Add Oracle DB as vector store

application/core/settings.py

@@ -61,6 +61,9 @@ class Settings(BaseSettings):
     QDRANT_PATH: Optional[str] = None
     QDRANT_DISTANCE_FUNC: str = "Cosine"
 
+    # ORACLE_URI="your_username/your_password@localhost:1521/docsgpt"
+    ORACLE_URI: Optional[str] = None  # Oracle DB connection string
+
     BRAVE_SEARCH_API_KEY: Optional[str] = None
 
     FLASK_DEBUG_MODE: bool = False

application/requirements.txt

@@ -16,6 +16,7 @@ javalang==0.13.0
 langchain==0.1.4
 langchain-openai==0.0.5
 openapi3_parser==1.1.16
+oracledb==2.2.1
 pandas==2.2.0
 pydantic_settings==2.1.0
 pymongo==4.6.3

application/vectorstore/oracledb.py

@@ -0,0 +1,101 @@
+# oracle_db.py
+from application.vectorstore.base import BaseVectorStore
+from application.core.settings import settings
+from application.vectorstore.document_class import Document
+
+
+class OracleDBVectorStore(BaseVectorStore):
+    def __init__(
+        self,
+        embeddings_key: str = "embeddings",
+        table: str = "documents",
+        text_key: str = "text",
+        embedding_key: str = "embedding",
+        database: str = "docsgpt",
+    ):
+        self._table = table
+        self._text_key = text_key
+        self._embedding_key = embedding_key
+        self._embeddings_key = embeddings_key
+        self._oracle_uri = settings.ORACLE_URI
+        self._embedding = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
+
+        try:
+            import oracledb
+        except ImportError:
+            raise ImportError(
+                "Could not import oracledb python package. "
+                "Please install it with `pip install oracledb`."
+            )
+
+        self._connection = oracledb.connect(self._oracle_uri)
+        self._cursor = self._connection.cursor()
+
+    def search(self, question, k=2, *args, **kwargs):
+        query_vector = self._embedding.embed_query(question)
+
+        query = f"""
+        SELECT {self._text_key}, {self._embedding_key}, METADATA
+        FROM {self._table}
+        ORDER BY SDO_GEOM.SDO_DISTANCE(SDO_GEOMETRY({query_vector}),
+        SDO_GEOMETRY({self._embedding_key})) ASC
+        FETCH FIRST {k} ROWS ONLY
+        """
+
+        self._cursor.execute(query)
+        results = []
+        for row in self._cursor.fetchall():
+            text, embedding, metadata = row
+            results.append(Document(text, metadata))
+        return results
+
+    def _insert_texts(self, texts, metadatas):
+        if not texts:
+            return []
+
+        embeddings = self._embedding.embed_documents(texts)
+        to_insert = [
+            (t, embedding, m) for t, m, embedding in zip(texts, metadatas, embeddings)
+        ]
+
+        query = f"""
+        INSERT INTO {self._table} ({self._text_key}, {self._embedding_key}, METADATA)
+        VALUES (:1, :2, :3)
+        """
+
+        self._cursor.executemany(query, to_insert)
+        self._connection.commit()
+        return [i[0] for i in self._cursor.fetchall()]
+
+    def add_texts(
+        self,
+        texts,
+        metadatas=None,
+        ids=None,
+        refresh_indices=True,
+        create_index_if_not_exists=True,
+        bulk_kwargs=None,
+        **kwargs,
+    ):
+        batch_size = 100
+        _metadatas = metadatas or ({} for _ in texts)
+        texts_batch = []
+        metadatas_batch = []
+        result_ids = []
+
+        for i, (text, metadata) in enumerate(zip(texts, _metadatas)):
+            texts_batch.append(text)
+            metadatas_batch.append(metadata)
+            if (i + 1) % batch_size == 0:
+                result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
+                texts_batch = []
+                metadatas_batch = []
+
+        if texts_batch:
+            result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
+        return result_ids
+
+    def delete_index(self, *args, **kwargs):
+        query = f"DELETE FROM {self._table} WHERE 1=1"
+        self._cursor.execute(query)
+        self._connection.commit()

application/vectorstore/vector_creator.py

@@ -2,6 +2,7 @@
 from application.vectorstore.elasticsearch import ElasticsearchStore
 from application.vectorstore.mongodb import MongoDBVectorStore
 from application.vectorstore.qdrant import QdrantStore
+from application.vectorstore.oracledb import OracleDBVectorStore
 
 
 class VectorCreator:
@@ -10,6 +11,7 @@ class VectorCreator:
         "elasticsearch": ElasticsearchStore,
         "mongodb": MongoDBVectorStore,
         "qdrant": QdrantStore,
+        "oracledb": OracleDBVectorStore,
     }
 
     @classmethod