优化了提示词

backend/app/api/services/18时33分49秒_副本_file.py

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+import base64
+import mimetypes
+import os
+from pathlib import Path
+from typing import Dict, List
+from uuid import uuid4
+
+from app.engine.index import get_index
+from llama_index.core import VectorStoreIndex
+from llama_index.core.ingestion import IngestionPipeline
+from llama_index.core.readers.file.base import (
+    _try_loading_included_file_formats as get_file_loaders_map,
+)
+from llama_index.core.readers.file.base import (
+    default_file_metadata_func,
+)
+from llama_index.core.schema import Document
+from llama_index.indices.managed.llama_cloud.base import LlamaCloudIndex
+from llama_index.readers.file import FlatReader
+
+
+def get_llamaparse_parser():
+    from app.engine.loaders import load_configs
+    from app.engine.loaders.file import FileLoaderConfig, llama_parse_parser
+
+    config = load_configs()
+    file_loader_config = FileLoaderConfig(**config["file"])
+    if file_loader_config.use_llama_parse:
+        return llama_parse_parser()
+    else:
+        return None
+
+
+def default_file_loaders_map():
+    default_loaders = get_file_loaders_map()
+    default_loaders[".txt"] = FlatReader
+    return default_loaders
+
+
+class PrivateFileService:
+    PRIVATE_STORE_PATH = "output/uploaded"
+
+    @staticmethod
+    def preprocess_base64_file(base64_content: str) -> tuple:
+        header, data = base64_content.split(",", 1)
+        mime_type = header.split(";")[0].split(":", 1)[1]
+        extension = mimetypes.guess_extension(mime_type)
+        # File data as bytes
+        return base64.b64decode(data), extension
+
+    @staticmethod
+    def store_and_parse_file(file_data, extension) -> List[Document]:
+        # Store file to the private directory
+        os.makedirs(PrivateFileService.PRIVATE_STORE_PATH, exist_ok=True)
+
+        # random file name
+        file_name = f"{uuid4().hex}{extension}"
+        file_path = Path(os.path.join(PrivateFileService.PRIVATE_STORE_PATH, file_name))
+
+        # write file
+        with open(file_path, "wb") as f:
+            f.write(file_data)
+
+        # Load file to documents
+        # If LlamaParse is enabled, use it to parse the file
+        # Otherwise, use the default file loaders
+        reader = get_llamaparse_parser()
+        if reader is None:
+            reader_cls = default_file_loaders_map().get(extension)
+            if reader_cls is None:
+                raise ValueError(f"File extension {extension} is not supported")
+            reader = reader_cls()
+        documents = reader.load_data(file_path)
+        # Add custom metadata
+        for doc in documents:
+            doc.metadata["file_name"] = file_name
+            doc.metadata["private"] = "true"
+        return documents
+
+    @staticmethod
+    def process_file(base64_content: str) -> List[str]:
+        file_data, extension = PrivateFileService.preprocess_base64_file(base64_content)
+        documents = PrivateFileService.store_and_parse_file(file_data, extension)
+
+        # Only process nodes, no store the index
+        pipeline = IngestionPipeline()
+        nodes = pipeline.run(documents=documents)
+
+        # Add the nodes to the index and persist it
+        current_index = get_index()
+
+        # Insert the documents into the index
+        if isinstance(current_index, LlamaCloudIndex):
+            # LlamaCloudIndex is a managed index so we don't need to process the nodes
+            # just insert the documents
+            for doc in documents:
+                current_index.insert(doc)
+        else:
+            # Only process nodes, no store the index
+            pipeline = IngestionPipeline()
+            nodes = pipeline.run(documents=documents)
+
+            # Add the nodes to the index and persist it
+            if current_index is None:
+                current_index = VectorStoreIndex(nodes=nodes)
+            else:
+                current_index.insert_nodes(nodes=nodes)
+            current_index.storage_context.persist(
+                persist_dir=os.environ.get("STORAGE_DIR", "storage")
+            )
+
+        # Return the document ids
+        return [doc.doc_id for doc in documents]

