Update version info (#564)

### What problem does this PR solve?

_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._

### Type of change

- [x] Documentation Update
- [x] Refactoring

---------

Signed-off-by: Jin Hai <haijin.chn@gmail.com>

.gitattributes

@@ -0,0 +1 @@
+*.sh text eol=lf

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,5 +1,5 @@
 name: Bug Report
-description: Create a bug issue for infinity
+description: Create a bug issue for RAGFlow
 title: "[Bug]: "
 labels: [bug]
 body:

.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,7 +1,7 @@
 ---
 name: Feature request
 title: '[Feature Request]: '
-about: Suggest an idea for Infinity
+about: Suggest an idea for RAGFlow
 labels: ''
 ---
 

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -1,5 +1,5 @@
 name: Feature request
-description: Propose a feature request for infinity.
+description: Propose a feature request for RAGFlow.
 title: "[Feature Request]: "
 labels: [feature request]
 body:

.github/ISSUE_TEMPLATE/question.yml

@@ -1,5 +1,5 @@
 name: Question
-description: Ask questions on infinity
+description: Ask questions on RAGFlow
 title: "[Question]: "
 labels: [question]
 body:

.github/ISSUE_TEMPLATE/subtask.yml

@@ -1,5 +1,5 @@
 name: Subtask
-description: "Propose a subtask for infinity"
+description: "Propose a subtask for RAGFlow"
 title: "[Subtask]: "
 labels: [subtask]
 

.github/pull_request_template.md

@@ -2,16 +2,11 @@
 
 _Briefly describe what this PR aims to solve. Include background context that will help reviewers understand the purpose of the PR._
 
-Issue link:#[Link the issue here]
-
 ### Type of change
 
 - [ ] Bug Fix (non-breaking change which fixes an issue)
 - [ ] New Feature (non-breaking change which adds functionality)
-- [ ] Breaking Change (fix or feature that could cause existing functionality not to work as expected)
 - [ ] Documentation Update
 - [ ] Refactoring
 - [ ] Performance Improvement
-- [ ] Test cases
-- [ ] Python SDK impacted, Need to update PyPI
 - [ ] Other (please describe):

.gitignore

@@ -21,3 +21,9 @@ Cargo.lock
 
 .idea/
 .vscode/
+
+# Exclude Mac generated files
+.DS_Store
+
+# Exclude the log folder
+docker/ragflow-logs/

Dockerfile.scratch.oc9

@@ -0,0 +1,56 @@
+FROM opencloudos/opencloudos:9.0
+USER root
+
+WORKDIR /ragflow
+
+RUN dnf update -y && dnf install -y wget curl gcc-c++ openmpi-devel
+
+RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
+    bash ~/miniconda.sh -b -p /root/miniconda3 && \
+    rm ~/miniconda.sh && ln -s /root/miniconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
+    echo ". /root/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc && \
+    echo "conda activate base" >> ~/.bashrc
+
+ENV PATH /root/miniconda3/bin:$PATH
+
+RUN conda create -y --name py11 python=3.11
+
+ENV CONDA_DEFAULT_ENV py11
+ENV CONDA_PREFIX /root/miniconda3/envs/py11
+ENV PATH $CONDA_PREFIX/bin:$PATH
+
+# RUN curl -sL https://rpm.nodesource.com/setup_14.x | bash -
+RUN dnf install -y nodejs
+
+RUN dnf install -y nginx
+
+ADD ./web ./web
+ADD ./api ./api
+ADD ./conf ./conf
+ADD ./deepdoc ./deepdoc
+ADD ./rag ./rag
+ADD ./requirements.txt ./requirements.txt
+
+RUN dnf install -y openmpi openmpi-devel python3-openmpi
+ENV C_INCLUDE_PATH /usr/include/openmpi-x86_64:$C_INCLUDE_PATH
+ENV LD_LIBRARY_PATH /usr/lib64/openmpi/lib:$LD_LIBRARY_PATH
+RUN rm /root/miniconda3/envs/py11/compiler_compat/ld
+RUN cd ./web && npm i && npm run build
+RUN conda run -n py11 pip install $(grep -ivE "mpi4py" ./requirements.txt) # without mpi4py==3.1.5
+RUN conda run -n py11 pip install redis
+
+RUN dnf update -y && \
+    dnf install -y glib2 mesa-libGL && \
+    dnf clean all
+
+RUN conda run -n py11 pip install ollama
+RUN conda run -n py11 python -m nltk.downloader punkt
+RUN conda run -n py11 python -m nltk.downloader wordnet
+
+ENV PYTHONPATH=/ragflow/
+ENV HF_ENDPOINT=https://hf-mirror.com
+
+ADD docker/entrypoint.sh ./entrypoint.sh
+RUN chmod +x ./entrypoint.sh
+
+ENTRYPOINT ["./entrypoint.sh"]

README.md

@@ -11,11 +11,14 @@
 </p>
 
 <p align="center">
+    <a href="https://github.com/infiniflow/ragflow/releases/latest">
+        <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
+    </a>
     <a href="https://demo.ragflow.io" target="_blank">
         <img alt="Static Badge" src="https://img.shields.io/badge/RAGFLOW-LLM-white?&labelColor=dd0af7"></a>
     <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v1.0-brightgreen"
-            alt="docker pull infiniflow/ragflow:v0.2.0"></a>
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.4.0-brightgreen"
+            alt="docker pull infiniflow/ragflow:v0.4.0"></a>
       <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
     <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?style=flat-square&labelColor=d4eaf7&color=7d09f1" alt="license">
   </a>
@@ -55,8 +58,10 @@
 
 ## 📌 Latest Features
 
+- 2024-04-26 Add file management.
+- 2024-04-19 Support conversation API ([detail](./docs/conversation_api.md)).
 - 2024-04-16 Add an embedding model 'bce-embedding-base_v1' from [BCEmbedding](https://github.com/netease-youdao/BCEmbedding).
-- 2024-04-16 Add [FastEmbed](https://github.com/qdrant/fastembed) is designed for light and speeding embedding.
+- 2024-04-16 Add [FastEmbed](https://github.com/qdrant/fastembed), which is designed specifically for light and speedy embedding.
 - 2024-04-11 Support [Xinference](./docs/xinference.md) for local LLM deployment.
 - 2024-04-10 Add a new layout recognization model for analyzing Laws documentation.
 - 2024-04-08 Support [Ollama](./docs/ollama.md) for local LLM deployment.
@@ -72,8 +77,9 @@
 
 ### 📝 Prerequisites
 
-- CPU >= 2 cores
-- RAM >= 8 GB
+- CPU >= 4 cores
+- RAM >= 16 GB
+- Disk >= 50 GB
 - Docker >= 24.0.0 & Docker Compose >= v2.26.1
   > If you have not installed Docker on your local machine (Windows, Mac, or Linux), see [Install Docker Engine](https://docs.docker.com/engine/install/).
 
@@ -137,9 +143,10 @@
     * Running on http://x.x.x.x:9380
     INFO:werkzeug:Press CTRL+C to quit
    ```
+   > If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network anomaly` error because, at that moment, your RAGFlow may not be fully initialized.  
 
 5. In your web browser, enter the IP address of your server and log in to RAGFlow.
-   > In the given scenario, you only need to enter `http://IP_OF_YOUR_MACHINE` (sans port number) as the default HTTP serving port `80` can be omitted when using the default configurations.
+   > With default settings, you only need to enter `http://IP_OF_YOUR_MACHINE` (**sans** port number) as the default HTTP serving port `80` can be omitted when using the default configurations.
 6. In [service_conf.yaml](./docker/service_conf.yaml), select the desired LLM factory in `user_default_llm` and update the `API_KEY` field with the corresponding API key.
 
    > See [./docs/llm_api_key_setup.md](./docs/llm_api_key_setup.md) for more information.
@@ -173,7 +180,7 @@ To build the Docker images from source:
 ```bash
 $ git clone https://github.com/infiniflow/ragflow.git
 $ cd ragflow/
-$ docker build -t infiniflow/ragflow:v0.2.0 .
+$ docker build -t infiniflow/ragflow:v0.4.0 .
 $ cd ragflow/docker
 $ chmod +x ./entrypoint.sh
 $ docker compose up -d

README_ja.md

@@ -11,11 +11,14 @@
 </p>
 
 <p align="center">
+    <a href="https://github.com/infiniflow/ragflow/releases/latest">
+        <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
+    </a>
     <a href="https://demo.ragflow.io" target="_blank">
         <img alt="Static Badge" src="https://img.shields.io/badge/RAGFLOW-LLM-white?&labelColor=dd0af7"></a>
     <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v1.0-brightgreen"
-            alt="docker pull infiniflow/ragflow:v0.2.0"></a>
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.4.0-brightgreen"
+            alt="docker pull infiniflow/ragflow:v0.4.0"></a>
       <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
     <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?style=flat-square&labelColor=d4eaf7&color=7d09f1" alt="license">
   </a>
@@ -55,6 +58,8 @@
 
 ## 📌 最新の機能
 
+- 2024-04-26 「ファイル管理」機能を追加しました。
+- 2024-04-19 会話 API をサポートします ([詳細](./docs/conversation_api.md))。
 - 2024-04-16 [BCEmbedding](https://github.com/netease-youdao/BCEmbedding) から埋め込みモデル「bce-embedding-base_v1」を追加します。
 - 2024-04-16 [FastEmbed](https://github.com/qdrant/fastembed) は、軽量かつ高速な埋め込み用に設計されています。
 - 2024-04-11 ローカル LLM デプロイメント用に [Xinference](./docs/xinference.md) をサポートします。
@@ -72,8 +77,9 @@
 
 ### 📝 必要条件
 
-- CPU >= 2 cores
-- RAM >= 8 GB
+- CPU >= 4 cores
+- RAM >= 16 GB
+- Disk >= 50 GB
 - Docker >= 24.0.0 & Docker Compose >= v2.26.1
   > ローカルマシン（Windows、Mac、または Linux）に Docker をインストールしていない場合は、[Docker Engine のインストール](https://docs.docker.com/engine/install/) を参照してください。
 
@@ -137,6 +143,7 @@
     * Running on http://x.x.x.x:9380
     INFO:werkzeug:Press CTRL+C to quit
    ```
+   > もし確認ステップをスキップして直接 RAGFlow にログインした場合、その時点で RAGFlow が完全に初期化されていない可能性があるため、ブラウザーがネットワーク異常エラーを表示するかもしれません。
 
 5. ウェブブラウザで、プロンプトに従ってサーバーの IP アドレスを入力し、RAGFlow にログインします。
    > デフォルトの設定を使用する場合、デフォルトの HTTP サービングポート `80` は省略できるので、与えられたシナリオでは、`http://IP_OF_YOUR_MACHINE`（ポート番号は省略）だけを入力すればよい。
@@ -173,7 +180,7 @@
 ```bash
 $ git clone https://github.com/infiniflow/ragflow.git
 $ cd ragflow/
-$ docker build -t infiniflow/ragflow:v0.2.0 .
+$ docker build -t infiniflow/ragflow:v0.4.0 .
 $ cd ragflow/docker
 $ chmod +x ./entrypoint.sh
 $ docker compose up -d

README_zh.md

@@ -11,11 +11,14 @@
 </p>
 
 <p align="center">
+    <a href="https://github.com/infiniflow/ragflow/releases/latest">
+        <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
+    </a>
     <a href="https://demo.ragflow.io" target="_blank">
         <img alt="Static Badge" src="https://img.shields.io/badge/RAGFLOW-LLM-white?&labelColor=dd0af7"></a>
     <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v1.0-brightgreen"
-            alt="docker pull infiniflow/ragflow:v0.2.0"></a>
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.4.0-brightgreen"
+            alt="docker pull infiniflow/ragflow:v0.4.0"></a>
       <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
     <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?style=flat-square&labelColor=d4eaf7&color=7d09f1" alt="license">
   </a>
@@ -55,6 +58,8 @@
 
 ## 📌 新增功能
 
+- 2024-04-26 增添了'文件管理'功能.
+- 2024-04-19 支持对话 API ([更多](./docs/conversation_api.md)).
 - 2024-04-16 添加嵌入模型 [BCEmbedding](https://github.com/netease-youdao/BCEmbedding) 。
 - 2024-04-16 添加 [FastEmbed](https://github.com/qdrant/fastembed) 专为轻型和高速嵌入而设计。
 - 2024-04-11 支持用 [Xinference](./docs/xinference.md) 本地化部署大模型。
@@ -72,8 +77,9 @@
 
 ### 📝 前提条件
 
-- CPU >= 2 核
-- RAM >= 8 GB
+- CPU >= 4 核
+- RAM >= 16 GB
+- Disk >= 50 GB
 - Docker >= 24.0.0 & Docker Compose >= v2.26.1
   > 如果你并没有在本机安装 Docker（Windows、Mac，或者 Linux）, 可以参考文档 [Install Docker Engine](https://docs.docker.com/engine/install/) 自行安装。
 
@@ -137,6 +143,7 @@
     * Running on http://x.x.x.x:9380
     INFO:werkzeug:Press CTRL+C to quit
    ```
+   > 如果您跳过这一步系统确认步骤就登录 RAGFlow，你的浏览器有可能会提示 `network anomaly` 或 `网络异常`，因为 RAGFlow 可能并未完全启动成功。
 
