use minio to store uploaded files; build dialog server; (#16)

* format code

* use minio to store uploaded files; build dialog server;

python/llm/__init__.py

@@ -1 +1,2 @@
 from .embedding_model import HuEmbedding
+from .chat_model import GptTurbo

python/llm/chat_model.py

@@ -0,0 +1,34 @@
+from abc import ABC
+import openapi
+import os
+
+class Base(ABC):
+    def chat(self, system, history, gen_conf):
+        raise NotImplementedError("Please implement encode method!")
+
+
+class GptTurbo(Base):
+    def __init__(self):
+        openapi.api_key = os.environ["OPENAPI_KEY"]
+
+    def chat(self, system, history, gen_conf):
+        history.insert(0, {"role": "system", "content": system})
+        res = openapi.ChatCompletion.create(model="gpt-3.5-turbo",
+                                           messages=history,
+                                          **gen_conf)
+        return res.choices[0].message.content.strip()
+
+
+class QWen(Base):
+    def chat(self, system, history, gen_conf):
+        from http import HTTPStatus
+        from dashscope import Generation
+        from dashscope.api_entities.dashscope_response import Role
+        response = Generation.call(
+                    Generation.Models.qwen_turbo,
+                    messages=messages,
+                    result_format='message'
+        )
+        if response.status_code == HTTPStatus.OK:
+            return response.output.choices[0]['message']['content']
+        return response.message

python/llm/embedding_model.py

@@ -1,6 +1,7 @@
 from abc import ABC
 from FlagEmbedding import FlagModel
 import torch
+import numpy as np
 
 class Base(ABC):
     def encode(self, texts: list, batch_size=32):
@@ -27,5 +28,5 @@ class HuEmbedding(Base):
     def encode(self, texts: list, batch_size=32):
         res = []
         for i in range(0, len(texts), batch_size):
-            res.extend(self.encode(texts[i:i+batch_size]))
+            res.extend(self.model.encode(texts[i:i+batch_size]).tolist())
-        return res
+        return np.array(res)

python/nlp/huchunk.py

@@ -372,7 +372,7 @@ class PptChunker(HuChunker):
 
     def __call__(self, fnm):
         from pptx import Presentation
-        ppt = Presentation(fnm)
+        ppt = Presentation(fnm) if isinstance(fnm, str) else Presentation(BytesIO(fnm))
         flds = self.Fields()
         flds.text_chunks = []
         for slide in ppt.slides:
@@ -396,7 +396,9 @@ class TextChunker(HuChunker):
     @staticmethod
     def is_binary_file(file_path):
         mime = magic.Magic(mime=True)
+        if isinstance(file_path, str):
             file_type = mime.from_file(file_path)
+        else:file_type = mime.from_buffer(file_path) 
         if 'text' in file_type:
             return False
         else:

