diff --git a/surfsense_backend/app/utils/document_converters.py b/surfsense_backend/app/utils/document_converters.py
index 8cc05af..69ba27c 100644
--- a/surfsense_backend/app/utils/document_converters.py
+++ b/surfsense_backend/app/utils/document_converters.py
@@ -1,10 +1,99 @@
 import hashlib
 
+from litellm import get_model_info, token_counter
+
 from app.config import config
 from app.db import Chunk
 from app.prompts import SUMMARY_PROMPT_TEMPLATE
 
 
+def get_model_context_window(model_name: str) -> int:
+    """Get the total context window size for a model (input + output tokens)."""
+    try:
+        model_info = get_model_info(model_name)
+        context_window = model_info.get("max_input_tokens", 4096)  # Default fallback
+        return context_window
+    except Exception as e:
+        print(
+            f"Warning: Could not get model info for {model_name}, using default 4096 tokens. Error: {e}"
+        )
+        return 4096  # Conservative fallback
+
+
+def optimize_content_for_context_window(
+    content: str, document_metadata: dict | None, model_name: str
+) -> str:
+    """
+    Optimize content length to fit within model context window using binary search.
+
+    Args:
+        content: Original document content
+        document_metadata: Optional metadata dictionary
+        model_name: Model name for token counting
+
+    Returns:
+        Optimized content that fits within context window
+    """
+    if not content:
+        return content
+
+    # Get model context window
+    context_window = get_model_context_window(model_name)
+
+    # Reserve tokens for: system prompt, metadata, template overhead, and output
+    # Conservative estimate: 2000 tokens for prompt + metadata + output buffer
+    # TODO: Calculate Summary System Prompt Token Count Here
+    reserved_tokens = 2000
+
+    # Add metadata token cost if present
+    if document_metadata:
+        metadata_text = (
+            f"<DOCUMENT_METADATA>\n\n{document_metadata}\n\n</DOCUMENT_METADATA>"
+        )
+        metadata_tokens = token_counter(
+            messages=[{"role": "user", "content": metadata_text}], model=model_name
+        )
+        reserved_tokens += metadata_tokens
+
+    available_tokens = context_window - reserved_tokens
+
+    if available_tokens <= 100:  # Minimum viable content
+        print(f"Warning: Very limited tokens available for content: {available_tokens}")
+        return content[:500]  # Fallback to first 500 chars
+
+    # Binary search to find optimal content length
+    left, right = 0, len(content)
+    optimal_length = 0
+
+    while left <= right:
+        mid = (left + right) // 2
+        test_content = content[:mid]
+
+        # Test token count for this content length
+        test_document = f"<DOCUMENT_CONTENT>\n\n{test_content}\n\n</DOCUMENT_CONTENT>"
+        test_tokens = token_counter(
+            messages=[{"role": "user", "content": test_document}], model=model_name
+        )
+
+        if test_tokens <= available_tokens:
+            optimal_length = mid
+            left = mid + 1
+        else:
+            right = mid - 1
+
+    optimized_content = (
+        content[:optimal_length] if optimal_length > 0 else content[:500]
+    )
+
+    if optimal_length < len(content):
+        print(
+            f"Content optimized: {len(content)} -> {optimal_length} chars "
+            f"to fit in {available_tokens} available tokens"
+        )
+
+    return optimized_content
+
+
 async def generate_document_summary(
     content: str,
     user_llm,
@@ -21,8 +110,16 @@ async def generate_document_summary(
     Returns:
         Tuple of (enhanced_summary_content, summary_embedding)
     """
+    # Get model name from user_llm for token counting
+    model_name = getattr(user_llm, "model", "gpt-3.5-turbo")  # Fallback to default
+
+    # Optimize content to fit within context window
+    optimized_content = optimize_content_for_context_window(
+        content, document_metadata, model_name
+    )
+
     summary_chain = SUMMARY_PROMPT_TEMPLATE | user_llm
-    content_with_metadata = f"<DOCUMENT><DOCUMENT_METADATA>\n\n{document_metadata}\n\n</DOCUMENT_METADATA>\n\n<DOCUMENT_CONTENT>\n\n{content}\n\n</DOCUMENT_CONTENT></DOCUMENT>"
+    content_with_metadata = f"<DOCUMENT><DOCUMENT_METADATA>\n\n{document_metadata}\n\n</DOCUMENT_METADATA>\n\n<DOCUMENT_CONTENT>\n\n{optimized_content}\n\n</DOCUMENT_CONTENT></DOCUMENT>"
     summary_result = await summary_chain.ainvoke({"document": content_with_metadata})
     summary_content = summary_result.content