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Step 2: ⌨️ Coding

• Modify agents-api/agents_api/models/agent/create_tools.py ✓ aed0768 Edit

Modify agents-api/agents_api/models/agent/create_tools.py with contents:
• At the top of the "create_tools.py" file, add a module-level docstring that briefly describes the module's purpose, e.g., "This module contains functions for creating tools in the CozoDB database."
• Add a docstring to the "create_tools_query" function that explains its purpose, parameters, and return type. For example: """ Constructs a datalog query for inserting tool records into the 'agent_functions' relation in the CozoDB.

Parameters:

• agent_id (UUID): The unique identifier for the agent.
• functions (list[FunctionDef]): A list of function definitions to be inserted.
• embeddings (list[list[float]]): A list of embeddings corresponding to each function.

Returns:

• pd.DataFrame: A DataFrame containing the results of the query execution.
  """
  • Insert an inline comment before the assertion in "create_tools_query" to explain its purpose, e.g., "# Ensure the number of functions matches the number of embeddings."
  • Add a comment above the loop that constructs 'functions_input', explaining its purpose, e.g., "# Construct the input records for the datalog query."
  • Before the datalog query string, insert a comment explaining its purpose, e.g., "# Datalog query for inserting new tool records into the 'agent_functions' relation."
  • Ensure that these comments and docstrings are concise and informative, providing clarity without overwhelming the reader with unnecessary details.

--- 
+++ 
@@ -1,3 +1,5 @@
+"""This module contains functions for creating tools in the CozoDB database."""
+
 from uuid import UUID, uuid4
 
 import pandas as pd
@@ -11,8 +13,21 @@
     functions: list[FunctionDef],
     embeddings: list[list[float]],
 ) -> pd.DataFrame:
+    """
+    Constructs a datalog query for inserting tool records into the 'agent_functions' relation in the CozoDB.
+
+    Parameters:
+    - agent_id (UUID): The unique identifier for the agent.
+    - functions (list[FunctionDef]): A list of function definitions to be inserted.
+    - embeddings (list[list[float]]): A list of embeddings corresponding to each function.
+
+    Returns:
+    - pd.DataFrame: A DataFrame containing the results of the query execution.
+    """
+    # Ensure the number of functions matches the number of embeddings
     assert len(functions) == len(embeddings)
 
+    # Construct the input records for the datalog query
     functions_input: list[list] = []
 
     for function, embedding in zip(functions, embeddings):
@@ -28,6 +43,7 @@
             ]
         )
 
+    # Datalog query for inserting new tool records into the 'agent_functions' relation
     query = """
         input[agent_id, tool_id, name, description, parameters, embedding] <- $records
         ?[agent_id, tool_id, name, description, parameters, embedding, updated_at] :=

• Running GitHub Actions for agents-api/agents_api/models/agent/create_tools.py ✓ Edit

Check agents-api/agents_api/models/agent/create_tools.py with contents:

Ran GitHub Actions for aed0768df4fb109e25a63755613f059e6c3083c1:

Step 3: 🔁 Code Review

I have finished reviewing the code for completeness. I did not find errors for sweep/add_docstrings_and_comments_to_agentsapi.

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