🚀 Here's the PR! #132

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Step 1: 🔎 Searching

I found the following snippets in your repository. I will now analyze these snippets and come up with a plan.

Some code snippets I think are relevant in decreasing order of relevance (click to expand). If some file is missing from here, you can mention the path in the ticket description.

https://github.com/julep-ai/infuse/blob/4199c15cc6ce4a0b6559f883ab893f816fde007c/agents-api/agents_api/models/entry/proc_mem_context.py#L1-L297

Step 2: ⌨️ Coding

[X] Modify agents-api/agents_api/models/entry/proc_mem_context.py ✓ https://github.com/julep-ai/infuse/commit/f5788f318e8e07b83a405a34eb54493993c10c73 Edit
Modify agents-api/agents_api/models/entry/proc_mem_context.py with contents:
• At the beginning of the `proc_mem_context_query` function, add a docstring that explains: - The function's purpose: "Executes a complex query to retrieve memory context based on session ID, tool and document embeddings." - Parameters: session_id (UUID), tool_query_embedding (list[float]), doc_query_embedding (list[float]), tools_confidence (float), docs_confidence (float), k_tools (int), k_docs (int), client (CozoClient). - Return type: A pandas DataFrame containing the query results.
• Before the query definition (line 25), add a comment: "# Define the datalog query to collect memory context."
• Before the section starting at line 52, add a comment: "# Collect situation details based on session ID."
• Before the section starting at line 115, add a comment: "# Collect tool information based on agent ID and tool query embedding."
• Before the section starting at line 154, add a comment: "# Collect document information based on agent ID and document query embedding."
• Before the section starting at line 216, add a comment: "# Collect all entries related to the session."
• Before the section starting at line 246, add a comment: "# Combine all collected data into a structured format."
• Ensure that all added comments and the docstring are concise and do not alter the functionality of the code.

--- 
+++ 
@@ -16,6 +16,21 @@
     k_docs: int = 2,
     client: CozoClient = client,
 ) -> pd.DataFrame:
+    """Executes a complex query to retrieve memory context based on session ID, tool and document embeddings.
+
+    Parameters:
+        session_id (UUID),
+        tool_query_embedding (list[float]),
+        doc_query_embedding (list[float]),
+        tools_confidence (float),
+        docs_confidence (float),
+        k_tools (int),
+        k_docs (int),
+        client (CozoClient).
+
+    Return type:
+        A pandas DataFrame containing the query results.
+    """
     VECTOR_SIZE = 768
     session_id = str(session_id)
     assert len(tool_query_embedding) == len(doc_query_embedding) == VECTOR_SIZE
@@ -23,6 +38,7 @@
     tools_radius: float = 1.0 - tools_confidence
     docs_radius: float = 1.0 - docs_confidence

+    # Define the datalog query to collect memory context.
     query = f"""
     {{
         # Input table for the query
@@ -49,6 +65,7 @@
             doc_query: ,
         }}
     }} {{
+        # Collect situation details based on session ID.
         # Collect situation
         ?[role, name, content, token_count, created_at, index] :=
             *_input{{session_id}},
@@ -110,6 +127,7 @@
             index: Float,
         }}
     }} {{
+        # Collect tool information based on agent ID and tool query embedding.
         # Collect all tools

         # Search for tools
@@ -150,6 +168,7 @@
             index: Float,
         }}
     }} {{
+        # Collect document information based on agent ID and document query embedding.
         # Collect docs

         # Search for agent docs
@@ -214,6 +233,7 @@
             index: Float,
         }}
     }} {{
+        # Collect all entries related to the session.
         # Collect all entries
         ?[role, name, content, token_count, created_at, index] :=
             *_input{{session_id}},
@@ -244,6 +264,7 @@
             index: Float,
         }}
     }} {{
+        # Combine all collected data into a structured format.
         # Combine all
         ?[role, name, content, token_count, created_at, index] :=
             *_preamble{{

[X] Running GitHub Actions for agents-api/agents_api/models/entry/proc_mem_context.py ✓ Edit
Check agents-api/agents_api/models/entry/proc_mem_context.py with contents:

Ran GitHub Actions for f5788f318e8e07b83a405a34eb54493993c10c73:

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julep-ai / julep

Sweep: Add docstrings and comments to agents-api/agents_api/models/entry/proc_mem_context.py #126

🚀 Here's the PR! #132

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Step 2: ⌨️ Coding

Step 3: 🔁 Code Review