 5. 在你的浏览器中输入你的服务器对应的 IP 地址并登录 RAGFlow。
    > 上面这个例子中，您只需输入 http://IP_OF_YOUR_MACHINE 即可：未改动过配置则无需输入端口（默认的 HTTP 服务端口 80）。
@@ -173,7 +180,7 @@
 ```bash
 $ git clone https://github.com/infiniflow/ragflow.git
 $ cd ragflow/
-$ docker build -t infiniflow/ragflow:v0.2.0 .
+$ docker build -t infiniflow/ragflow:v0.4.0 .
 $ cd ragflow/docker
 $ chmod +x ./entrypoint.sh
 $ docker compose up -d

api/apps/__init__.py

@@ -54,7 +54,7 @@ app.errorhandler(Exception)(server_error_response)
 #app.config["LOGIN_DISABLED"] = True
 app.config["SESSION_PERMANENT"] = False
 app.config["SESSION_TYPE"] = "filesystem"
-app.config['MAX_CONTENT_LENGTH'] = os.environ.get("MAX_CONTENT_LENGTH", 128 * 1024 * 1024)
+app.config['MAX_CONTENT_LENGTH'] = int(os.environ.get("MAX_CONTENT_LENGTH", 128 * 1024 * 1024))
 
 Session(app)
 login_manager = LoginManager()

api/apps/api_app.py

@@ -13,18 +13,28 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import os
+import re
 from datetime import datetime, timedelta
 from flask import request
 from flask_login import login_required, current_user
+
+from api.db import FileType, ParserType
 from api.db.db_models import APIToken, API4Conversation
+from api.db.services import duplicate_name
 from api.db.services.api_service import APITokenService, API4ConversationService
 from api.db.services.dialog_service import DialogService, chat
+from api.db.services.document_service import DocumentService
+from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.user_service import UserTenantService
 from api.settings import RetCode
 from api.utils import get_uuid, current_timestamp, datetime_format
 from api.utils.api_utils import server_error_response, get_data_error_result, get_json_result, validate_request
 from itsdangerous import URLSafeTimedSerializer
 
+from api.utils.file_utils import filename_type, thumbnail
+from rag.utils import MINIO
+
 
 def generate_confirmation_token(tenent_id):
     serializer = URLSafeTimedSerializer(tenent_id)
@@ -105,8 +115,8 @@ def stats():
         res = {
             "pv": [(o["dt"], o["pv"]) for o in objs],
             "uv": [(o["dt"], o["uv"]) for o in objs],
-            "speed": [(o["dt"], o["tokens"]/o["duration"]) for o in objs],
-            "tokens": [(o["dt"], o["tokens"]/1000.) for o in objs],
+            "speed": [(o["dt"], float(o["tokens"])/(float(o["duration"]+0.1))) for o in objs],
+            "tokens": [(o["dt"], float(o["tokens"])/1000.) for o in objs],
             "round": [(o["dt"], o["round"]) for o in objs],
             "thumb_up": [(o["dt"], o["thumb_up"]) for o in objs]
         }
@@ -115,8 +125,7 @@ def stats():
         return server_error_response(e)
 
 
-@manager.route('/new_conversation', methods=['POST'])
-@validate_request("user_id")
+@manager.route('/new_conversation', methods=['GET'])
 def set_conversation():
     token = request.headers.get('Authorization').split()[1]
     objs = APIToken.query(token=token)
@@ -131,7 +140,7 @@ def set_conversation():
         conv = {
             "id": get_uuid(),
             "dialog_id": dia.id,
-            "user_id": req["user_id"],
+            "user_id": request.args.get("user_id", ""),
             "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}]
         }
         API4ConversationService.save(**conv)
@@ -177,7 +186,6 @@ def completion():
         conv.reference.append(ans["reference"])
         conv.message.append({"role": "assistant", "content": ans["answer"]})
         API4ConversationService.append_message(conv.id, conv.to_dict())
-        APITokenService.APITokenService(token)
         return get_json_result(data=ans)
     except Exception as e:
         return server_error_response(e)
@@ -194,3 +202,73 @@ def get(conversation_id):
         return get_json_result(data=conv.to_dict())
     except Exception as e:
         return server_error_response(e)
+
+
+@manager.route('/document/upload', methods=['POST'])
+@validate_request("kb_name")
+def upload():
+    token = request.headers.get('Authorization').split()[1]
+    objs = APIToken.query(token=token)
+    if not objs:
+        return get_json_result(
+            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
+
+    kb_name = request.form.get("kb_name").strip()
+    tenant_id = objs[0].tenant_id
+
+    try:
+        e, kb = KnowledgebaseService.get_by_name(kb_name, tenant_id)
+        if not e:
+            return get_data_error_result(
+                retmsg="Can't find this knowledgebase!")
+        kb_id = kb.id
+    except Exception as e:
+        return server_error_response(e)
+
+    if 'file' not in request.files:
+        return get_json_result(
+            data=False, retmsg='No file part!', retcode=RetCode.ARGUMENT_ERROR)
+
+    file = request.files['file']
+    if file.filename == '':
+        return get_json_result(
+            data=False, retmsg='No file selected!', retcode=RetCode.ARGUMENT_ERROR)
+    try:
+        if DocumentService.get_doc_count(kb.tenant_id) >= int(os.environ.get('MAX_FILE_NUM_PER_USER', 8192)):
+            return get_data_error_result(
+                retmsg="Exceed the maximum file number of a free user!")
+
+        filename = duplicate_name(
+            DocumentService.query,
+            name=file.filename,
+            kb_id=kb_id)
+        filetype = filename_type(filename)
+        if not filetype:
+            return get_data_error_result(
+                retmsg="This type of file has not been supported yet!")
+
+        location = filename
+        while MINIO.obj_exist(kb_id, location):
+            location += "_"
+        blob = request.files['file'].read()
+        MINIO.put(kb_id, location, blob)
+        doc = {
+            "id": get_uuid(),
+            "kb_id": kb.id,
+            "parser_id": kb.parser_id,
+            "parser_config": kb.parser_config,
+            "created_by": kb.tenant_id,
+            "type": filetype,
+            "name": filename,
+            "location": location,
+            "size": len(blob),
+            "thumbnail": thumbnail(filename, blob)
+        }
+        if doc["type"] == FileType.VISUAL:
+            doc["parser_id"] = ParserType.PICTURE.value
+        if re.search(r"\.(ppt|pptx|pages)$", filename):
+            doc["parser_id"] = ParserType.PRESENTATION.value
+        doc = DocumentService.insert(doc)
+        return get_json_result(data=doc.to_json())
+    except Exception as e:
+        return server_error_response(e)

api/apps/document_app.py

@@ -23,6 +23,9 @@ import flask
 from elasticsearch_dsl import Q
 from flask import request
 from flask_login import login_required, current_user
+
+from api.db.services.file2document_service import File2DocumentService
+from api.db.services.file_service import FileService
 from rag.nlp import search
 from rag.utils import ELASTICSEARCH
 from api.db.services import duplicate_name
@@ -58,7 +61,8 @@ def upload():
         if not e:
             return get_data_error_result(
                 retmsg="Can't find this knowledgebase!")
-        if DocumentService.get_doc_count(kb.tenant_id) >= int(os.environ.get('MAX_FILE_NUM_PER_USER', 8192)):
+        MAX_FILE_NUM_PER_USER = int(os.environ.get('MAX_FILE_NUM_PER_USER', 0))
+        if MAX_FILE_NUM_PER_USER > 0 and DocumentService.get_doc_count(kb.tenant_id) >= MAX_FILE_NUM_PER_USER:
             return get_data_error_result(
                 retmsg="Exceed the maximum file number of a free user!")
 
@@ -67,7 +71,7 @@ def upload():
             name=file.filename,
             kb_id=kb.id)
         filetype = filename_type(filename)
-        if not filetype:
+        if filetype == FileType.OTHER.value:
             return get_data_error_result(
                 retmsg="This type of file has not been supported yet!")
 
@@ -217,26 +221,37 @@ def change_status():
 @validate_request("doc_id")
 def rm():
     req = request.json
+    doc_ids = req["doc_id"]
+    if isinstance(doc_ids, str): doc_ids = [doc_ids]
+    errors = ""
+    for doc_id in doc_ids:
         try:
-        e, doc = DocumentService.get_by_id(req["doc_id"])
+            e, doc = DocumentService.get_by_id(doc_id)
+
             if not e:
                 return get_data_error_result(retmsg="Document not found!")
-        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
+            tenant_id = DocumentService.get_tenant_id(doc_id)
             if not tenant_id:
                 return get_data_error_result(retmsg="Tenant not found!")
+
             ELASTICSEARCH.deleteByQuery(
                 Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
-
             DocumentService.increment_chunk_num(
                 doc.id, doc.kb_id, doc.token_num * -1, doc.chunk_num * -1, 0)
             if not DocumentService.delete(doc):
                 return get_data_error_result(
                     retmsg="Database error (Document removal)!")
 
+            informs = File2DocumentService.get_by_document_id(doc_id)
+            if not informs:
                 MINIO.rm(doc.kb_id, doc.location)
-        return get_json_result(data=True)
+            else:
+                File2DocumentService.delete_by_document_id(doc_id)
         except Exception as e:
-        return server_error_response(e)
+            errors += str(e)
+
+    if errors: return server_error_response(e)
+    return get_json_result(data=True)
 
 
 @manager.route('/run', methods=['POST'])
@@ -288,6 +303,11 @@ def rename():
             return get_data_error_result(
                 retmsg="Database error (Document rename)!")
 
+        informs = File2DocumentService.get_by_document_id(req["doc_id"])
+        if informs:
+            e, file = FileService.get_by_id(informs[0].file_id)
+            FileService.update_by_id(file.id, {"name": req["name"]})
+
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
@@ -301,7 +321,13 @@ def get(doc_id):
         if not e:
             return get_data_error_result(retmsg="Document not found!")
 
+        informs = File2DocumentService.get_by_document_id(doc_id)
+        if not informs:
             response = flask.make_response(MINIO.get(doc.kb_id, doc.location))
+        else:
+            e, file = FileService.get_by_id(informs[0].file_id)
+            response = flask.make_response(MINIO.get(file.parent_id, doc.location))
+
         ext = re.search(r"\.([^.]+)$", doc.name)
         if ext:
             if doc.type == FileType.VISUAL.value:

api/apps/file2document_app.py

@@ -0,0 +1,137 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License
+#
+from elasticsearch_dsl import Q
+
+from api.db.db_models import File2Document
+from api.db.services.file2document_service import File2DocumentService
+from api.db.services.file_service import FileService
+
+from flask import request
+from flask_login import login_required, current_user
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from api.utils import get_uuid
+from api.db import FileType
+from api.db.services.document_service import DocumentService
+from api.settings import RetCode
+from api.utils.api_utils import get_json_result
+from rag.nlp import search
+from rag.utils import ELASTICSEARCH
+
+
+@manager.route('/convert', methods=['POST'])
+@login_required
+@validate_request("file_ids", "kb_ids")
+def convert():
+    req = request.json
+    kb_ids = req["kb_ids"]
+    file_ids = req["file_ids"]
+    file2documents = []
+
+    try:
+        for file_id in file_ids:
+            e, file = FileService.get_by_id(file_id)
+            file_ids_list = [file_id]
+            if file.type == FileType.FOLDER.value:
+                file_ids_list = FileService.get_all_innermost_file_ids(file_id, [])
+            for id in file_ids_list:
+                informs = File2DocumentService.get_by_file_id(id)
+                # delete
+                for inform in informs:
+                    doc_id = inform.document_id
+                    e, doc = DocumentService.get_by_id(doc_id)
+                    if not e:
+                        return get_data_error_result(retmsg="Document not found!")
+                    tenant_id = DocumentService.get_tenant_id(doc_id)
+                    if not tenant_id:
+                        return get_data_error_result(retmsg="Tenant not found!")
+                    ELASTICSEARCH.deleteByQuery(
+                        Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
+                    DocumentService.increment_chunk_num(
+                        doc.id, doc.kb_id, doc.token_num * -1, doc.chunk_num * -1, 0)
+                    if not DocumentService.delete(doc):
+                        return get_data_error_result(
+                            retmsg="Database error (Document removal)!")
+                File2DocumentService.delete_by_file_id(id)
+
+                # insert
+                for kb_id in kb_ids:
+                    e, kb = KnowledgebaseService.get_by_id(kb_id)
+                    if not e:
+                        return get_data_error_result(
+                            retmsg="Can't find this knowledgebase!")
+                    e, file = FileService.get_by_id(id)
+                    if not e:
+                        return get_data_error_result(
+                            retmsg="Can't find this file!")
+
+                    doc = DocumentService.insert({
+                        "id": get_uuid(),
+                        "kb_id": kb.id,
+                        "parser_id": kb.parser_id,
+                        "parser_config": kb.parser_config,
+                        "created_by": current_user.id,
+                        "type": file.type,
+                        "name": file.name,
+                        "location": file.location,
+                        "size": file.size
+                    })
+                    file2document = File2DocumentService.insert({
+                        "id": get_uuid(),
+                        "file_id": id,
+                        "document_id": doc.id,
+                    })
+                    file2documents.append(file2document.to_json())
+        return get_json_result(data=file2documents)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rm', methods=['POST'])
+@login_required
+@validate_request("file_ids")
+def rm():
+    req = request.json
+    file_ids = req["file_ids"]
+    if not file_ids:
+        return get_json_result(
+            data=False, retmsg='Lack of "Files ID"', retcode=RetCode.ARGUMENT_ERROR)
+    try:
+        for file_id in file_ids:
+            informs = File2DocumentService.get_by_file_id(file_id)
+            if not informs:
+                return get_data_error_result(retmsg="Inform not found!")
+            for inform in informs:
+                if not inform:
+                    return get_data_error_result(retmsg="Inform not found!")
+                File2DocumentService.delete_by_file_id(file_id)
+                doc_id = inform.document_id
+                e, doc = DocumentService.get_by_id(doc_id)
+                if not e:
+                    return get_data_error_result(retmsg="Document not found!")
+                tenant_id = DocumentService.get_tenant_id(doc_id)
+                if not tenant_id:
+                    return get_data_error_result(retmsg="Tenant not found!")
+                ELASTICSEARCH.deleteByQuery(
+                    Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
+                DocumentService.increment_chunk_num(
+                    doc.id, doc.kb_id, doc.token_num * -1, doc.chunk_num * -1, 0)
+                if not DocumentService.delete(doc):
+                    return get_data_error_result(
+                        retmsg="Database error (Document removal)!")
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)

api/apps/file_app.py

@@ -0,0 +1,347 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License
+#
+import os
+import pathlib
+import re
+
+import flask
+from elasticsearch_dsl import Q
+from flask import request
+from flask_login import login_required, current_user
+
+from api.db.services.document_service import DocumentService
+from api.db.services.file2document_service import File2DocumentService
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from api.utils import get_uuid
+from api.db import FileType
+from api.db.services import duplicate_name
+from api.db.services.file_service import FileService
+from api.settings import RetCode
+from api.utils.api_utils import get_json_result
+from api.utils.file_utils import filename_type
+from rag.nlp import search
+from rag.utils import ELASTICSEARCH
+from rag.utils.minio_conn import MINIO
+
+
+@manager.route('/upload', methods=['POST'])
+@login_required
+# @validate_request("parent_id")
+def upload():
+    pf_id = request.form.get("parent_id")
+
+    if not pf_id:
+        root_folder = FileService.get_root_folder(current_user.id)
+        pf_id = root_folder.id
+
+    if 'file' not in request.files:
+        return get_json_result(
+            data=False, retmsg='No file part!', retcode=RetCode.ARGUMENT_ERROR)
+    file_objs = request.files.getlist('file')
+
+    for file_obj in file_objs:
+        if file_obj.filename == '':
+            return get_json_result(
+                data=False, retmsg='No file selected!', retcode=RetCode.ARGUMENT_ERROR)
+    file_res = []
+    try:
+        for file_obj in file_objs:
+            e, file = FileService.get_by_id(pf_id)
+            if not e:
+                return get_data_error_result(
+                    retmsg="Can't find this folder!")
+            MAX_FILE_NUM_PER_USER = int(os.environ.get('MAX_FILE_NUM_PER_USER', 0))
+            if MAX_FILE_NUM_PER_USER > 0 and DocumentService.get_doc_count(current_user.id) >= MAX_FILE_NUM_PER_USER:
+                return get_data_error_result(
+                    retmsg="Exceed the maximum file number of a free user!")
+
+            # split file name path
+            if not file_obj.filename:
+                e, file = FileService.get_by_id(pf_id)
+                file_obj_names = [file.name, file_obj.filename]
+            else:
+                full_path = '/' + file_obj.filename
+                file_obj_names = full_path.split('/')
+            file_len = len(file_obj_names)
+
+            # get folder
+            file_id_list = FileService.get_id_list_by_id(pf_id, file_obj_names, 1, [pf_id])
+            len_id_list = len(file_id_list)
+
+            # create folder
+            if file_len != len_id_list:
+                e, file = FileService.get_by_id(file_id_list[len_id_list - 1])
+                if not e:
+                    return get_data_error_result(retmsg="Folder not found!")
+                last_folder = FileService.create_folder(file, file_id_list[len_id_list - 1], file_obj_names,
+                                                        len_id_list)
+            else:
+                e, file = FileService.get_by_id(file_id_list[len_id_list - 2])
+                if not e:
+                    return get_data_error_result(retmsg="Folder not found!")
+                last_folder = FileService.create_folder(file, file_id_list[len_id_list - 2], file_obj_names,
+                                                        len_id_list)
+
+            # file type
+            filetype = filename_type(file_obj_names[file_len - 1])
+            location = file_obj_names[file_len - 1]
+            while MINIO.obj_exist(last_folder.id, location):
+                location += "_"
+            blob = file_obj.read()
+            filename = duplicate_name(
+                FileService.query,
+                name=file_obj_names[file_len - 1],
+                parent_id=last_folder.id)
+            file = {
+                "id": get_uuid(),
+                "parent_id": last_folder.id,
+                "tenant_id": current_user.id,
+                "created_by": current_user.id,
+                "type": filetype,
+                "name": filename,
+                "location": location,
+                "size": len(blob),
+            }
+            file = FileService.insert(file)
+            MINIO.put(last_folder.id, location, blob)
+            file_res.append(file.to_json())
+        return get_json_result(data=file_res)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/create', methods=['POST'])
+@login_required
+@validate_request("name")
+def create():
+    req = request.json
+    pf_id = request.json.get("parent_id")
+    input_file_type = request.json.get("type")
+    if not pf_id:
+        root_folder = FileService.get_root_folder(current_user.id)
+        pf_id = root_folder.id
+
+    try:
+        if not FileService.is_parent_folder_exist(pf_id):
+            return get_json_result(
+                data=False, retmsg="Parent Folder Doesn't Exist!", retcode=RetCode.OPERATING_ERROR)
+        if FileService.query(name=req["name"], parent_id=pf_id):
+            return get_data_error_result(
+                retmsg="Duplicated folder name in the same folder.")
+
+        if input_file_type == FileType.FOLDER.value:
+            file_type = FileType.FOLDER.value
+        else:
+            file_type = FileType.VIRTUAL.value
+
+        file = FileService.insert({
+            "id": get_uuid(),
+            "parent_id": pf_id,
+            "tenant_id": current_user.id,
+            "created_by": current_user.id,
+            "name": req["name"],
+            "location": "",
+            "size": 0,
+            "type": file_type
+        })
+
+        return get_json_result(data=file.to_json())
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/list', methods=['GET'])
+@login_required
+def list():
+    pf_id = request.args.get("parent_id")
+
+    keywords = request.args.get("keywords", "")
+
+    page_number = int(request.args.get("page", 1))
+    items_per_page = int(request.args.get("page_size", 15))
+    orderby = request.args.get("orderby", "create_time")
+    desc = request.args.get("desc", True)
+    if not pf_id:
+        root_folder = FileService.get_root_folder(current_user.id)
+        pf_id = root_folder.id
+    try:
+        e, file = FileService.get_by_id(pf_id)
+        if not e:
+            return get_data_error_result(retmsg="Folder not found!")
+
+        files, total = FileService.get_by_pf_id(
+            current_user.id, pf_id, page_number, items_per_page, orderby, desc, keywords)
+
+        parent_folder = FileService.get_parent_folder(pf_id)
+        if not FileService.get_parent_folder(pf_id):
+            return get_json_result(retmsg="File not found!")
+
+        return get_json_result(data={"total": total, "files": files, "parent_folder": parent_folder.to_json()})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/root_folder', methods=['GET'])
+@login_required
+def get_root_folder():
+    try:
+        root_folder = FileService.get_root_folder(current_user.id)
+        return get_json_result(data={"root_folder": root_folder.to_json()})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/parent_folder', methods=['GET'])
+@login_required
+def get_parent_folder():
+    file_id = request.args.get("file_id")
+    try:
+        e, file = FileService.get_by_id(file_id)
+        if not e:
+            return get_data_error_result(retmsg="Folder not found!")
+
+        parent_folder = FileService.get_parent_folder(file_id)
+        return get_json_result(data={"parent_folder": parent_folder.to_json()})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/all_parent_folder', methods=['GET'])
+@login_required
+def get_all_parent_folders():
+    file_id = request.args.get("file_id")
+    try:
+        e, file = FileService.get_by_id(file_id)
