import chainlit as cl
from llama_index import ServiceContext
from llama_index.node_parser.simple import SimpleNodeParser
from llama_index.langchain_helpers.text_splitter import TokenTextSplitter
from llama_index.llms import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index import VectorStoreIndex
from llama_index.vector_stores import ChromaVectorStore
from llama_index.storage.storage_context import StorageContext
import chromadb
from llama_index.readers.wikipedia import WikipediaReader
from llama_index.tools import FunctionTool
from llama_index.vector_stores.types import (
    VectorStoreInfo,
    MetadataInfo,
    ExactMatchFilter,
    MetadataFilters,
)
from llama_index.retrievers import VectorIndexRetriever
from llama_index.query_engine import RetrieverQueryEngine

from typing import List, Tuple, Any
from pydantic import BaseModel, Field
from llama_index.agent import OpenAIAgent

import pandas as pd
from sqlalchemy import create_engine
from llama_index import SQLDatabase
from llama_index.indices.struct_store.sql_query import NLSQLTableQueryEngine
from llama_index.tools.query_engine import QueryEngineTool


openai.api_key = os.environ["OPENAI_API_KEY"]

embed_model = OpenAIEmbedding()
chunk_size = 1000
llm = OpenAI(
    temperature=0, 
    model="gpt-3.5-turbo", 
    streaming=True
)

service_context = ServiceContext.from_defaults(
    llm=llm, 
    chunk_size=chunk_size, 
    embed_model=embed_model
)

text_splitter = TokenTextSplitter(
    chunk_size=chunk_size
)

node_parser = SimpleNodeParser(
    text_splitter=text_splitter
)

chroma_client = chromadb.Client()
chroma_collection = chroma_client.create_collection("wikipedia_barbie_opp")

vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
wiki_vector_index = VectorStoreIndex([], storage_context=storage_context, service_context=service_context)

movie_list = ["Barbie (film)", "Oppenheimer (film)"]

wiki_docs = WikipediaReader().load_data(pages=movie_list, auto_suggest=False)

class AutoRetrieveModel(BaseModel):
    query: str = Field(..., description="natural language query string")
    filter_key_list: List[str] = Field(
        ..., description="List of metadata filter field names"
    )
    filter_value_list: List[str] = Field(
        ...,
        description=(
            "List of metadata filter field values (corresponding to names specified in filter_key_list)"
        )
    )

def auto_retrieve_fn(
    query: str, filter_key_list: List[str], filter_value_list: List[str]
):
    """Auto retrieval function.
    Performs auto-retrieval from a vector database, and then applies a set of filters.
    """
    query = query or "Query"

    exact_match_filters = [
        ExactMatchFilter(key=k, value=v)
        for k, v in zip(filter_key_list, filter_value_list)
    ]
    retriever = VectorIndexRetriever(
        wiki_vector_index, filters=MetadataFilters(filters=exact_match_filters), top_k=top_k
    )
    query_engine = RetrieverQueryEngine.from_args(retriever)

    response = query_engine.query(query)
    return str(response)



@cl.author_rename
def rename(orig_author: str):
    rename_dict = {"RetrievalQA": "Consulting The Llamaindex Tools"}
    return rename_dict.get(orig_author, orig_author)

@cl.on_chat_start
async def init():
    msg = cl.Message(content=f"Building Index...")
    await msg.send()

    for movie, wiki_doc in zip(movie_list, wiki_docs):
        nodes = node_parser.get_nodes_from_documents([wiki_doc])
        for node in nodes:
            node.metadata = {'title' : movie}
        wiki_vector_index.insert_nodes(nodes)

    top_k = 3
    vector_store_info = VectorStoreInfo(
        content_info="semantic information about movies",
        metadata_info=[MetadataInfo(
            name="title",
            type="str",
            description="title of the movie, one of [Barbie (film), Oppenheimer (film)]",
        )]
    )

    description = f"""\
    Use this tool to look up semantic information about films.
    The vector database schema is given below:
    {vector_store_info.json()}
    """
    
    auto_retrieve_tool = FunctionTool.from_defaults(
        fn=auto_retrieve_fn,
        name="auto_retrieve_tool",
        description=description,
        fn_schema=AutoRetrieveModel,
    )

    agent = OpenAIAgent.from_tools(
        [auto_retrieve_tool], llm=llm, verbose=True
    )    

    barbie_df = pd.read_csv ('./data/barbie.csv')
    oppenheimer_df = pd.read_csv ('./data/oppenheimer.csv')

    engine = create_engine("sqlite+pysqlite:///:memory:")

    barbie_df.to_sql(
        "barbie",
        engine
    )

    oppenheimer_df.to_sql(
        "oppenheimer",
        engine
    )

    sql_query_engine = NLSQLTableQueryEngine(
        sql_database=sql_database,
        tables=['barbie', 'oppenheimer']
    )    

    sql_tool = QueryEngineTool.from_defaults(
        query_engine=sql_query_engine,
        name='sql_tool',
        description=(
            "Useful for translating a natural language query into a SQL query over a table containing: " +
            "barbie, containing information related to reviews of the Barbie movie" +
            "oppenheimer, containing information related to reviews of the Oppenheimer movie"
        ),
    )

    agent = OpenAIAgent.from_tools(
        [sql_tool], llm=llm, verbose=True
    )

    barbenheimer_agent = OpenAIAgent.from_tools(
        [auto_retrieve_tool, sql_tool], llm=llm, verbose=True
    )
    
    msg.content = f"Index built!"
    await msg.send()

    cl.user_session.set("barbenheimer_agent", barbenheimer_agent)


@cl.on_message
async def main(message):
    barbenheimer_agent = cl.user_session.get("barbenheimer_agent")
    response = barbenheimer_agent.chat(message)

    await cl.Message(content=str(response)).send()