jarvis-models/src/blackbox/chroma_upsert.py

from typing import Any, Coroutine

from fastapi import Request, Response, status
from fastapi.responses import JSONResponse
from .blackbox import Blackbox

import requests
import json

from langchain_community.document_loaders.csv_loader import CSVLoader
from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader, TextLoader, UnstructuredHTMLLoader, JSONLoader, Docx2txtLoader, UnstructuredExcelLoader
from langchain_community.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
import chromadb
        
import os
import tempfile

import logging
from ..log.logging_time import logging_time
import re

logger = logging.getLogger
DEFAULT_COLLECTION_ID = "123"

from injector import singleton
@singleton
class ChromaUpsert(Blackbox):
    
    def __init__(self, *args, **kwargs) -> None:
        # config = read_yaml(args[0])
        # load embedding model
        self.embedding_model_1 = SentenceTransformerEmbeddings(model_name="/Workspace/Models/BAAI/bge-large-zh-v1.5", model_kwargs={"device": "cuda"})
        # load chroma db
        self.client_1 = chromadb.HttpClient(host='192.168.0.200', port=7000)

    def __call__(self, *args, **kwargs):
        return self.processing(*args, **kwargs)

    def valid(self, *args, **kwargs) -> bool:
        data = args[0]
        return isinstance(data, list)

    # @logging_time(logger=logger)    
    def processing(self, file, string, context: list, settings: dict) -> str:
        # 用户的操作历史
        if context is None:
            context = []
            
        # context = [
        #     {
        #         "collection_id": "123",
        #         "action": "query",
        #         "content": "你吃饭了吗",
        #         "answer": "吃了",
        #     },
        #     {
        #         "collection_id": "123",
        #         "action": "upsert",
        #         "content": "file_name or string",
        #         "answer": "collection 123 has 12472 documents. /tmp/Cheap and Quick：Efficient Vision-Language Instruction Tuning for Large Language Models.pdf ids is 0~111",
        #     },
        # ]

        if settings is None:
            settings = {}
        print("\nSettings: ", settings)
        # # chroma_query settings
        if "settings" in settings:
            chroma_embedding_model = settings["settings"].get("chroma_embedding_model")
            chroma_host = settings["settings"].get("chroma_host")
            chroma_port = settings["settings"].get("chroma_port")
            chroma_collection_id = settings["settings"].get("chroma_collection_id")     
        else:
            chroma_embedding_model = settings.get("chroma_embedding_model")
            chroma_host = settings.get("chroma_host")
            chroma_port = settings.get("chroma_port")
            chroma_collection_id = settings.get("chroma_collection_id")

        if chroma_embedding_model is None or chroma_embedding_model.isspace() or chroma_embedding_model == "":
            chroma_embedding_model = "/Workspace/Models/BAAI/bge-large-zh-v1.5"

        if chroma_host is None or chroma_host.isspace() or chroma_host == "":
            chroma_host = "10.6.82.192"
        
        if chroma_port is None or chroma_port.isspace() or chroma_port == "":
            chroma_port = "8000"
        
        if chroma_collection_id is None or chroma_collection_id.isspace() or chroma_collection_id == "":
            chroma_collection_id = "g2e"

        # load client and embedding model from init
        if re.search(r"10.6.82.192", chroma_host) and re.search(r"8000", chroma_port):
            client = self.client_1
        else:
            client = chromadb.HttpClient(host=chroma_host, port=chroma_port)
        print(f"chroma_embedding_model: {chroma_embedding_model}")
        if re.search(r"/Workspace/Models/BAAI/bge-large-zh-v1.5", chroma_embedding_model):
            embedding_model = self.embedding_model_1
        else:
            embedding_model = SentenceTransformerEmbeddings(model_name=chroma_embedding_model, device = "cuda:0")


        if file is not None:
            file_type = file.split(".")[-1]
            print("file_type: ",file_type)
            if file_type == "pdf":
                loader = PyPDFLoader(file)
            elif file_type == "txt":
                loader = TextLoader(file)
            elif file_type == "csv":
                loader = CSVLoader(file)
            elif file_type == "html":
                loader = UnstructuredHTMLLoader(file)
            elif file_type == "json":
                loader = JSONLoader(file, jq_schema='.', text_content=False)
            elif file_type == "docx":
                loader = Docx2txtLoader(file)
            elif file_type == "xlsx":
                loader = UnstructuredExcelLoader(file)

            documents = loader.load()
            text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=0)

            docs = text_splitter.split_documents(documents)

            ids = [str(file)+str(i) for i in range(len(docs))]

            Chroma.from_documents(documents=docs, embedding=embedding_model, ids=ids, collection_name=chroma_collection_id, client=client)
            
            collection_number = client.get_collection(chroma_collection_id).count()
            response_file = f"collection {chroma_collection_id} has {collection_number} documents. {file} ids is 0~{len(docs)-1}"

        if string is not None:
            # 生成一个新的id  ids_string: 1
            # ids =  setting.ChromaSetting.string_ids[0] + 1
            ids =  "1"

            Chroma.from_texts(texts=[string], embedding=embedding_model, ids=[ids], collection_name=chroma_collection_id, client=client)


            collection_number = client.get_collection(chroma_collection_id).count()
            response_string = f"collection {chroma_collection_id} has {collection_number} documents. {string} ids is {ids}"
        
        
        if file is not None and string is not None:
            return response_file + " \n and " + response_string
        elif file is not None and string is None:
            return response_file
        elif file is None and string is not None:
            return response_string



    async def fast_api_handler(self, request: Request) -> Response:
 
        user_file = (await request.form()).get("file")
        user_string = (await request.form()).get("string")
        context = (await request.form()).get("context")
        setting: dict = (await request.form()).get("settings")

        if isinstance(setting, str):
            try:
                setting = json.loads(setting)  # 尝试将字符串转换为字典
            except json.JSONDecodeError:
                return JSONResponse(content={"error": "Invalid settings format"}, status_code=status.HTTP_400_BAD_REQUEST)
        
        if user_file is None and user_string is None:
            return JSONResponse(content={"error": "file or string is required"}, status_code=status.HTTP_400_BAD_REQUEST)

        if user_file is not None:
            pdf_bytes = await user_file.read()

            custom_filename = user_file.filename
            # 获取系统的临时目录路径
            safe_filename = os.path.join(tempfile.gettempdir(), os.path.basename(custom_filename))
        
            with open(safe_filename, "wb") as f:
                f.write(pdf_bytes)
        else:
            safe_filename = None

        try:
            txt = self.processing(safe_filename, user_string, context, setting)
            print(txt)
        except ValueError as e:
            return JSONResponse(content={"error": str(e)}, status_code=status.HTTP_400_BAD_REQUEST)
        return JSONResponse(content={"text": txt}, status_code=status.HTTP_200_OK)
            
        # return JSONResponse(
        #     content={"response": self.processing(safe_filename, user_string, context, setting)},
        #     status_code=status.HTTP_200_OK)