Feat: add vision LLM PDF parser (#6173)

### What problem does this PR solve?

Add vision LLM PDF parser

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

---------

Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>

rag/app/naive.py

@@ -26,8 +26,10 @@ from markdown import markdown
 from PIL import Image
 from tika import parser
 
+from api.db import LLMType
+from api.db.services.llm_service import LLMBundle
 from deepdoc.parser import DocxParser, ExcelParser, HtmlParser, JsonParser, MarkdownParser, PdfParser, TxtParser
-from deepdoc.parser.pdf_parser import PlainParser
+from deepdoc.parser.pdf_parser import PlainParser, VisionParser
 from rag.nlp import concat_img, find_codec, naive_merge, naive_merge_docx, rag_tokenizer, tokenize_chunks, tokenize_chunks_docx, tokenize_table
 from rag.utils import num_tokens_from_string
 
@@ -237,9 +239,16 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         return res
 
     elif re.search(r"\.pdf$", filename, re.IGNORECASE):
-        pdf_parser = Pdf()
-        if parser_config.get("layout_recognize", "DeepDOC") == "Plain Text":
+        layout_recognizer = parser_config.get("layout_recognize", "DeepDOC")
+
+        if layout_recognizer == "DeepDOC":
+            pdf_parser = Pdf()
+        elif layout_recognizer == "Plain Text":
             pdf_parser = PlainParser()
+        else:
+            vision_model = LLMBundle(kwargs["tenant_id"], LLMType.IMAGE2TEXT, llm_name=layout_recognizer, lang=lang)
+            pdf_parser = VisionParser(vision_model=vision_model, **kwargs)
+
         sections, tables = pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page,
                                       callback=callback)
         res = tokenize_table(tables, doc, is_english)

rag/app/picture.py

@@ -21,8 +21,9 @@ from PIL import Image
 
 from api.db import LLMType
 from api.db.services.llm_service import LLMBundle
-from rag.nlp import tokenize
 from deepdoc.vision import OCR
+from rag.nlp import tokenize
+from rag.utils import clean_markdown_block
 
 ocr = OCR()
 
@@ -57,3 +58,32 @@ def chunk(filename, binary, tenant_id, lang, callback=None, **kwargs):
         callback(prog=-1, msg=str(e))
 
     return []
+
+
+def vision_llm_chunk(binary, vision_model, prompt=None, callback=None):
+    """
+    A simple wrapper to process image to markdown texts via VLM.
+
+    Returns:
+        Simple markdown texts generated by VLM.
+    """
+    callback = callback or (lambda prog, msg: None)
+
+    img = binary
+    txt = ""
+
+    try:
+        img_binary = io.BytesIO()
+        img.save(img_binary, format='JPEG')
+        img_binary.seek(0)
+
+        ans = clean_markdown_block(vision_model.describe_with_prompt(img_binary.read(), prompt))
+
+        txt += "\n" + ans
+
+        return txt
+
+    except Exception as e:
+        callback(-1, str(e))
+
+    return []

rag/llm/cv_model.py

@@ -13,31 +13,36 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-from openai.lib.azure import AzureOpenAI
-from zhipuai import ZhipuAI
-import io
-from abc import ABC
-from ollama import Client
-from PIL import Image
-from openai import OpenAI
-import os
 import base64
-from io import BytesIO
+import io
 import json
-import requests
+import os
+from abc import ABC
+from io import BytesIO
+
+import requests
+from ollama import Client
+from openai import OpenAI
+from openai.lib.azure import AzureOpenAI
+from PIL import Image
+from zhipuai import ZhipuAI
 
-from rag.nlp import is_english
 from api.utils import get_uuid
 from api.utils.file_utils import get_project_base_directory
+from rag.nlp import is_english
+from rag.prompts import vision_llm_describe_prompt
 
 
 class Base(ABC):
     def __init__(self, key, model_name):
         pass
 
-    def describe(self, image, max_tokens=300):
+    def describe(self, image):
         raise NotImplementedError("Please implement encode method!")
-        
+
+    def describe_with_prompt(self, image, prompt=None):
+        raise NotImplementedError("Please implement encode method!")
+
     def chat(self, system, history, gen_conf, image=""):
         if system:
             history[-1]["content"] = system + history[-1]["content"] + "user query: " + history[-1]["content"]
@@ -90,7 +95,7 @@ class Base(ABC):
             yield ans + "\n**ERROR**: " + str(e)
 
         yield tk_count
-        
+
     def image2base64(self, image):
         if isinstance(image, bytes):
             return base64.b64encode(image).decode("utf-8")
@@ -122,6 +127,25 @@ class Base(ABC):
             }
         ]
 
