# Create in-memory ChromaDB client
    chroma_name = (
        f"analysis_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}"
    )
    print(f"Chroma collection name: {chroma_name}")
    chroma_client = chromadb.EphemeralClient()
    chroma_collection = chroma_client.get_or_create_collection(chroma_name)
    # Create ChromaVectorStore
    print(f"Creating ChromaVectorStore for collection: {chroma_name}")
    vector_store = ChromaVectorStore(
        collection_name=chroma_name, chroma_collection=chroma_collection
    )
    print(
        f"ChromaVectorStore created from collection: {vector_store.collection_name}"
    )

                Traceback (most recent call last):
  File "/Users/zachhandley/Documents/GitHub/my-project/api/app/db/vector_stores_temp.py", line 295, in <module>
    asyncio.run(main())
  File "/Users/zachhandley/Documents/GitHub/my-project/api/.venv/lib/python3.11/site-packages/nest_asyncio.py", line 30, in run
    return loop.run_until_complete(task)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/zachhandley/Documents/GitHub/my-project/api/.venv/lib/python3.11/site-packages/nest_asyncio.py", line 98, in run_until_complete
    return f.result()
           ^^^^^^^^^^
  File "/usr/local/Cellar/python@3.11/3.11.7_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/futures.py", line 203, in result
    raise self._exception.with_traceback(self._exception_tb)
  File "/usr/local/Cellar/python@3.11/3.11.7_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/tasks.py", line 277, in __step
    result = coro.send(None)
             ^^^^^^^^^^^^^^^
  File "/Users/zachhandley/Documents/GitHub/my-project/api/app/db/vector_stores_temp.py", line 238, in main
    user_images = await vector_store_temp.get_user_images()
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/zachhandley/Documents/GitHub/my-project/api/app/db/vector_stores_temp.py", line 191, in get_user_images
    return await self._image_retriever.aretrieve(query_str)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/zachhandley/Documents/GitHub/my-project/api/app/ai/zimage_retriever.py", line 249, in aretrieve
    return await self._atext_to_image_retrieve(query)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/zachhandley/Documents/GitHub/my-project/api/app/ai/zimage_retriever.py", line 220, in _atext_to_image_retrieve
    engine = index.as_chat_engine(
             ^^^^^^^^^^^^^^^^^^^^^
  File "/Users/zachhandley/Documents/GitHub/my-project/api/.venv/lib/python3.11/site-packages/llama_index/core/indices/base.py", line 413, in as_chat_engine
    resolve_llm(llm, callback_manager=self._callback_manager)
  File "/Users/zachhandley/Documents/GitHub/my-project/api/.venv/lib/python3.11/site-packages/llama_index/core/llms/utils.py", line 101, in resolve_llm
    llm.callback_manager = callback_manager or Settings.callback_manager
    ^^^^^^^^^^^^^^^^^^^^
  File "pydantic/main.py", line 357, in pydantic.main.BaseModel.__setattr__
ValueError: "OpenAIMultiModal" object has no field "callback_manager"

"Error generating summary: 'NodeWithScore' object has no attribute 'image_mimetype'"

image_doc = ImageDocument(
            doc_id=f"{image_request.userId}_{image_id}",
            image=image_data,
            text=image_request.text_request.text
            if image_request.text_request
            else "",
            image_mimetype=image_request.mimetype,
        )

import openai
import os
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
from llama_index.indices.multi_modal.base import MultiModalVectorStoreIndex
from llama_index.query_engine import SimpleMultiModalQueryEngine
from llama_index import SimpleDirectoryReader
from llama_index.vector_stores import QdrantVectorStore
from qdrant_client import QdrantClient

from dotenv import load_dotenv

load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
openai.api_key = OPENAI_API_KEY