python/nlp/search.py

@@ -0,0 +1,221 @@
+import re
+from elasticsearch_dsl import Q,Search,A
+from typing import List, Optional, Tuple,Dict, Union
+from dataclasses import dataclass
+from util import setup_logging, rmSpace
+from nlp import huqie, query
+from datetime import datetime
+from sklearn.metrics.pairwise import cosine_similarity as CosineSimilarity
+import numpy as np
+from copy import deepcopy
+
+class Dealer:
+    def __init__(self, es, emb_mdl):
+        self.qryr = query.EsQueryer(es)
+        self.qryr.flds = ["title_tks^10", "title_sm_tks^5", "content_ltks^2", "content_sm_ltks"]
+        self.es = es
+        self.emb_mdl = emb_mdl
+
+    @dataclass
+    class SearchResult:
+        total:int
+        ids: List[str]
+        query_vector: List[float] = None
+        field: Optional[Dict] = None
+        highlight: Optional[Dict] = None
+        aggregation: Union[List, Dict, None] = None
+        keywords: Optional[List[str]] = None
+        group_docs: List[List] = None
+
+    def _vector(self, txt, sim=0.8, topk=10):
+        return {
+            "field": "q_vec",
+            "k": topk,
+            "similarity": sim,
+            "num_candidates": 1000,
+            "query_vector": self.emb_mdl.encode_queries(txt)
+        }
+
+    def search(self, req, idxnm, tks_num=3):
+        keywords = []
+        qst = req.get("question", "")
+
+        bqry,keywords = self.qryr.question(qst)
+        if req.get("kb_ids"): bqry.filter.append(Q("terms", kb_id=req["kb_ids"]))
+        bqry.filter.append(Q("exists", field="q_tks"))
+        bqry.boost = 0.05
+        print(bqry)
+
+        s = Search()
+        pg = int(req.get("page", 1))-1
+        ps = int(req.get("size", 1000))
+        src = req.get("field", ["docnm_kwd", "content_ltks", "kb_id",
+                                "image_id", "doc_id", "q_vec"])
+
+        s = s.query(bqry)[pg*ps:(pg+1)*ps]
+        s = s.highlight("content_ltks")
+        s = s.highlight("title_ltks")
+        if not qst: s = s.sort({"create_time":{"order":"desc", "unmapped_type":"date"}})
+
+        s = s.highlight_options(
+                                fragment_size = 120,
+                                number_of_fragments=5,
+                                boundary_scanner_locale="zh-CN",
+                                boundary_scanner="SENTENCE",
+                                boundary_chars=",./;:\\!()，。？：！……（）——、"
+                                )
+        s = s.to_dict()
+        q_vec = []
+        if req.get("vector"): 
+            s["knn"] = self._vector(qst, req.get("similarity", 0.4), ps)
+            s["knn"]["filter"] = bqry.to_dict()
+            del s["highlight"]
+            q_vec = s["knn"]["query_vector"]
+        res = self.es.search(s, idxnm=idxnm, timeout="600s",src=src)
+        print("TOTAL: ", self.es.getTotal(res))
+        if self.es.getTotal(res) == 0 and "knn" in s:
+            bqry,_ = self.qryr.question(qst, min_match="10%")
+            if req.get("kb_ids"): bqry.filter.append(Q("terms", kb_id=req["kb_ids"]))
+            s["query"] = bqry.to_dict()
+            s["knn"]["filter"] = bqry.to_dict()
+            s["knn"]["similarity"] = 0.7
+            res = self.es.search(s, idxnm=idxnm, timeout="600s",src=src)
+
+        kwds = set([])
+        for k in keywords:
+            kwds.add(k)
+            for kk in huqie.qieqie(k).split(" "):
+                if len(kk) < 2:continue
+                if kk in kwds:continue
+                kwds.add(kk)
+
+        aggs = self.getAggregation(res, "docnm_kwd")
+
+        return self.SearchResult(
+            total = self.es.getTotal(res),
+            ids = self.es.getDocIds(res),
+            query_vector = q_vec,
+            aggregation = aggs,
+            highlight = self.getHighlight(res),
+            field = self.getFields(res, ["docnm_kwd", "content_ltks",
+                                         "kb_id","image_id", "doc_id", "q_vec"]),
+            keywords = list(kwds)
+        )
+
+    def getAggregation(self, res, g):
