import os
import readpdf
import extractdata
from qdrant_client import QdrantClient
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.chat_models import ChatOllama
from langchain_community.document_loaders import TextLoader
from langchain.chains import RetrievalQA
from langchain.schema.document import Document
from qdrant_client.http.models import Distance, VectorParams
from langchain_community.vectorstores import Qdrant
from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
from langchain_community.embeddings import OllamaEmbeddings
import sys

def sendFile(filename,hash):
	text= readpdf.returnTextFromFile("./"+filename)
	pages=[]

	# define the text splitter
	r_splitter = RecursiveCharacterTextSplitter(
    	  chunk_size=2048,
          chunk_overlap=100,
          #separators=["\n\n", "\n", " ", ""]
	)

	pages = r_splitter.split_text(text)
	docs = [Document(page_content=x) for x in pages]
	# set up Ollama Embeddings: https://python.langchain.com/docs/integrations/text_embedding/ollama
	embeddings = OllamaEmbeddings(model='llama3',base_url="http://10.128.0.3:11434")
	#url="http://localhost:6333"
	#client = QdrantClient(url=url)

	#client.create_collection(
	#    collection_name="C834_DSC_TEST",
	#    vectors_config=VectorParams(size=3072, distance=Distance.COSINE),
	#)
	# set up the qdrant database
	qdrant = Qdrant.from_documents(
    	 docs,
    	 embeddings,
    	 path="./vectLlama3",
    	 collection_name=hash,
    	 force_recreate=True,
	)
	del qdrant
	print ("FINE EMBEDDING")
	#os.remove("./"+filename)
	#print(f"File '{filename}' file rimosso.")
	result = extractdata.extractDataFromDoc(hash)
	return result