Fixes to database manager
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parent
86555460d2
commit
bf9c80653e
5 changed files with 34 additions and 26 deletions
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@ -41,6 +41,8 @@ RUN apt-get update -q && \
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/var/tmp/*
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WORKDIR /app
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# Set the PYTHONPATH environment variable to include the /app directory
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ENV PYTHONPATH=/app
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COPY cognitive_architecture/ /app/cognitive_architecture
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COPY main.py /app
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@ -36,6 +36,7 @@ class Config:
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db_user: str = os.getenv("DB_USER", "cognee")
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db_password: str = os.getenv("DB_PASSWORD", "cognee")
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sqlalchemy_logging: bool = os.getenv("SQLALCHEMY_LOGGING", True)
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graph_name = os.getenv("GRAPH_NAME", "cognee_graph.pkl")
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# Model parameters
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model: str = "gpt-4-1106-preview"
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@ -1,10 +1,16 @@
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import pickle
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from pathlib import Path
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from cognitive_architecture.config import Config
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import networkx as nx
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config = Config()
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config = config.load()
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class NetworkXGraphDB:
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def __init__(self, filename="cognee_graph.pkl"):
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"""A class to manage a graph database using NetworkX"""
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graph_path = (Path(config.base_path) / config.graph_name).absolute()
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def __init__(self, filename=graph_path):
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self.filename = filename
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try:
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self.graph = self.load_graph() # Attempt to load an existing graph
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@ -12,12 +12,11 @@ parent_dir = os.path.dirname(current_dir)
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# Add the parent directory to sys.path
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sys.path.insert(0, parent_dir)
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# API_ENABLED = os.environ.get("API_ENABLED", "False").lower() == "true"
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environment = os.getenv("AWS_ENV", "dev")
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def fetch_secret(secret_name, region_name, env_file_path):
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def fetch_secret(secret_name:str, region_name:str, env_file_path:str):
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"""Fetch the secret from AWS Secrets Manager and write it to the .env file."""
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print("Initializing session")
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session = boto3.session.Session()
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print("Session initialized")
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@ -28,20 +27,19 @@ def fetch_secret(secret_name, region_name, env_file_path):
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response = client.get_secret_value(SecretId=secret_name)
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except Exception as e:
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print(f"Error retrieving secret: {e}")
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return None
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return f"Error retrieving secret: {e}"
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if "SecretString" in response:
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secret = response["SecretString"]
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else:
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secret = response["SecretBinary"]
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with open(env_file_path, "w") as env_file:
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env_file.write(secret)
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print("Secrets are added to the .env file.")
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if os.path.exists(env_file_path):
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print(f"The .env file is located at: {env_file_path}")
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with open(env_file_path, "w") as env_file:
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env_file.write(secret)
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print("Secrets are added to the .env file.")
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load_dotenv()
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print("The .env file is loaded.")
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else:
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@ -1,3 +1,5 @@
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""" This module contains utility functions for the cognitive architecture. """
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import os
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import random
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import string
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@ -13,7 +15,13 @@ from cognitive_architecture.database.relationaldb.models.user import User
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.future import select
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import logging
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from cognitive_architecture.database.relationaldb.models.operation import Operation
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from cognitive_architecture.database.relationaldb.database_crud import (
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session_scope,
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add_entity,
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update_entity,
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fetch_job_id,
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)
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class Node:
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def __init__(self, id, description, color):
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@ -72,6 +80,7 @@ def get_document_names(doc_input):
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def format_dict(d):
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""" Format a dictionary as a string."""
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# Initialize an empty list to store formatted items
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formatted_items = []
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@ -93,6 +102,7 @@ def format_dict(d):
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def append_uuid_to_variable_names(variable_mapping):
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""" Append a UUID to the variable names to make them unique."""
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unique_variable_mapping = {}
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for original_name in variable_mapping.values():
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unique_name = f"{original_name}_{uuid.uuid4().hex}"
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@ -102,6 +112,7 @@ def append_uuid_to_variable_names(variable_mapping):
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# Update the functions to use the unique variable names
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def create_node_variable_mapping(nodes):
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""" Create a mapping of node identifiers to unique variable names."""
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mapping = {}
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for node in nodes:
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variable_name = f"{node['category']}{node['id']}".lower()
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@ -110,6 +121,7 @@ def create_node_variable_mapping(nodes):
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def create_edge_variable_mapping(edges):
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""" Create a mapping of edge identifiers to unique variable names."""
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mapping = {}
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for edge in edges:
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# Construct a unique identifier for the edge
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@ -124,17 +136,10 @@ def generate_letter_uuid(length=8):
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return "".join(random.choice(letters) for _ in range(length))
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from cognitive_architecture.database.relationaldb.models.operation import Operation
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from cognitive_architecture.database.relationaldb.database_crud import (
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session_scope,
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add_entity,
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update_entity,
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fetch_job_id,
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)
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async def get_vectordb_namespace(session: AsyncSession, user_id: str):
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""" Asynchronously retrieves the latest memory names for a given user."""
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try:
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result = await session.execute(
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select(MemoryModel.memory_name)
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@ -151,6 +156,7 @@ async def get_vectordb_namespace(session: AsyncSession, user_id: str):
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async def get_vectordb_document_name(session: AsyncSession, user_id: str):
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""" Asynchronously retrieves the latest memory names for a given user."""
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try:
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result = await session.execute(
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select(DocsModel.doc_name)
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@ -167,6 +173,7 @@ async def get_vectordb_document_name(session: AsyncSession, user_id: str):
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async def get_model_id_name(session: AsyncSession, id: str):
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""" Asynchronously retrieves the latest memory names for a given user."""
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try:
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result = await session.execute(
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select(MemoryModel.memory_name)
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@ -236,12 +243,6 @@ async def get_unsumarized_vector_db_namespace(session: AsyncSession, user_id: st
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return memory_details, docs
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# except Exception as e:
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# # Handle the exception as needed
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# print(f"An error occurred: {e}")
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# return None
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async def get_memory_name_by_doc_id(session: AsyncSession, docs_id: str):
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"""
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Asynchronously retrieves memory names associated with a specific document ID.
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