+        if not e:
+            return get_data_error_result(retmsg="Folder not found!")
+
+        parent_folders = FileService.get_all_parent_folders(file_id)
+        parent_folders_res = []
+        for parent_folder in parent_folders:
+            parent_folders_res.append(parent_folder.to_json())
+        return get_json_result(data={"parent_folders": parent_folders_res})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rm', methods=['POST'])
+@login_required
+@validate_request("file_ids")
+def rm():
+    req = request.json
+    file_ids = req["file_ids"]
+    try:
+        for file_id in file_ids:
+            e, file = FileService.get_by_id(file_id)
+            if not e:
+                return get_data_error_result(retmsg="File or Folder not found!")
+            if not file.tenant_id:
+                return get_data_error_result(retmsg="Tenant not found!")
+
+            if file.type == FileType.FOLDER.value:
+                file_id_list = FileService.get_all_innermost_file_ids(file_id, [])
+                for inner_file_id in file_id_list:
+                    e, file = FileService.get_by_id(inner_file_id)
+                    if not e:
+                        return get_data_error_result(retmsg="File not found!")
+                    MINIO.rm(file.parent_id, file.location)
+                FileService.delete_folder_by_pf_id(current_user.id, file_id)
+            else:
+                if not FileService.delete(file):
+                    return get_data_error_result(
+                        retmsg="Database error (File removal)!")
+
+            # delete file2document
+            informs = File2DocumentService.get_by_file_id(file_id)
+            for inform in informs:
+                doc_id = inform.document_id
+                e, doc = DocumentService.get_by_id(doc_id)
+                if not e:
+                    return get_data_error_result(retmsg="Document not found!")
+                tenant_id = DocumentService.get_tenant_id(doc_id)
+                if not tenant_id:
+                    return get_data_error_result(retmsg="Tenant not found!")
+                ELASTICSEARCH.deleteByQuery(
+                    Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
+                DocumentService.increment_chunk_num(
+                    doc.id, doc.kb_id, doc.token_num * -1, doc.chunk_num * -1, 0)
+                if not DocumentService.delete(doc):
+                    return get_data_error_result(
+                        retmsg="Database error (Document removal)!")
+            File2DocumentService.delete_by_file_id(file_id)
+
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rename', methods=['POST'])
+@login_required
+@validate_request("file_id", "name")
+def rename():
+    req = request.json
+    try:
+        e, file = FileService.get_by_id(req["file_id"])
+        if not e:
+            return get_data_error_result(retmsg="File not found!")
+        if pathlib.Path(req["name"].lower()).suffix != pathlib.Path(
+                file.name.lower()).suffix:
+            return get_json_result(
+                data=False,
+                retmsg="The extension of file can't be changed",
+                retcode=RetCode.ARGUMENT_ERROR)
+        if FileService.query(name=req["name"], pf_id=file.parent_id):
+            return get_data_error_result(
+                retmsg="Duplicated file name in the same folder.")
+
+        if not FileService.update_by_id(
+                req["file_id"], {"name": req["name"]}):
+            return get_data_error_result(
+                retmsg="Database error (File rename)!")
+
+        informs = File2DocumentService.get_by_file_id(req["file_id"])
+        if informs:
+            if not DocumentService.update_by_id(
+                    informs[0].document_id, {"name": req["name"]}):
+                return get_data_error_result(
+                    retmsg="Database error (Document rename)!")
+
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/get/<file_id>', methods=['GET'])
+# @login_required
+def get(file_id):
+    try:
+        e, doc = FileService.get_by_id(file_id)
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+
+        response = flask.make_response(MINIO.get(doc.parent_id, doc.location))
+        ext = re.search(r"\.([^.]+)$", doc.name)
+        if ext:
+            if doc.type == FileType.VISUAL.value:
+                response.headers.set('Content-Type', 'image/%s' % ext.group(1))
+            else:
+                response.headers.set(
+                    'Content-Type',
+                    'application/%s' %
+                    ext.group(1))
+        return response
+    except Exception as e:
+        return server_error_response(e)

api/apps/kb_app.py

@@ -111,7 +111,7 @@ def detail():
 @login_required
 def list():
     page_number = request.args.get("page", 1)
-    items_per_page = request.args.get("page_size", 15)
+    items_per_page = request.args.get("page_size", 150)
     orderby = request.args.get("orderby", "create_time")
     desc = request.args.get("desc", True)
     try:

api/apps/llm_app.py

@@ -28,7 +28,7 @@ from rag.llm import EmbeddingModel, ChatModel
 def factories():
     try:
         fac = LLMFactoriesService.get_all()
-        return get_json_result(data=[f.to_dict() for f in fac if f.name not in ["QAnything", "FastEmbed"]])
+        return get_json_result(data=[f.to_dict() for f in fac if f.name not in ["Youdao", "FastEmbed"]])
     except Exception as e:
         return server_error_response(e)
 
@@ -174,7 +174,7 @@ def list():
         llms = [m.to_dict()
                 for m in llms if m.status == StatusEnum.VALID.value]
         for m in llms:
-            m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in ["QAnything","FastEmbed"]
+            m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in ["Youdao","FastEmbed"]
 
         llm_set = set([m["llm_name"] for m in llms])
         for o in objs:

api/apps/user_app.py

@@ -14,6 +14,7 @@
 #  limitations under the License.
 #
 import re
+from datetime import datetime
 
 from flask import request, session, redirect
 from werkzeug.security import generate_password_hash, check_password_hash
@@ -22,11 +23,12 @@ from flask_login import login_required, current_user, login_user, logout_user
 from api.db.db_models import TenantLLM
 from api.db.services.llm_service import TenantLLMService, LLMService
 from api.utils.api_utils import server_error_response, validate_request
-from api.utils import get_uuid, get_format_time, decrypt, download_img
-from api.db import UserTenantRole, LLMType
+from api.utils import get_uuid, get_format_time, decrypt, download_img, current_timestamp, datetime_format
+from api.db import UserTenantRole, LLMType, FileType
 from api.settings import RetCode, GITHUB_OAUTH, CHAT_MDL, EMBEDDING_MDL, ASR_MDL, IMAGE2TEXT_MDL, PARSERS, API_KEY, \
     LLM_FACTORY, LLM_BASE_URL
 from api.db.services.user_service import UserService, TenantService, UserTenantService
+from api.db.services.file_service import FileService
 from api.settings import stat_logger
 from api.utils.api_utils import get_json_result, cors_reponse
 
@@ -56,6 +58,8 @@ def login():
         response_data = user.to_json()
         user.access_token = get_uuid()
         login_user(user)
+        user.update_time = current_timestamp(),
+        user.update_date = datetime_format(datetime.now()),
         user.save()
         msg = "Welcome back!"
         return cors_reponse(data=response_data, auth=user.get_id(), retmsg=msg)
@@ -218,6 +222,17 @@ def user_register(user_id, user):
         "invited_by": user_id,
         "role": UserTenantRole.OWNER
     }
+    file_id = get_uuid()
+    file = {
+        "id": file_id,
+        "parent_id": file_id,
+        "tenant_id": user_id,
+        "created_by": user_id,
+        "name": "/",
+        "type": FileType.FOLDER.value,
+        "size": 0,
+        "location": "",
+    }
     tenant_llm = []
     for llm in LLMService.query(fid=LLM_FACTORY):
         tenant_llm.append({"tenant_id": user_id,
@@ -233,6 +248,7 @@ def user_register(user_id, user):
     TenantService.insert(**tenant)
     UserTenantService.insert(**usr_tenant)
     TenantLLMService.insert_many(tenant_llm)
+    FileService.insert(file)
     return UserService.query(email=user["email"])
 
 

api/db/__init__.py

@@ -45,6 +45,8 @@ class FileType(StrEnum):
     VISUAL = 'visual'
     AURAL = 'aural'
     VIRTUAL = 'virtual'
+    FOLDER = 'folder'
+    OTHER = "other"
 
 
 class LLMType(StrEnum):

api/db/db_models.py

@@ -629,7 +629,7 @@ class Document(DataBaseModel):
         max_length=128,
         null=False,
         default="local",
-        help_text="where dose this document from")
+        help_text="where dose this document come from")
     type = CharField(max_length=32, null=False, help_text="file extension")
     created_by = CharField(
         max_length=32,
@@ -669,6 +669,61 @@ class Document(DataBaseModel):
         db_table = "document"
 
 
+class File(DataBaseModel):
+    id = CharField(
+        max_length=32,
+        primary_key=True,
+    )
+    parent_id = CharField(
+        max_length=32,
+        null=False,
+        help_text="parent folder id",
+        index=True)
+    tenant_id = CharField(
+        max_length=32,
+        null=False,
+        help_text="tenant id",
+        index=True)
+    created_by = CharField(
+        max_length=32,
+        null=False,
+        help_text="who created it")
+    name = CharField(
+        max_length=255,
+        null=False,
+        help_text="file name or folder name",
+        index=True)
+    location = CharField(
+        max_length=255,
+        null=True,
+        help_text="where dose it store")
+    size = IntegerField(default=0)
+    type = CharField(max_length=32, null=False, help_text="file extension")
+
+    class Meta:
+        db_table = "file"
+
+
+class File2Document(DataBaseModel):
+    id = CharField(
+        max_length=32,
+        primary_key=True,
+    )
+    file_id = CharField(
+        max_length=32,
+        null=True,
+        help_text="file id",
+        index=True)
+    document_id = CharField(
+        max_length=32,
+        null=True,
+        help_text="document id",
+        index=True)
+
+    class Meta:
+        db_table = "file2document"
+
+
 class Task(DataBaseModel):
     id = CharField(max_length=32, primary_key=True)
     doc_id = CharField(max_length=32, null=False, index=True)
@@ -697,7 +752,7 @@ class Dialog(DataBaseModel):
         null=True,
         default="Chinese",
         help_text="English|Chinese")
-    llm_id = CharField(max_length=32, null=False, help_text="default llm ID")
+    llm_id = CharField(max_length=128, null=False, help_text="default llm ID")
     llm_setting = JSONField(null=False, default={"temperature": 0.1, "top_p": 0.3, "frequency_penalty": 0.7,
                                                  "presence_penalty": 0.4, "max_tokens": 215})
     prompt_type = CharField(

api/db/init_data.py

@@ -120,7 +120,7 @@ factory_infos = [{
     "tags": "LLM,TEXT EMBEDDING,SPEECH2TEXT,MODERATION",
         "status": "1",
 },{
-    "name": "QAnything",
+    "name": "Youdao",
     "logo": "",
     "tags": "LLM,TEXT EMBEDDING,SPEECH2TEXT,MODERATION",
         "status": "1",
@@ -323,7 +323,7 @@ def init_llm_factory():
             "max_tokens": 2147483648,
             "model_type": LLMType.EMBEDDING.value
         },
-        # ------------------------ QAnything -----------------------
+        # ------------------------ Youdao -----------------------
         {
             "fid": factory_infos[7]["name"],
             "llm_name": "maidalun1020/bce-embedding-base_v1",
@@ -347,7 +347,9 @@ def init_llm_factory():
     LLMService.filter_delete([LLM.fid == "Local"])
     LLMService.filter_delete([LLM.fid == "Moonshot", LLM.llm_name == "flag-embedding"])
     TenantLLMService.filter_delete([TenantLLM.llm_factory == "Moonshot", TenantLLM.llm_name == "flag-embedding"])
-
+    LLMFactoriesService.filter_delete([LLMFactoriesService.model.name == "QAnything"])
+    LLMService.filter_delete([LLMService.model.fid == "QAnything"])
+    TenantLLMService.filter_update([TenantLLMService.model.llm_factory == "QAnything"], {"llm_factory": "Youdao"})
     """
     drop table llm;
     drop table llm_factories;

api/db/services/api_service.py

@@ -40,8 +40,8 @@ class API4ConversationService(CommonService):
     @classmethod
     @DB.connection_context()
     def append_message(cls, id, conversation):
-        cls.model.update_by_id(id, conversation)
-        return cls.model.update(round=cls.model.round + 1).where(id=id).execute()
+        cls.update_by_id(id, conversation)
+        return cls.model.update(round=cls.model.round + 1).where(cls.model.id==id).execute()
 
     @classmethod
     @DB.connection_context()

api/db/services/document_service.py

@@ -15,6 +15,11 @@
 #
 from peewee import Expression
 
+from elasticsearch_dsl import Q
+from rag.utils import ELASTICSEARCH
+from rag.utils.minio_conn import MINIO
+from rag.nlp import search
+
 from api.db import FileType, TaskStatus
 from api.db.db_models import DB, Knowledgebase, Tenant
 from api.db.db_models import Document
@@ -69,6 +74,20 @@ class DocumentService(CommonService):
             raise RuntimeError("Database error (Knowledgebase)!")
         return cls.delete_by_id(doc.id)
 
+    @classmethod
+    @DB.connection_context()
+    def remove_document(cls, doc, tenant_id):
+        ELASTICSEARCH.deleteByQuery(
+            Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
+
+        cls.increment_chunk_num(
+            doc.id, doc.kb_id, doc.token_num * -1, doc.chunk_num * -1, 0)
+        if not cls.delete(doc):
+            raise RuntimeError("Database error (Document removal)!")
+
+        MINIO.rm(doc.kb_id, doc.location)
+        return cls.delete_by_id(doc.id)
+
     @classmethod
     @DB.connection_context()
     def get_newly_uploaded(cls, tm, mod=0, comm=1, items_per_page=64):

api/db/services/file2document_service.py

@@ -0,0 +1,66 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from datetime import datetime
+
+from api.db.db_models import DB
+from api.db.db_models import File, Document, File2Document
+from api.db.services.common_service import CommonService
+from api.utils import current_timestamp, datetime_format
+
+
+class File2DocumentService(CommonService):
+    model = File2Document
+
+    @classmethod
+    @DB.connection_context()
+    def get_by_file_id(cls, file_id):
+        objs = cls.model.select().where(cls.model.file_id == file_id)
+        return objs
+
+    @classmethod
+    @DB.connection_context()
+    def get_by_document_id(cls, document_id):
+        objs = cls.model.select().where(cls.model.document_id == document_id)
+        return objs
+
+    @classmethod
+    @DB.connection_context()
+    def insert(cls, obj):
+        if not cls.save(**obj):
+            raise RuntimeError("Database error (File)!")
+        e, obj = cls.get_by_id(obj["id"])
+        if not e:
+            raise RuntimeError("Database error (File retrieval)!")
+        return obj
+
+    @classmethod
+    @DB.connection_context()
+    def delete_by_file_id(cls, file_id):
+        return cls.model.delete().where(cls.model.file_id == file_id).execute()
+
+    @classmethod
+    @DB.connection_context()
+    def delete_by_document_id(cls, doc_id):
+        return cls.model.delete().where(cls.model.document_id == doc_id).execute()
+
+    @classmethod
+    @DB.connection_context()
+    def update_by_file_id(cls, file_id, obj):
+        obj["update_time"] = current_timestamp()
+        obj["update_date"] = datetime_format(datetime.now())
+        num = cls.model.update(obj).where(cls.model.id == file_id).execute()
+        e, obj = cls.get_by_id(cls.model.id)
+        return obj

api/db/services/file_service.py

@@ -0,0 +1,243 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from flask_login import current_user
+from peewee import fn
+
+from api.db import FileType
+from api.db.db_models import DB, File2Document, Knowledgebase
+from api.db.db_models import File, Document
+from api.db.services.common_service import CommonService
+from api.utils import get_uuid
+from rag.utils import MINIO
+
+
+class FileService(CommonService):
+    model = File
+
+    @classmethod
+    @DB.connection_context()
+    def get_by_pf_id(cls, tenant_id, pf_id, page_number, items_per_page,
+                     orderby, desc, keywords):
+        if keywords:
+            files = cls.model.select().where(
+                (cls.model.tenant_id == tenant_id)
+                & (cls.model.parent_id == pf_id), (fn.LOWER(cls.model.name).like(f"%%{keywords.lower()}%%")))
+        else:
+            files = cls.model.select().where((cls.model.tenant_id == tenant_id)
+                                             & (cls.model.parent_id == pf_id))
+        count = files.count()
+        if desc:
+            files = files.order_by(cls.model.getter_by(orderby).desc())
+        else:
+            files = files.order_by(cls.model.getter_by(orderby).asc())
+
+        files = files.paginate(page_number, items_per_page)
+
+        res_files = list(files.dicts())
+        for file in res_files:
+            if file["type"] == FileType.FOLDER.value:
+                file["size"] = cls.get_folder_size(file["id"])
+                file['kbs_info'] = []
+                continue
+            kbs_info = cls.get_kb_id_by_file_id(file['id'])
+            file['kbs_info'] = kbs_info
+
+        return res_files, count
+
+    @classmethod
+    @DB.connection_context()
+    def get_kb_id_by_file_id(cls, file_id):
+        kbs = (cls.model.select(*[Knowledgebase.id, Knowledgebase.name])
+               .join(File2Document, on=(File2Document.file_id == file_id))
+               .join(Document, on=(File2Document.document_id == Document.id))
+               .join(Knowledgebase, on=(Knowledgebase.id == Document.kb_id))
+               .where(cls.model.id == file_id))
+        if not kbs: return []
+        kbs_info_list = []
+        for kb in list(kbs.dicts()):
+            kbs_info_list.append({"kb_id": kb['id'], "kb_name": kb['name']})
+        return kbs_info_list
+
+    @classmethod
+    @DB.connection_context()
+    def get_by_pf_id_name(cls, id, name):
+        file = cls.model.select().where((cls.model.parent_id == id) & (cls.model.name == name))
+        if file.count():
+            e, file = cls.get_by_id(file[0].id)
+            if not e:
+                raise RuntimeError("Database error (File retrieval)!")
+            return file
+        return None
+
+    @classmethod
+    @DB.connection_context()
+    def get_id_list_by_id(cls, id, name, count, res):
+        if count < len(name):
+            file = cls.get_by_pf_id_name(id, name[count])
+            if file:
+                res.append(file.id)
+                return cls.get_id_list_by_id(file.id, name, count + 1, res)
+            else:
+                return res
+        else:
+            return res
+
+    @classmethod
+    @DB.connection_context()
+    def get_all_innermost_file_ids(cls, folder_id, result_ids):
+        subfolders = cls.model.select().where(cls.model.parent_id == folder_id)
+        if subfolders.exists():
+            for subfolder in subfolders:
+                cls.get_all_innermost_file_ids(subfolder.id, result_ids)
+        else:
+            result_ids.append(folder_id)
+        return result_ids
+
+    @classmethod
+    @DB.connection_context()
+    def create_folder(cls, file, parent_id, name, count):
+        if count > len(name) - 2:
+            return file
+        else:
+            file = cls.insert({
+                "id": get_uuid(),
+                "parent_id": parent_id,
+                "tenant_id": current_user.id,
+                "created_by": current_user.id,
+                "name": name[count],
+                "location": "",
+                "size": 0,
+                "type": FileType.FOLDER.value
+            })
+            return cls.create_folder(file, file.id, name, count + 1)
+
+    @classmethod
+    @DB.connection_context()
+    def is_parent_folder_exist(cls, parent_id):
+        parent_files = cls.model.select().where(cls.model.id == parent_id)
+        if parent_files.count():
+            return True
+        cls.delete_folder_by_pf_id(parent_id)
+        return False
+
+    @classmethod
+    @DB.connection_context()
+    def get_root_folder(cls, tenant_id):
+        file = cls.model.select().where(cls.model.tenant_id == tenant_id and
+                                        cls.model.parent_id == cls.model.id)
+        if not file:
+            file_id = get_uuid()
+            file = {
+                "id": file_id,
+                "parent_id": file_id,
+                "tenant_id": tenant_id,
+                "created_by": tenant_id,
+                "name": "/",
+                "type": FileType.FOLDER.value,
+                "size": 0,
+                "location": "",
+            }
+            cls.save(**file)
+        else:
+            file_id = file[0].id
+
+        e, file = cls.get_by_id(file_id)
+        if not e:
+            raise RuntimeError("Database error (File retrieval)!")
+        return file
+
+    @classmethod
+    @DB.connection_context()
+    def get_parent_folder(cls, file_id):
+        file = cls.model.select().where(cls.model.id == file_id)
+        if file.count():
+            e, file = cls.get_by_id(file[0].parent_id)
+            if not e:
+                raise RuntimeError("Database error (File retrieval)!")
+        else:
+            raise RuntimeError("Database error (File doesn't exist)!")
+        return file
+
+    @classmethod
+    @DB.connection_context()
+    def get_all_parent_folders(cls, start_id):
+        parent_folders = []
+        current_id = start_id
+        while current_id:
+            e, file = cls.get_by_id(current_id)
+            if file.parent_id != file.id and e:
+                parent_folders.append(file)
+                current_id = file.parent_id
+            else:
+                parent_folders.append(file)
+                break
+        return parent_folders
+
+    @classmethod
+    @DB.connection_context()
+    def insert(cls, file):
+        if not cls.save(**file):
+            raise RuntimeError("Database error (File)!")
+        e, file = cls.get_by_id(file["id"])
+        if not e:
+            raise RuntimeError("Database error (File retrieval)!")
+        return file
+
+    @classmethod
+    @DB.connection_context()
+    def delete(cls, file):
+        return cls.delete_by_id(file.id)
+
+    @classmethod
+    @DB.connection_context()
+    def delete_by_pf_id(cls, folder_id):
+        return cls.model.delete().where(cls.model.parent_id == folder_id).execute()
+
+    @classmethod
+    @DB.connection_context()
+    def delete_folder_by_pf_id(cls, user_id, folder_id):
+        try:
+            files = cls.model.select().where((cls.model.tenant_id == user_id)
+                                             & (cls.model.parent_id == folder_id))
+            for file in files:
+                cls.delete_folder_by_pf_id(user_id, file.id)
+            return cls.model.delete().where((cls.model.tenant_id == user_id)
+                                            & (cls.model.id == folder_id)).execute(),
+        except Exception as e:
+            print(e)
+            raise RuntimeError("Database error (File retrieval)!")
+
+    @classmethod
+    @DB.connection_context()
+    def get_file_count(cls, tenant_id):
