# Wrap the Polygon API spec with LoadAndSearchToolSpec
wrapped_tools = LoadAndSearchToolSpec.from_defaults(
    api_spec.to_tool_list()[0],
).to_tool_list()

agent = ReActAgent.from_tools(
    [*wrapped_tools, requests_spec.to_tool_list()[0]]
    , verbose=True
    , llm=llm
    , context=CONTEXT
    , max_iterations=20
)

agent.chat("What are all the exchanges you have access to?")

File /workspaces/pye/pye/.venv/lib/python3.10/site-packages/llama_hub/tools/requests/base.py:75, in RequestsToolSpec._get_headers_for_url(self, url)
     74 def _get_headers_for_url(self, url: str) -> dict:
---> 75     return self.domain_headers[self._get_domain(url)]

KeyError: 'api.polygon.io'

llamaindex-cli new-package --kind "packs" --name "pack-test"

Traceback (most recent call last):
  File "/workspaces/llama_index/.venv/bin/llamaindex-cli", line 6, in <module>
    sys.exit(main())
  File "/workspaces/llama_index/.venv/lib/python3.10/site-packages/llama_index/cli/command_line.py", line 269, in main
    args.func(args)
  File "/workspaces/llama_index/.venv/lib/python3.10/site-packages/llama_index/cli/command_line.py", line 263, in <lambda>
    new_package_parser.set_defaults(func=lambda args: handle_init_package(**vars(args)))
  File "/workspaces/llama_index/.venv/lib/python3.10/site-packages/llama_index/cli/command_line.py", line 26, in handle_init_package
    init_new_package(integration_name=name, integration_type=kind, prefix=prefix)
  File "/workspaces/llama_index/.venv/lib/python3.10/site-packages/llama_index/cli/new_package/base.py", line 120, in init_new_package
    shutil.copyfile(common_path + "/BUILD", pkg_path + "/BUILD")
  File "/usr/local/python/3.10.13/lib/python3.10/shutil.py", line 254, in copyfile
    with open(src, 'rb') as fsrc:
FileNotFoundError: [Errno 2] No such file or directory: '/workspaces/llama_index/.venv/lib/python3.10/site-packages/llama_index/cli/new_package/common/BUILD'

(.venv) @no-dice-io ➜ /workspaces/koda-retriever (main) $ llamaindex-cli upgrade .
Module not found: VectorStoreQueryMode
Switching to core
Module not found: BaseNodePostprocessor
Switching to core
Module not found: QueryType
Switching to core
Module not found: Settings
Switching to core
New installs:
pip install llama-index-llms-openai
pip install llama-index-vector-stores-pinecone
pip install llama-index-embeddings-openai

from cerebro.cbcore.utils.vector_store import CerebroVectorStore
#from asyncio import run
from os import name

query = "Who does Paul Graham think of with the word schtick"
vector_store = CerebroVectorStore()
#if name == 'nt':
#    from asyncio import set_event_loop_policy, WindowsSelectorEventLoopPolicy
#    set_event_loop_policy(WindowsSelectorEventLoopPolicy()) # this fixed it

vector_store.hybrid_search(query)

AttributeError: 'NoneType' object has no attribute 'send'
Exception ignored in: <function _SSLProtocolTransport.__del__ at 0x000001951368DB40>
Traceback (most recent call last):
...character limit...
Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.3056.0_x64__qbz5n2kfra8p0\lib\asyncio\base_events.py", line 515, in _check_closed
RuntimeError: Event loop is closed

    async def ingest(self, chunks: list[dict]):
        '''Ingests a list of chunks into the vector store asynchronously'''
        
        if hasattr(self, 'ingestion_pipeline') == False:
            self.init_ingestion_pipeline()
        print('################ vector store and ingestion pipeline initialized')
        
        processed_chunks = await process_chunks(chunks)
        print('################ chunks processed')

        return await self.ingestion_pipeline.arun(documents=processed_chunks)