backend/app/api/services/18时33分49秒_副本_llama_cloud.py

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+import logging
+import os
+from typing import Any, Dict, List, Optional
+
+import requests
+from app.api.routers.models import LlamaCloudFile
+
+logger = logging.getLogger("uvicorn")
+
+
+class LLamaCloudFileService:
+    LLAMA_CLOUD_URL = "https://cloud.llamaindex.ai/api/v1"
+    LOCAL_STORE_PATH = "output/llamacloud"
+
+    DOWNLOAD_FILE_NAME_TPL = "{pipeline_id}${filename}"
+
+    @classmethod
+    def get_all_projects(cls) -> List[Dict[str, Any]]:
+        url = f"{cls.LLAMA_CLOUD_URL}/projects"
+        return cls._make_request(url)
+    
+    @classmethod
+    def get_all_pipelines(cls) -> List[Dict[str, Any]]:
+        url = f"{cls.LLAMA_CLOUD_URL}/pipelines"
+        return cls._make_request(url)
+    
+    @classmethod
+    def get_all_projects_with_pipelines(cls) -> List[Dict[str, Any]]:
+        try:
+            projects = cls.get_all_projects()
+            pipelines = cls.get_all_pipelines()
+            return [
+                {
+                    **project,
+                    "pipelines": [p for p in pipelines if p["project_id"] == project["id"]],
+                }
+                for project in projects
+            ]
+        except Exception as error:
+            logger.error(f"Error listing projects and pipelines: {error}")
+            return []
+
+    @classmethod
+    def _get_files(cls, pipeline_id: str) -> List[Dict[str, Any]]:
+        url = f"{cls.LLAMA_CLOUD_URL}/pipelines/{pipeline_id}/files"
+        return cls._make_request(url)
+
+    @classmethod
+    def _get_file_detail(cls, project_id: str, file_id: str) -> Dict[str, Any]:
+        url = f"{cls.LLAMA_CLOUD_URL}/files/{file_id}/content?project_id={project_id}"
+        return cls._make_request(url)
+
+    @classmethod
+    def _download_file(cls, url: str, local_file_path: str):
+        logger.info(f"Downloading file to {local_file_path}")
+        # Create directory if it doesn't exist
+        os.makedirs(cls.LOCAL_STORE_PATH, exist_ok=True)
+        # Download the file
+        with requests.get(url, stream=True) as r:
+            r.raise_for_status()
+            with open(local_file_path, "wb") as f:
+                for chunk in r.iter_content(chunk_size=8192):
+                    f.write(chunk)
+        logger.info("File downloaded successfully")
+
+    @classmethod
+    def download_llamacloud_pipeline_file(
+        cls,
+        file: LlamaCloudFile,
+        force_download: bool = False,
+    ):
+        file_name = file.file_name
+        pipeline_id = file.pipeline_id
+
+        # Check is the file already exists
+        downloaded_file_path = cls.get_file_path(file_name, pipeline_id)
+        if os.path.exists(downloaded_file_path) and not force_download:
+            logger.debug(f"File {file_name} already exists in local storage")
+            return
+        try:
+            logger.info(f"Downloading file {file_name} for pipeline {pipeline_id}")
+            files = cls._get_files(pipeline_id)
+            if not files or not isinstance(files, list):
+                raise Exception("No files found in LlamaCloud")
+            for file_entry in files:
+                if file_entry["name"] == file_name:
+                    file_id = file_entry["file_id"]
+                    project_id = file_entry["project_id"]
+                    file_detail = cls._get_file_detail(project_id, file_id)
+                    cls._download_file(file_detail["url"], downloaded_file_path)
+                    break
+        except Exception as error:
+            logger.info(f"Error fetching file from LlamaCloud: {error}")
+
+    @classmethod
+    def get_file_name(cls, name: str, pipeline_id: str) -> str:
+        return cls.DOWNLOAD_FILE_NAME_TPL.format(pipeline_id=pipeline_id, filename=name)
+
+    @classmethod
+    def get_file_path(cls, name: str, pipeline_id: str) -> str:
+        return os.path.join(cls.LOCAL_STORE_PATH, cls.get_file_name(name, pipeline_id))
+
+    @staticmethod
+    def _make_request(
+        url: str, data=None, headers: Optional[Dict] = None, method: str = "get"
+    ):
+        if headers is None:
+            headers = {
+                "Accept": "application/json",
+                "Authorization": f'Bearer {os.getenv("LLAMA_CLOUD_API_KEY")}',
+            }
+        response = requests.request(method, url, headers=headers, data=data)
+        response.raise_for_status()
+        return response.json()

backend/app/api/services/18时33分49秒_副本_suggestion.py

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+from typing import List
+
+from app.api.routers.models import Message
+from llama_index.core.prompts import PromptTemplate
+from llama_index.core.settings import Settings
+from pydantic import BaseModel
+
+NEXT_QUESTIONS_SUGGESTION_PROMPT = PromptTemplate(
+    "你是一个乐于助人的助手！你的任务是对用户可能会问的下一个问题给出建议。 "
+    "\n这是对话历史记录"
+    "\n---------------------\n{conversation}\n---------------------"
+    "考虑到对话历史记录，仅限于现在知识库已有内容, 请给我 $number_of_questions 个你接下来可能会问题的问题！"
+)
+N_QUESTION_TO_GENERATE = 3
+
+
+class NextQuestions(BaseModel):
+    """A list of questions that user might ask next"""
+
+    questions: List[str]
+
+
+class NextQuestionSuggestion:
+    @staticmethod
+    async def suggest_next_questions(
+        messages: List[Message],
+        number_of_questions: int = N_QUESTION_TO_GENERATE,
+    ) -> List[str]:
+        # Reduce the cost by only using the last two messages
+        last_user_message = None
+        last_assistant_message = None
+        for message in reversed(messages):
+            if message.role == "user":
+                last_user_message = f"User: {message.content}"
+            elif message.role == "assistant":
+                last_assistant_message = f"Assistant: {message.content}"
+            if last_user_message and last_assistant_message:
+                break
+        conversation: str = f"{last_user_message}\n{last_assistant_message}"
+
+        output: NextQuestions = await Settings.llm.astructured_predict(
+            NextQuestions,
+            prompt=NEXT_QUESTIONS_SUGGESTION_PROMPT,
+            conversation=conversation,
+            nun_questions=number_of_questions,
+        )
+
+        return output.questions