add dockerfile for cuda envirement. Refine table search strategy, (#123)

Dockerfile

@@ -14,6 +14,7 @@ ADD ./rag ./rag
 ENV PYTHONPATH=/ragflow/
 ENV HF_ENDPOINT=https://hf-mirror.com
 
+/root/miniconda3/envs/py11/bin/pip install peewee==3.17.1
 ADD docker/entrypoint.sh ./entrypoint.sh
 RUN chmod +x ./entrypoint.sh
 

Dockerfile.cuda

@@ -0,0 +1,26 @@
+FROM swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow-base:v1.0
+USER  root
+
+WORKDIR /ragflow
+
+## for cuda > 12.0
+RUN /root/miniconda3/envs/py11/bin/pip uninstall -y onnxruntime-gpu
+RUN /root/miniconda3/envs/py11/bin/pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
+
+
+ADD ./web ./web
+RUN cd ./web && npm i && npm run build
+
+ADD ./api ./api
+ADD ./conf ./conf
+ADD ./deepdoc ./deepdoc
+ADD ./rag ./rag
+
+ENV PYTHONPATH=/ragflow/
+ENV HF_ENDPOINT=https://hf-mirror.com
+
+/root/miniconda3/envs/py11/bin/pip install peewee==3.17.1
+ADD docker/entrypoint.sh ./entrypoint.sh
+RUN chmod +x ./entrypoint.sh
+
+ENTRYPOINT ["./entrypoint.sh"]

api/apps/conversation_app.py

@@ -21,7 +21,7 @@ from api.db.services.dialog_service import DialogService, ConversationService
 from api.db import LLMType
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.llm_service import LLMService, LLMBundle
-from api.settings import access_logger, stat_logger, retrievaler
+from api.settings import access_logger, stat_logger, retrievaler, chat_logger
 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
 from api.utils import get_uuid
 from api.utils.api_utils import get_json_result
@@ -183,10 +183,10 @@ def chat(dialog, messages, **kwargs):
     field_map = KnowledgebaseService.get_field_map(dialog.kb_ids)
     ## try to use sql if field mapping is good to go
     if field_map:
-        stat_logger.info("Use SQL to retrieval.")
-        markdown_tbl, chunks = use_sql("\n".join(questions), field_map, dialog.tenant_id, chat_mdl)
+        chat_logger.info("Use SQL to retrieval:{}".format(questions[-1]))
+        markdown_tbl, chunks = use_sql(questions[-1], field_map, dialog.tenant_id, chat_mdl)
         if markdown_tbl:
-            return {"answer": markdown_tbl, "retrieval": {"chunks": chunks}}
+            return {"answer": markdown_tbl, "reference": {"chunks": chunks, "doc_aggs": []}}
 
     prompt_config = dialog.prompt_config
     for p in prompt_config["parameters"]:
@@ -201,6 +201,7 @@ def chat(dialog, messages, **kwargs):
                                     dialog.similarity_threshold,
                                     dialog.vector_similarity_weight, top=1024, aggs=False)
     knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
+    chat_logger.info("{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
 
     if not knowledges and prompt_config.get("empty_response"):
         return {"answer": prompt_config["empty_response"], "reference": kbinfos}
@@ -212,7 +213,7 @@ def chat(dialog, messages, **kwargs):
     if "max_tokens" in gen_conf:
         gen_conf["max_tokens"] = min(gen_conf["max_tokens"], llm.max_tokens - used_token_count)
     answer = chat_mdl.chat(prompt_config["system"].format(**kwargs), msg, gen_conf)
-    stat_logger.info("User: {}|Assistant: {}".format(msg[-1]["content"], answer))
+    chat_logger.info("User: {}|Assistant: {}".format(msg[-1]["content"], answer))
 
     if knowledges:
         answer, idx = retrievaler.insert_citations(answer,
@@ -237,47 +238,83 @@ def use_sql(question, field_map, tenant_id, chat_mdl):
 