+        if not "aggregations" in res or "aggs_"+g not in res["aggregations"]:return
+        bkts = res["aggregations"]["aggs_"+g]["buckets"]
+        return [(b["key"], b["doc_count"]) for b in bkts]
+
+    def getHighlight(self, res):
+        def rmspace(line):
+            eng = set(list("qwertyuioplkjhgfdsazxcvbnm"))
+            r = []
+            for t in line.split(" "):
+                if not t:continue
+                if len(r)>0 and len(t)>0 and r[-1][-1] in eng and t[0] in eng:r.append(" ")
+                r.append(t)
+            r = "".join(r)
+            return r
+
+        ans = {}
+        for d in res["hits"]["hits"]:
+            hlts = d.get("highlight")
+            if not hlts:continue
+            ans[d["_id"]] = "".join([a for a in list(hlts.items())[0][1]])
+        return ans
+
+    def getFields(self, sres, flds):
+        res = {}
+        if not flds:return {}
+        for d in  self.es.getSource(sres):
+            m = {n:d.get(n) for n in flds if d.get(n) is not None}
+            for n,v in m.items():
+                if type(v) == type([]):
+                    m[n] = "\t".join([str(vv) for vv in v])
+                    continue
+                if type(v) != type(""):m[n] = str(m[n])
+                m[n] = rmSpace(m[n])
+
+            if m:res[d["id"]] = m
+        return res
+
+
+    @staticmethod
+    def trans2floats(txt):
+        return [float(t) for t in txt.split("\t")]
+
+
+    def insert_citations(self, ans, top_idx, sres, vfield = "q_vec", cfield="content_ltks"):
+
+        ins_embd = [Dealer.trans2floats(sres.field[sres.ids[i]][vfield]) for i in top_idx]
+        ins_tw =[sres.field[sres.ids[i]][cfield].split(" ") for i in top_idx]
+        s = 0
+        e = 0
+        res = ""
+        def citeit():
+            nonlocal s, e, ans, res
+            if not ins_embd:return
+            embd = self.emb_mdl.encode(ans[s: e])
+            sim = self.qryr.hybrid_similarity(embd, 
+                                              ins_embd, 
+                                              huqie.qie(ans[s:e]).split(" "),
+                                              ins_tw)
+            print(ans[s: e], sim)
+            mx = np.max(sim)*0.99
+            if mx < 0.55:return
+            cita = list(set([top_idx[i] for i in range(len(ins_embd)) if sim[i] >mx]))[:4]
+            for i in cita: res += f"@?{i}?@"
+
+            return cita
+
+        punct = set("；。？!！")
+        if not self.qryr.isChinese(ans): 
+            punct.add("?")
+            punct.add(".")
+        while e < len(ans):
+            if e - s < 12 or ans[e] not in punct:
+                e += 1
+                continue
+            if ans[e] == "." and e+1<len(ans) and re.match(r"[0-9]", ans[e+1]):
+                e += 1
+                continue
+            if ans[e] == "." and e-2>=0 and  ans[e-2] == "\n":
+                e += 1
+                continue
+            res += ans[s: e]
+            citeit()
+            res += ans[e]
+            e += 1
+            s = e
+
+        if s< len(ans): 
+            res += ans[s:]
+            citeit()
+
+        return res
+
+
+    def rerank(self, sres, query, tkweight=0.3, vtweight=0.7, vfield="q_vec", cfield="content_ltks"):
+        ins_embd = [Dealer.trans2floats(sres.field[i]["q_vec"]) for i in sres.ids]
+        if not ins_embd: return []
+        ins_tw =[sres.field[i][cfield].split(" ") for i in sres.ids]
+        #return CosineSimilarity([sres.query_vector], ins_embd)[0]
+        sim = self.qryr.hybrid_similarity(sres.query_vector, 
+                                          ins_embd, 
+                                          huqie.qie(query).split(" "),
+                                          ins_tw, tkweight, vtweight)
+        return sim
+
+
+
+if __name__ == "__main__": 
+    from util import es_conn
+    SE = Dealer(es_conn.HuEs("infiniflow"))
+    qs = [
+        "胡凯",
+        ""
+    ]
+    for q in qs: 
+        print(">>>>>>>>>>>>>>>>>>>>", q)
+        print(SE.search({"question": q, "kb_ids": "64f072a75f3b97c865718c4a"}, "infiniflow_*"))