+        files = cls.model.select(cls.model.id).where(cls.model.tenant_id == tenant_id)
+        return len(files)
+
+    @classmethod
+    @DB.connection_context()
+    def get_folder_size(cls, folder_id):
+        size = 0
+
+        def dfs(parent_id):
+            nonlocal size
+            for f in cls.model.select(*[cls.model.id, cls.model.size, cls.model.type]).where(
+                    cls.model.parent_id == parent_id, cls.model.id != parent_id):
+                size += f.size
+                if f.type == FileType.FOLDER.value:
+                    dfs(f.id)
+
+        dfs(folder_id)
+        return size

api/db/services/knowledgebase_service.py

@@ -27,7 +27,8 @@ class KnowledgebaseService(CommonService):
                           page_number, items_per_page, orderby, desc):
         kbs = cls.model.select().where(
             ((cls.model.tenant_id.in_(joined_tenant_ids) & (cls.model.permission ==
-             TenantPermission.TEAM.value)) | (cls.model.tenant_id == user_id))
+                                                            TenantPermission.TEAM.value)) | (
+                         cls.model.tenant_id == user_id))
             & (cls.model.status == StatusEnum.VALID.value)
         )
         if desc:
@@ -56,7 +57,8 @@ class KnowledgebaseService(CommonService):
             cls.model.chunk_num,
             cls.model.parser_id,
             cls.model.parser_config]
-        kbs = cls.model.select(*fields).join(Tenant, on=((Tenant.id == cls.model.tenant_id) & (Tenant.status == StatusEnum.VALID.value))).where(
+        kbs = cls.model.select(*fields).join(Tenant, on=(
+                    (Tenant.id == cls.model.tenant_id) & (Tenant.status == StatusEnum.VALID.value))).where(
             (cls.model.id == kb_id),
             (cls.model.status == StatusEnum.VALID.value)
         )
@@ -86,6 +88,7 @@ class KnowledgebaseService(CommonService):
                     old[k] = list(set(old[k] + v))
                 else:
                     old[k] = v
+
         dfs_update(m.parser_config, config)
         cls.update_by_id(id, {"parser_config": m.parser_config})
 
@@ -97,3 +100,15 @@ class KnowledgebaseService(CommonService):
             if k.parser_config and "field_map" in k.parser_config:
                 conf.update(k.parser_config["field_map"])
         return conf
+
+    @classmethod
+    @DB.connection_context()
+    def get_by_name(cls, kb_name, tenant_id):
+        kb = cls.model.select().where(
+            (cls.model.name == kb_name)
+            & (cls.model.tenant_id == tenant_id)
+            & (cls.model.status == StatusEnum.VALID.value)
+        )
+        if kb:
+            return True, kb[0]
+        return False, None

api/db/services/llm_service.py

@@ -81,7 +81,7 @@ class TenantLLMService(CommonService):
         if not model_config:
             if llm_type == LLMType.EMBEDDING.value:
                 llm = LLMService.query(llm_name=llm_name)
-                if llm and llm[0].fid in ["QAnything", "FastEmbed"]:
+                if llm and llm[0].fid in ["Youdao", "FastEmbed"]:
                     model_config = {"llm_factory": llm[0].fid, "api_key":"", "llm_name": llm_name, "api_base": ""}
             if not model_config:
                 if llm_name == "flag-embedding":

api/db/services/task_service.py

@@ -13,12 +13,15 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import random
+
 from peewee import Expression
 from api.db.db_models import DB
 from api.db import StatusEnum, FileType, TaskStatus
 from api.db.db_models import Task, Document, Knowledgebase, Tenant
 from api.db.services.common_service import CommonService
 from api.db.services.document_service import DocumentService
+from api.utils import current_timestamp
 
 
 class TaskService(CommonService):
@@ -26,7 +29,7 @@ class TaskService(CommonService):
 
     @classmethod
     @DB.connection_context()
-    def get_tasks(cls, tm, mod=0, comm=1, items_per_page=64):
+    def get_tasks(cls, tm, mod=0, comm=1, items_per_page=1, takeit=True):
         fields = [
             cls.model.id,
             cls.model.doc_id,
@@ -45,6 +48,7 @@ class TaskService(CommonService):
             Tenant.img2txt_id,
             Tenant.asr_id,
             cls.model.update_time]
+        with DB.lock("get_task", -1):
             docs = cls.model.select(*fields) \
                 .join(Document, on=(cls.model.doc_id == Document.id)) \
                 .join(Knowledgebase, on=(Document.kb_id == Knowledgebase.id)) \
@@ -54,11 +58,37 @@ class TaskService(CommonService):
                     Document.run == TaskStatus.RUNNING.value,
                     ~(Document.type == FileType.VIRTUAL.value),
                     cls.model.progress == 0,
-                cls.model.update_time >= tm,
-                (Expression(cls.model.create_time, "%%", comm) == mod))\
+                    #cls.model.update_time >= tm,
+                    #(Expression(cls.model.create_time, "%%", comm) == mod)
+                )\
                 .order_by(cls.model.update_time.asc())\
-            .paginate(1, items_per_page)
-        return list(docs.dicts())
+                .paginate(0, items_per_page)
+            docs = list(docs.dicts())
+            if not docs: return []
+            if not takeit: return docs
+
+            cls.model.update(progress_msg=cls.model.progress_msg + "\n" + "Task has been received.", progress=random.random()/10.).where(
+                cls.model.id == docs[0]["id"]).execute()
+            return docs
+
+    @classmethod
+    @DB.connection_context()
+    def get_ongoing_doc_name(cls):
+        with DB.lock("get_task", -1):
+            docs = cls.model.select(*[Document.kb_id, Document.location]) \
+                .join(Document, on=(cls.model.doc_id == Document.id)) \
+                .where(
+                    Document.status == StatusEnum.VALID.value,
+                    Document.run == TaskStatus.RUNNING.value,
+                    ~(Document.type == FileType.VIRTUAL.value),
+                    cls.model.progress >= 0,
+                    cls.model.progress < 1,
+                    cls.model.create_time >= current_timestamp() - 180000
+                )
+            docs = list(docs.dicts())
+            if not docs: return []
+
+            return list(set([(d["kb_id"], d["location"]) for d in docs]))
 
     @classmethod
     @DB.connection_context()
@@ -74,6 +104,7 @@ class TaskService(CommonService):
     @classmethod
     @DB.connection_context()
     def update_progress(cls, id, info):
+        with DB.lock("update_progress", -1):
             if info["progress_msg"]:
                 cls.model.update(progress_msg=cls.model.progress_msg + "\n" + info["progress_msg"]).where(
                     cls.model.id == id).execute()

api/utils/file_utils.py

@@ -147,7 +147,7 @@ def filename_type(filename):
         return FileType.PDF.value
 
     if re.match(
-            r".*\.(docx|doc|ppt|pptx|yml|xml|htm|json|csv|txt|ini|xls|xlsx|wps|rtf|hlp|pages|numbers|key|md)$", filename):
+            r".*\.(doc|docx|ppt|pptx|yml|xml|htm|json|csv|txt|ini|xls|xlsx|wps|rtf|hlp|pages|numbers|key|md)$", filename):
         return FileType.DOC.value
 
     if re.match(
@@ -155,7 +155,9 @@ def filename_type(filename):
         return FileType.AURAL.value
 
     if re.match(r".*\.(jpg|jpeg|png|tif|gif|pcx|tga|exif|fpx|svg|psd|cdr|pcd|dxf|ufo|eps|ai|raw|WMF|webp|avif|apng|icon|ico|mpg|mpeg|avi|rm|rmvb|mov|wmv|asf|dat|asx|wvx|mpe|mpa|mp4)$", filename):
-        return FileType.VISUAL
+        return FileType.VISUAL.value
+
+    return FileType.OTHER.value
 
 
 def thumbnail(filename, blob):

conf/mapping.json

@@ -1,7 +1,7 @@
  {
   "settings": {
     "index": {
-      "number_of_shards": 4,
+      "number_of_shards": 2,
       "number_of_replicas": 0,
       "refresh_interval" : "1000ms"
     },

conf/service_conf.yaml

@@ -13,11 +13,16 @@ minio:
   user: 'rag_flow'
   password: 'infini_rag_flow'
   host: 'minio:9000'
+redis:
+  db: 1
+  password: 'infini_rag_flow'
+  host: 'redis:6379'
 es:
   hosts: 'http://es01:9200'
 user_default_llm:
   factory: 'Tongyi-Qianwen'
   api_key: 'sk-xxxxxxxxxxxxx'
+  base_url: ''
 oauth:
   github:
     client_id: xxxxxxxxxxxxxxxxxxxxxxxxx

deepdoc/README_zh.md

@@ -1 +1,116 @@
 [English](./README.md) | 简体中文
+
+# *Deep*Doc
+
+- [*Deep*Doc](#deepdoc)
+  - [1. 介绍](#1-介绍)
+  - [2. 视觉处理](#2-视觉处理)
+  - [3. 解析器](#3-解析器)
+    - [简历](#简历)
+
+<a name="1"></a>
+## 1. 介绍
+
+对于来自不同领域、具有不同格式和不同检索要求的大量文档，准确的分析成为一项极具挑战性的任务。*Deep*Doc 就是为了这个目的而诞生的。到目前为止，*Deep*Doc 中有两个组成部分：视觉处理和解析器。如果您对我们的OCR、布局识别和TSR结果感兴趣，您可以运行下面的测试程序。
+
+```bash
+python deepdoc/vision/t_ocr.py -h
+usage: t_ocr.py [-h] --inputs INPUTS [--output_dir OUTPUT_DIR]
+
+options:
+  -h, --help            show this help message and exit
+  --inputs INPUTS       Directory where to store images or PDFs, or a file path to a single image or PDF
+  --output_dir OUTPUT_DIR
+                        Directory where to store the output images. Default: './ocr_outputs'
+```
+
+```bash
+python deepdoc/vision/t_recognizer.py -h
+usage: t_recognizer.py [-h] --inputs INPUTS [--output_dir OUTPUT_DIR] [--threshold THRESHOLD] [--mode {layout,tsr}]
+
+options:
+  -h, --help            show this help message and exit
+  --inputs INPUTS       Directory where to store images or PDFs, or a file path to a single image or PDF
+  --output_dir OUTPUT_DIR
+                        Directory where to store the output images. Default: './layouts_outputs'
+  --threshold THRESHOLD
+                        A threshold to filter out detections. Default: 0.5
+  --mode {layout,tsr}   Task mode: layout recognition or table structure recognition
+```
+
+HuggingFace为我们的模型提供服务。如果你在下载HuggingFace模型时遇到问题，这可能会有所帮助！！
+
+```bash
+export HF_ENDPOINT=https://hf-mirror.com
+```
+
+<a name="2"></a>
+## 2. 视觉处理
+
+作为人类，我们使用视觉信息来解决问题。
+
+  - **OCR（Optical Character Recognition，光学字符识别）**。由于许多文档都是以图像形式呈现的，或者至少能够转换为图像，因此OCR是文本提取的一个非常重要、基本，甚至通用的解决方案。
+
+    ```bash
+    python deepdoc/vision/t_ocr.py --inputs=path_to_images_or_pdfs --output_dir=path_to_store_result
+    ```
+
+    输入可以是图像或PDF的目录，或者单个图像、PDF文件。您可以查看文件夹 `path_to_store_result` ，其中有演示结果位置的图像，以及包含OCR文本的txt文件。
+    
+    <div align="center" style="margin-top:20px;margin-bottom:20px;">
+    <img src="https://github.com/infiniflow/ragflow/assets/12318111/f25bee3d-aaf7-4102-baf5-d5208361d110" width="900"/>
+    </div>
+
+  - 布局识别（Layout recognition）。来自不同领域的文件可能有不同的布局，如报纸、杂志、书籍和简历在布局方面是不同的。只有当机器有准确的布局分析时，它才能决定这些文本部分是连续的还是不连续的，或者这个部分需要表结构识别（Table Structure Recognition，TSR）来处理，或者这个部件是一个图形并用这个标题来描述。我们有10个基本布局组件，涵盖了大多数情况：
+      - 文本
+      - 标题
+      - 配图
+      - 配图标题
+      - 表格
+      - 表格标题
+      - 页头
+      - 页尾
+      - 参考引用
+      - 公式
+      
+     请尝试以下命令以查看布局检测结果。
+
+    ```bash
+    python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=layout --output_dir=path_to_store_result
+    ```
+
+    输入可以是图像或PDF的目录，或者单个图像、PDF文件。您可以查看文件夹 `path_to_store_result` ，其中有显示检测结果的图像，如下所示：
+    <div align="center" style="margin-top:20px;margin-bottom:20px;">
+    <img src="https://github.com/infiniflow/ragflow/assets/12318111/07e0f625-9b28-43d0-9fbb-5bf586cd286f" width="1000"/>
+    </div>
+  
+  - **TSR（Table Structure Recognition，表结构识别）**。数据表是一种常用的结构，用于表示包括数字或文本在内的数据。表的结构可能非常复杂，比如层次结构标题、跨单元格和投影行标题。除了TSR，我们还将内容重新组合成LLM可以很好理解的句子。TSR任务有五个标签：
+      - 列
+      - 行
+      - 列标题
+      - 行标题
+      - 合并单元格
+      
+    请尝试以下命令以查看布局检测结果。
+
+    ```bash
+    python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=tsr --output_dir=path_to_store_result
+    ```
+
+    输入可以是图像或PDF的目录，或者单个图像、PDF文件。您可以查看文件夹 `path_to_store_result` ，其中包含图像和html页面，这些页面展示了以下检测结果：
+
+    <div align="center" style="margin-top:20px;margin-bottom:20px;">
+    <img src="https://github.com/infiniflow/ragflow/assets/12318111/cb24e81b-f2ba-49f3-ac09-883d75606f4c" width="1000"/>
+    </div>
+        
+<a name="3"></a>
+## 3. 解析器
+
+PDF、DOCX、EXCEL和PPT四种文档格式都有相应的解析器。最复杂的是PDF解析器，因为PDF具有灵活性。PDF解析器的输出包括：
+  - 在PDF中有自己位置的文本块（页码和矩形位置）。
+  - 带有PDF裁剪图像的表格，以及已经翻译成自然语言句子的内容。
+  - 图中带标题和文字的图。
+  
+### 简历
+
+简历是一种非常复杂的文件。一份由各种布局的非结构化文本组成的简历可以分解为由近百个字段组成的结构化数据。我们还没有打开解析器，因为我们在解析过程之后打开了处理方法。

deepdoc/parser/excel_parser.py

@@ -3,6 +3,8 @@ from openpyxl import load_workbook
 import sys
 from io import BytesIO
 
+from rag.nlp import find_codec
+
 
 class HuExcelParser:
     def html(self, fnm):
@@ -66,7 +68,8 @@ class HuExcelParser:
                 return total
 
         if fnm.split(".")[-1].lower() in ["csv", "txt"]:
-            txt = binary.decode("utf-8")
+            encoding = find_codec(binary)
+            txt = binary.decode(encoding)
             return len(txt.split("\n"))
 
 

deepdoc/parser/pdf_parser.py

@@ -11,7 +11,7 @@ import pdfplumber
 import logging
 from PIL import Image, ImageDraw
 import numpy as np
-
+from timeit import default_timer as timer
 from PyPDF2 import PdfReader as pdf2_read
 
 from api.utils.file_utils import get_project_base_directory
@@ -43,11 +43,12 @@ class HuParser:
                 model_dir, "updown_concat_xgb.model"))
         except Exception as e:
             model_dir = snapshot_download(
-                repo_id="InfiniFlow/text_concat_xgb_v1.0")
+                repo_id="InfiniFlow/text_concat_xgb_v1.0",
+                local_dir=os.path.join(get_project_base_directory(), "rag/res/deepdoc"),
+                local_dir_use_symlinks=False)
             self.updown_cnt_mdl.load_model(os.path.join(
                 model_dir, "updown_concat_xgb.model"))
 
-
         self.page_from = 0
         """
         If you have trouble downloading HuggingFace models, -_^ this might help!!
@@ -62,7 +63,7 @@ class HuParser:
         """
 
     def __char_width(self, c):
-        return (c["x1"] - c["x0"]) // len(c["text"])
+        return (c["x1"] - c["x0"]) // max(len(c["text"]), 1)
 
     def __height(self, c):
         return c["bottom"] - c["top"]
@@ -828,9 +829,13 @@ class HuParser:
         pn = [bx["page_number"]]
         top = bx["top"] - self.page_cum_height[pn[0] - 1]
         bott = bx["bottom"] - self.page_cum_height[pn[0] - 1]
+        page_images_cnt = len(self.page_images)
+        if pn[-1] - 1 >= page_images_cnt: return ""
         while bott * ZM > self.page_images[pn[-1] - 1].size[1]:
             bott -= self.page_images[pn[-1] - 1].size[1] / ZM
             pn.append(pn[-1] + 1)
+            if pn[-1] - 1 >= page_images_cnt:
+                return ""
 
         return "@@{}\t{:.1f}\t{:.1f}\t{:.1f}\t{:.1f}##" \
             .format("-".join([str(p) for p in pn]),
@@ -930,6 +935,7 @@ class HuParser:
         self.page_cum_height = [0]
         self.page_layout = []
         self.page_from = page_from
+        st = timer()
         try:
             self.pdf = pdfplumber.open(fnm) if isinstance(
                 fnm, str) else pdfplumber.open(BytesIO(fnm))
@@ -968,6 +974,7 @@ class HuParser:
                         self.outlines.append((a["/Title"], depth))
                         continue
                     dfs(a, depth + 1)
+
             dfs(outlines, 0)
         except Exception as e:
             logging.warning(f"Outlines exception: {e}")
@@ -983,7 +990,9 @@ class HuParser:
             self.is_english = True
         else:
             self.is_english = False
+        self.is_english = False
 
+        st = timer()
         for i, img in enumerate(self.page_images):
             chars = self.page_chars[i] if not self.is_english else []
             self.mean_height.append(
@@ -1001,15 +1010,11 @@ class HuParser:
                                                                        chars[j]["width"]) / 2:
                     chars[j]["text"] += " "
                 j += 1
-            # if i > 0:
-            #     if not chars:
-            #         self.page_cum_height.append(img.size[1] / zoomin)
-            #     else:
-            #         self.page_cum_height.append(
-            #             np.max([c["bottom"] for c in chars]))
+
             self.__ocr(i + 1, img, chars, zoomin)
-            if callback:
+            if callback and i % 6 == 5:
                 callback(prog=(i + 1) * 0.6 / len(self.page_images), msg="")
+        # print("OCR:", timer()-st)
 
         if not self.is_english and not any(
                 [c for c in self.page_chars]) and self.boxes:

deepdoc/vision/layout_recognizer.py

@@ -43,7 +43,9 @@ class LayoutRecognizer(Recognizer):
                     "rag/res/deepdoc")
             super().__init__(self.labels, domain, model_dir)
         except Exception as e:
-            model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc")
+            model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc",
+                                          local_dir=os.path.join(get_project_base_directory(), "rag/res/deepdoc"),
+                                          local_dir_use_symlinks=False)
             super().__init__(self.labels, domain, model_dir)
 
         self.garbage_layouts = ["footer", "header", "reference"]

deepdoc/vision/ocr.py

@@ -486,7 +486,9 @@ class OCR(object):
                 self.text_detector = TextDetector(model_dir)
                 self.text_recognizer = TextRecognizer(model_dir)
             except Exception as e:
-                model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc")
+                model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc",
+                                              local_dir=os.path.join(get_project_base_directory(), "rag/res/deepdoc"),
+                                              local_dir_use_symlinks=False)
                 self.text_detector = TextDetector(model_dir)
                 self.text_recognizer = TextRecognizer(model_dir)
 

deepdoc/vision/recognizer.py

@@ -41,7 +41,9 @@ class Recognizer(object):
                         "rag/res/deepdoc")
             model_file_path = os.path.join(model_dir, task_name + ".onnx")
             if not os.path.exists(model_file_path):
-                model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc")
+                model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc",
+                                              local_dir=os.path.join(get_project_base_directory(), "rag/res/deepdoc"),
+                                              local_dir_use_symlinks=False)
                 model_file_path = os.path.join(model_dir, task_name + ".onnx")
         else:
             model_file_path = os.path.join(model_dir, task_name + ".onnx")

deepdoc/vision/table_structure_recognizer.py

@@ -39,7 +39,9 @@ class TableStructureRecognizer(Recognizer):
                     get_project_base_directory(),
                     "rag/res/deepdoc"))
         except Exception as e:
-            super().__init__(self.labels, "tsr", snapshot_download(repo_id="InfiniFlow/deepdoc"))
+            super().__init__(self.labels, "tsr", snapshot_download(repo_id="InfiniFlow/deepdoc",
+                                              local_dir=os.path.join(get_project_base_directory(), "rag/res/deepdoc"),
+                                              local_dir_use_symlinks=False))
 
     def __call__(self, images, thr=0.2):
         tbls = super().__call__(images, thr)

docker/.env

@@ -11,16 +11,24 @@ ES_PORT=1200
 KIBANA_PORT=6601
 
 # Increase or decrease based on the available host memory (in bytes)
-MEM_LIMIT=4073741824
+
+MEM_LIMIT=8073741824
+
 
 MYSQL_PASSWORD=infini_rag_flow
 MYSQL_PORT=5455
 
+# Port to expose minio to the host
+MINIO_CONSOLE_PORT=9001
+MINIO_PORT=9000
+
 MINIO_USER=rag_flow
 MINIO_PASSWORD=infini_rag_flow
 
 SVR_HTTP_PORT=9380
 
+RAGFLOW_VERSION=v0.4.0
+
 TIMEZONE='Asia/Shanghai'
 
 ######## OS setup for ES ###########

docker/docker-compose-CN-oc9.yml

@@ -0,0 +1,29 @@
+include:
+  - path: ./docker-compose-base.yml
+    env_file: ./.env
+
+services:
+   ragflow:
+    depends_on:
+      mysql:
+        condition: service_healthy
+      es01:
+        condition: service_healthy
+    image: edwardelric233/ragflow:oc9
+    container_name: ragflow-server
+    ports:
+      - ${SVR_HTTP_PORT}:9380
+      - 80:80
+      - 443:443
+    volumes:
+      - ./service_conf.yaml:/ragflow/conf/service_conf.yaml
+      - ./ragflow-logs:/ragflow/logs
+      - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf
+      - ./nginx/proxy.conf:/etc/nginx/proxy.conf
+      - ./nginx/nginx.conf:/etc/nginx/nginx.conf
+    environment:
+      - TZ=${TIMEZONE}
+      - HF_ENDPOINT=https://hf-mirror.com
+    networks:
+      - ragflow
+    restart: always

docker/docker-compose-CN.yml

@@ -9,7 +9,7 @@ services:
         condition: service_healthy
       es01:
         condition: service_healthy
-    image: swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:v0.2.0
+    image: swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:${RAGFLOW_VERSION}
     container_name: ragflow-server
     ports:
       - ${SVR_HTTP_PORT}:9380

docker/docker-compose-base.yml

@@ -29,23 +29,23 @@ services:
       - ragflow
     restart: always
 
-  kibana:
-    depends_on:
-        es01:
-          condition: service_healthy
-    image: docker.elastic.co/kibana/kibana:${STACK_VERSION}
-    container_name: ragflow-kibana
-    volumes:
-      - kibanadata:/usr/share/kibana/data
-    ports:
-      - ${KIBANA_PORT}:5601
-    environment:
-      - SERVERNAME=kibana
-      - ELASTICSEARCH_HOSTS=http://es01:9200
-      - TZ=${TIMEZONE}
-    mem_limit: ${MEM_LIMIT}
-    networks:
-      - ragflow
+  #kibana:
+  #  depends_on:
+  #      es01:
+  #        condition: service_healthy
+  #  image: docker.elastic.co/kibana/kibana:${STACK_VERSION}
+  #  container_name: ragflow-kibana
+  #  volumes:
+  #    - kibanadata:/usr/share/kibana/data
+  #  ports:
+  #    - ${KIBANA_PORT}:5601
+  #  environment:
+  #    - SERVERNAME=kibana
+  #    - ELASTICSEARCH_HOSTS=http://es01:9200
+  #    - TZ=${TIMEZONE}
+  #  mem_limit: ${MEM_LIMIT}
+  #  networks:
+  #    - ragflow
 
   mysql:
     image: mysql:5.7.18
@@ -80,8 +80,8 @@ services:
     container_name: ragflow-minio
     command: server --console-address ":9001" /data
     ports:
-      - 9000:9000
-      - 9001:9001
+      - ${MINIO_PORT}:9000
+      - ${MINIO_CONSOLE_PORT}:9001
     environment:
       - MINIO_ROOT_USER=${MINIO_USER}
       - MINIO_ROOT_PASSWORD=${MINIO_PASSWORD}
@@ -96,8 +96,8 @@ services:
 volumes:
   esdata01:
     driver: local
-  kibanadata:
-    driver: local
+#  kibanadata:
+#    driver: local
   mysql_data:
     driver: local
   minio_data:

docker/docker-compose.yml

@@ -9,7 +9,7 @@ services:
         condition: service_healthy
       es01:
         condition: service_healthy
-    image: infiniflow/ragflow:v0.2.0
+    image: infiniflow/ragflow:${RAGFLOW_VERSION}
     container_name: ragflow-server
     ports:
       - ${SVR_HTTP_PORT}:9380

docker/entrypoint.sh

@@ -23,13 +23,12 @@ function watch_broker(){
 }
 
 function task_bro(){
-    sleep 160;
     watch_broker;
 }
 
 task_bro &
 
-WS=2
+WS=1
 for ((i=0;i<WS;i++))
 do
   task_exe $i $WS &

docker/service_conf.yaml

@@ -13,6 +13,10 @@ minio:
   user: 'rag_flow'
   password: 'infini_rag_flow'
   host: 'minio:9000'
+redis:
+  db: 1
+  password: 'infini_rag_flow'
+  host: 'redis:6379'
 es:
   hosts: 'http://es01:9200'
 user_default_llm:

docs/conversation_api.md

@@ -1,5 +1,9 @@
 # Conversation API Instruction
 
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/infiniflow/ragflow/assets/12318111/df0dcc3d-789a-44f7-89f1-7a5f044ab729" width="830"/>
+</div>
+
 ## Base URL
 ```buildoutcfg
 https://demo.ragflow.io/v1/
@@ -7,7 +11,7 @@ https://demo.ragflow.io/v1/
 