 问题如下：
 {}
-请写出SQL，且只要SQL，不要有其他说明及文字。
+请写出SQL, 且只要SQL，不要有其他说明及文字。
 """.format(
         index_name(tenant_id),
         "\n".join([f"{k}: {v}" for k, v in field_map.items()]),
         question
     )
-    sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_promt}], {"temperature": 0.06})
-    stat_logger.info(f"“{question}” get SQL: {sql}")
-    sql = re.sub(r"[\r\n]+", " ", sql.lower())
-    sql = re.sub(r".*?select ", "select ", sql.lower())
-    sql = re.sub(r" +", " ", sql)
-    sql = re.sub(r"([;；]|```).*", "", sql)
-    if sql[:len("select ")] != "select ":
-        return None, None
-    if sql[:len("select *")] != "select *":
-        sql = "select doc_id,docnm_kwd," + sql[6:]
-    else:
-        flds = []
-        for k in field_map.keys():
-            if k in forbidden_select_fields4resume:continue
-            if len(flds) > 11:break
-            flds.append(k)
-        sql = "select doc_id,docnm_kwd," + ",".join(flds) + sql[8:]
-
-    stat_logger.info(f"“{question}” get SQL(refined): {sql}")
-    tbl = retrievaler.sql_retrieval(sql, format="json")
-    if not tbl or len(tbl["rows"]) == 0: return None, None
+    tried_times = 0
+    def get_table():
+        nonlocal sys_prompt, user_promt, question, tried_times
+        sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_promt}], {"temperature": 0.06})
+        print(user_promt, sql)
+        chat_logger.info(f"“{question}”==>{user_promt} get SQL: {sql}")
+        sql = re.sub(r"[\r\n]+", " ", sql.lower())
+        sql = re.sub(r".*select ", "select ", sql.lower())
+        sql = re.sub(r" +", " ", sql)
+        sql = re.sub(r"([;；]|```).*", "", sql)
+        if sql[:len("select ")] != "select ":
+            return None, None
+        if not re.search(r"((sum|avg|max|min)\(|group by )", sql.lower()):
+            if sql[:len("select *")] != "select *":
+                sql = "select doc_id,docnm_kwd," + sql[6:]
+            else:
+                flds = []
+                for k in field_map.keys():
+                    if k in forbidden_select_fields4resume:continue
+                    if len(flds) > 11:break
+                    flds.append(k)
+                sql = "select doc_id,docnm_kwd," + ",".join(flds) + sql[8:]
+
+        print(f"“{question}” get SQL(refined): {sql}")
+
+        chat_logger.info(f"“{question}” get SQL(refined): {sql}")
+        tried_times += 1
+        return retrievaler.sql_retrieval(sql, format="json"), sql
+
+    tbl, sql = get_table()
+    if tbl.get("error") and tried_times <= 2:
+        user_promt = """
+        表名：{}；
+        数据库表字段说明如下：
+        {}
+
+        问题如下：
+        {}
+        
+        你上一次给出的错误SQL如下：
+        {}
+        
+        后台报错如下：
+        {}
+        
+        请纠正SQL中的错误再写一遍，且只要SQL，不要有其他说明及文字。
+        """.format(
+            index_name(tenant_id),
+            "\n".join([f"{k}: {v}" for k, v in field_map.items()]),
+            question, sql, tbl["error"]
+        )
+        tbl, sql = get_table()
+        chat_logger.info("TRY it again: {}".format(sql))
+
+    chat_logger.info("GET table: {}".format(tbl))
+    print(tbl)
+    if tbl.get("error") or len(tbl["rows"]) == 0: return None, None
 
     docid_idx = set([ii for ii, c in enumerate(tbl["columns"]) if c["name"] == "doc_id"])
     docnm_idx = set([ii for ii, c in enumerate(tbl["columns"]) if c["name"] == "docnm_kwd"])
     clmn_idx = [ii for ii in range(len(tbl["columns"])) if ii not in (docid_idx | docnm_idx)]
 