python/parser/docx_parser.py

@@ -3,6 +3,7 @@ import re
 import pandas as pd
 from collections import Counter
 from nlp import huqie
+from io import BytesIO
 
 
 class HuDocxParser:
@@ -97,7 +98,7 @@ class HuDocxParser:
         return ["\n".join(lines)]
 
     def __call__(self, fnm):
-        self.doc = Document(fnm)
+        self.doc = Document(fnm) if isinstance(fnm, str) else Document(BytesIO(fnm))
         secs = [(p.text, p.style.name) for p in self.doc.paragraphs]
         tbls = [self.__extract_table_content(tb) for tb in self.doc.tables]
         return secs, tbls

python/parser/excel_parser.py

@@ -1,10 +1,12 @@
 from openpyxl import load_workbook
 import sys
+from io import BytesIO
 
 
 class HuExcelParser:
     def __call__(self, fnm):
-        wb = load_workbook(fnm)
+        if isinstance(fnm, str):wb = load_workbook(fnm)
+        else: wb = load_workbook(BytesIO(fnm))
         res = []
         for sheetname in wb.sheetnames:
             ws = wb[sheetname]

python/parser/pdf_parser.py

@@ -1,4 +1,5 @@
 import xgboost as xgb
+from io import BytesIO
 import torch
 import re
 import pdfplumber
@@ -1525,7 +1526,7 @@ class HuParser:
         return "\n\n".join(res)
 
     def __call__(self, fnm, need_image=True, zoomin=3, return_html=False):
-        self.pdf = pdfplumber.open(fnm)
+        self.pdf = pdfplumber.open(fnm) if isinstance(fnm, str) else pdfplumber.open(BytesIO(fnm))
         self.lefted_chars = []
         self.mean_height = []
         self.mean_width = []