+    def vision_llm_prompt(self, b64, prompt=None):
+        return [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                                "url": f"data:image/jpeg;base64,{b64}"
+                        },
+                    },
+                    {
+                        "type": "text",
+                        "text": prompt if prompt else vision_llm_describe_prompt(),
+                    },
+                ],
+            }
+        ]
+
     def chat_prompt(self, text, b64):
         return [
             {
@@ -140,12 +164,12 @@ class Base(ABC):
 class GptV4(Base):
     def __init__(self, key, model_name="gpt-4-vision-preview", lang="Chinese", base_url="https://api.openai.com/v1"):
         if not base_url:
-            base_url="https://api.openai.com/v1"
+            base_url = "https://api.openai.com/v1"
         self.client = OpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name
         self.lang = lang
 
-    def describe(self, image, max_tokens=300):
+    def describe(self, image):
         b64 = self.image2base64(image)
         prompt = self.prompt(b64)
         for i in range(len(prompt)):
@@ -159,6 +183,16 @@ class GptV4(Base):
         )
         return res.choices[0].message.content.strip(), res.usage.total_tokens
 
+    def describe_with_prompt(self, image, prompt=None):
+        b64 = self.image2base64(image)
+        vision_prompt = self.vision_llm_prompt(b64, prompt) if prompt else self.vision_llm_prompt(b64)
+
+        res = self.client.chat.completions.create(
+            model=self.model_name,
+            messages=vision_prompt,
+        )
+        return res.choices[0].message.content.strip(), res.usage.total_tokens
+
 
 class AzureGptV4(Base):
     def __init__(self, key, model_name, lang="Chinese", **kwargs):
@@ -168,7 +202,7 @@ class AzureGptV4(Base):
         self.model_name = model_name
         self.lang = lang
 
-    def describe(self, image, max_tokens=300):
+    def describe(self, image):
         b64 = self.image2base64(image)
         prompt = self.prompt(b64)
         for i in range(len(prompt)):
@@ -182,6 +216,16 @@ class AzureGptV4(Base):
         )
         return res.choices[0].message.content.strip(), res.usage.total_tokens
 
+    def describe_with_prompt(self, image, prompt=None):
+        b64 = self.image2base64(image)
+        vision_prompt = self.vision_llm_prompt(b64, prompt) if prompt else self.vision_llm_prompt(b64)
+
+        res = self.client.chat.completions.create(
+            model=self.model_name,
+            messages=vision_prompt,
+        )
+        return res.choices[0].message.content.strip(), res.usage.total_tokens
+
 
 class QWenCV(Base):
     def __init__(self, key, model_name="qwen-vl-chat-v1", lang="Chinese", **kwargs):
@@ -212,23 +256,57 @@ class QWenCV(Base):
             }
         ]
 
+    def vision_llm_prompt(self, binary, prompt=None):
+        # stupid as hell
+        tmp_dir = get_project_base_directory("tmp")
+        if not os.path.exists(tmp_dir):
+            os.mkdir(tmp_dir)
+        path = os.path.join(tmp_dir, "%s.jpg" % get_uuid())
+        Image.open(io.BytesIO(binary)).save(path)
+
+        return [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "image": f"file://{path}"
+                    },
+                    {
+                        "text":  prompt if prompt else vision_llm_describe_prompt(),
+                    },
+                ],
+            }
+        ]
+
     def chat_prompt(self, text, b64):
         return [
             {"image": f"{b64}"},
             {"text": text},
         ]
-    
-    def describe(self, image, max_tokens=300):
+
+    def describe(self, image):
         from http import HTTPStatus
+
         from dashscope import MultiModalConversation
-        response = MultiModalConversation.call(model=self.model_name,
-                                               messages=self.prompt(image))
+        response = MultiModalConversation.call(model=self.model_name, messages=self.prompt(image))
+        if response.status_code == HTTPStatus.OK:
+            return response.output.choices[0]['message']['content'][0]["text"], response.usage.output_tokens
+        return response.message, 0
+
+    def describe_with_prompt(self, image, prompt=None):
+        from http import HTTPStatus
+
+        from dashscope import MultiModalConversation
+
+        vision_prompt = self.vision_llm_prompt(image, prompt) if prompt else self.vision_llm_prompt(image)
+        response = MultiModalConversation.call(model=self.model_name, messages=vision_prompt)
         if response.status_code == HTTPStatus.OK:
             return response.output.choices[0]['message']['content'][0]["text"], response.usage.output_tokens
         return response.message, 0
 
