工程名称下拉项获取兼容.md文件，同时新增自定义答案合成类

backend/app/engine/engine.py

@@ -10,6 +10,8 @@ from sqlalchemy import create_engine
 from util.register import *
 from app.engine.prompt import text_qa_template, refine_template, summary_template, simple_template
 from app.engine.retriever.HybridRetriever import HybridRetriever
+from app.engine.response.treeSummResponse import CustomTreeResponse
+from llama_index.core.settings import Settings
 
 ModelPlateCategory = '模型平台'
 
@@ -65,6 +67,14 @@ def get_Retriever(index,**kwargs):
     return retriever
 
 
+def get_synthesizer():
+    return CustomTreeResponse(
+            llm=Settings.llm,
+            summary_template=summary_template,
+            use_async=True,
+            streaming=False,
+    )
+
 sql_database = None
 sql_obj_index = None
 

backend/app/engine/loaders/__init__.py

@@ -3,7 +3,7 @@ import yaml
 from app.engine.loaders.db import DBLoaderConfig, get_db_documents
 from app.engine.loaders.file import FileLoaderConfig, get_file_documents
 from app.engine.loaders.web import WebLoaderConfig, get_web_documents
-from app.engine.loaders.projectJson import getProjectName
+from app.engine.loaders.file import getProjectName
 import os
 
 

backend/app/engine/loaders/file.py

@@ -6,6 +6,9 @@ from llama_index.core.readers.base import BaseReader
 from llama_index.core.readers.json import JSONReader
 from llama_parse import LlamaParse
 from pydantic import BaseModel, validator
+from app.engine.loaders.markdownReader import ChunkMarkdownReader
+from app.engine.loaders.projectJson import ProjectJson
+
 
 logger = logging.getLogger(__name__)
 
@@ -20,7 +23,6 @@ class FileLoaderConfig(BaseModel):
             raise ValueError(f"Directory '{v}' does not exist")
         return v
 
-
 def llama_parse_parser():
     if os.getenv("LLAMA_CLOUD_API_KEY") is None:
         raise ValueError(
@@ -35,7 +37,6 @@ def llama_parse_parser():
     )
     return parser
 
-
 def llama_parse_extractor() -> Dict[str, LlamaParse]:
     from llama_parse.utils import SUPPORTED_FILE_TYPES
 
@@ -43,8 +44,11 @@ def llama_parse_extractor() -> Dict[str, LlamaParse]:
     return {file_type: parser for file_type in SUPPORTED_FILE_TYPES}
 
 def llama_local_extractor() -> Dict[str, BaseReader]:
-    return {".json" : JSONReader(clean_json=False,levels_back=0)}
+    parser = {
-
+        ".json" : JSONReader(clean_json=False,levels_back=0),
+        ".md" : ChunkMarkdownReader(),
+        }
+    return parser
 
 def get_file_documents(config: FileLoaderConfig,childPath: str):
     from llama_index.core.readers import SimpleDirectoryReader
@@ -86,3 +90,32 @@ def get_file_documents(config: FileLoaderConfig,childPath: str):
         else:
             # Raise the error if it is not the case of empty data dir
             raise e
+
+def prjFileSuffix(dir:str):
+    entries = os.listdir(dir)
+    file_names = [entry for entry in entries if os.path.isfile(os.path.join(dir, entry))]
+    if len(file_names) > 0:
+        return os.path.splitext(file_names[0])[1]
+    return ''
+
+def getProjectName(dir:str):
+    suffix = prjFileSuffix(dir)
+    if suffix== '.json':
+        prjJson = ProjectJson(dir)
+        prjJson.parse()
+        tb = prjJson.table('工程属性')
+        records = tb.records()
+        for record in records:
+            name = record.value('名称') 
+            if name == '工程名称':
+                return record.value('值') 
+    elif suffix == '.md':
+        md_files = [f for f in os.listdir(dir) if f.endswith('.md')]
+        for md_file in md_files:
+            prjPath = os.path.join(dir, md_file)
+            basename = os.path.splitext(md_file)[0]
+            if basename =='工程属性':
+                rd = ChunkMarkdownReader()
+                rd.load_data(prjPath)
+                return rd.findValue("名称=='工程名称'",'值')
+    return ''

backend/app/engine/loaders/markdownReader.py

@@ -13,6 +13,8 @@ class ChunkMarkdownReader(MarkdownReader):
     ) -> None:
         self._chunkSize = chunkSize
         self._tokenizer = get_tokenizer()
+        self._colheader = ''
+        self._rows = []
         super().__init__(*args,**kwargs)
 
     def markdown_to_tups(self, markdown_text: str) -> List[Tuple[Optional[str], str]]:
@@ -34,6 +36,8 @@ class ChunkMarkdownReader(MarkdownReader):
                 tokensNum =  headerSize
                 current_lines.clear()
             current_lines.append(line)
+            if strTitle!='' and strheader!='':
+                self._rows.append(line)
 
             if line == '\n' or line == '\r':
                 if tokensNum > self._chunkSize:
@@ -43,10 +47,12 @@ class ChunkMarkdownReader(MarkdownReader):
                 current_lines.clear()
                         
             if line.startswith("|---"):
+                self._colheader = current_lines[0]
                 strheader = "\n".join(current_lines) 
                 headerSize= headerSize + self._token_size(strheader)  
                 current_lines.clear() 
 
