pg_vector_store = PGVectorStore.from_params(
                    **POSTGRES_SETTINGS.model_dump(exclude_none=True),
                    table_name="embeddings",
                    embed_dim=384,
                )
  pipeline = IngestionPipeline(
    transformations=[
      HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5"),
    ],
    docstore=postgres_docstore,
    vector_store=pg_vector_store,
  )

validation_error = ValidationError(model='IngestionPipeline', errors=[{'loc': ('vector_store',), 'msg': "Can't instantiate abstract class...VectorStore without an implementation for abstract methods 'add', 'client', 'delete', 'query'", 'type': 'type_error'}])

    def __init__(__pydantic_self__, **data: Any) -> None:
        """
        Create a new model by parsing and validating input data from keyword arguments.
    
        Raises ValidationError if the input data cannot be parsed to form a valid model.
        """
        # Uses something other than `self` the first arg to allow "self" as a settable attribute
        values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
        if validation_error:
>           raise validation_error
E           pydantic.v1.error_wrappers.ValidationError: 1 validation error for IngestionPipeline
E           vector_store
E             Can't instantiate abstract class BasePydanticVectorStore without an implementation for abstract methods 'add', 'client', 'delete', 'query' (type=type_error)

query_stream = chat_service.stream_chat(
                    messages=all_messages,
                    use_context=True,
                )

from pydantic import BaseModel
from llama_index.core.llms import ChatResponse
class MyModel(BaseModel):
  foo: str

class CompletionWithAttributions(ChatResponse):
  attributions: list[MyModel] | None = None

from pydantic import BaseModel
class MyModel(BaseModel):
  foo: str

class CompletionWithAttributions(BaseModel):
  attributions: list[MyModel] | None = None

from llama_index.core.bridge.pydantic import BaseModel

   doc = docstore.get_document(llama_id)
    assert doc != None
    doc_content = doc.get_content()
    assert doc_content != None

docstore_ref_doc_info = docstore.get_all_ref_doc_info()

NotImplementedError: Vector store integrations that store text in the vector store are not supported by ref_doc_info yet.

from llama_index.core.bridge.pydantic import (
    BaseModel
)

class ChatBody(BaseModel):
    messages: list[OpenAIMessage]
    use_context: bool = False
    conversation_id: str | None = None
    context_filter: MetadataFilters | None = None
    include_sources: bool = True
    stream: bool = False

hf = HuggingFaceTextGenInference(
                    inference_server_url="https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta",
                    max_new_tokens=512,
                    top_k=10,
                    top_p=0.95,
                    typical_p=0.95,
                    temperature=0.01,
                    repetition_penalty=1.03,
                )
                prompt_style = get_prompt_style(settings.huggingface.prompt_style)
                self.llm = LangChainLLM(llm=hf)