 ## Authorization
 
-All the APIs are authorized with API-Key. Please keep it save and private. Don't reveal it in any way from the front-end. 
+All the APIs are authorized with API-Key. Please keep it safe and private. Don't reveal it in any way from the front-end. 
 The API-Key should put in the header of request:
 ```buildoutcfg
 Authorization: Bearer {API_KEY}
@@ -299,5 +303,61 @@ This will be called to get the answer to users' questions.
 ## Get document content or image
 
 This is usually used when display content of citation.
-### Path: /document/get/\<id\>
+### Path: /api/document/get/\<id\>
 ### Method: GET
+
+## Upload file
+
+This is usually used when upload a file to.
+### Path: /api/document/upload/
+### Method: POST
+
+### Parameter:
+
+| name    | type   | optional | description                            |
+|---------|--------|----------|----------------------------------------|
+| file    | file   | No       | Upload file.                           |
+| kb_name | string | No       | Choose the upload knowledge base name. |
+
+### Response 
+```json
+{
+    "data": {
+        "chunk_num": 0,
+        "create_date": "Thu, 25 Apr 2024 14:30:06 GMT",
+        "create_time": 1714026606921,
+        "created_by": "553ec818fd5711ee8ea63043d7ed348e",
+        "id": "41e9324602cd11ef9f5f3043d7ed348e",
+        "kb_id": "06802686c0a311ee85d6246e9694c130",
+        "location": "readme.txt",
+        "name": "readme.txt",
+        "parser_config": {
+            "field_map": {
+            },
+            "pages": [
+                [
+                    0,
+                    1000000
+                ]
+            ]
+        },
+        "parser_id": "general",
+        "process_begin_at": null,
+        "process_duation": 0.0,
+        "progress": 0.0,
+        "progress_msg": "",
+        "run": "0",
+        "size": 929,
+        "source_type": "local",
+        "status": "1",
+        "thumbnail": null,
+        "token_num": 0,
+        "type": "doc",
+        "update_date": "Thu, 25 Apr 2024 14:30:06 GMT",
+        "update_time": 1714026606921
+    },
+    "retcode": 0,
+    "retmsg": "success"
+}
+
+```

docs/faq.md

@@ -2,95 +2,187 @@
 
 ## General
 
-### What sets RAGFlow apart from other RAG products?
+### 1. What sets RAGFlow apart from other RAG products?
 
 The "garbage in garbage out" status quo remains unchanged despite the fact that LLMs have advanced Natural Language Processing (NLP) significantly. In response, RAGFlow introduces two unique features compared to other Retrieval-Augmented Generation (RAG) products.
 
 - Fine-grained document parsing: Document parsing involves images and tables, with the flexibility for you to intervene as needed.
 - Traceable answers with reduced hallucinations: You can trust RAGFlow's responses as you can view the citations and references supporting them.
 
-### Which languages does RAGFlow support?
+### 2. Which languages does RAGFlow support?
 
 English, simplified Chinese, traditional Chinese for now. 
 
 ## Performance
 
-### Why does it take longer for RAGFlow to parse a document than LangChain?
+### 1. Why does it take longer for RAGFlow to parse a document than LangChain?
 
 We put painstaking effort into document pre-processing tasks like layout analysis, table structure recognition, and OCR (Optical Character Recognition) using our vision model. This contributes to the additional time required. 
 
+### 2. Why does RAGFlow require more resources than other projects?
+
+RAGFlow has a number of built-in models for document structure parsing, which account for the additional computational resources.
+
 ## Feature
 
-### Which architectures or devices does RAGFlow support?
+### 1. Which architectures or devices does RAGFlow support?
 
-ARM64 and Ascend GPU are not supported.
+Currently, we only support x86 CPU and Nvidia GPU. 
 
-### Do you offer an API for integration with third-party applications?
+### 2. Do you offer an API for integration with third-party applications?
 
-These APIs are still in development. Contributions are welcome.
+The corresponding APIs are now available. See the [Conversation API](./conversation_api.md) for more information. 
 
-### Do you support stream output?
+### 3. Do you support stream output?
 
 No, this feature is still in development. Contributions are welcome. 
 
-### Is it possible to share dialogue through URL?
+### 4. Is it possible to share dialogue through URL?
+
+Yes, this feature is now available.
+
+### 5. Do you support multiple rounds of dialogues, i.e., referencing previous dialogues as context for the current dialogue?
 
 This feature and the related APIs are still in development. Contributions are welcome.
 
-### Do you support multiple rounds of dialogues, i.e., referencing previous dialogues as context for the current dialogue?
 
-This feature and the related APIs are still in development. Contributions are welcome.
+## Troubleshooting
 
-## Configurations
+### 1. Issues with docker images
 
-### How to increase the length of RAGFlow responses?
+#### 1.1 How to build the RAGFlow image from scratch?
 
-1. Right click the desired dialog to display the **Chat Configuration** window.
-2. Switch to the **Model Setting** tab and adjust the **Max Tokens** slider to get the desired length.
-3. Click **OK** to confirm your change. 
+```
+$ git clone https://github.com/infiniflow/ragflow.git
+$ cd ragflow
+$ docker build -t infiniflow/ragflow:v0.4.0 .
+$ cd ragflow/docker
+$ chmod +x ./entrypoint.sh
+$ docker compose up -d
+```
 
+#### 1.2 `process "/bin/sh -c cd ./web && npm i && npm run build"` failed
 
-### What does Empty response mean? How to set it?
+1. Check your network from within Docker, for example:
+```bash
+curl https://hf-mirror.com
+```
 
-You limit what the system responds to what you specify in **Empty response** if nothing is retrieved from your knowledge base. If you do not specify anything in **Empty response**, you let your LLM improvise, giving it a chance to hallucinate.
+2. If your network works fine, the issue lies with the Docker network configuration. Replace the Docker building command:
+```bash
+docker build -t infiniflow/ragflow:vX.Y.Z.
+```
+   With this:  
+```bash
+docker build -t infiniflow/ragflow:vX.Y.Z. --network host
+```
 
-### Can I set the base URL for OpenAI somewhere?
+### 2. Issues with huggingface models
 
-![](https://github.com/infiniflow/ragflow/assets/93570324/8cfb6fa4-8a97-415d-b9fa-b6f405a055f3)
+#### 2.1 Cannot access https://huggingface.co
  
+A *locally* deployed RAGflow downloads OCR and embedding modules from [Huggingface website](https://huggingface.co) by default. If your machine is unable to access this site, the following error occurs and PDF parsing fails: 
 
-### How to run RAGFlow with a locally deployed LLM?
+```
+FileNotFoundError: [Errno 2] No such file or directory: '/root/.cache/huggingface/hub/models--InfiniFlow--deepdoc/snapshots/be0c1e50eef6047b412d1800aa89aba4d275f997/ocr.res'
+```
+ To fix this issue, use https://hf-mirror.com instead:
 
-You can use Ollama to deploy local LLM. See [here](https://github.com/infiniflow/ragflow/blob/main/docs/ollama.md) for more information. 
+ 1. Stop all containers and remove all related resources:
 
-### How to link up ragflow and ollama servers?
+ ```bash
+ cd ragflow/docker/
+ docker compose down
+ ```
 
-- If RAGFlow is locally deployed, ensure that your RAGFlow and Ollama are in the same LAN. 
-- If you are using our online demo, ensure that the IP address of your Ollama server is public and accessible.
+ 2. Replace `https://huggingface.co` with `https://hf-mirror.com` in **ragflow/docker/docker-compose.yml**.
  
-### How to configure RAGFlow to respond with 100% matched results, rather than utilizing LLM?
+ 3. Start up the server: 
 
-1. Click the **Knowledge Base** tab in the middle top of the page.
-2. Right click the desired knowledge base to display the **Configuration** dialogue. 
-3. Choose **Q&A** as the chunk method and click **Save** to confirm your change. 
+ ```bash
+ docker compose up -d 
+ ```
 
-## Debugging
+#### 2.2. `MaxRetryError: HTTPSConnectionPool(host='hf-mirror.com', port=443)`
 
-### How to handle `WARNING: can't find /raglof/rag/res/borker.tm`?
+This error suggests that you do not have Internet access or are unable to connect to hf-mirror.com. Try the following: 
+
+1. Manually download the resource files from [huggingface.co/InfiniFlow/deepdoc](https://huggingface.co/InfiniFlow/deepdoc) to your local folder **~/deepdoc**. 
+2. Add a volumes to **docker-compose.yml**, for example:
+```
+- ~/deepdoc:/ragflow/rag/res/deepdoc
+```
+
+#### 2.3 `FileNotFoundError: [Errno 2] No such file or directory: '/root/.cache/huggingface/hub/models--InfiniFlow--deepdoc/snapshots/FileNotFoundError: [Errno 2] No such file or directory: '/ragflow/rag/res/deepdoc/ocr.res'be0c1e50eef6047b412d1800aa89aba4d275f997/ocr.res'`
+
+1. Check your network from within Docker, for example: 
+```bash
+curl https://hf-mirror.com
+```
+2. Run `ifconfig` to check the `mtu` value. If the server's `mtu` is `1450` while the NIC's `mtu` in the container is `1500`, this mismatch may cause network instability. Adjust the `mtu` policy as follows:
+
+```
+vim docker-compose-base.yml
+# Original configuration：
+networks:
+  ragflow:
+    driver: bridge
+# Modified configuration：
+networks:
+  ragflow:
+    driver: bridge
+    driver_opts:
+      com.docker.network.driver.mtu: 1450
+```
+
+### 3. Issues with RAGFlow servers
+
+#### 3.1 `WARNING: can't find /raglof/rag/res/borker.tm`
 
 Ignore this warning and continue. All system warnings can be ignored.
 
-### How to handle `Realtime synonym is disabled, since no redis connection`?
+#### 3.2 `network anomaly There is an abnormality in your network and you cannot connect to the server.`
+
+![anomaly](https://github.com/infiniflow/ragflow/assets/93570324/beb7ad10-92e4-4a58-8886-bfb7cbd09e5d)
+
+You will not log in to RAGFlow unless the server is fully initialized. Run `docker logs -f ragflow-server`.
+
+*The server is successfully initialized, if your system displays the following:*
+
+```
+    ____                 ______ __
+   / __ \ ____ _ ____ _ / ____// /____  _      __
+  / /_/ // __ `// __ `// /_   / // __ \| | /| / /
+ / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ /
+/_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/
+              /____/
+
+ * Running on all addresses (0.0.0.0)
+ * Running on http://127.0.0.1:9380
+ * Running on http://x.x.x.x:9380
+ INFO:werkzeug:Press CTRL+C to quit
+```
+
+
+### 4. Issues with RAGFlow backend services
+
+#### 4.1 `dependency failed to start: container ragflow-mysql is unhealthy`
+
+`dependency failed to start: container ragflow-mysql is unhealthy` means that your MySQL container failed to start. Try replacing `mysql:5.7.18` with `mariadb:10.5.8` in **docker-compose-base.yml**.
+
+#### 4.2 `Realtime synonym is disabled, since no redis connection`
 
 Ignore this warning and continue. All system warnings can be ignored.
 
 ![](https://github.com/infiniflow/ragflow/assets/93570324/ef5a6194-084a-4fe3-bdd5-1c025b40865c)
 
-### Why does it take so long to parse a 2MB document?
+#### 4.3 Why does it take so long to parse a 2MB document?
 
 Parsing requests have to wait in queue due to limited server resources. We are currently enhancing our algorithms and increasing computing power.
 
-### Why does my document parsing stall at under one percent?
+#### 4.4 Why does my document parsing stall at under one percent?
+
+![stall](https://github.com/infiniflow/ragflow/assets/93570324/3589cc25-c733-47d5-bbfc-fedb74a3da50)
 
 If your RAGFlow is deployed *locally*, try the following: 
 
@@ -98,20 +190,21 @@ If your RAGFlow is deployed *locally*, try the following:
 ```bash
 docker logs -f ragflow-server
 ```
-2. Check if the **tast_executor.py** process exist.
+2. Check if the **task_executor.py** process exists.
 3. Check if your RAGFlow server can access hf-mirror.com or huggingface.com.
 
-### How to handle `Index failure`?
+
+#### 4.5 `Index failure`
 
 An index failure usually indicates an unavailable Elasticsearch service.
 
-### How to check the log of RAGFlow?
+#### 4.6 How to check the log of RAGFlow?
 
 ```bash
 tail -f path_to_ragflow/docker/ragflow-logs/rag/*.log
 ```
 
-### How to check the status of each component in RAGFlow?
+#### 4.7 How to check the status of each component in RAGFlow?
 
 ```bash
 $ docker ps
@@ -119,13 +212,13 @@ $ docker ps
 *The system displays the following if all your RAGFlow components are running properly:* 
 
 ```
-5bc45806b680   infiniflow/ragflow:v0.2.0     "./entrypoint.sh"        11 hours ago   Up 11 hours               0.0.0.0:80->80/tcp, :::80->80/tcp, 0.0.0.0:443->443/tcp, :::443->443/tcp, 0.0.0.0:9380->9380/tcp, :::9380->9380/tcp   ragflow-server
+5bc45806b680   infiniflow/ragflow:v0.4.0     "./entrypoint.sh"        11 hours ago   Up 11 hours               0.0.0.0:80->80/tcp, :::80->80/tcp, 0.0.0.0:443->443/tcp, :::443->443/tcp, 0.0.0.0:9380->9380/tcp, :::9380->9380/tcp   ragflow-server
 91220e3285dd   docker.elastic.co/elasticsearch/elasticsearch:8.11.3   "/bin/tini -- /usr/l…"   11 hours ago   Up 11 hours (healthy)     9300/tcp, 0.0.0.0:9200->9200/tcp, :::9200->9200/tcp           ragflow-es-01
 d8c86f06c56b   mysql:5.7.18        "docker-entrypoint.s…"   7 days ago     Up 16 seconds (healthy)   0.0.0.0:3306->3306/tcp, :::3306->3306/tcp     ragflow-mysql
 cd29bcb254bc   quay.io/minio/minio:RELEASE.2023-12-20T01-00-02Z       "/usr/bin/docker-ent…"   2 weeks ago    Up 11 hours      0.0.0.0:9001->9001/tcp, :::9001->9001/tcp, 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp     ragflow-minio
 ```
 
-### How to handle `Exception: Can't connect to ES cluster`?
+#### 4.8 `Exception: Can't connect to ES cluster`
 
