[Python] Trying to deploy my first modal app with a chrona database but the data is not being...

I am having trouble figuring out why I can't see the print statements in the terminal for my retrieveInfoForQuery function and trying to figure out what is wrong. I have verified the chroma db is on the volume. Here is the code.

from langchain_core.tools import tool

from langchain_core.messages import SystemMessage
from langchain import hub
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import CSVLoader
from langgraph.graph import MessagesState, StateGraph
from langchain_chroma import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough, RunnableMap
from langchain_core.documents import Document
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
from langchain_community.llms import HuggingFaceHub
from typing_extensions import List, TypedDict
from langchain.chat_models import init_chat_model
from langchain_openai import OpenAIEmbeddings
import sys
import modal
import os


# Create an image with dependencies
image = modal.Image.debian_slim().pip_install(
"openai", "langchain", "langchain_community", "langchain_core",
"langchain_huggingface", "langchain_openai", "langgraph", "langchain_chroma"
)

# Create Modal app
app = modal.App("rag-modal-deployment", image=image)

# Define image correctly


# Persistent storage
vectorstore_volume = modal.Volume.from_name("gotquestions-storage",create_if_missing=True)

# Define CSV processing function



# Define RAG function
class State(MessagesState):
context: List[Document]
@app.function(volumes={"/vectorstore":vectorstore_volume},secrets=[modal.Secret.from_name("openai-secret"),modal.Secret.from_name("langsmith-secret")],timeout=6000)
def loadData(forceUpload):
# Load or create vectorstore
vectorstore_path = "/vectorstore"
if forceUpload == "true":
print("Created new vector store.")

# Load CSV
loader = CSVLoader(file_path="/vectorstore/gotquestions.csv", encoding="utf8", csv_args={'delimiter': ',', 'quotechar': '"'}, metadata_columns=["url", "question"])
docs = loader.load()

# Split Documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=500)
splits = text_splitter.split_documents(docs)

# Create Vector Store
vectorstore = Chroma.from_documents(
documents=splits,
embedding=OpenAIEmbeddings(model="text-embedding-3-large"),
persist_directory=vectorstore_path
)
else:
print("Loaded existing vector store.")
vectorstore = Chroma(persist_directory=vectorstore_path, embedding_function=OpenAIEmbeddings(model="text-embedding-3-large"))
print("done")


return vectorstore


@app.function(secrets=[modal.Secret.from_name("openai-secret"),modal.Secret.from_name("langsmith-secret")], volumes={"/vectorstore": vectorstore_volume},timeout=6000)
@modal.fastapi_endpoint(docs=True)
def getDataAndAnswerQuestion(question: str,forceUpload:str):


# Set environment variables
#os.environ["OPENAI_API_KEY"] = modal.Secret().get("OPENAI_API_KEY")
#os.environ["HUGGINGFACEHUB_API_TOKEN"] = modal.Secret().get("HUGGINGFACEHUB_API_TOKEN")

# Load data
#loadData.remote(forceUpload)
graph_builder = StateGraph(State)
from langgraph.graph import END
from langgraph.prebuilt import ToolNode, tools_condition

graph_builder.add_node(query_or_respond)
graph_builder.add_node(generate)
graph_builder.set_entry_point("query_or_respond")
graph_builder.add_edge("query_or_respond", "generate")
graph_builder.add_edge("generate", END)

graph = graph_builder.compile()
finalAnswer = graph.invoke({"messages": [{"role": "user", "content": question}], "context": ""})
#for step in graph.stream({"messages": [{"role": "user", "content": question}], "context": ""},stream_mode="values"):
#step["messages"][-1].pretty_print()
# Return formatted results
sources_html = "".join(f'<a href="{doc.metadata["url"]}">{doc.metadata["question"]}</a><br>' for doc in finalAnswer["context"])

return {"content": finalAnswer["messages"][-1].content, "sources": sources_html}


@tool(response_format="content_and_artifact")

def retrieveInfoForQuery(query: str):
"""Retrieve information related to a query."""
print("retrieving... "+query)
vectorstore_path = "/vectorstore"
vectorstore=loadData.remote("false")
if isinstance(vectorstore, Chroma): # Ensure it's properly loaded
retrieved_docs = vectorstore.similarity_search(query, k=2)
else:
raise ValueError("Vectorstore did not initialize correctly.")
retrieved_docs = vectorstore.similarity_search(query, k=2)


#print("retrieved... "+str(retrieved_docs))

serialized = "\n\n".join(
(f"Source: {doc.metadata}\n" f"Content: {doc.page_content}")
for doc in retrieved_docs
)
return serialized, retrieved_docs

def query_or_respond(state: MessagesState):
"""Generate tool call for retrieval or respond."""
llm = init_chat_model("gpt-4o", model_provider="openai")
llm_with_tools = llm.bind_tools([retrieveInfoForQuery])
response = llm_with_tools.invoke(state["messages"])

return {"messages": [response]}

def generate(state: State):
"""Generate answer."""
tool_messages = [
message for message in reversed(state["messages"])
if message.type == "tool"
][::-1]

docs_content = "\n\n".join(doc.content for doc in tool_messages)
system_message_content = (
"You are an assistant for question-answering tasks. "
"Use the following pieces of retrieved context to answer "
"the question. If you don't know the answer, say that you "
"don't know. Keep the answer concise. Only use data from the tool."
"\n\n"
f"{docs_content}"
)

conversation_messages = [
message for message in state["messages"]
if message.type in ("human", "system") or (message.type == "ai" and not message.tool_calls)
]

prompt = [SystemMessage(system_message_content)] + conversation_messages
llm = init_chat_model("gpt-4o", model_provider="openai")
response = llm.invoke(prompt)

context = []
for tool_message in tool_messages:
context.extend(tool_message.artifact)

return {"messages": [response], "context": context}


@app.local_entrypoint()
def main():
#retrieveInfoForQuery("who was Jesus")
vector=loadData.remote("true")
print(type(vector))




Thanks for any ehlp you can provide.

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