python/svr/dialog_svr.py

@@ -0,0 +1,164 @@
+#-*- coding:utf-8 -*-
+import sys, os, re,inspect,json,traceback,logging,argparse, copy
+sys.path.append(os.path.realpath(os.path.dirname(inspect.getfile(inspect.currentframe())))+"/../")
+from tornado.web import RequestHandler,Application
+from tornado.ioloop import IOLoop
+from tornado.httpserver import HTTPServer
+from tornado.options import define,options
+from util import es_conn, setup_logging
+from svr import sec_search as search
+from svr.rpc_proxy import RPCProxy
+from sklearn.metrics.pairwise import cosine_similarity as CosineSimilarity
+from nlp import huqie
+from nlp import query as Query
+from llm import HuEmbedding, GptTurbo
+import numpy as np
+from io import BytesIO
+from util import config
+from timeit import default_timer as timer
+from collections import OrderedDict
+
+SE = None
+CFIELD="content_ltks"
+EMBEDDING = HuEmbedding()
+LLM = GptTurbo()
+
+def get_QA_pairs(hists):
+    pa = []
+    for h in hists:
+        for k in ["user", "assistant"]:
+            if h.get(k):
+                pa.append({
+                    "content": h[k],
+                    "role": k,
+                })
+
+    for p in pa[:-1]: assert len(p) == 2, p
+    return pa
+
+
+
+def get_instruction(sres, top_i, max_len=8096 fld="content_ltks"):
+    max_len //= len(top_i)
+    # add instruction to prompt
+    instructions = [re.sub(r"[\r\n]+", " ", sres.field[sres.ids[i]][fld]) for i in top_i]
+    if len(instructions)>2:
+        # Said that LLM is sensitive to the first and the last one, so
+        # rearrange the order of references
+        instructions.append(copy.deepcopy(instructions[1]))
+        instructions.pop(1)
+
+    def token_num(txt):
+        c = 0
+        for tk in re.split(r"[，。/？‘’”“：；:;!！]", txt):
+            if re.match(r"[a-zA-Z-]+$", tk):
+                c += 1
+                continue
+            c += len(tk)
+        return c
+
+    _inst = ""
+    for ins in instructions:
+        if token_num(_inst) > 4096:
+            _inst += "\n知识库：" + instructions[-1][:max_len]
+            break
+        _inst += "\n知识库：" + ins[:max_len]
+    return _inst
+
+
+def prompt_and_answer(history, inst):
+    hist = get_QA_pairs(history)
+    chks = []
+    for s in re.split(r"[：:;；。\n\r]+", inst):
+        if s: chks.append(s)
+    chks = len(set(chks))/(0.1+len(chks))
+    print("Duplication portion:", chks)
+    
+    system = """
+你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答%s。当所有知识库内容都与问题无关时，你的回答必须包括"知识库中未找到您要的答案！这是我所知道的，仅作参考。"这句话。回答需要考虑聊天历史。
+以下是知识库：
+%s
+以上是知识库。
+"""%(("，最好总结成表格" if chks<0.6 and chks>0 else ""), inst)
+
+    print("【PROMPT】:", system)
+    start = timer()
+    response = LLM.chat(system, hist, {"temperature": 0.2, "max_tokens": 512})
+    print("GENERATE: ", timer()-start)
+    print("===>>", response)
+    return response
+
+
+class Handler(RequestHandler):
+    def post(self):
+        global SE,MUST_TK_NUM
+        param = json.loads(self.request.body.decode('utf-8'))
+        try:
+            question = param.get("history",[{"user": "Hi!"}])[-1]["user"]
+            res = SE.search({
+                "question": question,
+                "kb_ids": param.get("kb_ids", []),
+                "size": param.get("topn", 15)
+            })
+
+            sim = SE.rerank(res, question)  
+            rk_idx = np.argsort(sim*-1)
+            topidx = [i for i in rk_idx if sim[i] >= aram.get("similarity", 0.5)][:param.get("topn",12)]
+            inst = get_instruction(res, topidx)
+
+            ans, topidx = prompt_and_answer(param["history"], inst)
+            ans = SE.insert_citations(ans, topidx, res)
+
+            refer = OrderedDict()
+            docnms = {}
+            for i in rk_idx:
+                 did = res.field[res.ids[i]]["doc_id"])
+                 if did not in docnms: docnms[did] = res.field[res.ids[i]]["docnm_kwd"])
+                 if did not in refer: refer[did] = []
+                 refer[did].append({
+                     "chunk_id": res.ids[i],
+                     "content": res.field[res.ids[i]]["content_ltks"]),
+                     "image": ""
+                 })
+
+            print("::::::::::::::", ans)
+            self.write(json.dumps({
+                "code":0,
+                "msg":"success",
+                "data":{
+                    "uid": param["uid"],
+                    "dialog_id": param["dialog_id"],
+                    "assistant": ans
+                    "refer": [{
+                        "did": did,
+                        "doc_name": docnms[did],
+                        "chunks": chunks
+                    } for did, chunks in refer.items()]
+                }
+            }))
+            logging.info("SUCCESS[%d]"%(res.total)+json.dumps(param, ensure_ascii=False))
+
+        except Exception as e:
+            logging.error("Request 500: "+str(e))
+            self.write(json.dumps({
+                "code":500,
+                "msg":str(e),
+                "data":{}
+            }))
+            print(traceback.format_exc())
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--port", default=4455, type=int, help="Port used for service")
+    ARGS = parser.parse_args()
+    
+    SE = search.ResearchReportSearch(es_conn.HuEs("infiniflow"), EMBEDDING)
+
+    app = Application([(r'/v1/chat/completions', Handler)],debug=False)
+    http_server = HTTPServer(app)
+    http_server.bind(ARGS.port)
+    http_server.start(3)
+
+    IOLoop.current().start()
+

python/svr/parse_user_docs.py

@@ -34,18 +34,14 @@ DOC = DocxChunker(DocxParser())
 EXC = ExcelChunker(ExcelParser())
 PPT = PptChunker()
 
-UPLOAD_LOCATION = os.environ.get("UPLOAD_LOCATION", "./")
+def chuck_doc(name, binary):
-logging.warning(f"The files are stored in {UPLOAD_LOCATION}, please check it!")
-
-
-def chuck_doc(name):
     suff = os.path.split(name)[-1].lower().split(".")[-1]
-    if suff.find("pdf") >= 0: return PDF(name)
+    if suff.find("pdf") >= 0: return PDF(binary)
-    if suff.find("doc") >= 0: return DOC(name)
+    if suff.find("doc") >= 0: return DOC(binary)
-    if re.match(r"(xlsx|xlsm|xltx|xltm)", suff): return EXC(name)
+    if re.match(r"(xlsx|xlsm|xltx|xltm)", suff): return EXC(binary)
-    if suff.find("ppt") >= 0: return PPT(name)
+    if suff.find("ppt") >= 0: return PPT(binary)
     