 1. Check the status of your Elasticsearch component:
 
@@ -137,7 +230,7 @@ $ docker ps
 91220e3285dd   docker.elastic.co/elasticsearch/elasticsearch:8.11.3   "/bin/tini -- /usr/l…"   11 hours ago   Up 11 hours (healthy)     9300/tcp, 0.0.0.0:9200->9200/tcp, :::9200->9200/tcp           ragflow-es-01
 ```
 
-2. If your container keeps restarting, ensure `vm.max_map_count` >= 262144 as per [this README](https://github.com/infiniflow/ragflow?tab=readme-ov-file#-start-up-the-server).
+2. If your container keeps restarting, ensure `vm.max_map_count` >= 262144 as per [this README](https://github.com/infiniflow/ragflow?tab=readme-ov-file#-start-up-the-server). Updating the `vm.max_map_count` value in **/etc/sysctl.conf** is required, if you wish to keep your change permanent. This configuration works only for Linux.
 
 
 3. If your issue persists, ensure that the ES host setting is correct:
@@ -153,12 +246,104 @@ $ docker ps
     ```
 
 
-### How to handle `{"data":null,"retcode":100,"retmsg":"<NotFound '404: Not Found'>"}`?
+#### 4.9 `{"data":null,"retcode":100,"retmsg":"<NotFound '404: Not Found'>"}`
 
-Your IP address or port number may be incorrect. If you are using the default configurations, enter http://<IP_OF_YOUR_MACHINE> (**NOT `localhost`, NOT 9380, AND NO PORT NUMBER REQUIRED!**) in your browser. This should work.
+Your IP address or port number may be incorrect. If you are using the default configurations, enter http://<IP_OF_YOUR_MACHINE> (**NOT 9380, AND NO PORT NUMBER REQUIRED!**) in your browser. This should work.
+
+#### 4.10 `Ollama - Mistral instance running at 127.0.0.1:11434 but cannot add Ollama as model in RagFlow`
+
+A correct Ollama IP address and port is crucial to adding models to Ollama:
+
+- If you are on demo.ragflow.io, ensure that the server hosting Ollama has a publicly accessible IP address.Note that 127.0.0.1 is not a publicly accessible IP address.
+- If you deploy RAGFlow locally, ensure that Ollama and RAGFlow are in the same LAN and can comunicate with each other.
+
+#### 4.11 Do you offer examples of using deepdoc to parse PDF or other files?
+
+Yes, we do. See the Python files under the **rag/app** folder. 
+
+#### 4.12 Why did I fail to upload a 10MB+ file to my locally deployed RAGFlow?
+
+You probably forgot to update the **MAX_CONTENT_LENGTH** environment variable:
+
+1. Add environment variable `MAX_CONTENT_LENGTH` to **ragflow/docker/.env**:
+```
+MAX_CONTENT_LENGTH=100000000
+```
+2. Update **docker-compose.yml**:
+```
+environment:
+  - MAX_CONTENT_LENGTH=${MAX_CONTENT_LENGTH}
+```
+3. Restart the RAGFlow server:
+```
+docker compose up ragflow -d
+```
+   *Now you should be able to upload files of sizes less than 100MB.*
+
+#### 4.13 `Table 'rag_flow.document' doesn't exist`
+
+This exception occurs when starting up the RAGFlow server. Try the following: 
+
+  1. Prolong the sleep time: Go to **docker/entrypoint.sh**, locate line 26, and replace `sleep 60` with `sleep 280`.
+  2. If using Windows, ensure that the **entrypoint.sh** has LF end-lines.
+  3. Go to **docker/docker-compose.yml**, add the following:
+  ```
+  ./entrypoint.sh:/ragflow/entrypoint.sh
+  ```
+  4. Change directory:
+  ```bash
+  cd docker
+  ```
+  5. Stop the RAGFlow server:
+  ```bash
+  docker compose stop
+  ```
+  6. Restart up the RAGFlow server:
+  ```bash
+  docker compose up
+  ```
+
+#### 4.14 `hint : 102  Fail to access model  Connection error`
+
+![hint102](https://github.com/infiniflow/ragflow/assets/93570324/6633d892-b4f8-49b5-9a0a-37a0a8fba3d2)
+
+1. Ensure that the RAGFlow server can access the base URL.
+2. Do not forget to append **/v1/** to **http://IP:port**: 
+   **http://IP:port/v1/**
 
 
+## Usage
+
+### 1. How to increase the length of RAGFlow responses?
+
+1. Right click the desired dialog to display the **Chat Configuration** window.
+2. Switch to the **Model Setting** tab and adjust the **Max Tokens** slider to get the desired length.
+3. Click **OK** to confirm your change. 
 
 
+### 2. What does Empty response mean? How to set it?
 
+You limit what the system responds to what you specify in **Empty response** if nothing is retrieved from your knowledge base. If you do not specify anything in **Empty response**, you let your LLM improvise, giving it a chance to hallucinate.
 
+### 3. Can I set the base URL for OpenAI somewhere?
+
+![](https://github.com/infiniflow/ragflow/assets/93570324/8cfb6fa4-8a97-415d-b9fa-b6f405a055f3)
+
+### 4. How to run RAGFlow with a locally deployed LLM?
+
+You can use Ollama to deploy local LLM. See [here](https://github.com/infiniflow/ragflow/blob/main/docs/ollama.md) for more information. 
+
+### 5. How to link up ragflow and ollama servers?
+
+- If RAGFlow is locally deployed, ensure that your RAGFlow and Ollama are in the same LAN. 
+- If you are using our online demo, ensure that the IP address of your Ollama server is public and accessible.
+
+### 6. How to configure RAGFlow to respond with 100% matched results, rather than utilizing LLM?
+
+1. Click the **Knowledge Base** tab in the middle top of the page.
+2. Right click the desired knowledge base to display the **Configuration** dialogue. 
+3. Choose **Q&A** as the chunk method and click **Save** to confirm your change. 
+
+### Do I need to connect to Redis?
+
+No, connecting to Redis is not required to use RAGFlow. 

printEnvironment.sh

@@ -0,0 +1,67 @@
+#!/bin/bash
+
+# The function is used to obtain distribution information
+get_distro_info() {
+    local distro_id=$(lsb_release -i -s 2>/dev/null)
+    local distro_version=$(lsb_release -r -s 2>/dev/null)
+    local kernel_version=$(uname -r)
+
+    # If lsd_release is not available, try parsing the/etc/* - release file
+    if [ -z "$distro_id" ] || [ -z "$distro_version" ]; then
+        distro_id=$(grep '^ID=' /etc/*-release | cut -d= -f2 | tr -d '"')
+        distro_version=$(grep '^VERSION_ID=' /etc/*-release | cut -d= -f2 | tr -d '"')
+    fi
+
+    echo "$distro_id $distro_version (Kernel version: $kernel_version)"
+}
+
+# get Git repo name
+git_repo_name=''
+if git rev-parse --is-inside-work-tree > /dev/null 2>&1; then
+    git_repo_name=$(basename "$(git rev-parse --show-toplevel)")
+    if [ $? -ne 0 ]; then
+        git_repo_name="(Can't get repo name)"
+    fi
+else
+    git_repo_name="It NOT a Git repo"
+fi
+
+# get CPU type
+cpu_model=$(uname -m)
+
+# get memory size
+memory_size=$(free -h | grep Mem | awk '{print $2}')
+
+# get docker version
+docker_version=''
+if command -v docker &> /dev/null; then
+    docker_version=$(docker --version | cut -d ' ' -f3)
+else
+    docker_version="Docker not installed"
+fi
+
+# get python version
+python_version=''
+if command -v python &> /dev/null; then
+    python_version=$(python --version | cut -d ' ' -f2)
+else
+    python_version="Python not installed"
+fi
+
+# Print all infomation
+echo "Current Repo: $git_repo_name"
+
+# get Commit ID
+git_version=$(git log -1 --pretty=format:'%h')
+
+if [ -z "$git_version" ]; then
+    echo "Commit Id: The current directory is not a Git repository, or the Git command is not installed."
+else
+    echo "Commit Id: $git_version"
+fi
+
+echo "Operating system: $(get_distro_info)"
+echo "CPU Type: $cpu_model"
+echo "Memory: $memory_size"
+echo "Docker Version: $docker_version"
+echo "Python Version: $python_version"

rag/app/book.py

@@ -11,11 +11,13 @@
 #  limitations under the License.
 #
 import copy
+from tika import parser
 import re
 from io import BytesIO
 
 from rag.nlp import bullets_category, is_english, tokenize, remove_contents_table, \
-    hierarchical_merge, make_colon_as_title, naive_merge, random_choices, tokenize_table, add_positions, tokenize_chunks
+    hierarchical_merge, make_colon_as_title, naive_merge, random_choices, tokenize_table, add_positions, \
+    tokenize_chunks, find_codec
 from rag.nlp import huqie
 from deepdoc.parser import PdfParser, DocxParser, PlainParser
 
@@ -36,7 +38,7 @@ class Pdf(PdfParser):
         start = timer()
         self._layouts_rec(zoomin)
         callback(0.67, "Layout analysis finished")
-        print("paddle layouts:", timer() - start)
+        print("layouts:", timer() - start)
         self._table_transformer_job(zoomin)
         callback(0.68, "Table analysis finished")
         self._text_merge()
@@ -66,7 +68,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
     doc["title_sm_tks"] = huqie.qieqie(doc["title_tks"])
     pdf_parser = None
     sections, tbls = [], []
-    if re.search(r"\.docx?$", filename, re.IGNORECASE):
+    if re.search(r"\.docx$", filename, re.IGNORECASE):
         callback(0.1, "Start to parse.")
         doc_parser = DocxParser()
         # TODO: table of contents need to be removed
@@ -74,6 +76,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
             binary if binary else filename, from_page=from_page, to_page=to_page)
         remove_contents_table(sections, eng=is_english(
             random_choices([t for t, _ in sections], k=200)))
+        tbls = [((None, lns), None) for lns in tbls]
         callback(0.8, "Finish parsing.")
 
     elif re.search(r"\.pdf$", filename, re.IGNORECASE):
@@ -87,7 +90,8 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         callback(0.1, "Start to parse.")
         txt = ""
         if binary:
-            txt = binary.decode("utf-8")
+            encoding = find_codec(binary)
+            txt = binary.decode(encoding)
         else:
             with open(filename, "r") as f:
                 while True:
@@ -101,9 +105,19 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
             random_choices([t for t, _ in sections], k=200)))
         callback(0.8, "Finish parsing.")
 
+    elif re.search(r"\.doc$", filename, re.IGNORECASE):
+        callback(0.1, "Start to parse.")
+        binary = BytesIO(binary)
+        doc_parsed = parser.from_buffer(binary)
+        sections = doc_parsed['content'].split('\n')
+        sections = [(l, "") for l in sections if l]
+        remove_contents_table(sections, eng=is_english(
+            random_choices([t for t, _ in sections], k=200)))
+        callback(0.8, "Finish parsing.")
+
     else:
         raise NotImplementedError(
-            "file type not supported yet(docx, pdf, txt supported)")
+            "file type not supported yet(doc, docx, pdf, txt supported)")
 
     make_colon_as_title(sections)
     bull = bullets_category(

rag/app/laws.py

@@ -11,13 +11,14 @@
 #  limitations under the License.
 #
 import copy
+from tika import parser
 import re
 from io import BytesIO
 from docx import Document
 
 from api.db import ParserType
 from rag.nlp import bullets_category, is_english, tokenize, remove_contents_table, hierarchical_merge, \
-    make_colon_as_title, add_positions, tokenize_chunks
+    make_colon_as_title, add_positions, tokenize_chunks, find_codec
 from rag.nlp import huqie
 from deepdoc.parser import PdfParser, DocxParser, PlainParser
 from rag.settings import cron_logger
@@ -71,7 +72,7 @@ class Pdf(PdfParser):
         start = timer()
         self._layouts_rec(zoomin)
         callback(0.67, "Layout analysis finished")
-        cron_logger.info("paddle layouts:".format(
+        cron_logger.info("layouts:".format(
             (timer() - start) / (self.total_page + 0.1)))
         self._naive_vertical_merge()
 
@@ -93,7 +94,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
     doc["title_sm_tks"] = huqie.qieqie(doc["title_tks"])
     pdf_parser = None
     sections = []
-    if re.search(r"\.docx?$", filename, re.IGNORECASE):
+    if re.search(r"\.docx$", filename, re.IGNORECASE):
         callback(0.1, "Start to parse.")
         for txt in Docx()(filename, binary):
             sections.append(txt)
@@ -111,7 +112,8 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         callback(0.1, "Start to parse.")
         txt = ""
         if binary:
-            txt = binary.decode("utf-8")
+            encoding = find_codec(binary)
+            txt = binary.decode(encoding)
         else:
             with open(filename, "r") as f:
                 while True:
@@ -122,9 +124,18 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         sections = txt.split("\n")
         sections = [l for l in sections if l]
         callback(0.8, "Finish parsing.")
+
+    elif re.search(r"\.doc$", filename, re.IGNORECASE):
+        callback(0.1, "Start to parse.")
+        binary = BytesIO(binary)
+        doc_parsed = parser.from_buffer(binary)
+        sections = doc_parsed['content'].split('\n')
+        sections = [l for l in sections if l]
+        callback(0.8, "Finish parsing.")
+
     else:
         raise NotImplementedError(
-            "file type not supported yet(docx, pdf, txt supported)")
+            "file type not supported yet(doc, docx, pdf, txt supported)")
 
     # is it English
     eng = lang.lower() == "english"  # is_english(sections)

rag/app/manual.py

@@ -32,7 +32,7 @@ class Pdf(PdfParser):
 
         self._layouts_rec(zoomin)
         callback(0.65, "Layout analysis finished.")
-        print("paddle layouts:", timer() - start)
+        print("layouts:", timer() - start)
         self._table_transformer_job(zoomin)
         callback(0.67, "Table analysis finished.")
         self._text_merge()

rag/app/naive.py

@@ -10,12 +10,13 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+from tika import parser
 from io import BytesIO
 from docx import Document
+from timeit import default_timer as timer
 import re
 from deepdoc.parser.pdf_parser import PlainParser
-from rag.app import laws
-from rag.nlp import huqie, is_english, tokenize, naive_merge, tokenize_table, add_positions, tokenize_chunks
+from rag.nlp import huqie, naive_merge, tokenize_table, tokenize_chunks, find_codec
 from deepdoc.parser import PdfParser, ExcelParser, DocxParser
 from rag.settings import cron_logger
 
@@ -67,6 +68,7 @@ class Docx(DocxParser):
 class Pdf(PdfParser):
     def __call__(self, filename, binary=None, from_page=0,
                  to_page=100000, zoomin=3, callback=None):
+        start = timer()
         callback(msg="OCR is running...")
         self.__images__(
             filename if not binary else binary,
@@ -76,12 +78,11 @@ class Pdf(PdfParser):
             callback
         )
         callback(msg="OCR finished")
+        cron_logger.info("OCR({}~{}): {}".format(from_page, to_page, timer() - start))
 
-        from timeit import default_timer as timer
         start = timer()
         self._layouts_rec(zoomin)
         callback(0.63, "Layout analysis finished.")
-        print("paddle layouts:", timer() - start)
         self._table_transformer_job(zoomin)
         callback(0.65, "Table analysis finished.")
         self._text_merge()
@@ -91,8 +92,7 @@ class Pdf(PdfParser):
         self._concat_downward()
         #self._filter_forpages()
 
-        cron_logger.info("paddle layouts:".format(
-            (timer() - start) / (self.total_page + 0.1)))
+        cron_logger.info("layouts: {}".format(timer() - start))
         return [(b["text"], self._line_tag(b, zoomin))
                 for b in self.boxes], tbls
 
@@ -118,7 +118,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
     res = []
     pdf_parser = None
     sections = []
-    if re.search(r"\.docx?$", filename, re.IGNORECASE):
+    if re.search(r"\.docx$", filename, re.IGNORECASE):
         callback(0.1, "Start to parse.")
         sections, tbls = Docx()(filename, binary)
         res = tokenize_table(tbls, doc, eng)
@@ -136,11 +136,12 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         excel_parser = ExcelParser()
         sections = [(excel_parser.html(binary), "")]
 
-    elif re.search(r"\.txt$", filename, re.IGNORECASE):
+    elif re.search(r"\.(txt|md)$", filename, re.IGNORECASE):
         callback(0.1, "Start to parse.")
         txt = ""
         if binary:
-            txt = binary.decode("utf-8")
+            encoding = find_codec(binary)
+            txt = binary.decode(encoding)
         else:
             with open(filename, "r") as f:
                 while True:
@@ -152,16 +153,26 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         sections = [(l, "") for l in sections if l]
         callback(0.8, "Finish parsing.")
 
+    elif re.search(r"\.doc$", filename, re.IGNORECASE):
+        callback(0.1, "Start to parse.")
+        binary = BytesIO(binary)
+        doc_parsed = parser.from_buffer(binary)
+        sections = doc_parsed['content'].split('\n')
+        sections = [(l, "") for l in sections if l]
+        callback(0.8, "Finish parsing.")
+
     else:
         raise NotImplementedError(
-            "file type not supported yet(docx, pdf, txt supported)")
+            "file type not supported yet(doc, docx, pdf, txt supported)")
 
+    st = timer()
     chunks = naive_merge(
         sections, parser_config.get(
             "chunk_token_num", 128), parser_config.get(
             "delimiter", "\n!?。；！？"))
 
     res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser))
+    cron_logger.info("naive_merge({}): {}".format(filename, timer() - st))
     return res
 
 

rag/app/one.py

@@ -10,9 +10,11 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+from tika import parser
+from io import BytesIO
 import re
 from rag.app import laws
-from rag.nlp import huqie, tokenize
+from rag.nlp import huqie, tokenize, find_codec
 from deepdoc.parser import PdfParser, ExcelParser, PlainParser
 
 
@@ -33,7 +35,7 @@ class Pdf(PdfParser):
         start = timer()
         self._layouts_rec(zoomin, drop=False)
         callback(0.63, "Layout analysis finished.")
-        print("paddle layouts:", timer() - start)
+        print("layouts:", timer() - start)
         self._table_transformer_job(zoomin)
         callback(0.65, "Table analysis finished.")
         self._text_merge()
@@ -60,7 +62,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
 
     eng = lang.lower() == "english"  # is_english(cks)
 
-    if re.search(r"\.docx?$", filename, re.IGNORECASE):
+    if re.search(r"\.docx$", filename, re.IGNORECASE):
         callback(0.1, "Start to parse.")
         sections = [txt for txt in laws.Docx()(filename, binary) if txt]
         callback(0.8, "Finish parsing.")
@@ -82,7 +84,8 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         callback(0.1, "Start to parse.")
         txt = ""
         if binary:
-            txt = binary.decode("utf-8")
+            encoding = find_codec(binary)
+            txt = binary.decode(encoding)
         else:
             with open(filename, "r") as f:
                 while True:
@@ -94,9 +97,17 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         sections = [s for s in sections if s]
         callback(0.8, "Finish parsing.")
 
+    elif re.search(r"\.doc$", filename, re.IGNORECASE):
+        callback(0.1, "Start to parse.")
+        binary = BytesIO(binary)
+        doc_parsed = parser.from_buffer(binary)
+        sections = doc_parsed['content'].split('\n')
+        sections = [l for l in sections if l]
+        callback(0.8, "Finish parsing.")
+
     else:
         raise NotImplementedError(
-            "file type not supported yet(docx, pdf, txt supported)")
+            "file type not supported yet(doc, docx, pdf, txt supported)")
 
     doc = {
         "docnm_kwd": filename,

rag/app/paper.py

@@ -42,7 +42,7 @@ class Pdf(PdfParser):
         start = timer()
         self._layouts_rec(zoomin)
         callback(0.63, "Layout analysis finished")
-        print("paddle layouts:", timer() - start)
+        print("layouts:", timer() - start)
         self._table_transformer_job(zoomin)
         callback(0.68, "Table analysis finished")
         self._text_merge()
@@ -78,7 +78,7 @@ class Pdf(PdfParser):
         title = ""
         authors = []
         i = 0
-        while i < min(32, len(self.boxes)):
+        while i < min(32, len(self.boxes)-1):
             b = self.boxes[i]
             i += 1
             if b.get("layoutno", "").find("title") >= 0:

rag/app/qa.py

@@ -15,7 +15,7 @@ from copy import deepcopy
 from io import BytesIO
 from nltk import word_tokenize
 from openpyxl import load_workbook
-from rag.nlp import is_english, random_choices
+from rag.nlp import is_english, random_choices, find_codec
 from rag.nlp import huqie
 from deepdoc.parser import ExcelParser
 
@@ -106,7 +106,8 @@ def chunk(filename, binary=None, lang="Chinese", callback=None, **kwargs):
         callback(0.1, "Start to parse.")
         txt = ""
         if binary:
-            txt = binary.decode("utf-8")
+            encoding = find_codec(binary)
+            txt = binary.decode(encoding)
         else:
             with open(filename, "r") as f:
                 while True:

rag/app/table.py

@@ -20,7 +20,7 @@ from openpyxl import load_workbook
 from dateutil.parser import parse as datetime_parse
 
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from rag.nlp import huqie, is_english, tokenize
+from rag.nlp import huqie, is_english, tokenize, find_codec
 from deepdoc.parser import ExcelParser
 
 
@@ -147,7 +147,8 @@ def chunk(filename, binary=None, from_page=0, to_page=10000000000,
         callback(0.1, "Start to parse.")
         txt = ""
         if binary:
-            txt = binary.decode("utf-8")
+            encoding = find_codec(binary)
+            txt = binary.decode(encoding)
         else:
             with open(filename, "r") as f:
                 while True:
@@ -199,7 +200,7 @@ def chunk(filename, binary=None, from_page=0, to_page=10000000000,
                 re.sub(
                     r"(/.*|（[^（）]+?）|\([^()]+?\))",
                     "",
-                    n),
+                    str(n)),
                 '_')[0] for n in clmns]
         clmn_tys = []
         for j in range(len(clmns)):
@@ -208,7 +209,7 @@ def chunk(filename, binary=None, from_page=0, to_page=10000000000,
             df[clmns[j]] = cln
             if ty == "text":
                 txts.extend([str(c) for c in cln if c])
-        clmns_map = [(py_clmns[i].lower() + fieds_map[clmn_tys[i]], clmns[i].replace("_", " "))
+        clmns_map = [(py_clmns[i].lower() + fieds_map[clmn_tys[i]], str(clmns[i]).replace("_", " "))
                      for i in range(len(clmns))]
 
         eng = lang.lower() == "english"  # is_english(txts)
@@ -223,8 +224,8 @@ def chunk(filename, binary=None, from_page=0, to_page=10000000000,
                     continue
                 if not str(row[clmns[j]]):
                     continue
-                #if pd.isna(row[clmns[j]]):
-                #    continue
+                if pd.isna(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

@@ -25,7 +25,7 @@ EmbeddingModel = {
     "Tongyi-Qianwen": HuEmbedding, #QWenEmbed,
     "ZHIPU-AI": ZhipuEmbed,
     "FastEmbed": FastEmbed,
-    "QAnything": QAnythingEmbed
+    "Youdao": YoudaoEmbed
 }
 
 

rag/llm/chat_model.py

@@ -153,7 +153,7 @@ class OllamaChat(Base):
                 options=options
             )
             ans = response["message"]["content"].strip()
-            return ans, response["eval_count"] + response["prompt_eval_count"]
+            return ans, response["eval_count"] + response.get("prompt_eval_count", 0)
         except Exception as e:
             return "**ERROR**: " + str(e), 0
 

rag/llm/embedding_model.py

@@ -14,6 +14,8 @@
 #  limitations under the License.
 #
 from typing import Optional
+
+from huggingface_hub import snapshot_download
 from zhipuai import ZhipuAI
 import os
 from abc import ABC
@@ -35,7 +37,10 @@ try:
         query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
         use_fp16=torch.cuda.is_available())
 except Exception as e:
-    flag_model = FlagModel("BAAI/bge-large-zh-v1.5",
+    model_dir = snapshot_download(repo_id="BAAI/bge-large-zh-v1.5",
+                                  local_dir=os.path.join(get_project_base_directory(), "rag/res/bge-large-zh-v1.5"),
+                                  local_dir_use_symlinks=False)
+    flag_model = FlagModel(model_dir,
                            query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
                            use_fp16=torch.cuda.is_available())
 
@@ -224,19 +229,19 @@ class XinferenceEmbed(Base):
         return np.array(res.data[0].embedding), res.usage.total_tokens
 
 
-class QAnythingEmbed(Base):
+class YoudaoEmbed(Base):
     _client = None
 
     def __init__(self, key=None, model_name="maidalun1020/bce-embedding-base_v1", **kwargs):
         from BCEmbedding import EmbeddingModel as qanthing
-        if not QAnythingEmbed._client:
+        if not YoudaoEmbed._client:
