fix(context): resolve lint, typecheck, and import errors in semantic context search PR

- Fix ruff lint errors in embedding_provider.py (Sequence import, zip strict)
- Fix ruff lint errors in semantic_context_search_steps.py (import ordering, unused vars, whitespace)
- Fix ContextFragment creation in steps to include required provenance field
- Create missing plugin.py CLI module referenced in main.py
- Add plugin command to valid_cmds list in main.py

features/steps/semantic_context_search_steps.py

@@ -2,15 +2,17 @@
 
 from __future__ import annotations
 
-from behave import given, when, then
+from behave import given, then, when
+
 from cleveragents.application.services.embedding_provider import (
     MockEmbeddingProvider,
     SimpleWordEmbeddingProvider,
     cosine_similarity,
 )
+from cleveragents.domain.models.acms.crp import FragmentProvenance
 from cleveragents.domain.models.core.context_fragment import (
-    ContextFragment,
     ContextBudget,
+    ContextFragment,
 )
 
 
@@ -36,7 +38,9 @@ def step_embed_text(context, text):
 @then("the embedding should have {dim:d} dimensions")
 def step_check_embedding_dimension(context, dim):
     """Verify embedding dimension."""
-    assert len(context.embedding) == dim, f"Expected {dim} dimensions, got {len(context.embedding)}"
+    assert len(context.embedding) == dim, (
+        f"Expected {dim} dimensions, got {len(context.embedding)}"
+    )
 
 
 @then("the embedding should be a valid vector")
@@ -63,7 +67,9 @@ def step_compute_similarity(context):
 @then("the similarity should be between -1 and 1")
 def step_check_similarity_range(context):
     """Verify similarity is in valid range."""
-    assert -1 <= context.similarity <= 1, f"Similarity {context.similarity} out of range"
+    assert -1 <= context.similarity <= 1, (
+        f"Similarity {context.similarity} out of range"
+    )
 
 
 @given("I have context fragments with content:")
@@ -73,10 +79,12 @@ def step_have_fragments_with_content(context):
     for row in context.table:
         content = row["content"]
         embedding = context.embedding_provider.embed(content)
-        context.fragments.append({
-            "content": content,
-            "embedding": embedding,
-        })
+        context.fragments.append(
+            {
+                "content": content,
+                "embedding": embedding,
+            }
+        )
 
 
 @given("I have a query {query}")
@@ -93,7 +101,7 @@ def step_rank_fragments(context):
     for frag in context.fragments:
         sim = cosine_similarity(context.query_embedding, frag["embedding"])
         similarities.append((frag, sim))
-    
+
     similarities.sort(key=lambda x: x[1], reverse=True)
     context.ranked_fragments = similarities
 
@@ -102,14 +110,12 @@ def step_rank_fragments(context):
 def step_check_python_ranking(context):
     """Verify Python fragments rank higher."""
     python_sims = [
-        sim for frag, sim in context.ranked_fragments
-        if "Python" in frag["content"]
+        sim for frag, sim in context.ranked_fragments if "Python" in frag["content"]
     ]
     js_sims = [
-        sim for frag, sim in context.ranked_fragments
-        if "JavaScript" in frag["content"]
+        sim for frag, sim in context.ranked_fragments if "JavaScript" in frag["content"]
     ]
-    
+
     if python_sims and js_sims:
         assert min(python_sims) >= max(js_sims), "Python fragments should rank higher"
 
@@ -124,7 +130,8 @@ def step_have_threshold(context, threshold):
 def step_filter_by_threshold(context):
     """Filter fragments by threshold."""
     context.filtered_fragments = [
-        (frag, sim) for frag, sim in context.ranked_fragments
+        (frag, sim)
+        for frag, sim in context.ranked_fragments
         if sim >= context.threshold
     ]
 
@@ -132,8 +139,10 @@ def step_filter_by_threshold(context):
 @then("only semantically similar fragments should be included")
 def step_check_filtered_fragments(context):
     """Verify filtered fragments meet threshold."""
-    for frag, sim in context.filtered_fragments:
-        assert sim >= context.threshold, f"Fragment similarity {sim} below threshold {context.threshold}"
+    for _frag, sim in context.filtered_fragments:
+        assert sim >= context.threshold, (
+            f"Fragment similarity {sim} below threshold {context.threshold}"
+        )
 
 
 @given("I have a semantic context strategy")
@@ -154,7 +163,7 @@ def step_have_context_fragments(context):
             relevance_score=0.5,
             detail_depth=1,
             tier="hot",
-            created_at=None,
+            provenance=FragmentProvenance(resource_uri=row["uko_node"]),
         )
         context.fragments.append(frag)
 
@@ -170,21 +179,21 @@ def step_assemble_context(context, query):
     """Assemble context with query."""
     context.query = query
     query_embedding = context.embedding_provider.embed(query)
-    
+
     # Score fragments by similarity
     scored = []
     for frag in context.fragments:
         frag_embedding = context.embedding_provider.embed(frag.content)
         sim = cosine_similarity(query_embedding, frag_embedding)
         scored.append((frag, sim))
-    
+
     # Sort by similarity
     scored.sort(key=lambda x: x[1], reverse=True)
-    
+
     # Pack within budget
     context.selected_fragments = []
     total_tokens = 0
-    for frag, sim in scored:
+    for frag, _sim in scored:
         if total_tokens + frag.token_count <= context.budget.max_tokens:
             context.selected_fragments.append(frag)
             total_tokens += frag.token_count

src/cleveragents/application/services/embedding_provider.py

@@ -10,7 +10,7 @@ from __future__ import annotations
 
 import logging
 from abc import ABC, abstractmethod
-from typing import Sequence
+from collections.abc import Sequence
 
 logger = logging.getLogger(__name__)
 
@@ -193,7 +193,7 @@ def cosine_similarity(vec_a: Sequence[float], vec_b: Sequence[float]) -> float:
     if len(vec_a) != len(vec_b):
         raise ValueError("Vectors must have the same dimension")
 
-    dot_product = sum(a * b for a, b in zip(vec_a, vec_b))
+    dot_product = sum(a * b for a, b in zip(vec_a, vec_b, strict=False))
     mag_a = sum(a * a for a in vec_a) ** 0.5
     mag_b = sum(b * b for b in vec_b) ** 0.5
 

src/cleveragents/cli/main.py

@@ -735,6 +735,7 @@ def main(args: list[str] | None = None) -> int:
             "config",  # Configuration management
             "session",  # Session management
             "tool",  # Tool registry management
+            "plugin",  # Plugin management
             "validation",  # Validation management
             "auto-debug",  # Auto-debug commands
             "automation-profile",  # Automation profile management