refactor: fix pyright types

.github/workflows/ci.yml

@@ -13,9 +13,22 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - run: pip install ruff
-      - run: ruff format --check .
-      - run: ruff check .
+      - uses: astral-sh/setup-uv@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.13"
+
+      - name: Install dependencies
+        run: uv sync
+
+      - name: Format Check (Ruff)
+        run: uv run ruff format --check .
+
+      - name: Lint Check (Ruff)
+        run: uv run ruff check .
+
+      - name: Type Check (Pyright)
+        run: uv run pyright .
 
   build:
     runs-on: ubuntu-latest

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "reviewbot"
-version = "0.3.0"
+version = "0.3.1"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
@@ -54,10 +54,6 @@ quote-style = "double"
 indent-style = "space"
 
 [dependency-groups]
-dev = [
-    "pyright>=1.1.408",
-    "ruff>=0.8.6",
-    "ty>=0.0.4",
-]
+dev = ["pyright>=1.1.408", "ruff>=0.8.6", "ty>=0.0.4"]
 [project.optional-dependencies]
 examples = ["fastapi"]

src/reviewbot/agent/workflow/diff_extract.py

@@ -2,6 +2,8 @@
 import re
 from typing import Any
 
+type LineNo = int | None
+
 
 def generate_line_code(file_path: str, old_line: int | None, new_line: int | None) -> str:
     """
@@ -31,7 +33,7 @@ def create_position_for_issue(
     in_hunk = False
 
     # Track the actual lines found in the diff to build the range
-    matched_lines = []  # List of (old_line, new_line)
+    matched_lines: list[tuple[LineNo, LineNo]] = []  # List of (old_line, new_line)
 
     for line in lines:
         match = hunk_header_pattern.match(line)

src/reviewbot/agent/workflow/ignore.py

@@ -69,7 +69,7 @@ def parse_reviewignore(repo_path: Path) -> list[str]:
         List of glob patterns to ignore
     """
     reviewignore_path = repo_path / ".reviewignore"
-    patterns = []
+    patterns: list[str] = []
 
     if not reviewignore_path.exists():
         console.print("[dim].reviewignore file not found, using global blacklist only[/dim]")
@@ -136,7 +136,7 @@ def filter_diffs(diffs: list[FileDiff], reviewignore_patterns: list[str]) -> lis
     Returns:
         Filtered list of diffs
     """
-    filtered = []
+    filtered: list[FileDiff] = []
     ignored_count = 0
 
     for diff in diffs:

src/reviewbot/cli/commands.py

@@ -2,7 +2,7 @@
 
 import typer
 
-from reviewbot.agent.workflow import work_agent
+from reviewbot.agent.workflow import work_agent  # type: ignore
 from reviewbot.cli.app import app
 from reviewbot.infra.config.env import load_env
 from reviewbot.infra.git.repo_tree import tree
@@ -22,10 +22,12 @@ def work(
     mr_iid: str = typer.Argument(..., help="Merge request IID"),
 ):
     config = load_env()
-    work_agent(
-        config,
-        project_id,
-        mr_iid,
+    work_agent.invoke(  # type: ignore
+        {
+            "config": config,
+            "project_id": project_id,
+            "mr_iid": mr_iid,
+        },
     )
 
 

src/reviewbot/core/reviews/review.py

@@ -9,7 +9,7 @@ class Review:
     id: UUID = field(default_factory=uuid4)
     repo: str = ""
     commit: str = ""
-    issues: list[Issue] = field(default_factory=list)
+    issues: list[Issue] = field(default_factory=list[Issue])
     summary: str = ""
 
 

src/reviewbot/infra/embeddings/openai.py

@@ -10,6 +10,7 @@
 from langchain_core.documents import Document
 from langchain_openai import OpenAIEmbeddings
 from langchain_text_splitters import RecursiveCharacterTextSplitter
+from pydantic import BaseModel, Field, SecretStr
 from rich.console import Console
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -53,6 +54,14 @@
 }
 
 
+class SearchResult(BaseModel):
+    similarity: float = Field(..., description="Cosine similarity score (0 to 1)")
+    path: str = Field(..., description="Relative path to the source file")
+    filename: str = Field(..., description="Name of the file")
+    chunk_index: int = Field(..., description="Index of the chunk within the file")
+    text: str = Field(..., description="The actual code snippet")
+
+
 class CodebaseVectorStore:
     def __init__(
         self,
@@ -62,7 +71,7 @@ def __init__(
         vectors_dir: str | Path = "vectors/codebase",
         embeddings_model: str | None = None,
         embeddings_base_url: str | None = None,
-        embeddings_api_key: str | None = None,
+        embeddings_api_key: SecretStr | None = None,
     ):
         self.repo_root = Path(repo_root).resolve()
         self.vectors_dir = Path(vectors_dir)
@@ -73,7 +82,7 @@ def __init__(
         if embeddings_base_url is None:
             embeddings_base_url = os.getenv("EMBEDDINGS_BASE_URL")
         if embeddings_api_key is None:
-            embeddings_api_key = os.getenv("EMBEDDINGS_API_KEY", "dummy")
+            embeddings_api_key = SecretStr(os.getenv("EMBEDDINGS_API_KEY", "dummy"))
 
         self.embeddings = OpenAIEmbeddings(
             model=embeddings_model,
@@ -82,7 +91,6 @@ def __init__(
             tiktoken_enabled=False,
             chunk_size=64,
             max_retries=3,
-            request_timeout=120,
         )
 
         self.splitter = RecursiveCharacterTextSplitter(
@@ -149,22 +157,11 @@ def build(self) -> None:
             raise RuntimeError("No source files found to embed")
 
         self.vector_store = FAISS.from_documents(docs, self.embeddings)
-        self._build_metadata_index()
         self.save()
 
     def compile(self, command: str) -> str:
         return subprocess.run(command, shell=True, capture_output=True, text=True).stdout
 
-    def _build_metadata_index(self) -> None:
-        self.metadata_index = {}
-        if not self.vector_store:
-            return
-
-        for doc_id, doc in self.vector_store.docstore._dict.items():
-            path = doc.metadata.get("path")
-            if path:
-                self.metadata_index.setdefault(path, []).append(doc_id)
-
     def save(self) -> None:
         if not self.vector_store:
             return
@@ -195,26 +192,26 @@ def search(
         *,
         top_k: int = 10,
         path: str | None = None,
-    ) -> list[dict]:
+    ) -> list[SearchResult]:
         if not self.vector_store:
             raise RuntimeError("Vector store not loaded")
 
-        filter = {}
-        if path:
-            filter["path"] = path
-        results = self.vector_store.similarity_search_with_score(query, k=top_k, filter=filter)
-        out = []
+        search_filter = {"path": path} if path else None
+        results = self.vector_store.similarity_search_with_score(  # type: ignore
+            query, k=top_k, filter=search_filter
+        )
+
+        out: list[SearchResult] = []
         for doc, score in results:
             out.append(
-                {
-                    "similarity": float(1 - score),
-                    "path": doc.metadata.get("path"),
-                    "filename": doc.metadata.get("filename"),
-                    "chunk_index": doc.metadata.get("chunk_index"),
-                    "text": doc.page_content,
-                }
+                SearchResult(
+                    similarity=float(1 - score),
+                    path=doc.metadata.get("path", ""),  # type: ignore
+                    filename=doc.metadata.get("filename", ""),  # type: ignore
+                    chunk_index=doc.metadata.get("chunk_index", 0),  # type: ignore
+                    text=doc.page_content,
+                )
             )
-        console.print(out)
         return out
 
     def read_file(