client = QdrantClient(url="http://localhost")
openai_mm_llm = OpenAIMultiModal(
    model="gpt-4-vision-preview",
    api_key=os.getenv("OPENAI_API_KEY"),
    max_new_tokens=1500,
)
vector_store = QdrantVectorStore(
    "global_text_store",
    client=client,
)
image_store = QdrantVectorStore(
    "global_image_store",
    client=client,
)
index = MultiModalVectorStoreIndex.from_vector_store(
    vector_store=vector_store,
    image_vector_store=image_store,
    use_async=False,
    show_progress=True,
)
retriever = index.as_retriever()
image_nodes = retriever.retrieve("Find images in the knowledgebase.")
print("Image Nodes: ", image_nodes)
query_engine = SimpleMultiModalQueryEngine(
    retriever=index.as_retriever(),
    openai_mm_llm=openai_mm_llm,
)

response_1 = query_engine.query(
    "Describe the images in your knowledgebase as if you were a blind person.",
)
print("Response: ", response_1)

document = Document(script, "script")
    CHAT_REFINE_PROMPT_TMPL_MSGS = [
        # optionally add a system prompt
        # SystemMessagePromptTemplate.from_template("Some system message"),
        HumanMessagePromptTemplate.from_template("{query_str}"),
        AIMessagePromptTemplate.from_template("{existing_answer}"),
        HumanMessagePromptTemplate.from_template(
            "We have the opportunity to refine the above answer "
            "(only if needed) with some more context below.\n"
            "------------\n"
            "{context_msg}\n"
            "------------\n"
            "Given the new context, refine the original answer to better "
            "answer the question. "
            "If the context isn't useful, output the original answer again.",
        ),
    ]

    CHAT_REFINE_PROMPT_LC = ChatPromptTemplate.from_messages(CHAT_REFINE_PROMPT_TMPL_MSGS)
    CHAT_REFINE_PROMPT = Prompt.from_langchain_prompt(CHAT_REFINE_PROMPT_LC)

    CHAT_QA_PROMPT_TMPL_MSGS = [
        # optionally add a system prompt
        # SystemMessagePromptTemplate.from_template("My system message"),
        HumanMessagePromptTemplate.from_template(
            "Context information is below. \n"
            "------------\n"
            "{context_msg}\n"
            "------------\n"
            "Given the context information and not prior knowledge, "
            "answer the question: {query_str}\n"
        ),
    ]

    CHAT_QA_PROMPT_LC = ChatPromptTemplate.from_messages(CHAT_QA_PROMPT_TMPL_MSGS)
    CHAT_QA_PROMPT = Prompt.from_langchain_prompt(CHAT_QA_PROMPT_LC)
    article_llm = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo"))
    article_service_context = ServiceContext.from_defaults(llm_predictor=article_llm, chunk_size=1024)
    query_engine = GPTVectorStoreIndex.from_documents([document]).as_query_engine(text_qa_template=CHAT_QA_PROMPT, refine_template=CHAT_REFINE_PROMPT)
    response = query_engine.query(script_prompt)
    print(f"Script: {response}")
    return response

  response = await query_engine.aquery(script_prompt)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/llama_index/indices/query/base.py", line 30, in aquery
    response = await self._aquery(str_or_query_bundle)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/llama_index/query_engine/sub_question_query_engine.py", line 124, in _aquery
    sub_questions = await self._question_gen.agenerate(
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/llama_index/question_gen/llm_generators.py", line 78, in agenerate
    parse = self._prompt.output_parser.parse(prediction)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/llama_index/question_gen/output_parser.py", line 10, in parse
    json_dict = parse_json_markdown(output)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/llama_index/output_parsers/utils.py", line 18, in parse_json_markdown
    raise OutputParserException(f"Got invalid JSON object. Error: {e}")
llama_index.output_parsers.base.OutputParserException: Got invalid JSON object. Error: Unterminated string starting at: line 49 column 22 (char 4229)

llm_predictor_chatgpt = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo"))
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor_chatgpt, chunk_size=1024)
# get the summary prompt
summory_prompt = ""
with open("summary_prompt.txt", "r") as f:
    summary_prompt = f.read()
summary_query = summory_prompt
print(f"Summary Query length {len(summary_query)}")
text = f"{post.title}\n{post.subtitle}\n{post.content}"
document = Document(text, article.url)
document_summary_index = DocumentSummaryIndex.from_documents([document], service_context=service_context)
index = document_summary_index.as_query_engine()
summary = index.query(summary_query)
print(f"Summary: {summary}")