-    return TextChunker()(name)
+    return TextChunker()(binary)
 
 
 def collect(comm, mod, tm):
@@ -115,7 +111,7 @@ def build(row):
     random.seed(time.time())
     set_progress(row["kb2doc_id"], random.randint(0, 20)/100., "Finished preparing! Start to slice file!")
     try:
-        obj = chuck_doc(os.path.join(UPLOAD_LOCATION, row["location"]))
+        obj = chuck_doc(row["doc_name"], MINIO.get("%s-upload"%str(row["uid"]), row["location"]))
     except Exception as e:
         if re.search("(No such file|not found)", str(e)):
             set_progress(row["kb2doc_id"], -1, "Can not find file <%s>"%row["doc_name"])
@@ -133,9 +129,11 @@ def build(row):
     doc = {
         "doc_id": row["did"],
         "kb_id": [str(row["kb_id"])],
+        "docnm_kwd": os.path.split(row["location"])[-1],
         "title_tks": huqie.qie(os.path.split(row["location"])[-1]),
         "updated_at": str(row["updated_at"]).replace("T", " ")[:19]
     }
+    doc["title_sm_tks"] = huqie.qieqie(doc["title_tks"])
     output_buffer = BytesIO()
     docs = []
     md5 = hashlib.md5()
@@ -144,11 +142,14 @@ def build(row):
         md5.update((txt + str(d["doc_id"])).encode("utf-8"))
         d["_id"] = md5.hexdigest()
         d["content_ltks"] = huqie.qie(txt)
+        d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"])
         if not img:
             docs.append(d)
             continue
         img.save(output_buffer, format='JPEG')
-        d["img_bin"] = str(output_buffer.getvalue())
+        MINIO.put("{}-{}".format(row["uid"], row["kb_id"]), d["_id"],
+                      output_buffer.getvalue())
+        d["img_id"] = "{}-{}".format(row["uid"], row["kb_id"])
         docs.append(d)
 
     for arr, img in obj.table_chunks:

src/api/doc_info.rs

@@ -1,9 +1,9 @@
 use std::collections::HashMap;
-use std::io::Write;
+use std::io::BufReader;
 use actix_multipart_extract::{ File, Multipart, MultipartForm };
-use actix_web::{get, HttpResponse, post, web};
+use actix_web::{ HttpResponse, post, web };
 use chrono::{ Utc, FixedOffset };
-use minio::s3::args::{BucketExistsArgs, MakeBucketArgs, UploadObjectArgs};
+use minio::s3::args::{ BucketExistsArgs, MakeBucketArgs, PutObjectArgs };
 use sea_orm::DbConn;
 use crate::api::JsonResponse;
 use crate::AppState;
@@ -12,9 +12,6 @@ use crate::errors::AppError;
 use crate::service::doc_info::{ Mutation, Query };
 use serde::Deserialize;
 