     # compose markdown table
-    clmns = "|".join([re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"], f"C{i}")) for i in clmn_idx]) + "|原文"
-    line = "|".join(["------" for _ in range(len(clmn_idx))]) + "|------"
-    rows = ["|".join([rmSpace(str(r[i])) for i in clmn_idx]).replace("None", " ") + "|" for r in tbl["rows"]]
+    clmns = "|"+"|".join([re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"], tbl["columns"][i]["name"])) for i in clmn_idx]) + ("|原文|" if docid_idx and docid_idx else "|")
+    line = "|"+"|".join(["------" for _ in range(len(clmn_idx))]) + ("|------|" if docid_idx and docid_idx else "")
+    rows = ["|"+"|".join([rmSpace(str(r[i])) for i in clmn_idx]).replace("None", " ") + "|" for r in tbl["rows"]]
     if not docid_idx or not docnm_idx:
-        access_logger.error("SQL missing field: " + sql)
+        chat_logger.warning("SQL missing field: " + sql)
         return "\n".join([clmns, line, "\n".join(rows)]), []
 
-    rows = "\n".join([r + f"##{ii}$$" for ii, r in enumerate(rows)])
+    rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
     docid_idx = list(docid_idx)[0]
     docnm_idx = list(docnm_idx)[0]
     return "\n".join([clmns, line, rows]), [{"doc_id": r[docid_idx], "docnm_kwd": r[docnm_idx]} for r in tbl["rows"]]

api/db/db_models.py

@@ -502,7 +502,7 @@ class Document(DataBaseModel):
     token_num = IntegerField(default=0)
     chunk_num = IntegerField(default=0)
     progress = FloatField(default=0)
-    progress_msg = CharField(max_length=4096, null=True, help_text="process message", default="")
+    progress_msg = TextField(null=True, help_text="process message", default="")
     process_begin_at = DateTimeField(null=True)
     process_duation = FloatField(default=0)
     run = CharField(max_length=1, null=True, help_text="start to run processing or cancel.(1: run it; 2: cancel)", default="0")
@@ -520,7 +520,7 @@ class Task(DataBaseModel):
     begin_at = DateTimeField(null=True)
     process_duation = FloatField(default=0)
     progress = FloatField(default=0)
-    progress_msg = TextField(max_length=4096, null=True, help_text="process message", default="")
+    progress_msg = TextField(null=True, help_text="process message", default="")
 
 
 class Dialog(DataBaseModel):

api/db/init_data.py

@@ -90,6 +90,17 @@ def init_llm_factory():
             "tags": "LLM,TEXT EMBEDDING,SPEECH2TEXT,MODERATION",
             "status": "1",
         },
+         {
+             "name": "Local",
+             "logo": "",
+             "tags": "LLM,TEXT EMBEDDING,SPEECH2TEXT,MODERATION",
+            "status": "0",
+        },{
+            "name": "Moonshot",
+             "logo": "",
+             "tags": "LLM,TEXT EMBEDDING",
+             "status": "1",
+        }
         # {
         #     "name": "文心一言",
         #     "logo": "",
@@ -155,6 +166,12 @@ def init_llm_factory():
             "tags": "LLM,CHAT,32K",
             "max_tokens": 32768,
             "model_type": LLMType.CHAT.value
+        },{
+            "fid": factory_infos[1]["name"],
+            "llm_name": "qwen-max-1201",
+            "tags": "LLM,CHAT,6K",
+            "max_tokens": 5899,
+            "model_type": LLMType.CHAT.value
         },{
             "fid": factory_infos[1]["name"],
             "llm_name": "text-embedding-v2",
@@ -201,6 +218,46 @@ def init_llm_factory():
             "max_tokens": 512,
             "model_type": LLMType.EMBEDDING.value
         },
+        # ---------------------- 本地 ----------------------
+        {
+            "fid": factory_infos[3]["name"],
+            "llm_name": "qwen-14B-chat",
+            "tags": "LLM,CHAT,",
+            "max_tokens": 8191,
+            "model_type": LLMType.CHAT.value
+        }, {
+            "fid": factory_infos[3]["name"],
+            "llm_name": "flag-enbedding",
+            "tags": "TEXT EMBEDDING,",
+            "max_tokens": 128 * 1000,
+            "model_type": LLMType.EMBEDDING.value
+        },
+        # ------------------------ Moonshot -----------------------
+        {
+            "fid": factory_infos[4]["name"],
+            "llm_name": "moonshot-v1-8k",
+            "tags": "LLM,CHAT,",
+            "max_tokens": 7900,
+            "model_type": LLMType.CHAT.value
+        }, {
+            "fid": factory_infos[4]["name"],
+            "llm_name": "flag-enbedding",
+            "tags": "TEXT EMBEDDING,",
+            "max_tokens": 128 * 1000,
+            "model_type": LLMType.EMBEDDING.value
+        },{
+            "fid": factory_infos[4]["name"],
+            "llm_name": "moonshot-v1-32k",
+            "tags": "LLM,CHAT,",
+            "max_tokens": 32768,
+            "model_type": LLMType.CHAT.value
+        },{
+            "fid": factory_infos[4]["name"],
+            "llm_name": "moonshot-v1-128k",
+            "tags": "LLM,CHAT",
+            "max_tokens": 128 * 1000,
+            "model_type": LLMType.CHAT.value
+        },
     ]
     for info in factory_infos:
         LLMFactoriesService.save(**info)