     def chat(self, system, history, gen_conf, image=""):
         from http import HTTPStatus
+
         from dashscope import MultiModalConversation
         if system:
             history[-1]["content"] = system + history[-1]["content"] + "user query: " + history[-1]["content"]
@@ -254,6 +332,7 @@ class QWenCV(Base):
 
     def chat_streamly(self, system, history, gen_conf, image=""):
         from http import HTTPStatus
+
         from dashscope import MultiModalConversation
         if system:
             history[-1]["content"] = system + history[-1]["content"] + "user query: " + history[-1]["content"]
@@ -292,15 +371,25 @@ class Zhipu4V(Base):
         self.model_name = model_name
         self.lang = lang
 
-    def describe(self, image, max_tokens=1024):
+    def describe(self, image):
         b64 = self.image2base64(image)
 
         prompt = self.prompt(b64)
         prompt[0]["content"][1]["type"] = "text"
-        
+
         res = self.client.chat.completions.create(
             model=self.model_name,
-            messages=prompt
+            messages=prompt,
+        )
+        return res.choices[0].message.content.strip(), res.usage.total_tokens
+
+    def describe_with_prompt(self, image, prompt=None):
+        b64 = self.image2base64(image)
+        vision_prompt = self.vision_llm_prompt(b64, prompt) if prompt else self.vision_llm_prompt(b64)
+
+        res = self.client.chat.completions.create(
+            model=self.model_name,
+            messages=vision_prompt
         )
         return res.choices[0].message.content.strip(), res.usage.total_tokens
 
@@ -334,7 +423,7 @@ class Zhipu4V(Base):
                     his["content"] = self.chat_prompt(his["content"], image)
 
             response = self.client.chat.completions.create(
-                model=self.model_name, 
+                model=self.model_name,
                 messages=history,
                 temperature=gen_conf.get("temperature", 0.3),
                 top_p=gen_conf.get("top_p", 0.7),
@@ -364,7 +453,7 @@ class OllamaCV(Base):
         self.model_name = model_name
         self.lang = lang
 
-    def describe(self, image, max_tokens=1024):
+    def describe(self, image):
         prompt = self.prompt("")
         try:
             response = self.client.generate(
@@ -377,6 +466,19 @@ class OllamaCV(Base):
         except Exception as e:
             return "**ERROR**: " + str(e), 0
 
+    def describe_with_prompt(self, image, prompt=None):
+        vision_prompt = self.vision_llm_prompt("", prompt) if prompt else self.vision_llm_prompt("")
+        try:
+            response = self.client.generate(
+                model=self.model_name,
+                prompt=vision_prompt[0]["content"][1]["text"],
+                images=[image],
+            )
+            ans = response["response"].strip()
+            return ans, 128
+        except Exception as e:
+            return "**ERROR**: " + str(e), 0
+
     def chat(self, system, history, gen_conf, image=""):
         if system:
             history[-1]["content"] = system + history[-1]["content"] + "user query: " + history[-1]["content"]
@@ -460,7 +562,7 @@ class XinferenceCV(Base):
         self.model_name = model_name
         self.lang = lang
 
-    def describe(self, image, max_tokens=300):
+    def describe(self, image):
         b64 = self.image2base64(image)
 
         res = self.client.chat.completions.create(
@@ -469,27 +571,49 @@ class XinferenceCV(Base):
         )
         return res.choices[0].message.content.strip(), res.usage.total_tokens
 
+    def describe_with_prompt(self, image, prompt=None):
+        b64 = self.image2base64(image)
+        vision_prompt = self.vision_llm_prompt(b64, prompt) if prompt else self.vision_llm_prompt(b64)
+
+        res = self.client.chat.completions.create(
+            model=self.model_name,
+            messages=vision_prompt,
+        )
+        return res.choices[0].message.content.strip(), res.usage.total_tokens
+
+
 class GeminiCV(Base):
     def __init__(self, key, model_name="gemini-1.0-pro-vision-latest", lang="Chinese", **kwargs):
-        from google.generativeai import client, GenerativeModel
+        from google.generativeai import GenerativeModel, client
         client.configure(api_key=key)
         _client = client.get_default_generative_client()
         self.model_name = model_name
         self.model = GenerativeModel(model_name=self.model_name)
         self.model._client = _client
-        self.lang = lang 
+        self.lang = lang
 
