fix typing errors

core/models/documents.py

@@ -21,6 +21,7 @@ class Document(BaseModel):
     content_type: str
     filename: Optional[str] = None
     metadata: Dict[str, Any] = Field(default_factory=dict)
+    """user-defined metadata"""
     storage_info: Dict[str, str] = Field(default_factory=dict)
     system_metadata: Dict[str, Any] = Field(
         default_factory=lambda: {
@@ -29,6 +30,9 @@ class Document(BaseModel):
             "version": 1,
         }
     )
+    """metadata such as creation date etc."""
+    additional_metadata: Dict[str, Any] = Field(default_factory=dict)
+    """metadata to help with querying eg. frame descriptions and time-stamped transcript for videos"""
     access_control: Dict[str, List[str]] = Field(
         default_factory=lambda: {"readers": [], "writers": [], "admins": []}
     )

core/models/video.py

@@ -1,8 +1,11 @@
 from collections import defaultdict
+from numbers import Number
 from typing import List, Tuple, Optional, Union, Dict
 from bisect import bisect_left
 import logging
 
+from pydantic import BaseModel
+
 from core.models.documents import Chunk
 
 logger = logging.getLogger(__name__)
@@ -18,6 +21,7 @@ class TimeSeriesData:
         """
         # Sort timestamps and content for binary search
         sorted_items = sorted(time_to_content.items(), key=lambda x: x[0])
+        self.time_to_content = time_to_content
         self.timestamps = [t for t, _ in sorted_items]
         self.contents = [c for _, c in sorted_items]
 
@@ -90,3 +94,9 @@ class TimeSeriesData:
             Chunk(content=content, metadata={"timestamp": timestamp})
             for content, timestamp in zip(self.contents, self.timestamps)
         ]
+
+
+class ParseVideoResult(BaseModel):
+    metadata: Dict[str, Number]
+    frame_descriptions: TimeSeriesData
+    transcript: TimeSeriesData

core/parser/base_parser.py

@@ -1,6 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import List, Union
-from fastapi import UploadFile
+from typing import Any, Dict, List, Tuple
 from core.models.documents import Chunk
 
 
@@ -13,6 +12,8 @@ class BaseParser(ABC):
         pass
 
     @abstractmethod
-    async def parse_file(self, file: bytes, content_type: str) -> List[Chunk]:
+    async def parse_file(
+        self, file: bytes, content_type: str
+    ) -> Tuple[Dict[str, Any], List[Chunk]]:
         """Parse file content into text chunks"""
         pass

core/parser/combined_parser.py

@@ -1,10 +1,11 @@
-from typing import List, Optional
+from typing import Any, Dict, List, Optional, Tuple
 import logging
 import os
 import tempfile
 import magic
 from core.models.documents import Chunk
 
+from core.models.video import TimeSeriesData
 from core.parser.base_parser import BaseParser
 from core.parser.unstructured_parser import UnstructuredAPIParser
 from core.parser.video.parse_video import VideoParser
@@ -76,7 +77,9 @@ class CombinedParser(BaseParser):
         """Split plain text into chunks using unstructured parser"""
         return await self.unstructured_parser.split_text(text)
 
-    async def parse_file(self, file: bytes, content_type: str) -> List[Chunk]:
+    async def parse_file(
+        self, file: bytes, content_type: str
+    ) -> Tuple[Dict[str, Any], List[Chunk]]:
         """Parse file content into text chunks"""
         is_video = self._is_video_file(file_bytes=file)
 
@@ -85,7 +88,7 @@ class CombinedParser(BaseParser):
         else:
             return await self.unstructured_parser.parse_file(file, content_type)
 
-    async def _parse_video(self, file: bytes) -> List[Chunk]:
+    async def _parse_video(self, file: bytes) -> Tuple[Dict[str, Any], List[Chunk]]:
         """Parse video file and combine transcript and frame descriptions into chunks"""
         # Save video to temporary file if needed
         temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
@@ -102,10 +105,16 @@ class CombinedParser(BaseParser):
             )
             results = parser.process_video()
             # Get all frame descriptions
-            frame_chunks = results["frame_descriptions"].to_chunks()
+            frame_descriptions = results.frame_descriptions
             # Get all transcript text
-            transcript_chunks = results["transcript"].to_chunks()
-            return frame_chunks + transcript_chunks
+            transcript_text = results.transcript
+            additional_metadata = {
+                "frame_descriptions": frame_descriptions.time_to_content,
+                "transcript": transcript_text.time_to_content,
+                "video_metadata": results.metadata,
+            }
+            chunks = frame_descriptions.to_chunks() + transcript_text.to_chunks()
+            return additional_metadata, chunks
 
         finally:
             # Clean up temporary file

core/parser/unstructured_parser.py

@@ -1,4 +1,4 @@
-from typing import List
+from typing import Any, Dict, List, Tuple
 import io
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_unstructured import UnstructuredLoader
@@ -32,7 +32,9 @@ class UnstructuredAPIParser(BaseParser):
             for chunk in self.text_splitter.split_text(text)
         ]
 