-const BUCKET_NAME: &'static str = "docgpt-upload";
-
-
 fn now() -> chrono::DateTime<FixedOffset> {
     Utc::now().with_timezone(&FixedOffset::east_opt(3600 * 8).unwrap())
 }
@@ -74,43 +71,61 @@ async fn upload(
 ) -> Result<HttpResponse, AppError> {
     let uid = payload.uid;
     let file_name = payload.file_field.name.as_str();
-    async fn add_number_to_filename(file_name: &str, conn:&DbConn, uid:i64, parent_id:i64) -> String {
+    async fn add_number_to_filename(
+        file_name: &str,
+        conn: &DbConn,
+        uid: i64,
+        parent_id: i64
+    ) -> String {
         let mut i = 0;
         let mut new_file_name = file_name.to_string();
         let arr: Vec<&str> = file_name.split(".").collect();
         let suffix = String::from(arr[arr.len() - 1]);
         let preffix = arr[..arr.len() - 1].join(".");
-        let mut docs = Query::find_doc_infos_by_name(conn, uid, &new_file_name, Some(parent_id)).await.unwrap();
+        let mut docs = Query::find_doc_infos_by_name(
+            conn,
+            uid,
+            &new_file_name,
+            Some(parent_id)
+        ).await.unwrap();
         while docs.len() > 0 {
             i += 1;
             new_file_name = format!("{}_{}.{}", preffix, i, suffix);
-            docs = Query::find_doc_infos_by_name(conn, uid, &new_file_name, Some(parent_id)).await.unwrap();
+            docs = Query::find_doc_infos_by_name(
+                conn,
+                uid,
+                &new_file_name,
+                Some(parent_id)
+            ).await.unwrap();
         }
         new_file_name
     }
     let fnm = add_number_to_filename(file_name, &data.conn, uid, payload.did).await;
 
-    let s3_client = &data.s3_client;
+    let bucket_name = format!("{}-upload", payload.uid);
+    let s3_client: &minio::s3::client::Client = &data.s3_client;
     let buckets_exists = s3_client
-        .bucket_exists(&BucketExistsArgs::new(BUCKET_NAME)?)
+        .bucket_exists(&BucketExistsArgs::new(&bucket_name).unwrap()).await
-        .await?;
+        .unwrap();
     if !buckets_exists {
-        s3_client
+        print!("Create bucket: {}", bucket_name.clone());
-            .make_bucket(&MakeBucketArgs::new(BUCKET_NAME)?)
+        s3_client.make_bucket(&MakeBucketArgs::new(&bucket_name).unwrap()).await.unwrap();
-            .await?;
+    } else {
+        print!("Existing bucket: {}", bucket_name.clone());
     }
 
-    s3_client
+    let location = format!("/{}/{}", payload.did, fnm);
-        .upload_object(
+    print!("===>{}", location.clone());
-            &mut UploadObjectArgs::new(
+    s3_client.put_object(
-                BUCKET_NAME,
+        &mut PutObjectArgs::new(
-                fnm.as_str(),
+            &bucket_name,
-                format!("/{}/{}-{}", payload.uid, payload.did, fnm).as_str()
+            &location,
+            &mut BufReader::new(payload.file_field.bytes.as_slice()),
+            Some(payload.file_field.bytes.len()),
+            None
         )?
-        )
+    ).await?;
-        .await?;
 
-    let location = format!("/{}/{}", BUCKET_NAME, fnm);
     let doc = Mutation::create_doc_info(&data.conn, Model {
         did: Default::default(),
         uid: uid,

src/api/tag.rs

@@ -1,58 +0,0 @@
-use std::collections::HashMap;
-use actix_web::{get, HttpResponse, post, web};
-use actix_web::http::Error;
-use crate::api::JsonResponse;
-use crate::AppState;
-use crate::entity::tag_info;
-use crate::service::tag_info::{Mutation, Query};
-
-#[post("/v1.0/create_tag")]
-async fn create(model: web::Json<tag_info::Model>, data: web::Data<AppState>) -> Result<HttpResponse, Error> {
-    let model = Mutation::create_tag(&data.conn, model.into_inner()).await.unwrap();
-
-    let mut result = HashMap::new();
-    result.insert("tid", model.tid.unwrap());
-
-    let json_response = JsonResponse {
-        code: 200,
-        err: "".to_owned(),
-        data: result,
-    };
-
-    Ok(HttpResponse::Ok()
-        .content_type("application/json")
-        .body(serde_json::to_string(&json_response).unwrap()))
-}
-
-#[post("/v1.0/delete_tag")]
-async fn delete(model: web::Json<tag_info::Model>, data: web::Data<AppState>) -> Result<HttpResponse, Error> {
-    let _ = Mutation::delete_tag(&data.conn, model.tid).await.unwrap();
-
-    let json_response = JsonResponse {
-        code: 200,
-        err: "".to_owned(),
-        data: (),
-    };
-
-    Ok(HttpResponse::Ok()
-        .content_type("application/json")
-        .body(serde_json::to_string(&json_response).unwrap()))
-}
-
-#[get("/v1.0/tags")]
-async fn list(data: web::Data<AppState>) -> Result<HttpResponse, Error> {
-    let tags = Query::find_tag_infos(&data.conn).await.unwrap();
-
-    let mut result = HashMap::new();
-    result.insert("tags", tags);
-
-    let json_response = JsonResponse {
-        code: 200,
-        err: "".to_owned(),
-        data: result,
-    };
-
-    Ok(HttpResponse::Ok()
-        .content_type("application/json")
-        .body(serde_json::to_string(&json_response).unwrap()))
-}