-    def describe(self, image, max_tokens=2048):
+    def describe(self, image):
         from PIL.Image import open
         prompt = "请用中文详细描述一下图中的内容，比如时间，地点，人物，事情，人物心情等，如果有数据请提取出数据。" if self.lang.lower() == "chinese" else \
             "Please describe the content of this picture, like where, when, who, what happen. If it has number data, please extract them out."
-        b64 = self.image2base64(image) 
-        img = open(BytesIO(base64.b64decode(b64))) 
-        input = [prompt,img]
+        b64 = self.image2base64(image)
+        img = open(BytesIO(base64.b64decode(b64)))
+        input = [prompt, img]
         res = self.model.generate_content(
             input
         )
-        return res.text,res.usage_metadata.total_token_count
+        return res.text, res.usage_metadata.total_token_count
+
+    def describe_with_prompt(self, image, prompt=None):
+        from PIL.Image import open
+        b64 = self.image2base64(image)
+        vision_prompt = self.vision_llm_prompt(b64, prompt) if prompt else self.vision_llm_prompt(b64)
+        img = open(BytesIO(base64.b64decode(b64)))
+        input = [vision_prompt, img]
+        res = self.model.generate_content(
+            input,
+        )
+        return res.text, res.usage_metadata.total_token_count
 
     def chat(self, system, history, gen_conf, image=""):
         from transformers import GenerationConfig
@@ -566,7 +690,7 @@ class LocalCV(Base):
     def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs):
         pass
 
-    def describe(self, image, max_tokens=1024):
+    def describe(self, image):
         return "", 0
 
 
@@ -590,7 +714,7 @@ class NvidiaCV(Base):
             )
         self.key = key
 
-    def describe(self, image, max_tokens=1024):
+    def describe(self, image):
         b64 = self.image2base64(image)
         response = requests.post(
             url=self.base_url,
@@ -609,6 +733,27 @@ class NvidiaCV(Base):
             response["usage"]["total_tokens"],
         )
 
+    def describe_with_prompt(self, image, prompt=None):
+        b64 = self.image2base64(image)
+        vision_prompt = self.vision_llm_prompt(b64, prompt) if prompt else self.vision_llm_prompt(b64)
+
+        response = requests.post(
+            url=self.base_url,
+            headers={
+                "accept": "application/json",
+                "content-type": "application/json",
+                "Authorization": f"Bearer {self.key}",
+            },
+            json={
+                "messages": vision_prompt,
+            },
+        )
+        response = response.json()
+        return (
+            response["choices"][0]["message"]["content"].strip(),
+            response["usage"]["total_tokens"],
+        )
+
     def prompt(self, b64):
         return [
             {
@@ -622,6 +767,17 @@ class NvidiaCV(Base):
             }
         ]
 
+    def vision_llm_prompt(self, b64, prompt=None):
+        return [
+            {
+                "role": "user",
+                "content": (
+                    prompt if prompt else vision_llm_describe_prompt()
+                )
+                + f' <img src="data:image/jpeg;base64,{b64}"/>',
+            }
+        ]
+
     def chat_prompt(self, text, b64):
         return [
             {
@@ -634,7 +790,7 @@ class NvidiaCV(Base):
 class StepFunCV(GptV4):
     def __init__(self, key, model_name="step-1v-8k", lang="Chinese", base_url="https://api.stepfun.com/v1"):
         if not base_url:
-            base_url="https://api.stepfun.com/v1"
+            base_url = "https://api.stepfun.com/v1"
         self.client = OpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name
         self.lang = lang
@@ -666,18 +822,18 @@ class TogetherAICV(GptV4):
     def __init__(self, key, model_name, lang="Chinese", base_url="https://api.together.xyz/v1"):
         if not base_url:
             base_url = "https://api.together.xyz/v1"
-        super().__init__(key, model_name,lang,base_url)
+        super().__init__(key, model_name, lang, base_url)
 
 
 class YiCV(GptV4):
-    def __init__(self, key, model_name, lang="Chinese",base_url="https://api.lingyiwanwu.com/v1",):
+    def __init__(self, key, model_name, lang="Chinese", base_url="https://api.lingyiwanwu.com/v1",):
         if not base_url:
             base_url = "https://api.lingyiwanwu.com/v1"
-        super().__init__(key, model_name,lang,base_url)
+        super().__init__(key, model_name, lang, base_url)
 
 
 class HunyuanCV(Base):
-    def __init__(self, key, model_name, lang="Chinese",base_url=None):
+    def __init__(self, key, model_name, lang="Chinese", base_url=None):
         from tencentcloud.common import credential
         from tencentcloud.hunyuan.v20230901 import hunyuan_client
 
