reader = SimpleDirectoryReader(
    input_dir="cases/"
)

documents = reader.load_data()

# Insert documents into vector index
# Each document has metadata of the city attached
for city, wiki_doc in zip(cities, wiki_docs):
    nodes = node_parser.get_nodes_from_documents([wiki_doc])
    # add metadata to each node
    for node in nodes:
        node.extra_info = {"title": city}
    vector_index.insert_nodes(nodes)

python3 main.py
dict_keys(['city_stats'])
[('Toronto', 2930000, 'Canada'), ('Tokyo', 13960000, 'Japan'), ('Berlin', 3645000, 'Germany')]
Traceback (most recent call last):
  File "/home/bi-ai/ai/txt-to-sql/main.py", line 107, in <module>
    node.extra_info = {"title": city}
  File "pydantic/main.py", line 357, in pydantic.main.BaseModel.__setattr__
ValueError: "TextNode" object has no field "extra_info"

Traceback (most recent call last):
  File "/home/bi/ai/llama_docs_bot/3_eval_baseline/main.py", line 110, in <module>
    response = query_engine.query(query)
  File "/usr/local/lib/python3.10/dist-packages/llama_index/indices/query/base.py", line 23, in query
    response = self._query(str_or_query_bundle)
  File "/usr/local/lib/python3.10/dist-packages/llama_index/query_engine/sub_question_query_engine.py", line 126, in _query
    sub_questions = self._question_gen.generate(self._metadatas, query_bundle)
  File "/usr/local/lib/python3.10/dist-packages/llama_index/question_gen/openai_generator.py", line 77, in generate
    question_list = self._program(query_str=query_str, tools_str=tools_str)
  File "/usr/local/lib/python3.10/dist-packages/llama_index/program/openai_program.py", line 101, in __call__
    chat_response = self._llm.chat(
  File "/usr/local/lib/python3.10/dist-packages/llama_index/llms/base.py", line 134, in wrapped_llm_chat
    CBEventType.LLM, payload={EventPayload.MESSAGES: args[0]}
IndexError: tuple index out of range

class MetadataExtractor(BaseExtractor):
    """Metadata extractor."""

    ...other code...

    in_place: bool = Field(
        default=True, description="Whether to process nodes in place."
    )

Traceback (most recent call last):
  File "/home/bi-ai/ai/bottoms-up-embeddings/main.py", line 40, in <module>
    embed_model=InstructorEmbeddings(embed_batch_size=2), chunk_size=512
TypeError: Can't instantiate abstract class InstructorEmbeddings with abstract methods _aget_query_embedding, class_name

def class_name(self) -> str:
        return "InstructorEmbeddings"
    
async def _aget_query_embedding(self, query: str) -> List[float]:
    return self._get_query_embedding(query)

Traceback (most recent call last):
  File "/home/bi-ai/ai/bottoms-up-embeddings/main.py", line 46, in <module>
    embed_model=InstructorEmbeddings(embed_batch_size=2), chunk_size=512
  File "/home/bi-ai/ai/bottoms-up-embeddings/main.py", line 19, in __init__
    self._model = INSTRUCTOR(instructor_model_name)
  File "pydantic/main.py", line 357, in pydantic.main.BaseModel.__setattr__
ValueError: "InstructorEmbeddings" object has no field "_model"

# Provide URI to constructor, or use environment variable

from markdown import Markdown
import pymongo
from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
from llama_index.indices.vector_store.base import VectorStoreIndex
from llama_index.storage.storage_context import StorageContext
from llama_index.readers.file.base import SimpleDirectoryReader

mongo_uri = "mongodb+srv://<username>:<password>@<host>/?retryWrites=true&w=majority"

mongodb_client = pymongo.MongoClient(mongo_uri)
store = MongoDBAtlasVectorSearch(mongodb_client)
storage_context = StorageContext.from_defaults(vector_store=store)
silva_docs = SimpleDirectoryReader(input_files=["data/Anderson_Silva.pdf"]).load_data()

index = VectorStoreIndex.from_documents(silva_docs, storage_context=storage_context)

response = index.as_query_engine().query("When was Anderson Silva born?")
print(f"<b>{response}</b>")

python3 main.py 
<b>None</b>

bi@bi:~/ai/quiz-maker-be$ python3 main.py 
Traceback (most recent call last):
  File "/home/bi/ai/quiz-maker-be/main.py", line 5, in <module>
    from llama_index import SimpleDirectoryReader
  File "/home/bi/.local/lib/python3.10/site-packages/llama_index/__init__.py", line 12, in <module>
    from llama_index.data_structs.struct_type import IndexStructType
  File "/home/bi/.local/lib/python3.10/site-packages/llama_index/data_structs/__init__.py", line 3, in <module>
    from llama_index.data_structs.data_structs import (
  File "/home/bi/.local/lib/python3.10/site-packages/llama_index/data_structs/data_structs.py", line 14, in <module>
    from llama_index.schema import BaseNode, TextNode
  File "/home/bi/.local/lib/python3.10/site-packages/llama_index/schema.py", line 9, in <module>
    from llama_index.bridge.langchain import Document as LCDocument
  File "/home/bi/.local/lib/python3.10/site-packages/llama_index/bridge/langchain.py", line 21, in <module>
    from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceBgeEmbeddings
ImportError: cannot import name 'HuggingFaceBgeEmbeddings' from 'langchain.embeddings' (/home/bi/.local/lib/python3.10/site-packages/langchain/embeddings/__init__.py)