-    async def parse_file(self, file: bytes, content_type: str) -> List[Chunk]:
+    async def parse_file(
+        self, file: bytes, content_type: str
+    ) -> Tuple[Dict[str, Any], List[Chunk]]:
         """Parse file content using unstructured"""
         # Parse with unstructured
         loader = UnstructuredLoader(
@@ -42,6 +44,6 @@ class UnstructuredAPIParser(BaseParser):
             chunking_strategy="by_title",
         )
         elements = loader.load()
-        return [
+        return {}, [
             Chunk(content=element.page_content, metadata={}) for element in elements
         ]

core/parser/video/parse_video.py

@@ -1,10 +1,11 @@
+from numbers import Number
 import cv2
-from typing import Dict
+from typing import Dict, Union
 import base64
 from openai import OpenAI
 import assemblyai as aai
 import logging
-from core.models.time_series import TimeSeriesData
+from core.models.video import TimeSeriesData, ParseVideoResult
 
 logger = logging.getLogger(__name__)
 
@@ -42,7 +43,9 @@ class VideoParser:
             speaker_labels=True
         )  # speech_model=aai.SpeechModel.nano
         self.transcriber = aai.Transcriber(config=aai_config)
-        self.transcript = None
+        self.transcript = TimeSeriesData(
+            time_to_content={}
+        )  # empty transcript initially - TODO: have this be a lateinit somehow
         self.gpt = OpenAI()
 
         logger.info(f"Video loaded: {self.duration:.2f}s duration, {self.fps:.2f} FPS")
@@ -80,7 +83,11 @@ class VideoParser:
         logger.info("Starting video transcription")
         transcript = self.get_transcript_object()
         # divide by 1000 because assemblyai timestamps are in milliseconds
-        time_to_text = {u.start / 1000: u.text for u in transcript.utterances}
+        time_to_text = (
+            {u.start / 1000: u.text for u in transcript.utterances}
+            if transcript.utterances
+            else {}
+        )
         debug_object("Time to text", time_to_text)
         self.transcript = TimeSeriesData(time_to_text)
         return self.transcript
@@ -125,7 +132,7 @@ class VideoParser:
                                 {last_description if last_description else 'No previous frame description available, this is the first frame'}
                                 ---
 
-                                In your response, only provide the description of the current frame, using the above information as context. 
+                                In your response, only provide the description of the current frame, using the above information as context.
                                 """,
                                 },
                                 {
@@ -147,7 +154,7 @@ class VideoParser:
         logger.info(f"Generated descriptions for {len(time_to_description)} frames")
         return TimeSeriesData(time_to_description)
 
-    def process_video(self) -> Dict:
+    def process_video(self) -> ParseVideoResult:
         """
         Process the video to get both transcript and frame descriptions
 
@@ -155,16 +162,17 @@ class VideoParser:
             Dictionary containing transcript and frame descriptions as TimeSeriesData objects
         """
         logger.info("Starting full video processing")
-        result = {
-            "metadata": {
-                "duration": self.duration,
-                "fps": self.fps,
-                "total_frames": self.total_frames,
-                "frame_sample_rate": self.frame_sample_rate,
-            },
-            "transcript": self.get_transcript(),
-            "frame_descriptions": self.get_frame_descriptions(),
+        metadata = {
+            "duration": self.duration,
+            "fps": self.fps,
+            "total_frames": self.total_frames,
+            "frame_sample_rate": self.frame_sample_rate,
         }
+        result = ParseVideoResult(
+            metadata=metadata,
+            transcript=self.get_transcript(),
+            frame_descriptions=self.get_frame_descriptions(),
+        )
         logger.info("Video processing completed successfully")
         return result
 

core/services/document_service.py

@@ -163,7 +163,11 @@ class DocumentService:
         if "write" not in auth.permissions:
             raise PermissionError("User does not have write permission")
 
-        # 1. Create document record
+        file_content = await file.read()
+        additional_metadata, chunks = await self.parser.parse_file(
+            file_content, file.content_type or ""
+        )
+
         doc = Document(
             content_type=file.content_type or "",
             filename=file.filename,
@@ -174,11 +178,10 @@ class DocumentService:
                 "writers": [auth.entity_id],
                 "admins": [auth.entity_id],
             },
+            additional_metadata=additional_metadata,
         )
         logger.info(f"Created file document record with ID {doc.external_id}")
 
-        # 2. Read and store file
-        file_content = await file.read()
         storage_info = await self.storage.upload_from_base64(
             base64.b64encode(file_content).decode(), doc.external_id, file.content_type
         )
@@ -187,8 +190,6 @@ class DocumentService:
             f"Stored file in bucket `{storage_info[0]}` with key `{storage_info[1]}`"
         )
 
-        # 3. Parse content into chunks
-        chunks = await self.parser.parse_file(file_content, file.content_type or "")
         if not chunks:
             raise ValueError("No content chunks extracted from file")
         logger.info(f"Parsed file into {len(chunks)} chunks")

core/vector_store/base_vector_store.py

@@ -1,6 +1,5 @@
 from abc import ABC, abstractmethod
 from typing import List, Optional, Tuple
-from core.models.auth import AuthContext
 from core.models.documents import DocumentChunk