@@ -689,12 +845,12 @@ class HunyuanCV(Base):
         self.client = hunyuan_client.HunyuanClient(cred, "")
         self.lang = lang
 
-    def describe(self, image, max_tokens=4096):
-        from tencentcloud.hunyuan.v20230901 import models
+    def describe(self, image):
         from tencentcloud.common.exception.tencent_cloud_sdk_exception import (
             TencentCloudSDKException,
         )
-        
+        from tencentcloud.hunyuan.v20230901 import models
+
         b64 = self.image2base64(image)
         req = models.ChatCompletionsRequest()
         params = {"Model": self.model_name, "Messages": self.prompt(b64)}
@@ -706,7 +862,24 @@ class HunyuanCV(Base):
             return ans, response.Usage.TotalTokens
         except TencentCloudSDKException as e:
             return ans + "\n**ERROR**: " + str(e), 0
-        
+
+    def describe_with_prompt(self, image, prompt=None):
+        from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException
+        from tencentcloud.hunyuan.v20230901 import models
+
+        b64 = self.image2base64(image)
+        vision_prompt = self.vision_llm_prompt(b64, prompt) if prompt else self.vision_llm_prompt(b64)
+        req = models.ChatCompletionsRequest()
+        params = {"Model": self.model_name, "Messages": vision_prompt}
+        req.from_json_string(json.dumps(params))
+        ans = ""
+        try:
+            response = self.client.ChatCompletions(req)
+            ans = response.Choices[0].Message.Content
+            return ans, response.Usage.TotalTokens
+        except TencentCloudSDKException as e:
+            return ans + "\n**ERROR**: " + str(e), 0
+
     def prompt(self, b64):
         return [
             {
@@ -725,4 +898,4 @@ class HunyuanCV(Base):
                     },
                 ],
             }
-        ]
+        ]

rag/prompts.py

@@ -18,13 +18,13 @@ import json
 import logging
 import re
 from collections import defaultdict
+
 import json_repair
+
 from api import settings
 from api.db import LLMType
-from api.db.services.document_service import DocumentService
-from api.db.services.llm_service import TenantLLMService, LLMBundle
 from rag.settings import TAG_FLD
-from rag.utils import num_tokens_from_string, encoder
+from rag.utils import encoder, num_tokens_from_string
 
 
 def chunks_format(reference):
@@ -44,9 +44,11 @@ def chunks_format(reference):
 
 
 def llm_id2llm_type(llm_id):
+    from api.db.services.llm_service import TenantLLMService
+
     llm_id, _ = TenantLLMService.split_model_name_and_factory(llm_id)
 
-    llm_factories = settings.FACTORY_LLM_INFOS  
+    llm_factories = settings.FACTORY_LLM_INFOS
     for llm_factory in llm_factories:
         for llm in llm_factory["llm"]:
             if llm_id == llm["llm_name"]:
@@ -92,6 +94,8 @@ def message_fit_in(msg, max_length=4000):
 
 
 def kb_prompt(kbinfos, max_tokens):
+    from api.db.services.document_service import DocumentService
+
     knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
     used_token_count = 0
     chunks_num = 0
@@ -166,15 +170,15 @@ Overall, while Musk enjoys Dogecoin and often promotes it, he also warns against
 
 def keyword_extraction(chat_mdl, content, topn=3):
     prompt = f"""
-Role: You're a text analyzer. 
+Role: You're a text analyzer.
 Task: extract the most important keywords/phrases of a given piece of text content.
-Requirements: 
+Requirements:
   - Summarize the text content, and give top {topn} important keywords/phrases.
   - The keywords MUST be in language of the given piece of text content.
   - The keywords are delimited by ENGLISH COMMA.
   - Keywords ONLY in output.
 
