Lossless Data Processing Analysis
1. What Must Be Preserved
Document Integrity
-
Original Content
- Every character must be preserved exactly as input
- Whitespace and formatting in code must be maintained
- Comments and documentation strings must be kept
- Line breaks and indentation must remain unchanged
-
Metadata Relationships
- Complete UUIDs must be maintained for absolute uniqueness
- All chunk relationships must be preserved
- Document lineage must be traceable
- Sequence information must be complete
Context
The current situation requires a decision because:
- Requirement 1
- Constraint 2
- Need 3
Status
Accepted | YYYY-MM-DD
2. Safe vs. Unsafe Compression
UNSAFE (Cannot Use):
# UNSAFE: Loses formatting and comments
def compress_code(content: str) -> str:
"""This type of compression loses information"""
# Remove comments
content = re.sub(r'\s*#.*$', '', content)
# Normalize whitespace
content = re.sub(r'\s+', ' ', content)
return content
SAFE (Can Use):
class LosslessMetadataHandler:
def __init__(self):
self.full_metadata_store = {} # Complete metadata storage
self.reference_map = {} # UUID reference mapping
def store_metadata(self, metadata: Dict) -> Dict:
"""Store complete metadata and return a reference structure"""
chunk_uuid = metadata["chunk_uuid"]
# Store complete metadata
self.full_metadata_store[chunk_uuid] = copy.deepcopy(metadata)
# Create reference structure (no data loss)
reference = {
"ref_id": chunk_uuid, # Full UUID preserved
"sequence": metadata["chunk_sequence"],
"type": "metadata_reference" # Explicit reference marker
}
self.reference_map[chunk_uuid] = reference
return reference
def retrieve_metadata(self, ref_id: str) -> Dict:
"""Retrieve complete metadata using reference"""
return copy.deepcopy(self.full_metadata_store.get(ref_id))
3. Safe Optimization Approaches
1. Reference-Based System
class LosslessDocumentProcessor:
def __init__(self):
self.metadata_handler = LosslessMetadataHandler()
self.content_store = {}
def process_document(self, content: str, doc_id: str) -> Dict:
"""Process document while preserving all information"""
# Store complete original content
self.content_store[doc_id] = content
# Create chunks without modifying content
chunks = self._create_chunks(content)
# Create reference structure
reference_structure = {
"doc_id": doc_id,
"total_chunks": len(chunks),
"chunks": []
}
for chunk in chunks:
# Store complete chunk data
chunk_ref = self._store_chunk(chunk)
reference_structure["chunks"].append(chunk_ref)
return reference_structure
def _store_chunk(self, chunk: Dict) -> Dict:
"""Store complete chunk data and return reference"""
chunk_id = chunk["metadata"]["chunk_uuid"]
self.content_store[chunk_id] = chunk["content"]
return {
"chunk_id": chunk_id,
"metadata_ref": self.metadata_handler.store_metadata(chunk["metadata"])
}
2. Token-Efficient API Calls
class LosslessAPIHandler:
def __init__(self, metadata_handler: LosslessMetadataHandler):
self.metadata_handler = metadata_handler
async def process_chunk(self, chunk_ref: Dict, prompt: str) -> Dict:
"""Make API calls with complete data but efficient structure"""
# Retrieve complete metadata
full_metadata = self.metadata_handler.retrieve_metadata(chunk_ref["metadata_ref"]["ref_id"])
# Structure API call efficiently without data loss
api_payload = {
"content": chunk_ref["content"],
"context": {
"doc_id": full_metadata["doc_uuid"],
"chunk_id": full_metadata["chunk_uuid"],
"sequence": full_metadata["chunk_sequence"],
"relationships": {
"previous": full_metadata["previous_uuid"],
"next": full_metadata["next_uuid"]
}
}
}
return await self._make_api_call(api_payload, prompt)
4. Database Integration for Complete Storage
1. Metadata Storage
CREATE TABLE document_metadata (
doc_uuid VARCHAR(36) PRIMARY KEY,
created_at TIMESTAMP,
total_chunks INTEGER,
status VARCHAR(50)
);
CREATE TABLE chunk_metadata (
chunk_uuid VARCHAR(36) PRIMARY KEY,
doc_uuid VARCHAR(36),
sequence_num INTEGER,
previous_uuid VARCHAR(36),
next_uuid VARCHAR(36),
FOREIGN KEY (doc_uuid) REFERENCES document_metadata(doc_uuid),
FOREIGN KEY (previous_uuid) REFERENCES chunk_metadata(chunk_uuid),
FOREIGN KEY (next_uuid) REFERENCES chunk_metadata(chunk_uuid)
);
2. Content Storage
class LosslessContentStore:
def __init__(self, db_connection):
self.db = db_connection
def store_chunk(self, chunk: Dict):
"""Store complete chunk content and metadata"""
with self.db.transaction():
# Store complete content
self.db.execute("""
INSERT INTO chunk_content (chunk_uuid, content, original_size)
VALUES (?, ?, ?)
""", (chunk["uuid"], chunk["content"], len(chunk["content"])))
# Store complete metadata
self.db.execute("""
INSERT INTO chunk_metadata
(chunk_uuid, doc_uuid, sequence_num, previous_uuid, next_uuid)
VALUES (?, ?, ?, ?, ?)
""", (chunk["uuid"], chunk["doc_uuid"], chunk["sequence"],
chunk["previous_uuid"], chunk["next_uuid"]))
5. Token Optimization Without Loss
1. Efficient Structure for API Calls
class EfficientAPIFormatter:
"""Format data efficiently for API calls without losing information"""
@staticmethod
def format_for_api(chunk: Dict, metadata: Dict) -> Dict:
"""Structure data efficiently for API while preserving all information"""
return {
"data": {
"content": chunk["content"], # Complete content preserved
"context": {
"id": metadata["chunk_uuid"],
"seq": metadata["chunk_sequence"],
"doc": metadata["doc_uuid"],
}
},
"relationships": {
"prev": metadata["previous_uuid"],
"next": metadata["next_uuid"]
}
}
6. Implementation Recommendations
-
Use Complete Storage with Efficient References
- Store all data completely
- Use references for API calls
- Maintain full relationship graph
-
Implement Verification Systems
class DataVerifier:
@staticmethod
def verify_chunk_integrity(original: Dict, processed: Dict) -> bool:
"""Verify no data loss occurred during processing"""
return (
original["content"] == processed["content"] and
original["metadata"]["chunk_uuid"] == processed["metadata"]["chunk_uuid"] and
original["metadata"]["sequence"] == processed["metadata"]["sequence"]
)
- Database-Backed Processing Pipeline
class LosslessProcessor:
def __init__(self, db_connection):
self.db = db_connection
self.verifier = DataVerifier()
async def process_document(self, document: str) -> Dict:
"""Process document with complete data preservation"""
# Store complete document
doc_id = str(uuid.uuid4())
self.store_document(doc_id, document)
# Process chunks
chunks = self.create_chunks(document)
chunk_refs = []
for chunk in chunks:
# Store complete chunk
stored_chunk = self.store_chunk(chunk)
# Verify storage
assert self.verifier.verify_chunk_integrity(chunk, stored_chunk)
# Create efficient reference
chunk_refs.append(self.create_chunk_reference(stored_chunk))
return {
"doc_id": doc_id,
"chunks": chunk_refs
}
The key insight is that we can maintain complete information while still being efficient in our API calls by:
- Storing everything completely in our system
- Using efficient references for API interactions
- Maintaining ability to reconstruct complete data
- Verifying data integrity at each step
Would you like me to focus on implementing any particular part of this lossless system?