feat(upload): changed to task

backend/celery_task.py

@@ -0,0 +1,19 @@
+from celery import shared_task
+from models.brains import Brain
+from repository.files.upload_file import DocumentSerializable
+from utils.vectors import Neurons
+
+
+@shared_task
+def create_embedding_for_document(
+    brain_id, doc_with_metadata, user_openai_api_key, file_sha1
+):
+    neurons = Neurons()
+    doc = DocumentSerializable.from_json(doc_with_metadata)
+    created_vector = neurons.create_vector(doc, user_openai_api_key)
+    # add_usage(stats_db, "embedding", "audio", metadata={"file_name": file_meta_name,"file_type": ".txt", "chunk_size": chunk_size, "chunk_overlap": chunk_overlap})
+
+    created_vector_id = created_vector[0]  # pyright: ignore reportPrivateUsage=none
+
+    brain = Brain(id=brain_id)
+    brain.create_brain_vector(created_vector_id, file_sha1)

backend/parsers/common.py

@@ -1,8 +1,8 @@
 import time
 
-from langchain.schema import Document
-from models import Brain, File
-from utils.vectors import Neurons
+from celery_task import create_embedding_for_document
+from models import File
+from repository.files.upload_file import DocumentSerializable
 
 
 async def process_file(
@@ -26,15 +26,12 @@ async def process_file(
             "date": dateshort,
             "summarization": "true" if enable_summarization else "false",
         }
-        doc_with_metadata = Document(page_content=doc.page_content, metadata=metadata)
+        doc_with_metadata = DocumentSerializable(
+            page_content=doc.page_content, metadata=metadata
+        )
 
-        neurons = Neurons()
-        created_vector = neurons.create_vector(doc_with_metadata, user_openai_api_key)
-        # add_usage(stats_db, "embedding", "audio", metadata={"file_name": file_meta_name,"file_type": ".txt", "chunk_size": chunk_size, "chunk_overlap": chunk_overlap})
-
-        created_vector_id = created_vector[0]  # pyright: ignore reportPrivateUsage=none
-
-        brain = Brain(id=brain_id)
-        brain.create_brain_vector(created_vector_id, file.file_sha1)
+        create_embedding_for_document.delay(
+            brain_id, doc_with_metadata.to_json(), user_openai_api_key, file.file_sha1
+        )
 
     return

backend/repository/files/upload_file.py

@@ -1,6 +1,9 @@
+import json
 from multiprocessing import get_logger
 
 from httpx import Response
+from langchain.pydantic_v1 import Field
+from langchain.schema import Document
 from models import get_supabase_client
 from supabase.client import Client
 
@@ -19,3 +22,35 @@ def upload_file_storage(file, file_identifier: str) -> Response:
         logger.error(e)
         print(e)
         return response
+
+
+class DocumentSerializable(Document):
+    """Class for storing a piece of text and associated metadata."""
+
+    page_content: str
+    metadata: dict = Field(default_factory=dict)
+
+    @property
+    def lc_serializable(self) -> bool:
+        return True
+
+    def __repr__(self):
+        return f"Document(page_content='{self.page_content[:50]}...', metadata={self.metadata})"
+
+    def __str__(self):
+        return self.__repr__()
+
+    def to_json(self) -> str:
+        """Convert the Document object to a JSON string."""
+        return json.dumps(
+            {
+                "page_content": self.page_content,
+                "metadata": self.metadata,
+            }
+        )
+
+    @classmethod
+    def from_json(cls, json_str: str):
+        """Create a Document object from a JSON string."""
+        data = json.loads(json_str)
+        return cls(page_content=data["page_content"], metadata=data["metadata"])