+
         if len(current_lines) > 0:
             if len(markdown_tups) == 0:
                 markdown_tups.append((strTitle + strheader , "\n".join(current_lines)))
@@ -62,4 +68,22 @@ class ChunkMarkdownReader(MarkdownReader):
         ]
 
     def _token_size(self, text: str) -> int:
-        return len(self._tokenizer(text))
+        return len(self._tokenizer(text))
+    
+    def findValue(self,expression:str,Field:str):
+        cols = self._colheader.split('|')
+        cols =  [item for item in cols if item]
+
+        for row in self._rows:
+            rowtrs = row.split('|')
+            rowdatas =  [item for item in rowtrs if item and (item!='\r' or item!='\n')]
+            if len(rowdatas) == 0:
+                continue
+            gData = {}
+            for cName,rValue in zip(cols,rowdatas):
+                gData[cName] = rValue
+            if eval(expression,gData):
+                return gData[Field]
+        return ''
+
+

backend/app/engine/loaders/projectJson.py

@@ -55,7 +55,6 @@ class JsonTable:
     def comment(self):
         return self._comment
 
-
 class ProjectJson:
     def __init__(self,dir:str) -> None:
         self._dir = dir
@@ -76,14 +75,5 @@ class ProjectJson:
     def tables(self):
         return self._tables
 
-def getProjectName(dir:str):
+
-    prjJson = ProjectJson(dir)
-    prjJson.parse()
-    tb:JsonTable = prjJson.table('工程属性')
-    records = tb.records()
-    for record in records:
-        name = record.value('名称') 
-        if name == '工程名称':
-            return record.value('值') 
-    return ''
 