             try:
                 print("LOADING BCE...")
-                QAnythingEmbed._client = qanthing(model_name_or_path=os.path.join(
+                YoudaoEmbed._client = qanthing(model_name_or_path=os.path.join(
                     get_project_base_directory(),
                     "rag/res/bce-embedding-base_v1"))
             except Exception as e:
-                QAnythingEmbed._client = qanthing(
+                YoudaoEmbed._client = qanthing(
                     model_name_or_path=model_name.replace(
                         "maidalun1020", "InfiniFlow"))
 
@@ -246,10 +251,10 @@ class QAnythingEmbed(Base):
         for t in texts:
             token_count += num_tokens_from_string(t)
         for i in range(0, len(texts), batch_size):
-            embds = QAnythingEmbed._client.encode(texts[i:i + batch_size])
+            embds = YoudaoEmbed._client.encode(texts[i:i + batch_size])
             res.extend(embds)
         return np.array(res), token_count
 
     def encode_queries(self, text):
-        embds = QAnythingEmbed._client.encode([text])
+        embds = YoudaoEmbed._client.encode([text])
         return np.array(embds[0]), num_tokens_from_string(text)

rag/nlp/__init__.py

@@ -6,6 +6,35 @@ from . import huqie
 import re
 import copy
 
+all_codecs = [
+    'utf-8', 'gb2312', 'gbk', 'utf_16', 'ascii', 'big5', 'big5hkscs',
+    'cp037', 'cp273', 'cp424', 'cp437',
+    'cp500', 'cp720', 'cp737', 'cp775', 'cp850', 'cp852', 'cp855', 'cp856', 'cp857',
+    'cp858', 'cp860', 'cp861', 'cp862', 'cp863', 'cp864', 'cp865', 'cp866', 'cp869',
+    'cp874', 'cp875', 'cp932', 'cp949', 'cp950', 'cp1006', 'cp1026', 'cp1125',
+    'cp1140', 'cp1250', 'cp1251', 'cp1252', 'cp1253', 'cp1254', 'cp1255', 'cp1256',
+    'cp1257', 'cp1258', 'euc_jp', 'euc_jis_2004', 'euc_jisx0213', 'euc_kr',
+    'gb2312', 'gb18030', 'hz', 'iso2022_jp', 'iso2022_jp_1', 'iso2022_jp_2',
+    'iso2022_jp_2004', 'iso2022_jp_3', 'iso2022_jp_ext', 'iso2022_kr', 'latin_1',
+    'iso8859_2', 'iso8859_3', 'iso8859_4', 'iso8859_5', 'iso8859_6', 'iso8859_7',
+    'iso8859_8', 'iso8859_9', 'iso8859_10', 'iso8859_11', 'iso8859_13',
+    'iso8859_14', 'iso8859_15', 'iso8859_16', 'johab', 'koi8_r', 'koi8_t', 'koi8_u',
+    'kz1048', 'mac_cyrillic', 'mac_greek', 'mac_iceland', 'mac_latin2', 'mac_roman',
+    'mac_turkish', 'ptcp154', 'shift_jis', 'shift_jis_2004', 'shift_jisx0213',
+    'utf_32', 'utf_32_be', 'utf_32_le''utf_16_be', 'utf_16_le', 'utf_7'
+]
+
+
+def find_codec(blob):
+    global all_codecs
+    for c in all_codecs:
+        try:
+            blob.decode(c)
+            return c
+        except Exception as e:
+            pass
+    return "utf-8"
+
 
 BULLET_PATTERN = [[
     r"第[零一二三四五六七八九十百0-9]+(分?编|部分)",

rag/nlp/huqie.py

@@ -8,6 +8,7 @@ import re
 import string
 import sys
 from hanziconv import HanziConv
+from huggingface_hub import snapshot_download
 from nltk import word_tokenize
 from nltk.stem import PorterStemmer, WordNetLemmatizer
 from api.utils.file_utils import get_project_base_directory

rag/nlp/search.py

@@ -68,7 +68,7 @@ class Dealer:
         pg = int(req.get("page", 1)) - 1
         ps = int(req.get("size", 1000))
         topk = int(req.get("topk", 1024))
-        src = req.get("fields", ["docnm_kwd", "content_ltks", "kb_id", "img_id",
+        src = req.get("fields", ["docnm_kwd", "content_ltks", "kb_id", "img_id", "title_tks", "important_kwd",
                                  "image_id", "doc_id", "q_512_vec", "q_768_vec", "position_int",
                                  "q_1024_vec", "q_1536_vec", "available_int", "content_with_weight"])
 
@@ -237,7 +237,7 @@ class Dealer:
             pieces_.append(t)
         es_logger.info("{} => {}".format(answer, pieces_))
         if not pieces_:
-            return answer
+            return answer, set([])
 
         ans_v, _ = embd_mdl.encode(pieces_)
         assert len(ans_v[0]) == len(chunk_v[0]), "The dimension of query and chunk do not match: {} vs. {}".format(
@@ -289,8 +289,18 @@ class Dealer:
                 sres.field[i].get("q_%d_vec" % len(sres.query_vector), "\t".join(["0"] * len(sres.query_vector)))) for i in sres.ids]
         if not ins_embd:
             return [], [], []
-        ins_tw = [sres.field[i][cfield].split(" ")
-                  for i in sres.ids]
+
+        for i in sres.ids:
+            if isinstance(sres.field[i].get("important_kwd", []), str):
+                sres.field[i]["important_kwd"] = [sres.field[i]["important_kwd"]]
+        ins_tw = []
+        for i in sres.ids:
+            content_ltks = sres.field[i][cfield].split(" ")
+            title_tks = [t for t in sres.field[i].get("title_tks", "").split(" ") if t]
+            important_kwd = sres.field[i].get("important_kwd", [])
+            tks = content_ltks + title_tks + important_kwd
+            ins_tw.append(tks)
+
         sim, tksim, vtsim = self.qryr.hybrid_similarity(sres.query_vector,
                                                         ins_embd,
                                                         keywords,
@@ -368,7 +378,7 @@ class Dealer:
 
     def sql_retrieval(self, sql, fetch_size=128, format="json"):
         from api.settings import chat_logger
-        sql = re.sub(r"[ ]+", " ", sql)
+        sql = re.sub(r"[ `]+", " ", sql)
         sql = sql.replace("%", "")
         es_logger.info(f"Get es sql: {sql}")
         replaces = []

rag/nlp/synonym.py

@@ -17,12 +17,12 @@ class Dealer:
         try:
             self.dictionary = json.load(open(path, 'r'))
         except Exception as e:
-            logging.warn("Miss synonym.json")
+            logging.warning("Missing synonym.json")
             self.dictionary = {}
 
         if not redis:
             logging.warning(
-                "Realtime synonym is disabled, since no redis connection.")
+                "Real-time synonym is disabled, since no redis connection.")
         if not len(self.dictionary.keys()):
             logging.warning(f"Fail to load synonym")
 

rag/settings.py

@@ -25,6 +25,11 @@ SUBPROCESS_STD_LOG_NAME = "std.log"
 
 ES = get_base_config("es", {})
 MINIO = decrypt_database_config(name="minio")
+try:
+    REDIS = decrypt_database_config(name="redis")
+except Exception as e:
+    REDIS = {}
+    pass
 DOC_MAXIMUM_SIZE = 128 * 1024 * 1024
 
 # Logger
@@ -39,5 +44,6 @@ LoggerFactory.LEVEL = 30
 es_logger = getLogger("es")
 minio_logger = getLogger("minio")
 cron_logger = getLogger("cron_logger")
+cron_logger.setLevel(20)
 chunk_logger = getLogger("chunk_logger")
 database_logger = getLogger("database")

rag/svr/cache_file_svr.py

@@ -0,0 +1,43 @@
+import random
+import time
+import traceback
+
+from api.db.db_models import close_connection
+from api.db.services.task_service import TaskService
+from rag.utils import MINIO
+from rag.utils.redis_conn import REDIS_CONN
+
+
+def collect():
+    doc_locations = TaskService.get_ongoing_doc_name()
+    #print(tasks)
+    if len(doc_locations) == 0:
+        time.sleep(1)
+        return
+    return doc_locations
+
+def main():
+    locations = collect()
+    if not locations:return
+    print("TASKS:", len(locations))
+    for kb_id, loc in locations:
+        try:
+            if REDIS_CONN.is_alive():
+                try:
+                    key = "{}/{}".format(kb_id, loc)
+                    if REDIS_CONN.exist(key):continue
+                    file_bin = MINIO.get(kb_id, loc)
+                    REDIS_CONN.transaction(key, file_bin, 12 * 60)
+                    print("CACHE:", loc)
+                except Exception as e:
+                    traceback.print_stack(e)
+        except Exception as e:
+            traceback.print_stack(e)
+
+
+
+if __name__ == "__main__":
+    while True:
+        main()
+        close_connection()
+        time.sleep(1)

rag/svr/task_broker.py

@@ -32,6 +32,9 @@ from api.db.services.document_service import DocumentService
 from api.settings import database_logger
 from api.utils import get_format_time, get_uuid
 from api.utils.file_utils import get_project_base_directory
+from rag.utils.redis_conn import REDIS_CONN
+from api.db.db_models import init_database_tables as init_web_db
+from api.db.init_data import init_web_data
 
 
 def collect(tm):
@@ -84,10 +87,16 @@ def dispatch():
 
         tsks = []
         try:
+            file_bin = MINIO.get(r["kb_id"], r["location"])
+            if REDIS_CONN.is_alive():
+                try:
+                    REDIS_CONN.set("{}/{}".format(r["kb_id"], r["location"]), file_bin, 12*60)
+                except Exception as e:
+                    cron_logger.warning("Put into redis[EXCEPTION]:" + str(e))
+
             if r["type"] == FileType.PDF.value:
                 do_layout = r["parser_config"].get("layout_recognize", True)
-                pages = PdfParser.total_page_number(
-                        r["name"], MINIO.get(r["kb_id"], r["location"]))
+                pages = PdfParser.total_page_number(r["name"], file_bin)
                 page_size = r["parser_config"].get("task_page_size", 12)
                 if r["parser_id"] == "paper":
                     page_size = r["parser_config"].get("task_page_size", 22)
@@ -110,8 +119,7 @@ def dispatch():
 
             elif r["parser_id"] == "table":
                 rn = HuExcelParser.row_number(
-                    r["name"], MINIO.get(
-                        r["kb_id"], r["location"]))
+                    r["name"], file_bin)
                 for i in range(0, rn, 3000):
                     task = new_task()
                     task["from_page"] = i
@@ -175,6 +183,9 @@ if __name__ == "__main__":
     peewee_logger.propagate = False
     peewee_logger.addHandler(database_logger.handlers[0])
     peewee_logger.setLevel(database_logger.level)
+    # init db
+    init_web_db()
+    init_web_data()
 
     while True:
         dispatch()

rag/svr/task_executor.py

@@ -24,7 +24,7 @@ import sys
 import time
 import traceback
 from functools import partial
-
+from rag.utils import MINIO
 from api.db.db_models import close_connection
 from rag.settings import database_logger
 from rag.settings import cron_logger, DOC_MAXIMUM_SIZE
@@ -34,7 +34,7 @@ from elasticsearch_dsl import Q
 from multiprocessing.context import TimeoutError
 from api.db.services.task_service import TaskService
 from rag.utils import ELASTICSEARCH
-from rag.utils import MINIO
+from timeit import default_timer as timer
 from rag.utils import rmSpace, findMaxTm
 
 from rag.nlp import search
@@ -47,6 +47,7 @@ from api.db import LLMType, ParserType
 from api.db.services.document_service import DocumentService
 from api.db.services.llm_service import LLMBundle
 from api.utils.file_utils import get_project_base_directory
+from rag.utils.redis_conn import REDIS_CONN
 
 BATCH_SIZE = 64
 
@@ -92,6 +93,7 @@ def set_progress(task_id, from_page=0, to_page=-1,
 
 def collect(comm, mod, tm):
     tasks = TaskService.get_tasks(tm, mod, comm)
+    #print(tasks)
     if len(tasks) == 0:
         time.sleep(1)
         return pd.DataFrame()
@@ -103,11 +105,22 @@ def collect(comm, mod, tm):
 
 def get_minio_binary(bucket, name):
     global MINIO
+    if REDIS_CONN.is_alive():
+        try:
+            for _ in range(30):
+                if REDIS_CONN.exist("{}/{}".format(bucket, name)):
+                    time.sleep(1)
+                    break
+                time.sleep(1)
+            r = REDIS_CONN.get("{}/{}".format(bucket, name))
+            if r: return r
+            cron_logger.warning("Cache missing: {}".format(name))
+        except Exception as e:
+            cron_logger.warning("Get redis[EXCEPTION]:" + str(e))
     return MINIO.get(bucket, name)
 
 
 def build(row):
-    from timeit import default_timer as timer
     if row["size"] > DOC_MAXIMUM_SIZE:
         set_progress(row["id"], prog=-1, msg="File size exceeds( <= %dMb )" %
                      (int(DOC_MAXIMUM_SIZE / 1024 / 1024)))
@@ -156,6 +169,7 @@ def build(row):
         "doc_id": row["doc_id"],
         "kb_id": [str(row["kb_id"])]
     }
+    el = 0
     for ck in cks:
         d = copy.deepcopy(doc)
         d.update(ck)
@@ -175,10 +189,13 @@ def build(row):
         else:
             d["image"].save(output_buffer, format='JPEG')
 
+        st = timer()
         MINIO.put(row["kb_id"], d["_id"], output_buffer.getvalue())
+        el += timer() - st
         d["img_id"] = "{}-{}".format(row["kb_id"], d["_id"])
         del d["image"]
         docs.append(d)
+    cron_logger.info("MINIO PUT({}):{}".format(row["name"], el))
 
     return docs
 
@@ -243,6 +260,7 @@ def main(comm, mod):
     tmf = open(tm_fnm, "a+")
     for _, r in rows.iterrows():
         callback = partial(set_progress, r["id"], r["from_page"], r["to_page"])
+        #callback(random.random()/10., "Task has been received.")
         try:
             embd_mdl = LLMBundle(r["tenant_id"], LLMType.EMBEDDING, llm_name=r["embd_id"], lang=r["language"])
         except Exception as e:
@@ -250,7 +268,9 @@ def main(comm, mod):
             callback(prog=-1, msg=str(e))
             continue
 
+        st = timer()
         cks = build(r)
+        cron_logger.info("Build chunks({}): {}".format(r["name"], timer()-st))
         if cks is None:
             continue
         if not cks:
@@ -262,17 +282,21 @@ def main(comm, mod):
         callback(
             msg="Finished slicing files(%d). Start to embedding the content." %
             len(cks))
+        st = timer()
         try:
             tk_count = embedding(cks, embd_mdl, r["parser_config"], callback)
         except Exception as e:
             callback(-1, "Embedding error:{}".format(str(e)))
             cron_logger.error(str(e))
             tk_count = 0
+        cron_logger.info("Embedding elapsed({}): {}".format(r["name"], timer()-st))
 
-        callback(msg="Finished embedding! Start to build index!")
+        callback(msg="Finished embedding({})! Start to build index!".format(timer()-st))
         init_kb(r)
         chunk_count = len(set([c["_id"] for c in cks]))
+        st = timer()
         es_r = ELASTICSEARCH.bulk(cks, search.index_name(r["tenant_id"]))
+        cron_logger.info("Indexing elapsed({}): {}".format(r["name"], timer()-st))
         if es_r:
             callback(-1, "Index failure!")
             ELASTICSEARCH.deleteByQuery(
@@ -287,8 +311,8 @@ def main(comm, mod):
             DocumentService.increment_chunk_num(
                 r["doc_id"], r["kb_id"], tk_count, chunk_count, 0)
             cron_logger.info(
-                "Chunk doc({}), token({}), chunks({})".format(
-                    r["id"], tk_count, len(cks)))
+                "Chunk doc({}), token({}), chunks({}), elapsed:{}".format(
+                    r["id"], tk_count, len(cks), timer()-st))
 
         tmf.write(str(r["update_time"]) + "\n")
     tmf.close()
@@ -300,9 +324,8 @@ if __name__ == "__main__":
     peewee_logger.addHandler(database_logger.handlers[0])
     peewee_logger.setLevel(database_logger.level)
 
-    from mpi4py import MPI
-
-    comm = MPI.COMM_WORLD
+    #from mpi4py import MPI
+    #comm = MPI.COMM_WORLD
     while True:
         main(int(sys.argv[2]), int(sys.argv[1]))
         close_connection()

rag/utils/es_conn.py

@@ -2,6 +2,7 @@ import re
 import json
 import time
 import copy
+
 import elasticsearch
 from elastic_transport import ConnectionTimeout
 from elasticsearch import Elasticsearch

rag/utils/minio_conn.py

@@ -56,7 +56,6 @@ class HuMinio(object):
         except Exception as e:
             minio_logger.error(f"Fail rm {bucket}/{fnm}: " + str(e))
 
-
     def get(self, bucket, fnm):
         for _ in range(1):
             try:

rag/utils/redis_conn.py

@@ -0,0 +1,74 @@
+import json
+
+import redis
+import logging
+from rag import settings
+from rag.utils import singleton
+
+@singleton
+class RedisDB:
+    def __init__(self):
+        self.REDIS = None
+        self.config = settings.REDIS
+        self.__open__()
+
+    def __open__(self):
+        try:
+            self.REDIS = redis.Redis(host=self.config.get("host", "redis").split(":")[0],
+                                     port=int(self.config.get("host", ":6379").split(":")[1]),
+                                     db=int(self.config.get("db", 1)),
+                                     password=self.config.get("password"))
+        except Exception as e:
+            logging.warning("Redis can't be connected.")
+        return self.REDIS
+
+    def is_alive(self):
+        return self.REDIS is not None
+
+    def exist(self, k):
+        if not self.REDIS: return
+        try:
+            return self.REDIS.exists(k)
+        except Exception as e:
+            logging.warning("[EXCEPTION]exist" + str(k) + "||" + str(e))
+            self.__open__()
+
+    def get(self, k):
+        if not self.REDIS: return
+        try:
+            return self.REDIS.get(k)
+        except Exception as e:
+            logging.warning("[EXCEPTION]get" + str(k) + "||" + str(e))
+            self.__open__()
+
+    def set_obj(self, k, obj, exp=3600):
+        try:
+            self.REDIS.set(k, json.dumps(obj, ensure_ascii=False), exp)
+            return True
+        except Exception as e:
+            logging.warning("[EXCEPTION]set_obj" + str(k) + "||" + str(e))
+            self.__open__()
+        return False
+
+    def set(self, k, v, exp=3600):
+        try:
+            self.REDIS.set(k, v, exp)
+            return True
+        except Exception as e:
+            logging.warning("[EXCEPTION]set" + str(k) + "||" + str(e))
+            self.__open__()
+        return False
+
+    def transaction(self, key, value, exp=3600):
+        try:
+            pipeline = self.REDIS.pipeline(transaction=True)
+            pipeline.set(key, value, exp, nx=True)
+            pipeline.execute()
+            return True
+        except Exception as e:
+            logging.warning("[EXCEPTION]set" + str(key) + "||" + str(e))
+            self.__open__()
+        return False
+
+
+REDIS_CONN = RedisDB()

requirements.txt

@@ -116,6 +116,7 @@ sniffio==1.3.1
 StrEnum==0.4.15
 sympy==1.12
 threadpoolctl==3.3.0
+tika==2.6.0
 tiktoken==0.6.0
 tokenizers==0.15.2
 torch==2.2.1

web/externals.d.ts

@@ -0,0 +1,138 @@
+// This file is generated by Umi automatically
+// DO NOT CHANGE IT MANUALLY!
+type CSSModuleClasses = { readonly [key: string]: string };
+declare module '*.css' {
+  const classes: CSSModuleClasses;
+  export default classes;
+}
+declare module '*.scss' {
+  const classes: CSSModuleClasses;
+  export default classes;
+}
+declare module '*.sass' {
+  const classes: CSSModuleClasses;
+  export default classes;
+}
+declare module '*.less' {
+  const classes: CSSModuleClasses;
+  export default classes;
+}
+declare module '*.styl' {
+  const classes: CSSModuleClasses;
+  export default classes;
+}
+declare module '*.stylus' {
+  const classes: CSSModuleClasses;
+  export default classes;
+}
+
+// images
+declare module '*.jpg' {
+  const src: string;
+  export default src;
+}
+declare module '*.jpeg' {
+  const src: string;
+  export default src;
+}
+declare module '*.png' {
+  const src: string;
+  export default src;
+}
+declare module '*.gif' {
+  const src: string;
+  export default src;
+}
+declare module '*.svg' {
+  import * as React from 'react';
+  export const ReactComponent: React.FunctionComponent<
+    React.SVGProps<SVGSVGElement> & { title?: string }
+  >;
+
+  const src: string;
+  export default src;
+}
+declare module '*.ico' {
+  const src: string;
+  export default src;
+}
+declare module '*.webp' {
+  const src: string;
+  export default src;
+}
+declare module '*.avif' {
+  const src: string;
+  export default src;
+}
+
+// media
+declare module '*.mp4' {
+  const src: string;
+  export default src;
+}
+declare module '*.webm' {
+  const src: string;
+  export default src;
+}
+declare module '*.ogg' {
+  const src: string;
+  export default src;
+}
+declare module '*.mp3' {
+  const src: string;
+  export default src;
+}
+declare module '*.wav' {
+  const src: string;
+  export default src;
+}
+declare module '*.flac' {
+  const src: string;
+  export default src;
+}
+declare module '*.aac' {
+  const src: string;
+  export default src;
+}
+
+// fonts
+declare module '*.woff' {
+  const src: string;
+  export default src;
+}
+declare module '*.woff2' {
+  const src: string;
+  export default src;
+}
+declare module '*.eot' {
+  const src: string;
+  export default src;
+}
+declare module '*.ttf' {
+  const src: string;
+  export default src;
+}
+declare module '*.otf' {
+  const src: string;
+  export default src;
+}
+
+// other
+declare module '*.wasm' {
+  const initWasm: (
+    options: WebAssembly.Imports,
+  ) => Promise<WebAssembly.Exports>;
+  export default initWasm;
+}
+declare module '*.webmanifest' {
+  const src: string;
+  export default src;
+}
+declare module '*.pdf' {
+  const src: string;
+  export default src;
+}
+declare module '*.txt' {
+  const src: string;
+  export default src;
+}

web/src/assets/svg/file-icon/folder.svg

@@ -0,0 +1,18 @@
+<svg width="24" height="18" viewBox="0 0 24 18" fill="none" xmlns="http://www.w3.org/2000/svg">
+    <path
+        d="M1.32202e-08 2.54731L21.5 2.54731C22.8807 2.54731 24 3.4977 24 4.67006L24 15.2838C24 16.4562 22.8807 17.4066 21.5 17.4066L12 17.4066L2.5 17.4066C1.11929 17.4066 8.54054e-08 16.4562 7.9321e-08 15.2838L1.32202e-08 2.54731Z"
+        fill="#FBBC1A" />
+    <path
+        d="M2.97454e-08 5.73144L7.49143e-08 14.4347C8.09987e-08 15.6071 1.11929 16.5575 2.5 16.5575L21.5 16.5575C22.8807 16.5575 24 15.6071 24 14.4347L24 5.51916C24 4.3468 22.8807 3.39641 21.5 3.39641L11 3.39641L11 4.45779C11 5.16121 10.3284 5.73144 9.5 5.73144L2.97454e-08 5.73144Z"
+        fill="url(#paint0_linear_2323_8307)" />
+    <path
+        d="M8.81345e-09 1.6982C3.94591e-09 0.760312 0.89543 -4.64716e-09 2 -1.03797e-08L9 -4.67088e-08C10.1046 -5.24413e-08 11 0.760312 11 1.6982L11 2.54731L1.32202e-08 2.54731L8.81345e-09 1.6982Z"
+        fill="#FBBC1A" />
+    <defs>
+        <linearGradient id="paint0_linear_2323_8307" x1="0" y1="0" x2="28.8004" y2="20.3231"
+            gradientUnits="userSpaceOnUse">
+            <stop stop-color="#FFE69C" />
+            <stop offset="1" stop-color="#FFC937" />
+        </linearGradient>
+    </defs>
+</svg>

web/src/base.ts

@@ -0,0 +1,48 @@
+import isObject from 'lodash/isObject';
+import { DvaModel } from 'umi';
+import { BaseState } from './interfaces/common';
+
+type State = Record<string, any>;
+type DvaModelKey<T> = keyof DvaModel<T>;
+
+export const modelExtend = <T>(
+  baseModel: Partial<DvaModel<any>>,
+  extendModel: DvaModel<any>,
+): DvaModel<T> => {
+  return Object.keys(extendModel).reduce<DvaModel<T>>((pre, cur) => {
+    const baseValue = baseModel[cur as DvaModelKey<State>];
+    const value = extendModel[cur as DvaModelKey<State>];
+
+    if (isObject(value) && isObject(baseValue) && typeof value !== 'string') {
+      const key = cur as Exclude<DvaModelKey<State>, 'namespace'>;
+
+      pre[key] = {
+        ...baseValue,
+        ...value,
+      } as any;
+    } else {
+      pre[cur as DvaModelKey<State>] = value as any;
+    }
+
+    return pre;
+  }, {} as DvaModel<T>);
+};
+
+export const paginationModel: Partial<DvaModel<BaseState>> = {
+  state: {
+    searchString: '',
+    pagination: {
+      total: 0,
+      current: 1,
+      pageSize: 10,
+    },
+  },
+  reducers: {
+    setSearchString(state, { payload }) {
+      return { ...state, searchString: payload };
+    },
+    setPagination(state, { payload }) {
+      return { ...state, pagination: { ...state.pagination, ...payload } };
+    },
+  },
+};

web/src/components/highlight-markdown/index.tsx

@@ -0,0 +1,36 @@
+import Markdown from 'react-markdown';
+import SyntaxHighlighter from 'react-syntax-highlighter';
+import remarkGfm from 'remark-gfm';
+
+const HightLightMarkdown = ({
+  children,
+}: {
+  children: string | null | undefined;
+}) => {
+  return (
+    <Markdown
+      remarkPlugins={[remarkGfm]}
+      components={
+        {
+          code(props: any) {
+            const { children, className, node, ...rest } = props;
+            const match = /language-(\w+)/.exec(className || '');
+            return match ? (
+              <SyntaxHighlighter {...rest} PreTag="div" language={match[1]}>
+                {String(children).replace(/\n$/, '')}
+              </SyntaxHighlighter>
+            ) : (
+              <code {...rest} className={className}>
+                {children}
+              </code>
+            );
+          },
+        } as any
+      }
+    >