api/settings.py

@@ -29,6 +29,7 @@ LoggerFactory.LEVEL = 10
 stat_logger = getLogger("stat")
 access_logger = getLogger("access")
 database_logger = getLogger("database")
+chat_logger = getLogger("chat")
 
 API_VERSION = "v1"
 RAG_FLOW_SERVICE_NAME = "ragflow"
@@ -69,9 +70,15 @@ default_llm = {
         "image2text_model": "glm-4v",
         "asr_model": "",
     },
-    "local": {
-        "chat_model": "",
-        "embedding_model": "",
+    "Local": {
+        "chat_model": "qwen-14B-chat",
+        "embedding_model": "flag-enbedding",
+        "image2text_model": "",
+        "asr_model": "",
+    },
+    "Moonshot": {
+        "chat_model": "moonshot-v1-8k",
+        "embedding_model": "flag-enbedding",
         "image2text_model": "",
         "asr_model": "",
     }
@@ -86,7 +93,7 @@ EMBEDDING_MDL = default_llm[LLM_FACTORY]["embedding_model"]
 ASR_MDL = default_llm[LLM_FACTORY]["asr_model"]
 IMAGE2TEXT_MDL = default_llm[LLM_FACTORY]["image2text_model"]
 
-API_KEY = LLM.get("api_key", "infiniflow API Key")
+API_KEY = LLM.get("api_key", "")
 PARSERS = LLM.get("parsers", "naive:General,qa:Q&A,resume:Resume,table:Table,laws:Laws,manual:Manual,book:Book,paper:Paper,presentation:Presentation,picture:Picture")
 
 # distribution

deepdoc/parser/excel_parser.py

@@ -34,7 +34,7 @@ class HuExcelParser:
             total = 0
             for sheetname in wb.sheetnames:
                 ws = wb[sheetname]
-                total += len(ws.rows)
+                total += len(list(ws.rows))
                 return total
 
         if fnm.split(".")[-1].lower() in ["csv", "txt"]:

deepdoc/parser/pdf_parser.py

@@ -655,14 +655,14 @@ class HuParser:
             #if min(tv, fv) > 2000:
             #    i += 1
             #    continue
-            if tv < fv:
+            if tv < fv and tk:
                 tables[tk].insert(0, c)
                 logging.debug(
                     "TABLE:" +
                     self.boxes[i]["text"] +
                     "; Cap: " +
                     tk)
-            else:
+            elif fk:
                 figures[fk].insert(0, c)
                 logging.debug(
                     "FIGURE:" +

deepdoc/parser/ppt_parser.py

@@ -31,7 +31,7 @@ class HuPptParser(object):
 
         if shape.shape_type == 6:
             texts = []
-            for p in shape.shapes:
+            for p in sorted(shape.shapes, key=lambda x: (x.top//10, x.left)):
                 t = self.__extract(p)
                 if t: texts.append(t)
             return "\n".join(texts)
@@ -46,7 +46,7 @@ class HuPptParser(object):
             if i < from_page: continue
             if i >= to_page:break
             texts = []
-            for shape in slide.shapes:
+            for shape in sorted(slide.shapes, key=lambda x: (x.top//10, x.left)):
                 txt = self.__extract(shape)
                 if txt: texts.append(txt)
             txts.append("\n".join(texts))

deepdoc/vision/ocr.py

@@ -64,10 +64,15 @@ def load_model(model_dir, nm):
         raise ValueError("not find model file path {}".format(
             model_file_path))
 
