linting

templates/neo4j-semantic-layer/ingest.py

@@ -12,7 +12,8 @@
 # Import movie information
 
 movies_query = """
-LOAD CSV WITH HEADERS FROM 'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/movies.csv' AS row
+LOAD CSV WITH HEADERS FROM 
+'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/movies.csv' AS row
 CALL {
     WITH row
     MERGE (m:Movie {id:row.movieId})
@@ -35,7 +36,8 @@
 
 # Import rating information
 rating_query = """
-LOAD CSV WITH HEADERS FROM 'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/ratings.csv' AS row
+LOAD CSV WITH HEADERS FROM 
+'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/ratings.csv' AS row
 CALL {
     WITH row
     MATCH (m:Movie {id:row.movieId})

templates/neo4j-semantic-layer/neo4j_semantic_layer/agent.py

@@ -22,9 +22,10 @@
     [
         (
             "system",
-            "You are a helpful assistant that finds information about movies and recommends them. "
-            "If tools require follow up questions, make sure to ask the user for clarification. "
-            "Make sure to include any available options that need to be clarified in the follow up questions",
+            "You are a helpful assistant that finds information about movies "
+            " and recommends them. If tools require follow up questions, "
+            "make sure to ask the user for clarification. Make sure to include "
+            "any available options that need to be clarified in the follow up questions",
         ),
         MessagesPlaceholder(variable_name="chat_history"),
         ("user", "{input}"),

templates/neo4j-semantic-layer/neo4j_semantic_layer/information_tool.py

@@ -18,7 +18,8 @@
 WITH m, type(r) as type, collect(coalesce(t.name, t.title)) as names
 WITH m, type+": "+reduce(s="", n IN names | s + n + ", ") as types
 WITH m, collect(types) as contexts
-WITH m, "type:" + labels(m)[0] + "\ntitle: "+ coalesce(m.title, m.name) + "\nyear: "+coalesce(m.released,"") +"\n" +
+WITH m, "type:" + labels(m)[0] + "\ntitle: "+ coalesce(m.title, m.name) 
+       + "\nyear: "+coalesce(m.released,"") +"\n" +
        reduce(s="", c in contexts | s + substring(c, 0, size(c)-2) +"\n") as context
 RETURN context LIMIT 1
 """
@@ -30,7 +31,10 @@ def get_information(entity: str, type: str) -> str:
         return "No information was found about the movie or person in the database"
     elif len(candidates) > 1:
         newline = "\n"
-        return f"Need additional information, which of these did you mean: {newline + newline.join(str(d) for d in candidates)}"
+        return (
+            "Need additional information, which of these "
+            f"did you mean: {newline + newline.join(str(d) for d in candidates)}"
+        )
     data = graph.query(
         description_query, params={"candidate": candidates[0]["candidate"]}
     )

templates/neo4j-semantic-layer/neo4j_semantic_layer/memory_tool.py

@@ -41,7 +41,10 @@ def store_movie_rating(movie: str, rating: int):
 class MemoryInput(BaseModel):
     movie: str = Field(description="movie the user liked")
     rating: int = Field(
-        description="Rating from 1 to 5, where one represents heavy dislike and 5 represent the user loved the movie"
+        description=(
+            "Rating from 1 to 5, where one represents heavy dislike "
+            "and 5 represent the user loved the movie"
+        )
     )
 
 

templates/neo4j-semantic-layer/neo4j_semantic_layer/recommendation_tool.py

@@ -55,7 +55,8 @@ def recommendation_query_movie(genre: bool) -> str:
 
 def recommend_movie(movie: Optional[str] = None, genre: Optional[str] = None) -> str:
     """
-    Recommends movies based on user's history and preference for a specific movie and/or genre.
+    Recommends movies based on user's history and preference
+    for a specific movie and/or genre.
     Returns:
         str: A string containing a list of recommended movies, or an error message.
     """
@@ -66,14 +67,14 @@ def recommend_movie(movie: Optional[str] = None, genre: Optional[str] = None) ->
         response = graph.query(recommendation_query_db_history, params)
         try:
             return ", ".join([el["movie"] for el in response])
-        except:
+        except Exception:
             return "Can you tell us about some of the movies you liked?"
     if not movie and genre:
         # Recommend top voted movies in the genre the user haven't seen before
         response = graph.query(recommendation_query_genre, params)
         try:
             return ", ".join([el["movie"] for el in response])
-        except:
+        except Exception:
             return "Something went wrong"
 
     candidates = get_candidates(movie, "movie")
@@ -84,7 +85,7 @@ def recommend_movie(movie: Optional[str] = None, genre: Optional[str] = None) ->
     response = graph.query(query, params)
     try:
         return ", ".join([el["movie"] for el in response])
-    except:
+    except Exception:
         return "Something went wrong"
 
 
@@ -114,7 +115,9 @@ def recommend_movie(movie: Optional[str] = None, genre: Optional[str] = None) ->
 class RecommenderInput(BaseModel):
     movie: Optional[str] = Field(description="movie used for recommendation")
     genre: Optional[str] = Field(
-        description=f"genre used for recommendation. Available options are: {all_genres}"
+        description=(
+            "genre used for recommendation. Available options are:" f"{all_genres}"
+        )
     )
 
 

templates/neo4j-semantic-layer/neo4j_semantic_layer/utils.py

@@ -34,7 +34,8 @@ def generate_full_text_query(input: str) -> str:
 candidate_query = """
 CALL db.index.fulltext.queryNodes($index, $fulltextQuery, {limit: $limit})
 YIELD node
-RETURN coalesce(node.name, node.title) AS candidate, [el in labels(node) WHERE el IN ['Person', 'Movie'] | el][0] AS label
+RETURN coalesce(node.name, node.title) AS candidate,
+       [el in labels(node) WHERE el IN ['Person', 'Movie'] | el][0] AS label
 """