3 files changed, 108 insertions, 0 deletions
diff --git a/rag/db/__init__.py b/rag/db/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/rag/db/__init__.py
diff --git a/rag/db/documents.py b/rag/db/documents.py
new file mode 100644
index 0000000..bdbf5a4
--- /dev/null
+++ b/rag/db/documents.py
@@ -0,0 +1,85 @@
+import os
+from typing import List
+import hashlib
+import psycopg
+from langchain_core.documents.base import Document
+
+TABLES = """
+CREATE TABLE IF NOT EXISTS chunk (
+        id serial PRIMARY KEY,
+        data text)
+
+CREATE TABLE IF NOT EXISTS document (
+        hash text PRIMARY KEY)
+"""
+
+
+class Documents:
+    def __init__(self) -> None:
+        self.conn = psycopg.connect(
+            f"dbname={os.environ['RAG_DB_NAME']} user={os.environ['RAG_USER']}"
+        )
+        self.__create_content_table()
+
+    def close(self):
+        self.conn.close()
+
+    def __create_content_table(self):
+        with self.conn.cursor() as cur:
+            cur.execute(TABLES)
+            self.conn.commit()
+
+    def __hash(self, chunks: List[Document]) -> str:
+        document = str.encode("".join([chunk.page_content for chunk in chunks]))
+        return hashlib.sha256(document).hexdigest()
+
+    def add_document(self, chunks: List[Document]) -> bool:
+        with self.conn.cursor() as cur:
+            hash = self.__hash(chunks)
+            cur.execute(
+                """
+                        SELECT * FROM document 
+                        WHERE
+                        hash = %s
+                        """,
+                hash,
+            )
+            exist = cur.fetchone()
+            if exist is None:
+                cur.execute(
+                    """
+                            INSERT INTO document 
+                            (hash) VALUES 
+                            (%s)
+                            """,
+                    hash,
+                )
+            self.conn.commit()
+        return exist is not None
+
+
+    def add_chunk(self, data: str):
+        with self.conn.cursor() as cur:
+            cur.execute(
+                """
+                        INSERT INTO chunk 
+                        (data) VALUES 
+                        (%s)
+                        """,
+                data,
+            )
+            self.conn.commit()
+
+    def get_chunk(self, id: int):
+        with self.conn.cursor() as cur:
+            cur.execute(
+                """
+                        SELECT * FROM chunk 
+                        WHERE
+                        id = %s
+                        """,
+                str(id),
+            )
+            chunk = cur.fetchone()
+            self.conn.commit()
+        return chunk
diff --git a/rag/db/vector.py b/rag/db/vector.py
new file mode 100644
index 0000000..f229ba7
--- /dev/null
+++ b/rag/db/vector.py
@@ -0,0 +1,23 @@
+from typing import Tuple
+import faiss
+import numpy as np
+
+# TODO: read from .env
+EMBEDDING_DIM = 1024
+
+
+# TODO: inner product distance metric?
+class VectorStore:
+    def __init__(self):
+        self.index = faiss.IndexFlatL2(EMBEDDING_DIM)
+        # TODO: load from file
+
+    def add(self, embeddings: np.ndarray):
+        # TODO: save to file
+        self.index.add(embeddings)
+
+    def search(
+        self, query: np.ndarray, neighbors: int = 4
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        score, indices = self.index.search(query, neighbors)
+        return score, indices