+    options = ort.SessionOptions()
+    options.enable_cpu_mem_arena = False
+    options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
+    options.intra_op_num_threads = 2
+    options.inter_op_num_threads = 2
     if ort.get_device() == "GPU":
-        sess = ort.InferenceSession(model_file_path, providers=['CUDAExecutionProvider'])
+        sess = ort.InferenceSession(model_file_path, options=options, providers=['CUDAExecutionProvider'])
     else:
-        sess = ort.InferenceSession(model_file_path, providers=['CPUExecutionProvider'])
+        sess = ort.InferenceSession(model_file_path, options=options, providers=['CPUExecutionProvider'])
     return sess, sess.get_inputs()[0]
 
 
@@ -325,7 +330,13 @@ class TextRecognizer(object):
 
             input_dict = {}
             input_dict[self.input_tensor.name] = norm_img_batch
-            outputs = self.predictor.run(None, input_dict)
+            for i in range(100000):
+                try:
+                    outputs = self.predictor.run(None, input_dict)
+                    break
+                except Exception as e:
+                    if i >= 3: raise e
+                    time.sleep(5)
             preds = outputs[0]
             rec_result = self.postprocess_op(preds)
             for rno in range(len(rec_result)):
@@ -430,7 +441,13 @@ class TextDetector(object):
         img = img.copy()
         input_dict = {}
         input_dict[self.input_tensor.name] = img
-        outputs = self.predictor.run(None, input_dict)
+        for i in range(100000):
+            try:
+                outputs = self.predictor.run(None, input_dict)
+                break
+            except Exception as e:
+                if i >= 3: raise e
+                time.sleep(5)
 
         post_result = self.postprocess_op({"maps": outputs[0]}, shape_list)
         dt_boxes = post_result[0]['points']

deepdoc/vision/recognizer.py

@@ -42,7 +42,9 @@ class Recognizer(object):
             raise ValueError("not find model file path {}".format(
                 model_file_path))
         if ort.get_device() == "GPU":
-            self.ort_sess = ort.InferenceSession(model_file_path, providers=['CUDAExecutionProvider'])
+            options = ort.SessionOptions()
+            options.enable_cpu_mem_arena = False
+            self.ort_sess = ort.InferenceSession(model_file_path, options=options, providers=[('CUDAExecutionProvider')])
         else:
             self.ort_sess = ort.InferenceSession(model_file_path, providers=['CPUExecutionProvider'])
         self.input_names = [node.name for node in self.ort_sess.get_inputs()]

rag/app/table.py

@@ -67,7 +67,7 @@ class Excel(ExcelParser):
 
 def trans_datatime(s):
     try:
-        return datetime_parse(s.strip()).strftime("%Y-%m-%dT%H:%M:%S")
+        return datetime_parse(s.strip()).strftime("%Y-%m-%d %H:%M:%S")
     except Exception as e:
         pass
 
@@ -80,6 +80,7 @@ def trans_bool(s):
 
 
 def column_data_type(arr):
+    arr = list(arr)
     uni = len(set([a for a in arr if a is not None]))
     counts = {"int": 0, "float": 0, "text": 0, "datetime": 0, "bool": 0}
     trans = {t: f for f, t in
@@ -130,7 +131,7 @@ def chunk(filename, binary=None, from_page=0, to_page=10000000000, lang="Chinese
     if re.search(r"\.xlsx?$", filename, re.IGNORECASE):
         callback(0.1, "Start to parse.")
         excel_parser = Excel()
-        dfs = excel_parser(filename, binary, callback)
+        dfs = excel_parser(filename, binary, from_page=from_page, to_page=to_page, callback=callback)
     elif re.search(r"\.(txt|csv)$", filename, re.IGNORECASE):
         callback(0.1, "Start to parse.")
         txt = ""
@@ -188,7 +189,7 @@ def chunk(filename, binary=None, from_page=0, to_page=10000000000, lang="Chinese
             df[clmns[j]] = cln
             if ty == "text":
                 txts.extend([str(c) for c in cln if c])
-        clmns_map = [(py_clmns[i] + fieds_map[clmn_tys[i]], clmns[i])
+        clmns_map = [(py_clmns[i] + fieds_map[clmn_tys[i]], clmns[i].replace("_", " "))
                      for i in range(len(clmns))]
 