src/main.rs

@@ -28,7 +28,7 @@ struct AppState {
 
 pub(crate) async fn validator(
     req: ServiceRequest,
-    credentials: BearerAuth,
+    credentials: BearerAuth
 ) -> Result<ServiceRequest, Error> {
     if let Some(token) = req.get_identity() {
         println!("{}, {}", credentials.token(), token);
@@ -52,26 +52,25 @@ async fn main() -> Result<(), AppError> {
     let port = env::var("PORT").expect("PORT is not set in .env file");
     let server_url = format!("{host}:{port}");
 
-    let s3_base_url = env::var("S3_BASE_URL").expect("S3_BASE_URL is not set in .env file");
+    let mut s3_base_url = env::var("MINIO_HOST").expect("MINIO_HOST is not set in .env file");
-    let s3_access_key = env::var("S3_ACCESS_KEY").expect("S3_ACCESS_KEY is not set in .env file");;
+    let s3_access_key = env::var("MINIO_USR").expect("MINIO_USR is not set in .env file");
-    let s3_secret_key = env::var("S3_SECRET_KEY").expect("S3_SECRET_KEY is not set in .env file");;
+    let s3_secret_key = env::var("MINIO_PWD").expect("MINIO_PWD is not set in .env file");
+    if s3_base_url.find("http") != Some(0) {
+        s3_base_url = format!("http://{}", s3_base_url);
+    }
 
     // establish connection to database and apply migrations
     // -> create post table if not exists
     let conn = Database::connect(&db_url).await.unwrap();
     Migrator::up(&conn, None).await.unwrap();
 
-    let static_provider = StaticProvider::new(
+    let static_provider = StaticProvider::new(s3_access_key.as_str(), s3_secret_key.as_str(), None);
-        s3_access_key.as_str(),
-        s3_secret_key.as_str(),
-        None,
-    );
 
     let s3_client = Client::new(
         s3_base_url.parse::<BaseUrl>()?,
         Some(Box::new(static_provider)),
         None,
-        None,
+        Some(true)
     )?;
 
     let state = AppState { conn, s3_client };
@@ -82,18 +81,20 @@ async fn main() -> Result<(), AppError> {
         App::new()
             .service(Files::new("/static", "./static"))
             .app_data(web::Data::new(state.clone()))
-            .wrap(IdentityService::new(
+            .wrap(
+                IdentityService::new(
                     CookieIdentityPolicy::new(&[0; 32])
                         .name("auth-cookie")
                         .login_deadline(Duration::seconds(120))
-                    .secure(false),
+                        .secure(false)
-            ))
+                )
+            )
             .wrap(
                 CookieSession::signed(&[0; 32])
                     .name("session-cookie")
                     .secure(false)
                     // WARNING(alex): This uses the `time` crate, not `std::time`!
-                    .expires_in_time(Duration::seconds(60)),
+                    .expires_in_time(Duration::seconds(60))
             )
             .wrap(middleware::Logger::default())
             .configure(init)