, namespace: Optional[str] = None, insert_kwargs: Optional[Dict] = None, add_sparse_vector: bool = False, tokenizer: Optional[Callable] = None, text_key: str = DEFAULT_TEXT_KEY, batch_size: int = DEFAULT_BATCH_SIZE, remove_text_from_metadata: bool = False, default_empty_query_vector: Optional[List[float]] = None, **kwargs: Any) -> "PineconeVectorStore"
• @classmethod class_name(cls) -> str
• add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]
• delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None
• @property client(self) -> Any
• query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult

确保你的 PineconeVectorStore 类实现了这些属性和方法，以避免进一步的问题。

详细信息

来源

• Pinecone retriever [AttributeError: 'Pinecone' object has no attribute 'query']
• [Bug]: AttributeError: 'str' object has no attribute 'upsert'
• AttributeError: 'LLMPredictor' object has no attribute 'get_text_from_nodes'
• llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/auto_retriever.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/pyproject.toml
• llama-index-legacy/llama_index/legacy/vector_stores/pinecone_utils.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/utils.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/base.py
• llama-index-legacy/llama_index/legacy/vector_stores/pinecone.py
  关于 Dosu

此响应旨在帮助您节省时间并提供有用的信息。它不是精确的解决方案，而是您自己研究的起点。请帮我变得更有用！如果这有帮助，请留下一个👍，如果无关紧要，请留下一个👎。要继续对话，请提及 @dosu。

根据类定义， service_context 是一个可选变量，它不应该抛出任何错误，对吗？

错误发生是因为 PineconeVectorStore 类没有 service_context 属性，而在 VectorIndexAutoRetriever 类中访问了它。尽管 service_context 是可选的，但代码仍然尝试访问它，导致了 AttributeError。
要解决这个问题，你可以在将 PineconeVectorStore 示例传递给 VectorIndexAutoRetriever 之前手动添加 service_context 属性。这是你的代码片段的更新版本：

import pinecone
from dotenv import load_dotenv
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
from load_envs import *
from llama_index.vector_stores.pinecone import PineconeVectorStore

load_dotenv()

pc = pinecone.Pinecone(api_key=PINECONE_API_KEY, environment=PINECONE_ZONE)

embed_model = OpenAIEmbedding(embed_batch_size=1, openai_api_key=OPENAI_API_KEY, model=OPENAI_EMBEDDING_MODEL_NAME)

vector_store = PineconeVectorStore(
    pinecone_index=pc,
    embedding=embed_model
)

# Manually add the service_context attribute
vector_store.service_context = None  # or set it to an appropriate value if available

vector_store_info = VectorStoreInfo(
    content_info="Information about blogs",
    metadata_info=[
        MetadataInfo(
            name="source",
            type="str",
            description="URL of the blog from which content is extracted from.",
        ),
        MetadataInfo(
            name="bucket",
            type="str",
            description="To which bucket does the data belong to",
        ),
        MetadataInfo(
            name="text",
            type="str",
            description="Content from the blog",
        ),
    ],
)

retriever = VectorIndexAutoRetriever(
    vector_store,
    vector_store_info=vector_store_info,
    empty_query_top_k=10,
    default_empty_query_vector=[0] * 1536,
    verbose=True,
)

通过向 vector_store 示例添加 service_context 属性，你应该能够避免 AttributeError。如果你为 service_context 有特定的值，可以相应地设置它，而不是 None。

详细信息

来源

• Pinecone retriever [AttributeError: 'Pinecone' object has no attribute 'query']
• [Bug]: AttributeError: 'str' object has no attribute 'upsert'
• llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/init.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/base.py
• llama-index-legacy/llama_index/legacy/vector_stores/pinecone.py
• docs/docs/examples/data_connectors/PineconeDemo.ipynb
• docs/docs/examples/vector_stores/PineconeIndexDemo.ipynb
• docs/docs/examples/vector_stores/existing_data/pinecone_existing_data.ipynb
• llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/pyproject.toml
  关于 Dosu

此响应旨在帮助您节省时间并提高效率。它不是精确的解决方案，而是您自己研究的起点。
请帮助我变得更有用！如果这有帮助，请留下一个 👍，如果不相关，请留下一个 👎。

• 要继续对话，请提及 @dosu。*

你好，@maheshchandrra28,VectorIndexAutoRetriever的第一个参数应该是VectorStoreIndex类型，而不是VectorStore类型。

vector_store = PineconeVectorStore(
    pinecone_index=pc,
    embedding=embed_model
)

...

+ storage_context = StorageContext.from_defaults(vector_store=vector_store)
+ index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

retriever = VectorIndexAutoRetriever(
-   vector_store,
+   index,
    vector_store_info=vector_store_info,
    empty_query_top_k=10,
    # this is a hack to allow for blank queries in pinecone
    default_empty_query_vector=[0] * 1536,
    verbose=True,
)

有关详细信息，请参阅https://docs.llamaindex.ai/en/stable/examples/vector_stores/PineconeIndexDemo/。

尝试将vector_store.service_context设置为None,仍然出现相同的错误。

ValueError Traceback (most recent call last)
 Cell In[28], line 18
 15 from llama_index.core import Settings
 17 embed_model = OpenAIEmbedding(embed_batch_size=1,openai_api_key=OPENAI_API_KEY,model=OPENAI_EMBEDDING_MODEL_NAME )
 ---> 18 vector_store.service_context = None # or set it to an appropriate value if available
 21 vector_store = PineconeVectorStore(
 22 pinecone_index=pc,
 23 embedding=embed_model
 24 )
 27 vector_store_info = VectorStoreInfo(
 28 content_info="Information about blogs",
 29 metadata_info=[
 (...)
 45 ],
 46 )
File ~/anaconda3/lib/python3.11/site-packages/pydantic/v1/main.py:357, in BaseModel.**setattr**(self, name, value)
 354 return object_setattr(self, name, value)
 356 if self.**config**.extra is not Extra.allow and name not in self.**fields**:
 --> 357 raise ValueError(f'"{self.**class**.**name**}" object has no field "{name}"')
 358 elif not self.**config**.allow_mutation or self.**config**.frozen:
 359 raise TypeError(f'"{self.**class**.**name**}" is immutable and does not support item assignment')
ValueError: "PineconeVectorStore" object has no field "service_context"`