+      {children}
+    </Markdown>
+  );
+};
+
+export default HightLightMarkdown;

web/src/components/line-chart/index.tsx

@@ -50,9 +50,10 @@ const data = [
 
 interface IProps extends CategoricalChartProps {
   data?: Array<{ xAxis: string; yAxis: number }>;
+  showLegend?: boolean;
 }
 
-const RagLineChart = ({ data }: IProps) => {
+const RagLineChart = ({ data, showLegend = false }: IProps) => {
   return (
     <ResponsiveContainer width="100%" height="100%">
       <LineChart
@@ -72,7 +73,7 @@ const RagLineChart = ({ data }: IProps) => {
         <XAxis dataKey="xAxis" />
         <YAxis />
         <Tooltip />
-        <Legend />
+        {showLegend && <Legend />}
         <Line
           type="monotone"
           dataKey="yAxis"

web/src/global.less

@@ -1,6 +1,19 @@
 @import url(./inter.less);
 
+html {
+  height: 100%;
+}
+
 body {
   font-family: Inter;
   margin: 0;
+  height: 100%;
+}
+
+#root {
+  height: 100%;
+}
+
+.ant-app {
+  height: 100%;
 }

web/src/hooks/chatHooks.ts

@@ -248,3 +248,55 @@ export const useSelectStats = () => {
 };
 
 //#endregion
+
+//#region shared chat
+
+export const useCreateSharedConversation = () => {
+  const dispatch = useDispatch();
+
+  const createSharedConversation = useCallback(
+    (userId?: string) => {
+      return dispatch<any>({
+        type: 'chatModel/createExternalConversation',
+        payload: { userId },
+      });
+    },
+    [dispatch],
+  );
+
+  return createSharedConversation;
+};
+
+export const useFetchSharedConversation = () => {
+  const dispatch = useDispatch();
+
+  const fetchSharedConversation = useCallback(
+    (conversationId: string) => {
+      return dispatch<any>({
+        type: 'chatModel/getExternalConversation',
+        payload: conversationId,
+      });
+    },
+    [dispatch],
+  );
+
+  return fetchSharedConversation;
+};
+
+export const useCompleteSharedConversation = () => {
+  const dispatch = useDispatch();
+
+  const completeSharedConversation = useCallback(
+    (payload: any) => {
+      return dispatch<any>({
+        type: 'chatModel/completeExternalConversation',
+        payload: payload,
+      });
+    },
+    [dispatch],
+  );
+
+  return completeSharedConversation;
+};
+
+//#endregion

web/src/hooks/documentHooks.ts

@@ -160,12 +160,12 @@ export const useRemoveDocument = () => {
   const { knowledgeId } = useGetKnowledgeSearchParams();
 
   const removeDocument = useCallback(
-    (documentId: string) => {
+    (documentIds: string[]) => {
       try {
         return dispatch<any>({
           type: 'kFModel/document_rm',
           payload: {
-            doc_id: documentId,
+            doc_id: documentIds,
             kb_id: knowledgeId,
           },
         });

web/src/hooks/fileManagerHooks.ts

@@ -0,0 +1,144 @@
+import {
+  IConnectRequestBody,
+  IFileListRequestBody,
+} from '@/interfaces/request/file-manager';
+import { UploadFile } from 'antd';
+import { useCallback } from 'react';
+import { useDispatch, useSelector } from 'umi';
+
+export const useFetchFileList = () => {
+  const dispatch = useDispatch();
+
+  const fetchFileList = useCallback(
+    (payload: IFileListRequestBody) => {
+      return dispatch<any>({
+        type: 'fileManager/listFile',
+        payload,
+      });
+    },
+    [dispatch],
+  );
+
+  return fetchFileList;
+};
+
+export const useRemoveFile = () => {
+  const dispatch = useDispatch();
+
+  const removeFile = useCallback(
+    (fileIds: string[], parentId: string) => {
+      return dispatch<any>({
+        type: 'fileManager/removeFile',
+        payload: { fileIds, parentId },
+      });
+    },
+    [dispatch],
+  );
+
+  return removeFile;
+};
+
+export const useRenameFile = () => {
+  const dispatch = useDispatch();
+
+  const renameFile = useCallback(
+    (fileId: string, name: string, parentId: string) => {
+      return dispatch<any>({
+        type: 'fileManager/renameFile',
+        payload: { fileId, name, parentId },
+      });
+    },
+    [dispatch],
+  );
+
+  return renameFile;
+};
+
+export const useFetchParentFolderList = () => {
+  const dispatch = useDispatch();
+
+  const fetchParentFolderList = useCallback(
+    (fileId: string) => {
+      return dispatch<any>({
+        type: 'fileManager/getAllParentFolder',
+        payload: { fileId },
+      });
+    },
+    [dispatch],
+  );
+
+  return fetchParentFolderList;
+};
+
+export const useCreateFolder = () => {
+  const dispatch = useDispatch();
+
+  const createFolder = useCallback(
+    (parentId: string, name: string) => {
+      return dispatch<any>({
+        type: 'fileManager/createFolder',
+        payload: { parentId, name, type: 'folder' },
+      });
+    },
+    [dispatch],
+  );
+
+  return createFolder;
+};
+
+export const useSelectFileList = () => {
+  const fileList = useSelector((state) => state.fileManager.fileList);
+
+  return fileList;
+};
+
+export const useSelectParentFolderList = () => {
+  const parentFolderList = useSelector(
+    (state) => state.fileManager.parentFolderList,
+  );
+  return parentFolderList.toReversed();
+};
+
+export const useUploadFile = () => {
+  const dispatch = useDispatch();
+
+  const uploadFile = useCallback(
+    (fileList: UploadFile[], parentId: string) => {
+      try {
+        return dispatch<any>({
+          type: 'fileManager/uploadFile',
+          payload: {
+            file: fileList,
+            parentId,
+            path: fileList.map((file) => (file as any).webkitRelativePath),
+          },
+        });
+      } catch (errorInfo) {
+        console.log('Failed:', errorInfo);
+      }
+    },
+    [dispatch],
+  );
+
+  return uploadFile;
+};
+
+export const useConnectToKnowledge = () => {
+  const dispatch = useDispatch();
+
+  const uploadFile = useCallback(
+    (payload: IConnectRequestBody) => {
+      try {
+        return dispatch<any>({
+          type: 'fileManager/connectFileToKnowledge',
+          payload,
+        });
+      } catch (errorInfo) {
+        console.log('Failed:', errorInfo);
+      }
+    },
+    [dispatch],
+  );
+
+  return uploadFile;
+};

web/src/hooks/knowledgeHook.ts

@@ -127,13 +127,13 @@ export const useFetchKnowledgeBaseConfiguration = () => {
 
 export const useFetchKnowledgeList = (
   shouldFilterListWithoutDocument: boolean = false,
-): { list: IKnowledge[]; loading: boolean } => {
+) => {
   const dispatch = useDispatch();
   const loading = useOneNamespaceEffectsLoading('knowledgeModel', ['getList']);
 
   const knowledgeModel = useSelector((state: any) => state.knowledgeModel);
   const { data = [] } = knowledgeModel;
-  const list = useMemo(() => {
+  const list: IKnowledge[] = useMemo(() => {
     return shouldFilterListWithoutDocument
       ? data.filter((x: IKnowledge) => x.chunk_num > 0)
       : data;
@@ -149,7 +149,7 @@ export const useFetchKnowledgeList = (
     fetchList();
   }, [fetchList]);
 
-  return { list, loading };
+  return { list, loading, fetchList };
 };
 
 export const useSelectFileThumbnails = () => {

web/src/hooks/logicHooks.ts

@@ -1,9 +1,12 @@
 import { LanguageTranslationMap } from '@/constants/common';
+import { Pagination } from '@/interfaces/common';
 import { IKnowledgeFile } from '@/interfaces/database/knowledge';
 import { IChangeParserConfigRequestBody } from '@/interfaces/request/document';
-import { useCallback, useState } from 'react';
+import { PaginationProps } from 'antd';
+import { useCallback, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
-import { useSetModalState } from './commonHooks';
+import { useDispatch } from 'umi';
+import { useSetModalState, useTranslate } from './commonHooks';
 import { useSetDocumentParser } from './documentHooks';
 import { useOneNamespaceEffectsLoading } from './storeHooks';
 import { useSaveSetting } from './userSettingHook';
@@ -62,3 +65,51 @@ export const useChangeLanguage = () => {
 
   return changeLanguage;
 };
+
+export const useGetPagination = (
+  total: number,
+  page: number,
+  pageSize: number,
+  onPageChange: PaginationProps['onChange'],
+) => {
+  const { t } = useTranslate('common');
+
+  const pagination: PaginationProps = useMemo(() => {
+    return {
+      showQuickJumper: true,
+      total,
+      showSizeChanger: true,
+      current: page,
+      pageSize: pageSize,
+      pageSizeOptions: [1, 2, 10, 20, 50, 100],
+      onChange: onPageChange,
+      showTotal: (total) => `${t('total')} ${total}`,
+    };
+  }, [t, onPageChange, page, pageSize, total]);
+
+  return {
+    pagination,
+  };
+};
+
+export const useSetPagination = (namespace: string) => {
+  const dispatch = useDispatch();
+
+  const setPagination = useCallback(
+    (pageNumber = 1, pageSize?: number) => {
+      const pagination: Pagination = {
+        current: pageNumber,
+      } as Pagination;
+      if (pageSize) {
+        pagination.pageSize = pageSize;
+      }
+      dispatch({
+        type: `${namespace}/setPagination`,
+        payload: pagination,
+      });
+    },
+    [dispatch, namespace],
+  );
+
+  return setPagination;
+};

web/src/interfaces/common.ts

@@ -1,6 +1,7 @@
 export interface Pagination {
   current: number;
   pageSize: number;
+  total: number;
 }
 
 export interface BaseState {
@@ -13,5 +14,5 @@ export interface IModalProps<T> {
   hideModal(): void;
   visible: boolean;
   loading?: boolean;
-  onOk?(payload?: T): Promise<void> | void;
+  onOk?(payload?: T): Promise<any> | void;
 }

web/src/interfaces/database/file-manager.ts

@@ -0,0 +1,30 @@
+export interface IFile {
+  create_date: string;
+  create_time: number;
+  created_by: string;
+  id: string;
+  kbs_info: { kb_id: string; kb_name: string }[];
+  location: string;
+  name: string;
+  parent_id: string;
+  size: number;
+  tenant_id: string;
+  type: string;
+  update_date: string;
+  update_time: number;
+}
+
+export interface IFolder {
+  create_date: string;
+  create_time: number;
+  created_by: string;
+  id: string;
+  location: string;
+  name: string;
+  parent_id: string;
+  size: number;
+  tenant_id: string;
+  type: string;
+  update_date: string;
+  update_time: number;
+}

web/src/interfaces/request/base.ts

@@ -0,0 +1,7 @@
+export interface IPaginationRequestBody {
+  keywords?: string;
+  page?: number;
+  page_size?: number; // name|create|doc_num|create_time|update_time，default：create_time
+  orderby?: string;
+  desc?: string;
+}

web/src/interfaces/request/file-manager.ts

@@ -0,0 +1,14 @@
+import { IPaginationRequestBody } from './base';
+
+export interface IFileListRequestBody extends IPaginationRequestBody {
+  parent_id?: string; // folder id
+}
+
+interface BaseRequestBody {
+  parentId: string;
+}
+
+export interface IConnectRequestBody extends BaseRequestBody {
+  fileIds: string[];
+  kbIds: string[];
+}

web/src/layouts/components/header/index.tsx

@@ -1,4 +1,5 @@
 import { ReactComponent as StarIon } from '@/assets/svg/chat-star.svg';
+import { ReactComponent as FileIcon } from '@/assets/svg/file-management.svg';
 import { ReactComponent as KnowledgeBaseIcon } from '@/assets/svg/knowledge-base.svg';
 import { ReactComponent as Logo } from '@/assets/svg/logo.svg';
 import { useTranslate } from '@/hooks/commonHooks';
@@ -24,7 +25,7 @@ const RagHeader = () => {
     () => [
       { path: '/knowledge', name: t('knowledgeBase'), icon: KnowledgeBaseIcon },
       { path: '/chat', name: t('chat'), icon: StarIon },
-      // { path: '/file', name: 'File Management', icon: FileIcon },
+      { path: '/file', name: t('fileManager'), icon: FileIcon },
     ],
     [t],
   );

web/src/less/mixins.less

@@ -33,3 +33,12 @@
 .pointerCursor() {
   cursor: pointer;
 }
+
+.clearCardBody() {
+  :global {
+    .ant-card-body {
+      padding: 0;
+      margin: 0;
+    }
+  }
+}

web/src/locales/en.ts

@@ -22,6 +22,8 @@ export default {
       languagePlaceholder: 'select your language',
       copy: 'Copy',
       copied: 'Copied',
+      comingSoon: 'Coming Soon',
+      download: 'Download',
     },
     login: {
       login: 'Sign in',
@@ -52,6 +54,7 @@ export default {
       home: 'Home',
       setting: '用户设置',
       logout: '登出',
+      fileManager: 'File Management',
     },
     knowledgeList: {
       welcome: 'Welcome back',
@@ -70,7 +73,7 @@ export default {
       namePlaceholder: 'Please input name!',
       doc: 'Docs',
       datasetDescription:
-        "Hey, don't forget to adjust the chunk after adding the dataset! 😉",
+        '😉 Questions and answers can only be answered after the parsing is successful.',
       addFile: 'Add file',
       searchFiles: 'Search your files',
       localFiles: 'Local files',
@@ -171,7 +174,7 @@ export default {
       methodTitle: 'Chunking Method Description',
       methodExamples: 'Examples',
       methodExamplesDescription:
-        'This visual guides is in order to make understanding easier for you.',
+        'The following screenshots are presented to facilitate understanding.',
       dialogueExamplesTitle: 'Dialogue Examples',
       methodEmpty:
         'This will display a visual explanation of the knowledge base categories',
@@ -201,15 +204,27 @@ export default {
       presentation: `<p>The supported file formats are <b>PDF</b>, <b>PPTX</b>.</p><p>
       Every page will be treated as a chunk. And the thumbnail of every page will be stored.</p><p>
       <i>All the PPT files you uploaded will be chunked by using this method automatically, setting-up for every PPT file is not necessary.</i></p>`,
-      qa: `<p><b>EXCEL</b> and <b>CSV/TXT</b> files are supported.</p><p>
-      If the file is in excel format, there should be 2 columns question and answer without header.
-      And question column is ahead of answer column.
-      And it's O.K if it has multiple sheets as long as the columns are rightly composed.</p><p>
-    
-      If it's in csv format, it should be UTF-8 encoded. Use TAB as delimiter to separate question and answer.</p><p>
-    
-      <i>All the deformed lines will be ignored.
-      Every pair of Q&A will be treated as a chunk.</i></p>`,
+      qa: `
+      <p>
+      This chunk method supports <b>EXCEL</b> and <b>CSV/TXT</b> file formats.
+    </p>
+    <li>
+      If the file is in <b>Excel</b> format, it should consist of two columns
+      without headers: one for questions and the other for answers, with the
+      question column preceding the answer column. Multiple sheets are
+      acceptable as long as the columns are correctly structured.
+    </li>
+    <li>
+      If the file is in <b>CSV/TXT</b> format, it must be UTF-8 encoded with TAB
+      used as the delimiter to separate questions and answers.
+    </li>
+    <p>
+      <i>
+        Lines of texts that fail to follow the above rules will be ignored, and
+        each Q&A pair will be considered a distinct chunk.
+      </i>
+    </p>
+      `,
       resume: `<p>The supported file formats are <b>DOCX</b>, <b>PDF</b>, <b>TXT</b>.
       </p><p>
       The résumé comes in a variety of formats, just like a person’s personality, but we often have to organize them into structured data that makes it easy to search.
@@ -337,24 +352,31 @@ export default {
         'This sets the maximum length of the model’s output, measured in the number of tokens (words or pieces of words).',
       quote: 'Show Quote',
       quoteTip: 'Should the source of the original text be displayed?',
-      overview: 'Overview',
+      overview: 'Chat Bot API',
       pv: 'Number of messages',
       uv: 'Active user number',
       speed: 'Token output speed',
       tokens: 'Consume the token number',
       round: 'Session Interaction Number',
       thumbUp: 'customer satisfaction',
-      publicUrl: 'Public URL',
       preview: 'Preview',
       embedded: 'Embedded',
       serviceApiEndpoint: 'Service API Endpoint',
       apiKey: 'Api Key',
-      apiReference: 'Api Reference',
+      apiReference: 'API Documents',
       dateRange: 'Date Range:',
       backendServiceApi: 'Backend service API',
       createNewKey: 'Create new key',
       created: 'Created',
       action: 'Action',
+      embedModalTitle: 'Embed into website',
+      comingSoon: 'Coming Soon',
+      fullScreenTitle: 'Full Embed',
+      fullScreenDescription:
+        'Embed the following iframe into your website at the desired location',
+      partialTitle: 'Partial Embed',
+      extensionTitle: 'Chrome Extension',
+      tokenError: 'Please create API Token first!',
     },
     setting: {
       profile: 'Profile',
@@ -363,7 +385,7 @@ export default {
       passwordDescription:
         'Please enter your current password to change your password.',
       model: 'Model Providers',
-      modelDescription: 'Manage your account settings and preferences here.',
+      modelDescription: 'Set the model parameter and API Key here.',
       team: 'Team',
       logout: 'Log out',
       username: 'Username',
@@ -440,6 +462,7 @@ export default {
       renamed: 'Renamed',
       operated: 'Operated',
       updated: 'Updated',
+      uploaded: 'Uploaded',
       200: 'The server successfully returns the requested data.',
       201: 'Create or modify data successfully.',
       202: 'A request has been queued in the background (asynchronous task).',
@@ -461,6 +484,24 @@ export default {
       networkAnomaly: 'network anomaly',
       hint: 'hint',
     },
+    fileManager: {
+      name: 'Name',
+      uploadDate: 'Upload Date',
+      knowledgeBase: 'Knowledge Base',
+      size: 'Size',
+      action: 'Action',
+      addToKnowledge: 'Add to Knowledge Base',
+      pleaseSelect: 'Please select',
+      newFolder: 'New Folder',
+      file: 'File',
+      uploadFile: 'Upload File',
+      directory: 'Directory',
+      uploadTitle: 'Click or drag file to this area to upload',
+      uploadDescription:
+        'Support for a single or bulk upload. Strictly prohibited from uploading company data or other banned files.',
+      local: 'Local uploads',
+      s3: 'S3 uploads',
+    },
     footer: {
       profile: 'All rights reserved @ React',
     },

web/src/locales/zh-traditional.ts

@@ -22,6 +22,8 @@ export default {
       languagePlaceholder: '請選擇語言',
       copy: '複製',
       copied: '複製成功',
+      comingSoon: '即將推出',
+      download: '下載',
     },
     login: {
       login: '登入',
@@ -52,6 +54,7 @@ export default {
       home: '首頁',
       setting: '用戶設置',
       logout: '登出',
+      fileManager: '文件管理',
     },
     knowledgeList: {
       welcome: '歡迎回來',
@@ -69,7 +72,7 @@ export default {
       name: '名稱',
       namePlaceholder: '請輸入名稱',
       doc: '文件',
-      datasetDescription: '嘿，添加數據集後別忘了調整解析塊！😉',
+      datasetDescription: '😉 解析成功後才能問答哦。',
       addFile: '新增文件',
       searchFiles: '搜索文件',
       localFiles: '本地文件',
@@ -144,12 +147,12 @@ export default {
       languagePlaceholder: '請輸入語言',
       permissions: '權限',
       embeddingModel: '嵌入模型',
-      chunkTokenNumber: '塊令牌數',
-      chunkTokenNumberMessage: '塊令牌數是必填項',
+      chunkTokenNumber: '塊Token數',
+      chunkTokenNumberMessage: '塊Token數是必填項',
       embeddingModelTip:
         '用於嵌入塊的嵌入模型。一旦知識庫有了塊，它就無法更改。如果你想改變它，你需要刪除所有的塊。',
       permissionsTip: '如果權限是“團隊”，則所有團隊成員都可以操作知識庫。',
-      chunkTokenNumberTip: '它大致確定了一個塊的令牌數量。',
+      chunkTokenNumberTip: '它大致確定了一個塊的Token數量。',
       chunkMethod: '解析方法',
       chunkMethodTip: '說明位於右側。',
       upload: '上傳',
@@ -163,7 +166,7 @@ export default {
       cancel: '取消',
       methodTitle: '分塊方法說明',
       methodExamples: '示例',
-      methodExamplesDescription: '這個視覺指南是為了讓您更容易理解。',
+      methodExamplesDescription: '提出以下屏幕截圖以促進理解。',
       dialogueExamplesTitle: '對話示例',
       methodEmpty: '這將顯示知識庫類別的可視化解釋',
       book: `<p>支持的文件格式為<b>DOCX</b>、<b>PDF</b>、<b>TXT</b>。</p><p>
@@ -182,7 +185,7 @@ export default {
         <p>此方法將簡單的方法應用於塊文件：</p>
         <p>
         <li>系統將使用視覺檢測模型將連續文本分割成多個片段。</li>
-        <li>接下來，這些連續的片段被合併成令牌數不超過“令牌數”的塊。</li></p>`,
+        <li>接下來，這些連續的片段被合併成Token數不超過“Token數”的塊。</li></p>`,
       paper: `<p>僅支持<b>PDF</b>文件。</p><p>
         如果我們的模型運行良好，論文將按其部分進行切片，例如<i>摘要、1.1、1.2</i>等。</p><p>
         這樣做的好處是LLM可以更好的概括論文中相關章節的內容，
@@ -192,15 +195,24 @@ export default {
       presentation: `<p>支持的文件格式為<b>PDF</b>、<b>PPTX</b>。</p><p>
         每個頁面都將被視為一個塊。並且每個頁面的縮略圖都會被存儲。</p><p>
         <i>您上傳的所有PPT文件都會使用此方法自動分塊，無需為每個PPT文件進行設置。</i></p>`,
-      qa: `支持<p><b>EXCEL</b>和<b>CSV/TXT</b>文件。</p><p>
-        如果文件是Excel格式，應該有2列問題和答案，沒有標題。
-        問題欄位於答案欄之前。
-        如果有多個工作表也沒關係，只要列的組合正確即可。</p><p>
-     
-        如果是 csv 格式，則應採用 UTF-8 編碼。使用 TAB 作為分隔符來分隔問題和答案。</p><p>
-     
-        <i>所有變形的線都將被忽略。
-        每對問答都將被視為一個塊。</i></p>`,
+      qa: `<p>
+      此塊方法支持<b> excel </b>和<b> csv/txt </b>文件格式。
+    </p>
+    <li>
+      如果文件以<b> excel </b>格式，則應由兩個列組成
+      沒有標題：一個提出問題，另一個用於答案，
+      答案列之前的問題列。多張紙是
+      只要列正確結構，就可以接受。
+    </li>
+    <li>
+      如果文件以<b> csv/txt </b>格式為
+      用作分開問題和答案的定界符。
+    </li>
+    <p>
+      <i>
+        未能遵循上述規則的文本行將被忽略，並且
+        每個問答對將被認為是一個獨特的部分。
+      </i>`,
       resume: `<p>支持的文件格式為<b>DOCX</b>、<b>PDF</b>、<b>TXT</b>。
         </p><p>
         簡歷有多種格式，就像一個人的個性一樣，但我們經常必須將它們組織成結構化數據，以便於搜索。
@@ -209,7 +221,7 @@ export default {
         您只需與<i>'ragflow'</i>交談即可列出所有符合資格的候選人。
         </p>
           `,
-      table: `支持<p><b>excel</b>和<b>csv/txt</b>格式文件。</p><p>以下是一些提示： <ul> <li>对于Csv或Txt文件，列之间的分隔符为 <em><b>tab</b></em>。</li> <li>第一行必须是列标题。</li> <li>列标题必须是有意义的术语，以便我们的法学硕士能够理解。列举一些同义词时最好使用斜杠<i>'/'</i>来分隔，甚至更好使用方括号枚举值，例如 <i>“性別/性別（男性，女性）”</i>.<p>以下是标题的一些示例：<ol> <li>供应商/供货商<b>'tab'</b>顏色（黃色、紅色、棕色）<b>'tab'</b>性別（男、女）<b>'tab'</B>尺码（m、l、xl、xxl）</li> <li>姓名/名字<b>'tab'</b>電話/手機/微信<b>'tab'</b>最高学历（高中，职高，硕士，本科，博士，初中，中技，中专，专科，专升本，mpa，mba，emba）</li> </ol> </p> </li> <li>表中的每一行都将被视为一个块。</li> </ul>`,
+      table: `支持<p><b>excel</b>和<b>csv/txt</b>格式文件。</p><p>以下是一些提示： <ul> <li>对于Csv或Txt文件，列之间的分隔符为 <em><b>tab</b></em>。</li> <li>第一行必须是列标题。</li> <li>列标题必须是有意义的术语，以便我们的大語言模型能够理解。列举一些同义词时最好使用斜杠<i>'/'</i>来分隔，甚至更好使用方括号枚举值，例如 <i>“性別/性別（男性，女性）”</i>.<p>以下是标题的一些示例：<ol> <li>供应商/供货商<b>'tab'</b>顏色（黃色、紅色、棕色）<b>'tab'</b>性別（男、女）<b>'tab'</B>尺码（m、l、xl、xxl）</li> <li>姓名/名字<b>'tab'</b>電話/手機/微信<b>'tab'</b>最高学历（高中，职高，硕士，本科，博士，初中，中技，中专，专科，专升本，mpa，mba，emba）</li> </ol> </p> </li> <li>表中的每一行都将被视为一个块。</li> </ul>`,
       picture: `
        <p>支持圖像文件。視頻即將推出。</p><p>
         如果圖片中有文字，則應用 OCR 提取文字作為其文字描述。
@@ -312,24 +324,30 @@ export default {
         '這設置了模型輸出的最大長度，以標記（單詞或單詞片段）的數量來衡量。',
       quote: '顯示引文',
       quoteTip: '是否應該顯示原文出處？',
-      overview: '概覽',
+      overview: '聊天 API',
       pv: '消息數',
       uv: '活躍用戶數',
       speed: 'Token 輸出速度',
       tokens: '消耗Token數',
       round: '會話互動數',
       thumbUp: '用戶滿意度',
-      publicUrl: '公共url',
       preview: '預覽',
       embedded: '嵌入',
       serviceApiEndpoint: '服務 API 端點',
       apiKey: 'API 鍵',
-      apiReference: 'API參考',
+      apiReference: 'API 文件',
       dateRange: '日期範圍：',
       backendServiceApi: '後端服務 API',
       createNewKey: '創建新密鑰',
       created: '創建於',
       action: '操作',
+      embedModalTitle: '嵌入網站',
+      comingSoon: '即將推出',
+      fullScreenTitle: '全屏嵌入',
+      fullScreenDescription: '將以下iframe嵌入您的網站處於所需位置',
+      partialTitle: '部分嵌入',
+      extensionTitle: 'Chrome 插件',
+      tokenError: '請先創建 Api Token!',
     },
     setting: {
       profile: '概述',
@@ -337,7 +355,7 @@ export default {
       password: '密碼',
       passwordDescription: '請輸入您當前的密碼以更改您的密碼。',
       model: '模型提供商',
-      modelDescription: '在此管理您的帳戶設置和首選項。',
+      modelDescription: '在此設置模型參數和 API Key。',
       team: '團隊',
       logout: '登出',
       username: '使用者名稱',
@@ -409,12 +427,13 @@ export default {
       renamed: '重命名成功',
       operated: '操作成功',
       updated: '更新成功',
+      uploaded: '上傳成功',
       200: '服務器成功返回請求的數據。',
       201: '新建或修改數據成功。',
       202: '一個請求已經進入後台排隊（異步任務）。',
       204: '刪除數據成功。',
       400: '發出的請求有錯誤，服務器沒有進行新建或修改數據的操作。',
-      401: '用戶沒有權限（令牌、用戶名、密碼錯誤）。',
+      401: '用戶沒有權限（Token、用戶名、密碼錯誤）。',
       403: '用戶得到授權，但是訪問是被禁止的。',
       404: '發出的請求針對的是不存在的記錄，服務器沒有進行操作。',
       406: '請求的格式不可得。',
@@ -429,6 +448,23 @@ export default {
       networkAnomaly: '網絡異常',
       hint: '提示',
     },
+    fileManager: {
+      name: '名稱',
+      uploadDate: '上傳日期',
+      knowledgeBase: '知識庫',
+      size: '大小',
+      action: '操作',
+      addToKnowledge: '添加到知識庫',
+      pleaseSelect: '請選擇',
+      newFolder: '新建文件夾',
+      uploadFile: '上傳文件',
+      uploadTitle: '點擊或拖拽文件至此區域即可上傳',
+      uploadDescription: '支持單次或批量上傳。嚴禁上傳公司數據或其他違禁文件。',
+      file: '文件',
+      directory: '文件夾',
+      local: '本地上傳',
+      s3: 'S3 上傳',
+    },
     footer: {
       profile: '“保留所有權利 @ react”',
     },