From db013e471355cf6d3028f9e43be9823e68798743 Mon Sep 17 00:00:00 2001
From: Adityavardhan Agrawal <aa729@cornell.edu>
Date: Mon, 11 Nov 2024 18:51:52 -0500
Subject: [PATCH] init commit

---
 core.py | 171 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 171 insertions(+)
 create mode 100644 core.py

diff --git a/core.py b/core.py
new file mode 100644
index 0000000..1053d25
--- /dev/null
+++ b/core.py
@@ -0,0 +1,171 @@
+from typing import List, Dict, Any, Optional
+from abc import ABC, abstractmethod
+from datetime import datetime
+import uuid
+
+# Base Classes and Interfaces
+
+class Document:
+    def __init__(self, content: str, metadata: Dict[str, Any], owner_id: str):
+        self.id = str(uuid.uuid4())
+        self.content = content
+        self.metadata = metadata
+        self.owner_id = owner_id
+        self.created_at = datetime.utcnow()
+        self.chunks: List[DocumentChunk] = []
+
+class DocumentChunk:
+    def __init__(self, content: str, embedding: List[float], doc_id: str):
+        self.id = str(uuid.uuid4())
+        self.content = content
+        self.embedding = embedding
+        self.doc_id = doc_id
+
+class BaseParser(ABC):
+    @abstractmethod
+    def parse(self, content: str, metadata: Dict[str, Any]) -> List[str]:
+        """Parse content into chunks"""
+        pass
+
+
+class BasePlanner(ABC):
+    @abstractmethod
+    def plan_retrieval(self, query: str, **kwargs) -> Dict[str, Any]:
+        """Create execution plan for retrieval"""
+        pass
+
+
+class BaseVectorStore(ABC):
+    @abstractmethod
+    def store_embeddings(self, chunks: List[DocumentChunk]) -> bool:
+        """Store document chunks and their embeddings"""
+        pass
+
+    @abstractmethod
+    def query_similar(self, query_embedding: List[float], k: int, owner_id: str) -> List[DocumentChunk]:
+        """Find similar chunks"""
+        pass
+
+# Concrete Implementations
+
+class SimpleParser(BaseParser):
+    def __init__(self, chunk_size: int = 1000, chunk_overlap: int = 200):
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+
+    def parse(self, content: str, metadata: Dict[str, Any]) -> List[str]:
+        # Simple implementation - split by chunk_size
+        chunks = []
+        for i in range(0, len(content), self.chunk_size - self.chunk_overlap):
+            chunk = content[i:i + self.chunk_size]
+            if chunk:
+                chunks.append(chunk)
+        return chunks
+
+class SimpleRAGPlanner(BasePlanner):
+    def __init__(self, k: int = 3):
+        self.k = k
+
+    def plan_retrieval(self, query: str, **kwargs) -> Dict[str, Any]:
+        return {
+            "strategy": "simple_rag",
+            "k": kwargs.get("k", self.k),
+            "query": query
+        }
+
+# Main DataBridge Class
+
+class DataBridge:
+    def __init__(
+        self,
+        parser: BaseParser,
+        planner: BasePlanner,
+        vector_store: BaseVectorStore,
+        embedding_model: Any  # This would be your chosen embedding model
+    ):
+        self.parser = parser
+        self.planner = planner
+        self.vector_store = vector_store
+        self.embedding_model = embedding_model
+
+    async def ingest_document(
+        self,
+        content: str,
+        metadata: Dict[str, Any],
+        owner_id: str
+    ) -> Document:
+        # Create document
+        doc = Document(content, metadata, owner_id)
+
+        # Parse into chunks
+        chunk_texts = self.parser.parse(content, metadata)
+
+        # Create embeddings and chunks
+        for chunk_text in chunk_texts:
+            embedding = await self.embedding_model.embed(chunk_text)
+            chunk = DocumentChunk(chunk_text, embedding, doc.id)
+            doc.chunks.append(chunk)
+
+        # Store in vector store
+        success = self.vector_store.store_embeddings(doc.chunks)
+        if not success:
+            raise Exception("Failed to store embeddings")
+
+        return doc
+
+    async def query(
+        self,
+        query: str,
+        owner_id: str,
+        **kwargs
+    ) -> List[Dict[str, Any]]:
+        # Create plan
+        plan = self.planner.plan_retrieval(query, **kwargs)
+
+        # Get query embedding
+        query_embedding = await self.embedding_model.embed(query)
+
+        # Execute plan
+        chunks = self.vector_store.query_similar(
+            query_embedding,
+            k=plan["k"],
+            owner_id=owner_id
+        )
+
+        # Format results
+        results = []
+        for chunk in chunks:
+            results.append({
+                "content": chunk.content,
+                "doc_id": chunk.doc_id,
+                "chunk_id": chunk.id,
+                "score": chunk.score if hasattr(chunk, "score") else None
+            })
+
+        return results
+
+# Example usage
+"""
+# Initialize components
+parser = SimpleParser()
+planner = SimpleRAGPlanner()
+vector_store = YourVectorStore()  # Implement with chosen backend
+embedding_model = YourEmbeddingModel()  # Implement with chosen model
+
+# Create DataBridge instance
+db = DataBridge(parser, planner, vector_store, embedding_model)
+
+# Ingest a document
+doc = await db.ingest_document(
+    content="Your document content here",
+    metadata={"source": "pdf", "title": "Example Doc"},
+    owner_id="user123"
+)
+
+# Query the system
+results = await db.query(
+    query="Your query here",
+    owner_id="user123",
+    k=5  # optional override
+)
+"""
\ No newline at end of file