add image processing to ollama

core/completion/ollama_completion.py

@@ -5,6 +5,7 @@ from core.completion.base_completion import (
 )
 from ollama import AsyncClient
 
+BASE_64_PREFIX = "data:image/png;base64,"
 
 class OllamaCompletionModel(BaseCompletionModel):
     """Ollama completion model implementation"""
@@ -16,18 +17,33 @@ class OllamaCompletionModel(BaseCompletionModel):
     async def complete(self, request: CompletionRequest) -> CompletionResponse:
         """Generate completion using Ollama API"""
         # Construct prompt with context
-        context = "\n\n".join(request.context_chunks)
+        images, context = [], []
+        for chunk in request.context_chunks:
+            if chunk.startswith(BASE_64_PREFIX):
+                image_b64 = chunk.split(',', 1)[1]
+                images.append(image_b64)
+            else:
+                context.append(chunk)
+        context = "\n\n".join(context)
         prompt = f"""You are a helpful assistant. Use the provided context to answer questions accurately.
 
-Context:
+<QUESTION>
-{context}
+{request.query}
+</QUESTION>
 
-Question: {request.query}"""
+<CONTEXT>
+{context}
+</CONTEXT>
+"""
 
         # Call Ollama API
         response = await self.client.chat(
             model=self.model_name,
-            messages=[{"role": "user", "content": prompt}],
+            messages=[{
+                "role": "user",
+                "content": prompt,
+                "images": [images[0]],
+            }],
             options={
                 "num_predict": request.max_tokens,
                 "temperature": request.temperature,

core/models/documents.py

@@ -1,6 +1,7 @@
 from typing import Dict, Any, List, Optional, Literal
 from enum import Enum
 from datetime import UTC, datetime
+from PIL import Image
 from pydantic import BaseModel, Field, field_validator
 import uuid
 import logging
@@ -88,7 +89,7 @@ class ChunkResult(BaseModel):
     filename: Optional[str] = None
     download_url: Optional[str] = None
 
-    def augmented_content(self, doc: DocumentResult) -> str:
+    def augmented_content(self, doc: DocumentResult) -> str | Image.Image:
         match self.metadata:
             case m if "timestamp" in m:
                 # if timestamp present, then must be a video. In that case,
@@ -110,5 +111,21 @@ class ChunkResult(BaseModel):
                     for t in timestamps
                 ]
                 return "\n\n".join(augmented_contents)
+            # case m if m.get("is_image", False):
+            #     try:
+            #         # Handle data URI format "data:image/png;base64,..."
+            #         content = self.content
+            #         if content.startswith('data:'):
+            #             # Extract the base64 part after the comma
+            #             content = content.split(',', 1)[1]
+                    
+            #         # Now decode the base64 string
+            #         image_bytes = base64.b64decode(content)
+            #         content = Image.open(io.BytesIO(image_bytes))
+            #         return content
+            #     except Exception as e:
+            #         print(f"Error processing image: {str(e)}")
+            #         # Fall back to using the content as text
+            #         return self.content
             case _:
                 return self.content

core/services/document_service.py

@@ -27,7 +27,7 @@ from core.services.rules_processor import RulesProcessor
 from core.embedding.colpali_embedding_model import ColpaliEmbeddingModel
 from core.vector_store.multi_vector_store import MultiVectorStore
 import filetype
-from filetype.types import IMAGE, archive  # , DOCUMENT, document
+from filetype.types import IMAGE  # , DOCUMENT, document
 import pdf2image
 from PIL.Image import Image
 
@@ -363,7 +363,7 @@ class DocumentService:
             case file_type if file_type in IMAGE:
                 return [Chunk(content=file_content_base64, metadata={"is_image": True})]
             case "application/pdf":
-                logger.info(f"Working with PDF file!")
+                logger.info("Working with PDF file!")
                 images = pdf2image.convert_from_bytes(file_content)
                 images_b64 = [self.img_to_base64_str(image) for image in images]
                 return [

core/tests/integration/test_colpali_integrate_multivector.py

@@ -4,7 +4,6 @@ import os
 import logging
 from pathlib import Path
 from pdf2image import convert_from_path
-from PIL import Image
 
 from core.embedding.colpali_embedding_model import ColpaliEmbeddingModel
 from core.vector_store.multi_vector_store import MultiVectorStore

core/tests/unit/test_colpali_embedding.py

@@ -2,7 +2,6 @@ import pytest
 import base64
 import io
 import numpy as np
-import torch
 from PIL import Image
 
 from core.embedding.colpali_embedding_model import ColpaliEmbeddingModel

core/tests/unit/test_multivector.py

@@ -2,8 +2,7 @@ import pytest
 import asyncio
 import torch
 import numpy as np
-import psycopg
+from pgvector.psycopg import Bit
-from pgvector.psycopg import Bit, register_vector
 import logging
 from core.vector_store.multi_vector_store import MultiVectorStore
 from core.models.chunk import DocumentChunk

databridge.toml

@@ -12,7 +12,7 @@ dev_permissions = ["read", "write", "admin"]  # Default dev permissions
 
 [completion]
 provider = "ollama"
-model_name = "llama3.2"
+model_name = "llama3.2-vision"
 default_max_tokens = "1000"
 default_temperature = 0.7
 # base_url = "http://ollama:11434"  # Just use the service name

sdks/python/databridge/async_.py

@@ -7,7 +7,6 @@ from urllib.parse import urlparse
 import httpx
 import jwt
 from PIL.Image import Image as PILImage
-from PIL import Image
 
 from .models import (
     Document,