vector_store = PFVectorStore() #custom class I was referring to
run(vector_store.ingest(test_data)) #asyncio

    def init_ingestion_pipeline(self) -> IngestionPipeline:
        '''Initializes the ingestion pipeline for the vector store'''

        pipeline = IngestionPipeline(
            transformations=[
                AzureOpenAIEmbedding(
                    model="text-embedding-ada-002"
                    , azure_deployment="text-embedding-ada-002"
                    , azure_endpoint=str(settings.azure_openai_api_base)
                    , api_version=str(settings.azure_openai_api_version)
                    , api_key=str(settings.azure_openai_api_key)
                )
            ]
            , vector_store=self.vector_store
        )

        self.ingestion_pipeline = pipeline

        return pipeline

    async def ingest(self, chunks: list[dict]):
        '''Ingests a list of chunks into the vector store asynchronously'''
        
        if hasattr(self, 'ingestion_pipeline') == False:
            self.init_ingestion_pipeline()
        print('################ vector store and ingestion pipeline initialized')
        
        processed_chunks = await process_chunks(chunks)
        print('################ chunks processed')

        #await self.ingestion_pipeline.arun(documents=processed_chunks)
        nodes = self.ingestion_pipeline.run(documents=processed_chunks, show_progress=True)
        print('################ ingestion pipeline completed')

vector_store = PFVectorStore() #Custom class, not a vector store from LlamaIndex
run(vector_store.ingest(test_data)) #Referencing the ingest function from an ingestion pipeline, ingest is a custom wrapper over the ingestion pipeline

pydantic.error_wrappers.ValidationError: 1 validation error for IngestionPipeline
transformations -> 0
  value is not a valid dict (type=type_error.dict)

ingestion_pipeline = IngestionPipeline(
    transformations=[AzureOpenAIEmbedding]
    , vector_store=vector_store
)

ingestion_pipeline.run(documents=new_docs, show_progress=True)

## setup
@pytest.fixture
def setup() -> dict:

    os.environ["OPENAI_API_KEY"] = str(settings.openai_api_key)

    service_context = ServiceContext.from_defaults(
        embed_model=OpenAIEmbedding(
            model="text-embedding-ada-002"
        ),
        llm=OpenAI(
            model="gpt-3.5-turbo"
        )
    )

    shots = AlphaMatrix(data=DEFAULT_CATEGORIES)

    vector_index = VectorStoreIndex.from_documents(
        [Document.example()]
        , service_context=service_context
    )

    reranker = LLMRerank(service_context=service_context)

    retriever = CustomRetriever(
        index=vector_index,
        llm=service_context.llm,
        reranker=reranker,
        matrix=shots,
        verbose=True,
    )

    return {
        "retriever": retriever,
        "service_context": service_context,
        "vector_index": vector_index,
        "matrix": shots,
    }

#Where the error occurs:
retriever = VectorIndexRetriever(
            index=index,
            vector_store_query_mode="hybrid",
            alpha=default_alpha,
            **kwargs,  # filters, etc, added here
        )

def test_retrieve(setup):

    retriever = setup.get("retriever")
    query = "What are LLMs good at?"
    results = retriever.retrieve(query)

ValueError: Invalid query mode: hybrid

    service_context: ServiceContext = ServiceContext.from_defaults(
        embed_model= AzureOpenAIEmbedding(
            model="text-embedding-ada-002"
            , azure_deployment="text-embedding-ada-002"
            , azure_endpoint=str(settings.azure_openai_api_base)
            , api_version=str(settings.azure_openai_api_version)
            , api_key=str(settings.azure_openai_api_key)
        )
        , llm = AzureOpenAI(
            model="gpt-4"
            , azure_deployment="gpt-4"
            , azure_endpoint=str(settings.azure_openai_api_base)
            , api_version=str(settings.azure_openai_api_version)
            , api_key=str(settings.azure_openai_api_key)
        )
    )