         eng = lang.lower() == "english"#is_english(txts)
@@ -201,6 +202,8 @@ def chunk(filename, binary=None, from_page=0, to_page=10000000000, lang="Chinese
             for j in range(len(clmns)):
                 if row[clmns[j]] is None:
                     continue
+                if not str(row[clmns[j]]):
+                    continue
                 fld = clmns_map[j][0]
                 d[fld] = row[clmns[j]] if clmn_tys[j] != "text" else huqie.qie(
                     row[clmns[j]])

rag/llm/__init__.py

@@ -19,18 +19,20 @@ from .cv_model import *
 
 
 EmbeddingModel = {
-    "local": HuEmbedding,
+    "Local": HuEmbedding,
     "OpenAI": OpenAIEmbed,
     "通义千问": HuEmbedding, #QWenEmbed,
-    "智谱AI": ZhipuEmbed
+    "智谱AI": ZhipuEmbed,
+    "Moonshot": HuEmbedding
 }
 
 
 CvModel = {
     "OpenAI": GptV4,
-    "local": LocalCV,
+    "Local": LocalCV,
     "通义千问": QWenCV,
-    "智谱AI": Zhipu4V
+    "智谱AI": Zhipu4V,
+    "Moonshot": LocalCV
 }
 
 
@@ -38,6 +40,7 @@ ChatModel = {
     "OpenAI": GptTurbo,
     "智谱AI": ZhipuChat,
     "通义千问": QWenChat,
-    "local": LocalLLM
+    "Local": LocalLLM,
+    "Moonshot": MoonshotChat
 }
 

rag/llm/chat_model.py

@@ -14,11 +14,8 @@
 #  limitations under the License.
 #
 from abc import ABC
-from copy import deepcopy
-
 from openai import OpenAI
 import openai
-
 from rag.nlp import is_english
 from rag.utils import num_tokens_from_string
 
@@ -52,6 +49,12 @@ class GptTurbo(Base):
             return "**ERROR**: "+str(e), 0
 
 
+class MoonshotChat(GptTurbo):
+    def __init__(self, key, model_name="moonshot-v1-8k"):
+        self.client = OpenAI(api_key=key, base_url="https://api.moonshot.cn/v1",)
+        self.model_name = model_name
+
+
 from dashscope import Generation
 class QWenChat(Base):
     def __init__(self, key, model_name=Generation.Models.qwen_turbo):

rag/llm/rpc_server.py

@@ -4,7 +4,7 @@ import random
 import time
 from multiprocessing.connection import Listener
 from threading import Thread
-import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
 
 class RPCHandler:
@@ -47,14 +47,27 @@ tokenizer = None
 def chat(messages, gen_conf):
     global tokenizer
     model = Model()
-    roles = {"system":"System", "user": "User", "assistant": "Assistant"}
-    line = ["{}: {}".format(roles[m["role"].lower()], m["content"]) for m in messages]
-    line = "\n".join(line) + "\nAssistant: "
-    tokens = tokenizer([line], return_tensors='pt')
-    tokens = {k: tokens[k].to(model.device) if isinstance(tokens[k], torch.Tensor) else tokens[k] for k in
-              tokens.keys()}
-    res = [tokenizer.decode(t) for t in model.generate(**tokens, **gen_conf)][0]
-    return res.split("Assistant: ")[-1]
+    try:
+        conf = {"max_new_tokens": int(gen_conf.get("max_tokens", 256)), "temperature": float(gen_conf.get("temperature", 0.1))}
+        print(messages, conf)
+        text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+        model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+        generated_ids = model.generate(
+            model_inputs.input_ids,
+            **conf
+        )
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+        ]
+
+        return tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    except Exception as e:
+        return str(e)
 
 
 def Model():
@@ -71,20 +84,13 @@ if __name__ == "__main__":
     handler = RPCHandler()
     handler.register_function(chat)
 
