go through upload, create kb, add doc to kb (#11)

* add field progress msg into docinfo; add file processing procedure

* go through upload, create kb, add doc to kb

python/svr/parse_user_docs.py

@@ -0,0 +1,171 @@
+import json, re, sys, os, hashlib, copy, glob, util, time, random
+from util.es_conn import HuEs, Postgres
+from util import rmSpace, findMaxDt
+from FlagEmbedding import FlagModel
+from nlp import huchunk, huqie
+import base64, hashlib
+from io import BytesIO
+from elasticsearch_dsl import Q
+from parser import (
+    PdfParser,
+    DocxParser,
+    ExcelParser
+)
+from nlp.huchunk import (
+    PdfChunker,
+    DocxChunker,
+    ExcelChunker,
+    PptChunker,
+    TextChunker
+)
+
+ES = HuEs("infiniflow")
+BATCH_SIZE = 64
+PG = Postgres("infiniflow", "docgpt")
+
+PDF = PdfChunker(PdfParser())
+DOC = DocxChunker(DocxParser())
+EXC = ExcelChunker(ExcelParser())
+PPT = PptChunker()
+
+
+def chuck_doc(name):
+    name = os.path.split(name)[-1].lower().split(".")[-1]
+    if name.find("pdf") >= 0: return PDF(name)
+    if name.find("doc") >= 0: return DOC(name)
+    if name.find("xlsx") >= 0: return EXC(name)
+    if name.find("ppt") >= 0: return PDF(name)
+    if name.find("pdf") >= 0: return PPT(name)
+    
+    if re.match(r"(txt|csv)", name): return TextChunker(name)
+
+
+def collect(comm, mod, tm):
+    sql = f"""
+    select 
+    did,
+    uid,
+    doc_name,
+    location,
+    updated_at
+    from docinfo
+    where 
+    updated_at >= '{tm}' 
+    and kb_progress = 0
+    and type = 'doc'
+    and MOD(uid, {comm}) = {mod}
+    order by updated_at asc
+    limit 1000
+    """
+    df = PG.select(sql)
+    df = df.fillna("")
+    mtm = str(df["updated_at"].max())[:19]
+    print("TOTAL:", len(df), "To: ", mtm)
+    return df, mtm
+
+
+def set_progress(did, prog, msg):
+    sql = f"""
+    update docinfo set kb_progress={prog}, kb_progress_msg='{msg}' where did={did}
+    """
+    PG.update(sql)
+
+
+def build(row):
+    if row["size"] > 256000000:
+        set_progress(row["did"], -1, "File size exceeds( <= 256Mb )")
+        return  []
+    doc = {
+        "doc_id": row["did"],
+        "title_tks": huqie.qie(os.path.split(row["location"])[-1]),
+        "updated_at": row["updated_at"]
+    }
+    random.seed(time.time())
+    set_progress(row["did"], random.randint(0, 20)/100., "Finished preparing! Start to slice file!")
+    obj = chuck_doc(row["location"])
+    if not obj: 
+        set_progress(row["did"], -1, "Unsuported file type.")
+        return  []
+
+    set_progress(row["did"], random.randint(20, 60)/100.)
+
+    output_buffer = BytesIO()
+    docs = []
+    md5 = hashlib.md5()
+    for txt, img in obj.text_chunks:
+        d = copy.deepcopy(doc)
+        md5.update((txt + str(d["doc_id"])).encode("utf-8"))
+        d["_id"] = md5.hexdigest()
+        d["content_ltks"] = huqie.qie(txt)
+        d["docnm_kwd"] = rmSpace(d["docnm_tks"])
+        if not img:
+            docs.append(d)
+            continue
+        img.save(output_buffer, format='JPEG')
+        d["img_bin"] = base64.b64encode(output_buffer.getvalue())
+        docs.append(d)
+
+    for arr, img in obj.table_chunks:
+        for i, txt in enumerate(arr):
+            d = copy.deepcopy(doc)
+            d["content_ltks"] = huqie.qie(txt)
+            md5.update((txt + str(d["doc_id"])).encode("utf-8"))
+            d["_id"] = md5.hexdigest()
+            if not img:
+                docs.append(d)
+                continue
+            img.save(output_buffer, format='JPEG')
+            d["img_bin"] = base64.b64encode(output_buffer.getvalue())
+            docs.append(d)
+    set_progress(row["did"], random.randint(60, 70)/100., "Finished slicing. Start to embedding the content.")
+
+    return docs
+
+
+def index_name(uid):return f"docgpt_{uid}"
+
+def init_kb(row):
+    idxnm = index_name(row["uid"])
+    if ES.indexExist(idxnm): return
+    return ES.createIdx(idxnm, json.load(open("res/mapping.json", "r")))
+
+
+model = None
+def embedding(docs):
+    global model
+    tts = model.encode([rmSpace(d["title_tks"]) for d in docs])
+    cnts = model.encode([rmSpace(d["content_ltks"]) for d in docs])
+    vects = 0.1 * tts + 0.9 * cnts
+    assert len(vects) == len(docs)
+    for i,d in enumerate(docs):d["q_vec"] = vects[i].tolist()
+    for d in docs:
+        set_progress(d["doc_id"], random.randint(70, 95)/100., 
+                     "Finished embedding! Start to build index!")
+
+
+def main(comm, mod):
+    tm_fnm = f"res/{comm}-{mod}.tm"
+    tmf = open(tm_fnm, "a+")
+    tm = findMaxDt(tm_fnm)
+    rows, tm = collect(comm, mod, tm)
+    for r in rows:
+        if r["is_deleted"]: 
+            ES.deleteByQuery(Q("term", dock_id=r["did"]), index_name(r["uid"]))
+            continue
+
+        cks = build(r)
+        ## TODO: exception handler
+        ## set_progress(r["did"], -1, "ERROR: ")
+        embedding(cks)
+        if cks: init_kb(r)
+        ES.bulk(cks, index_name(r["uid"]))
+        tmf.write(str(r["updated_at"]) + "\n")
+    tmf.close()
+
+
+if __name__ == "__main__":
+    from mpi4py import MPI
+    comm = MPI.COMM_WORLD
+    rank = comm.Get_rank()
+    main(comm, rank)
+