Fixes to database manager

cognitive_architecture/config.py

@@ -72,6 +72,7 @@ class Config:
         or os.getenv("AWS_ENV") == "dev"
         or os.getenv("AWS_ENV") == "prd"
     ):
+        db_type = 'postgresql'
         db_host: str = os.getenv("POSTGRES_PROD_HOST")
         db_user: str = os.getenv("POSTGRES_USER")
         db_password: str = os.getenv("POSTGRES_PASSWORD")

cognitive_architecture/llm/queries.py

@@ -1,38 +1,38 @@
+""" This module contains the functions that are used to query the language model. """
 import os
-
-from ..shared.data_models import Node, Edge, KnowledgeGraph, GraphQLQuery, MemorySummary
-from ..config import Config
 import instructor
 from openai import OpenAI
+import logging
+from ..shared.data_models import  KnowledgeGraph,  MemorySummary
+from ..config import Config
+
+
 
 config = Config()
 config.load()
 
-print(config.model)
-print(config.openai_key)
-
 OPENAI_API_KEY = config.openai_key
 
 aclient = instructor.patch(OpenAI())
 
-import logging
-
 
 # Function to read query prompts from files
 def read_query_prompt(filename):
+    """Read a query prompt from a file."""
     try:
         with open(filename, "r") as file:
             return file.read()
     except FileNotFoundError:
-        logging.info(f"Error: File not found. Attempted to read: {filename}")
-        logging.info(f"Current working directory: {os.getcwd()}")
+        logging.info(f"Error: File not found. Attempted to read: %s {filename}")
+        logging.info(f"Current working directory: %s {os.getcwd()}")
         return None
     except Exception as e:
-        logging.info(f"An error occurred: {e}")
+        logging.info(f"An error occurred: %s {e}")
         return None
 
 
 def generate_graph(input) -> KnowledgeGraph:
+    """Generate a knowledge graph from a user query."""
     model = "gpt-4-1106-preview"
     user_prompt = f"Use the given format to extract information from the following input: {input}."
     system_prompt = read_query_prompt(
@@ -57,20 +57,26 @@ def generate_graph(input) -> KnowledgeGraph:
 
 
 async def generate_summary(input) -> MemorySummary:
+    """Generate a summary from a user query."""
     out = aclient.chat.completions.create(
         model=config.model,
         messages=[
             {
                 "role": "user",
-                "content": f"""Use the given format summarize and reduce the following input: {input}. """,
+                "content": f"""Use the given format summarize 
+                and reduce the following input: {input}. """,
             },
             {
                 "role": "system",
                 "content": """You are a top-tier algorithm
-                designed for summarizing existing knowledge graphs in structured formats based on a knowledge graph.
+                designed for summarizing existing knowledge 
+                graphs in structured formats based on a knowledge graph.
                 ## 1. Strict Compliance
-                Adhere to the rules strictly. Non-compliance will result in termination.
-                ## 2. Don't forget your main goal is to reduce the number of nodes in the knowledge graph while preserving the information contained in it.""",
+                Adhere to the rules strictly. 
+                Non-compliance will result in termination.
+                ## 2. Don't forget your main goal 
+                is to reduce the number of nodes in the knowledge graph 
+                while preserving the information contained in it.""",
             },
         ],
         response_model=MemorySummary,
@@ -79,6 +85,7 @@ async def generate_summary(input) -> MemorySummary:
 
 
 def user_query_to_edges_and_nodes(input: str) -> KnowledgeGraph:
+    """Generate a knowledge graph from a user query."""
     system_prompt = read_query_prompt(
         "cognitive_architecture/llm/prompts/generate_graph_prompt.txt"
     )
@@ -87,7 +94,8 @@ def user_query_to_edges_and_nodes(input: str) -> KnowledgeGraph:
         messages=[
             {
                 "role": "user",
-                "content": f"""Use the given format to extract information from the following input: {input}. """,
+                "content": f"""Use the given format to
+                 extract information from the following input: {input}. """,
             },
             {"role": "system", "content": system_prompt},
         ],