-    from transformers import AutoModelForCausalLM, AutoTokenizer
-    from transformers.generation.utils import GenerationConfig
-
     models = []
-    for _ in range(2):
+    for _ in range(1):
         m = AutoModelForCausalLM.from_pretrained(args.model_name,
                                                  device_map="auto",
-                                                 torch_dtype='auto',
-                                                 trust_remote_code=True)
-        m.generation_config = GenerationConfig.from_pretrained(args.model_name)
-        m.generation_config.pad_token_id = m.generation_config.eos_token_id
+                                                 torch_dtype='auto')
         models.append(m)
-    tokenizer = AutoTokenizer.from_pretrained(args.model_name, use_fast=False,
-                                              trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
 
     # Run the server
     rpc_server(handler, ('0.0.0.0', args.port), authkey=b'infiniflow-token4kevinhu')

rag/nlp/search.py

@@ -7,6 +7,7 @@ from elasticsearch_dsl import Q, Search
 from typing import List, Optional, Dict, Union
 from dataclasses import dataclass
 
+from api.settings import chat_logger
 from rag.settings import es_logger
 from rag.utils import rmSpace
 from rag.nlp import huqie, query
@@ -333,15 +334,16 @@ class Dealer:
         replaces = []
         for r in re.finditer(r" ([a-z_]+_l?tks)( like | ?= ?)'([^']+)'", sql):
             fld, v = r.group(1), r.group(3)
-            match = " MATCH({}, '{}', 'operator=OR;fuzziness=AUTO:1,3;minimum_should_match=30%') ".format(fld, huqie.qieqie(huqie.qie(v)))
+            match = " MATCH({}, '{}', 'operator=OR;minimum_should_match=30%') ".format(fld, huqie.qieqie(huqie.qie(v)))
             replaces.append(("{}{}'{}'".format(r.group(1), r.group(2), r.group(3)), match))
 
         for p, r in replaces: sql = sql.replace(p, r, 1)
-        es_logger.info(f"To es: {sql}")
+        chat_logger.info(f"To es: {sql}")
 
         try:
             tbl = self.es.sql(sql, fetch_size, format)
             return tbl
         except Exception as e:
-            es_logger.error(f"SQL failure: {sql} =>" + str(e))
+            chat_logger.error(f"SQL failure: {sql} =>" + str(e))
+            return {"error": str(e)}
 

rag/svr/task_executor.py

@@ -169,16 +169,25 @@ def init_kb(row):
 
 
 def embedding(docs, mdl, parser_config={}, callback=None):
+    batch_size = 32
     tts, cnts = [rmSpace(d["title_tks"]) for d in docs if d.get("title_tks")], [
         d["content_with_weight"] for d in docs]
     tk_count = 0
     if len(tts) == len(cnts):
-        tts, c = mdl.encode(tts)
-        tk_count += c
+        tts_ = np.array([])
+        for i in range(0, len(tts), batch_size):
+            vts, c = mdl.encode(tts[i: i + batch_size])
+            if len(tts_) == 0:
+                tts_ = vts
+            else:
+                tts_ = np.concatenate((tts_, vts), axis=0)
+            tk_count += c
+            callback(prog=0.6 + 0.1 * (i + 1) / len(tts), msg="")
+        tts = tts_
 
     cnts_ = np.array([])
-    for i in range(0, len(cnts), 8):
-        vts, c = mdl.encode(cnts[i: i+8])
+    for i in range(0, len(cnts), batch_size):
+        vts, c = mdl.encode(cnts[i: i+batch_size])
         if len(cnts_) == 0: cnts_ = vts
         else: cnts_ = np.concatenate((cnts_, vts), axis=0)
         tk_count += c

rag/utils/es_conn.py

@@ -249,6 +249,8 @@ class HuEs:
             except ConnectionTimeout as e:
                 es_logger.error("Timeout【Q】：" + sql)
                 continue
+            except Exception as e:
+                raise e
         es_logger.error("ES search timeout for 3 times!")
         raise ConnectionTimeout()