Merge pull request #397 from webcoderz/main

adding pgvector memory module

examples/pg-memory.py

@@ -0,0 +1,47 @@
+import controlflow as cf
+from controlflow.memory.memory import Memory
+from controlflow.memory.providers.postgres import PostgresMemory
+
+provider = PostgresMemory(
+    database_url="postgresql://postgres:postgres@localhost:5432/your_database",
+    # embedding_dimension=1536,
+    # embedding_fn=OpenAIEmbeddings(),
+    table_name="vector_db",
+)
+# Create a memory module for user preferences
+user_preferences = cf.Memory(
+    key="user_preferences",
+    instructions="Store and retrieve user preferences.",
+    provider=provider,
+)
+
+# Create an agent with access to the memory
+agent = cf.Agent(memories=[user_preferences])
+
+
+# Create a flow to ask for the user's favorite color
+@cf.flow
+def remember_color():
+    return cf.run(
+        "Ask the user for their favorite color and store it in memory",
+        agents=[agent],
+        interactive=True,
+    )
+
+
+# Create a flow to recall the user's favorite color
+@cf.flow
+def recall_color():
+    return cf.run(
+        "What is the user's favorite color?",
+        agents=[agent],
+    )
+
+
+if __name__ == "__main__":
+    print("First flow:")
+    remember_color()
+
+    print("\nSecond flow:")
+    result = recall_color()
+    print(result)

src/controlflow/memory/memory.py

@@ -166,4 +166,16 @@ def get_memory_provider(provider: str) -> MemoryProvider:
 
         return lance_providers.LanceMemory()
 
+    # --- Postgres ---
+    elif provider.startswith("postgres"):
+        try:
+            import sqlalchemy
+        except ImportError:
+            raise ImportError(
+                "To use Postgres as a memory provider, please install the `sqlalchemy` package."
+            )
+
+        import controlflow.memory.providers.postgres as postgres_providers
+
+        return postgres_providers.PostgresMemory()
     raise ValueError(f'Memory provider "{provider}" could not be loaded from a string.')