-### Text Content 
+### Text Content
 {content}
 
 """
@@ -194,9 +198,9 @@ Requirements:
 
 def question_proposal(chat_mdl, content, topn=3):
     prompt = f"""
-Role: You're a text analyzer. 
+Role: You're a text analyzer.
 Task:  propose {topn} questions about a given piece of text content.
-Requirements: 
+Requirements:
   - Understand and summarize the text content, and propose top {topn} important questions.
   - The questions SHOULD NOT have overlapping meanings.
   - The questions SHOULD cover the main content of the text as much as possible.
@@ -204,7 +208,7 @@ Requirements:
   - One question per line.
   - Question ONLY in output.
 
-### Text Content 
+### Text Content
 {content}
 
 """
@@ -223,6 +227,8 @@ Requirements:
 
 
 def full_question(tenant_id, llm_id, messages, language=None):
+    from api.db.services.llm_service import LLMBundle
+
     if llm_id2llm_type(llm_id) == "image2text":
         chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id)
     else:
@@ -239,7 +245,7 @@ def full_question(tenant_id, llm_id, messages, language=None):
     prompt = f"""
 Role: A helpful assistant
 
-Task and steps: 
+Task and steps:
     1. Generate a full user question that would follow the conversation.
     2. If the user's question involves relative date, you need to convert it into absolute date based on the current date, which is {today}. For example: 'yesterday' would be converted to {yesterday}.
 
@@ -300,11 +306,11 @@ Output: What's the weather in Rochester on {tomorrow}?
 
 def content_tagging(chat_mdl, content, all_tags, examples, topn=3):
     prompt = f"""
-Role: You're a text analyzer. 
+Role: You're a text analyzer.
 
 Task: Tag (put on some labels) to a given piece of text content based on the examples and the entire tag set.
 
-Steps:: 
+Steps::
   - Comprehend the tag/label set.
   - Comprehend examples which all consist of both text content and assigned tags with relevance score in format of JSON.
   - Summarize the text content, and tag it with top {topn} most relevant tags from the set of tag/label and the corresponding relevance score.
@@ -358,3 +364,32 @@ Output:
         except Exception as e:
             logging.exception(f"JSON parsing error: {result} -> {e}")
             raise e
+
+
+def vision_llm_describe_prompt(page=None) -> str:
+    prompt_en = """
+INSTRUCTION:
+Transcribe the content from the provided PDF page image into clean Markdown format.
+- Only output the content transcribed from the image.
+- Do NOT output this instruction or any other explanation.
+- If the content is missing or you do not understand the input, return an empty string.
+
+RULES:
+1. Do NOT generate examples, demonstrations, or templates.
+2. Do NOT output any extra text such as 'Example', 'Example Output', or similar.
+3. Do NOT generate any tables, headings, or content that is not explicitly present in the image.
+4. Transcribe content word-for-word. Do NOT modify, translate, or omit any content.
+5. Do NOT explain Markdown or mention that you are using Markdown.
+6. Do NOT wrap the output in ```markdown or ``` blocks.
+7. Only apply Markdown structure to headings, paragraphs, lists, and tables, strictly based on the layout of the image. Do NOT create tables unless an actual table exists in the image.
+8. Preserve the original language, information, and order exactly as shown in the image.
+"""
+
+    if page is not None:
+        prompt_en += f"\nAt the end of the transcription, add the page divider: `--- Page {page} ---`."
+
+    prompt_en += """
+FAILURE HANDLING:
+- If you do not detect valid content in the image, return an empty string.
+"""
+    return prompt_en

rag/utils/__init__.py

@@ -16,7 +16,9 @@
 
 import os
 import re
+
 import tiktoken
+
 from api.utils.file_utils import get_project_base_directory
 
 
@@ -54,7 +56,7 @@ def findMaxDt(fnm):
         pass
     return m
 
-  
+
 def findMaxTm(fnm):
     m = 0
     try:
@@ -91,11 +93,18 @@ def truncate(string: str, max_len: int) -> str:
     """Returns truncated text if the length of text exceed max_len."""
     return encoder.decode(encoder.encode(string)[:max_len])
 
+  
+def clean_markdown_block(text):
+    text = re.sub(r'^\s*```markdown\s*\n?', '', text)
+    text = re.sub(r'\n?\s*```\s*$', '', text)
+    return text.strip()
 
+  
 def get_float(v: str | None):
     if v is None:
         return float('-inf')
     try:
         return float(v)
     except Exception:
-        return float('-inf')
+        return float('-inf')
+