cognitive_architecture/openai_tools.py

@@ -1,3 +1,4 @@
+"""Tools for interacting with OpenAI's GPT-3, GPT-4 API"""
 import asyncio
 import random
 import os

cognitive_architecture/shared/data_models.py

@@ -1,9 +1,10 @@
+"""Data models for the cognitive architecture."""
 from typing import Optional, List
-
 from pydantic import BaseModel, Field
 
 
 class Node(BaseModel):
+    """Node in a knowledge graph."""
     id: int
     description: str
     category: str
@@ -14,6 +15,7 @@ class Node(BaseModel):
 
 
 class Edge(BaseModel):
+    """Edge in a knowledge graph."""
     source: int
     target: int
     description: str
@@ -23,14 +25,17 @@ class Edge(BaseModel):
 
 
 class KnowledgeGraph(BaseModel):
+    """Knowledge graph."""
     nodes: List[Node] = Field(..., default_factory=list)
     edges: List[Edge] = Field(..., default_factory=list)
 
 
 class GraphQLQuery(BaseModel):
+    """GraphQL query."""
     query: str
 
 
 class MemorySummary(BaseModel):
+    """ Memory summary. """
     nodes: List[Node] = Field(..., default_factory=list)
     edges: List[Edge] = Field(..., default_factory=list)

cognitive_architecture/shared/language_processing.py

@@ -1,9 +1,10 @@
+""" This module provides language processing functions for language detection and translation. """
+import logging
 import boto3
 from botocore.exceptions import BotoCoreError, ClientError
 from langdetect import detect, LangDetectException
 import iso639
 
-import logging
 
 # Basic configuration of the logging system
 logging.basicConfig(
@@ -30,7 +31,7 @@ def detect_language(text):
     try:
         # Detect the language using langdetect
         detected_lang_iso639_1 = detect(trimmed_text)
-        logging.info(f"Detected ISO 639-1 code: {detected_lang_iso639_1}")
+        logging.info(f"Detected ISO 639-1 code: %s {detected_lang_iso639_1}")
 
         # Special case: map 'hr' (Croatian) to 'sr' (Serbian ISO 639-2)
         if detected_lang_iso639_1 == "hr":
@@ -38,9 +39,9 @@ def detect_language(text):
         return detected_lang_iso639_1
 
     except LangDetectException as e:
-        logging.error(f"Language detection error: {e}")
+        logging.error(f"Language detection error: %s  {e}")
     except Exception as e:
-        logging.error(f"Unexpected error: {e}")
+        logging.error(f"Unexpected error: %s  {e}")
 
     return -1
 
@@ -57,8 +58,10 @@ def translate_text(
 
     Parameters:
     text (str): The text to be translated.
-    source_language (str): The source language code (e.g., 'sr' for Serbian). ISO 639-2 Code https://www.loc.gov/standards/iso639-2/php/code_list.php
-    target_language (str): The target language code (e.g., 'en' for English). ISO 639-2 Code https://www.loc.gov/standards/iso639-2/php/code_list.php
+    source_language (str): The source language code (e.g., 'sr' for Serbian).
+     ISO 639-2 Code https://www.loc.gov/standards/iso639-2/php/code_list.php
+    target_language (str): The target language code (e.g., 'en' for English).
+    ISO 639-2 Code https://www.loc.gov/standards/iso639-2/php/code_list.php
     region_name (str): AWS region name.
 
     Returns:
@@ -82,20 +85,9 @@ def translate_text(
         return result.get("TranslatedText", "No translation found.")
 
     except BotoCoreError as e:
-        logging.info(f"BotoCoreError occurred: {e}")
+        logging.info(f"BotoCoreError occurred: %s  {e}")
         return "Error with AWS Translate service configuration or request."
 
     except ClientError as e:
-        logging.info(f"ClientError occurred: {e}")
+        logging.info(f"ClientError occurred: %s  {e}")
         return "Error with AWS client or network issue."
-
-
-source_language = "sr"
-target_language = "en"
-text_to_translate = "Ja volim da pecam i idem na reku da šetam pored nje ponekad"
-
-translated_text = translate_text(text_to_translate, source_language, target_language)
-print(translated_text)
-
-
-# print(detect_language("Koliko krava ide u setnju?"))