src/controlflow/memory/providers/postgres.py

@@ -0,0 +1,197 @@
+import uuid
+from typing import Callable, Dict, Optional
+
+import sqlalchemy
+from pgvector.sqlalchemy import Vector
+from pydantic import Field
+from sqlalchemy import Column, String, select, text
+from sqlalchemy.dialects.postgresql import ARRAY
+from sqlalchemy.exc import ProgrammingError
+from sqlalchemy.orm import Session, declarative_base, sessionmaker
+from sqlalchemy_utils import create_database, database_exists
+
+import controlflow
+from controlflow.memory.memory import MemoryProvider
+
+try:
+    # For embeddings, we can use langchain_openai or any other library:
+    from langchain_openai import OpenAIEmbeddings
+except ImportError:
+    raise ImportError(
+        "To use an embedding function similar to LanceDB's default, "
+        "please install lancedb with: pip install lancedb"
+    )
+
+# SQLAlchemy base class for declarative models
+Base = declarative_base()
+
+
+class SQLMemoryTable(Base):
+    """
+    A simple declarative model that represents a memory record.
+
+    We’ll dynamically set the __tablename__ at runtime.
+    """
+
+    __abstract__ = True
+    id = Column(String, primary_key=True)
+    text = Column(String)
+    # Use pgvector for storing embeddings in a Postgres Vector column
+    # vector = Column(Vector(dim=1536))  # Adjust dimension to match your embedding model
+
+
+class PostgresMemory(MemoryProvider):
+    """
+    A ControlFlow MemoryProvider that stores text + embeddings in PostgreSQL
+    using SQLAlchemy and pg_vector. Each Memory module gets its own table.
+    """
+
+    # Default database URL. You can point this to your actual Postgres instance.
+    # Requires the pgvector extension installed and the sqlalchemy-pgvector package.
+    database_url: str = Field(
+        default="postgresql://user:password@localhost:5432/your_database",
+        description="SQLAlchemy-compatible database URL to a Postgres instance with pgvector.",
+    )
+    table_name: str = Field(
+        "memory_{key}",
+        description="""
+            Name of the table to store this memory partition. "{key}" will be replaced 
+            by the memory’s key attribute.
+        """,
+    )
+
+    embedding_dimension: int = Field(
+        default=1536,
+        description="Dimension of the embedding vectors. Match your model's output.",
+    )
+
+    embedding_fn: Callable = Field(
+        default_factory=lambda: OpenAIEmbeddings(
+            model="text-embedding-ada-002",
+        ),
+        description="A function that turns a string into a vector.",
+    )
+
+    # Internal: keep a cached Session maker
+    _SessionLocal: Optional[sessionmaker] = None
+
+    # This dict will map "table_name" -> "model class"
+    _table_class_cache: Dict[str, Base] = {}
+
+    def configure(self, memory_key: str) -> None:
+        """
+        Configure a SQLAlchemy session and ensure the table for this
+        memory partition is created if it does not already exist.
+        """
+        engine = sqlalchemy.create_engine(self.database_url)
+
+        # 2) If DB doesn't exist, create it!
+        if not database_exists(engine.url):
+            create_database(engine.url)
+
+        with engine.connect() as conn:
+            conn.execute(text("CREATE EXTENSION IF NOT EXISTS vector"))
+            conn.commit()
+
+        self._SessionLocal = sessionmaker(bind=engine)
+
+        # Dynamically create a specialized table model for this memory_key
+        table_name = self.table_name.format(key=memory_key)
+
+        # 1) Check if table already in metadata
+        if table_name not in Base.metadata.tables:
+            # 2) Create the dynamic class + table
+            memory_model = type(
+                f"SQLMemoryTable_{memory_key}",
+                (SQLMemoryTable,),
+                {
+                    "__tablename__": table_name,
+                    "vector": Column(Vector(dim=self.embedding_dimension)),
+                },
+            )
+
+            try:
+                Base.metadata.create_all(engine, tables=[memory_model.__table__])
+                # Store it in the cache
+                self._table_class_cache[table_name] = memory_model
+            except ProgrammingError as e:
+                raise RuntimeError(f"Failed to create table {table_name}: {e}")
+
+    def _get_session(self) -> Session:
+        if not self._SessionLocal:
+            raise RuntimeError(
+                "Session is not initialized. Make sure to call configure() first."
+            )
+        return self._SessionLocal()
+
+    def _get_table(self, memory_key: str) -> Base:
+        """
+        Return a dynamically generated declarative model class
+        mapped to the memory_{key} table. Each memory partition
+        has a separate table.
+        """
+        table_name = self.table_name.format(key=memory_key)
+
+        # Return the cached class if already built
+        if table_name in self._table_class_cache:
+            return self._table_class_cache[table_name]
+
+        # If for some reason it's not there, create it now (or raise error):
+        memory_model = type(
+            f"SQLMemoryTable_{memory_key}",
+            (SQLMemoryTable,),
+            {
+                "__tablename__": table_name,
+                "vector": Column(Vector(dim=self.embedding_dimension)),
+            },
+        )
+        self._table_class_cache[table_name] = memory_model
+        return memory_model
+
+    def add(self, memory_key: str, content: str) -> str:
+        """
+        Insert a new memory record into the Postgres table,
+        generating an embedding and storing it in a vector column.
+        Returns the memory’s ID (uuid).
+        """
+        memory_id = str(uuid.uuid4())
+        model_cls = self._get_table(memory_key)
+
+        # Generate an embedding for the content
+        embedding = self.embedding_fn.embed_query(content)
+
+        with self._get_session() as session:
+            record = model_cls(id=memory_id, text=content, vector=embedding)
+            session.add(record)
+            session.commit()
+
+        return memory_id
+
+    def delete(self, memory_key: str, memory_id: str) -> None:
+        """
+        Delete a memory record by its UUID.
+        """
+        model_cls = self._get_table(memory_key)
+
+        with self._get_session() as session:
+            session.query(model_cls).filter(model_cls.id == memory_id).delete()
+            session.commit()
+
+    def search(self, memory_key: str, query: str, n: int = 20) -> Dict[str, str]:
+        """
+        Uses pgvector’s approximate nearest neighbor search with the `<->` operator to find
+        the top N matching records for the embedded query. Returns a dict of {id: text}.
+        """
+        model_cls = self._get_table(memory_key)
+        # Generate embedding for the query
+        query_embedding = self.embedding_fn.embed_query(query)
+        embedding_col = model_cls.vector
+
+        with self._get_session() as session:
+            results = session.execute(
+                select(model_cls.id, model_cls.text)
+                .order_by(embedding_col.l2_distance(query_embedding))
+                .limit(n)
+            ).all()
+
+        return {row.id: row.text for row in results}