backend/app/engine/response/treeSummResponse.py

@@ -0,0 +1,234 @@
+from llama_index.core.response_synthesizers.tree_summarize import TreeSummarize
+from typing import Any, Optional, Sequence,List
+import asyncio
+from llama_index.core.callbacks.base import CallbackManager
+from llama_index.core.indices.prompt_helper import PromptHelper
+from llama_index.core.prompts import BasePromptTemplate
+from llama_index.core.service_context import ServiceContext
+from llama_index.core.service_context_elements.llm_predictor import LLMPredictorType
+from llama_index.core.types import BaseModel,RESPONSE_TEXT_TYPE
+from llama_index.core.async_utils import run_async_tasks
+from llama_index.core.utils import get_tokenizer
+from llama_index.core.prompts.prompt_utils import get_empty_prompt_txt
+
+class CustomTreeResponse(TreeSummarize):
+    def __init__(
+        self,
+        llm: Optional[LLMPredictorType] = None,
+        callback_manager: Optional[CallbackManager] = None,
+        prompt_helper: Optional[PromptHelper] = None,
+        summary_template: Optional[BasePromptTemplate] = None,
+        output_cls: Optional[BaseModel] = None,
+        streaming: bool = False,
+        use_async: bool = False,
+        verbose: bool = False,
+        service_context: Optional[ServiceContext] = None,
+    ) -> None:
+        self._tokenizer = get_tokenizer()
+        super().__init__(llm,callback_manager,prompt_helper,summary_template,output_cls
+                        ,streaming,use_async,verbose,service_context)
+        
+    async def aget_response(
+        self,
+        query_str: str,
+        text_chunks: Sequence[str],
+        **response_kwargs: Any,
+    ) -> RESPONSE_TEXT_TYPE:
+        """Get tree summarize response."""
+        summary_template = self._summary_template.partial_format(query_str=query_str)
+
+        text_chunks = self.repack(text_chunks=text_chunks)
+
+        if self._verbose:
+            print(f"{len(text_chunks)} text chunks after repacking")
+
+
+        # give final response if there is only one chunk
+        if len(text_chunks) == 1:
+            response: RESPONSE_TEXT_TYPE
+            if self._streaming:
+                response = await self._llm.astream(
+                    summary_template, context_str=text_chunks[0], **response_kwargs
+                )
+            else:
+                if self._output_cls is None:
+                    response = await self._llm.apredict(
+                        summary_template,
+                        context_str=text_chunks[0],
+                        **response_kwargs,
+                    )
+                else:
+                    response = await self._llm.astructured_predict(
+                        self._output_cls,
+                        summary_template,
+                        context_str=text_chunks[0],
+                        **response_kwargs,
+                    )
+
+            # return pydantic object if output_cls is specified
+            return response
+
+        else:
+            # summarize each chunk
+            if self._output_cls is None:
+                tasks = [
+                    self._llm.apredict(
+                        summary_template,
+                        context_str=text_chunk,
+                        **response_kwargs,
+                    )
+                    for text_chunk in text_chunks
+                ]
+            else:
+                tasks = [
+                    self._llm.astructured_predict(
+                        self._output_cls,
+                        summary_template,
+                        context_str=text_chunk,
+                        **response_kwargs,
+                    )
+                    for text_chunk in text_chunks
+                ]
+
+            summary_responses = await asyncio.gather(*tasks)
+            if self._output_cls is not None:
+                summaries = [summary.json() for summary in summary_responses]
+            else:
+                summaries = summary_responses
+
+            # recursively summarize the summaries
+            return await self.aget_response(
+                query_str=query_str,
+                text_chunks=summaries,
+                **response_kwargs,
+            )
+
+    def get_response(
+        self,
+        query_str: str,
+        text_chunks: Sequence[str],
+        **response_kwargs: Any,
+    ) -> RESPONSE_TEXT_TYPE:
+        """Get tree summarize response."""
+        summary_template = self._summary_template.partial_format(query_str=query_str)
+        text_chunks = self.repack(text_chunks=text_chunks)
+
+        if self._verbose:
+            print(f"{len(text_chunks)} text chunks after repacking")
+
+        # give final response if there is only one chunk
+        if len(text_chunks) == 1:
+            response: RESPONSE_TEXT_TYPE
+            if self._streaming:
+                response = self._llm.stream(
+                    summary_template, context_str=text_chunks[0], **response_kwargs
+                )
+            else:
+                if self._output_cls is None:
+                    response = self._llm.predict(
+                        summary_template,
+                        context_str=text_chunks[0],
+                        **response_kwargs,
+                    )
+                else:
+                    response = self._llm.structured_predict(
+                        self._output_cls,
+                        summary_template,
+                        context_str=text_chunks[0],
+                        **response_kwargs,
+                    )
+
+            return response
+
+        else:
+            # summarize each chunk
+            if self._use_async:
+                if self._output_cls is None:
+                    tasks = [
+                        self._llm.apredict(
+                            summary_template,
+                            context_str=text_chunk,
+                            **response_kwargs,
+                        )
+                        for text_chunk in text_chunks
+                    ]
+                else:
+                    tasks = [
+                        self._llm.astructured_predict(
+                            self._output_cls,
+                            summary_template,
+                            context_str=text_chunk,
+                            **response_kwargs,
+                        )
+                        for text_chunk in text_chunks
+                    ]
+
+                summary_responses = run_async_tasks(tasks)
+
+                if self._output_cls is not None:
+                    summaries = [summary.json() for summary in summary_responses]
+                else:
+                    summaries = summary_responses
+            else:
+                if self._output_cls is None:
+                    summaries = [
+                        self._llm.predict(
+                            summary_template,
+                            context_str=text_chunk,
+                            **response_kwargs,
+                        )
+                        for text_chunk in text_chunks
+                    ]
+                else:
+                    summaries = [
+                        self._llm.structured_predict(
+                            self._output_cls,
+                            summary_template,
+                            context_str=text_chunk,
+                            **response_kwargs,
+                        )
+                        for text_chunk in text_chunks
+                    ]
+                    summaries = [summary.json() for summary in summaries]
+
+            # recursively summarize the summaries
+            return self.get_response(
+                query_str=query_str, text_chunks=summaries, **response_kwargs
+            )
+        
+    def repack( self,text_chunks: Sequence[str],) ->List[str]:
+        prompt_str = get_empty_prompt_txt(self._summary_template)
+        num_prompt_tokens = self._token_size(prompt_str)
+        avaliableSize = self._get_available_context_size(num_prompt_tokens)
+        ava_chunks = []
+        sumSize = 0
+        results = []
+        for text_chunk in text_chunks:
+            one_chunk_size = self._token_size(text_chunk)
+            if one_chunk_size > avaliableSize:
+                raise ValueError("文本块大小大于可用上下文大小")
+            sumSize = sumSize +  one_chunk_size
+            if sumSize > avaliableSize:
+                results.append(self._merge_chunks(ava_chunks))
+                ava_chunks.clear()
+                sumSize = 0
+            ava_chunks.append(text_chunk)
+        if len(ava_chunks) > 0:
+            results.append(self._merge_chunks(ava_chunks))
+        return results
+
+    def _get_available_context_size(self, num_prompt_tokens: int) -> int:
+        llm_metadata = self._llm.metadata
+        context_size_tokens = llm_metadata.context_window - num_prompt_tokens - llm_metadata.num_output
+        if context_size_tokens < 0:
+            raise ValueError(
+                f"Calculated available context size {context_size_tokens} was"
+                " not non-negative."
+            )
+        return context_size_tokens
+    
+    def _token_size(self, text: str) -> int:
+        return len(self._tokenizer(text))
+    
+    def _merge_chunks(self,ava_chunks:list):
+        return "\n\n".join([c.strip() for c in ava_chunks if c.strip()])