Add ruff format check; Fix existing formatting

.github/workflows/ci-linter-ruff.yml

@@ -6,3 +6,6 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - uses: chartboost/ruff-action@v1
+      - uses: chartboost/ruff-action@v1
+        with:
+          args: 'format --check --diff'

01-resource-referral/debugging.py

@@ -6,6 +6,7 @@
 from langchain_core.runnables import RunnableLambda
 from langchain_core.prompt_values import PromptValue
 
+
 def stacktrace():
     traceback.print_stack()
 
@@ -20,6 +21,7 @@ def debug_here(local_vars):
 
 def debug_runnable(prefix: str):
     """Useful to see output/input between Runnables in a LangChain"""
+
     def debug_chainlink(x):
         print(f"{prefix if prefix else 'DEBUG_CHAINLINK'}")
         if isinstance(x, PromptValue):

01-resource-referral/langgraph-workflow.py

@@ -4,6 +4,7 @@
 from dotenv import main
 import operator
 from typing import TypedDict, Annotated, Sequence
+
 # import os
 import graphviz  # type: ignore
 
@@ -32,7 +33,6 @@ class WorkflowState(TypedDict):
 
 
 class MyWorkflow:
-
     def __init__(self, model_name: str, tools: list):
         main.load_dotenv()
         self.graph = self._init_graph()
@@ -42,7 +42,6 @@ def __init__(self, model_name: str, tools: list):
         # tool_executor will be used to call the tool specified by the LLM in the llm_chain
         self.tool_executor = ToolExecutor(tools)
 
-
     def _init_graph(self):
         graph = StateGraph(WorkflowState)
         graph.add_node("decision_node", self.check_for_final_answer)
@@ -92,7 +91,7 @@ def _draw_graph(self, graph: StateGraph):
 
     # Determines next node to call
     def decide_next_node(self, state):
-        print("\nNEXT_EDGE") # , json.dumps(state, indent=2))
+        print("\nNEXT_EDGE")  # , json.dumps(state, indent=2))
 
         if state["final_answer"]:
             return END
@@ -120,7 +119,7 @@ def check_for_final_answer(self, state):
         print("\nHAS_FINAL_ANSWER node: Waiting for more responses")
 
     def _got_responses_from_all_tools(self, state):
-        expected_tools = [ "spreadsheet", "211_api" ]
+        expected_tools = ["spreadsheet", "211_api"]
         return all(key in state["tool_responses"] for key in expected_tools)
 
     def run_llms(self, state):
@@ -148,7 +147,7 @@ def llm_spreadsheet_query(self, state):
         llm_response = self.invoke_user_message("query_spreadsheet", user_message)
         return {"messages": [llm_response]}
 
-    def invoke_user_message(self, tool, user_message): 
+    def invoke_user_message(self, tool, user_message):
         return self.llm_chain[tool].invoke(user_message)
 
     def call_211_tool(self, state):
@@ -235,5 +234,4 @@ def merge_results(self, state):
 final_state = runnable_graph.invoke(inputs)
 # print("\nFINAL_STATE", type(final_state), final_state)
 print("\nFINAL_ANSWER")
-print(final_state['final_answer'])
-
+print(final_state["final_answer"])

01-resource-referral/my_tools.py

@@ -11,15 +11,18 @@
 from debugging import debug_runnable
 
 TWO_ONE_ONE_BASE_SEARCH_ENDPOINT = "https://api.211.org/search/v1/api"
+
+
 # TODO: adjust parameters and get LLM to set correct parameters
 @tool
-def call_211_api(city: str, service_type:str | list[str]) -> str:
+def call_211_api(city: str, service_type: str | list[str]) -> str:
     """Calls National 211 API for the given city and service type, such as 'Consumer Services'"""
     print(f"211 args: city={city}; service_type={service_type}")
 
     return directly_call_211_api(city, service_type)
 
-def directly_call_211_api(city:str, keyword:str | list[str]) -> str:
+
+def directly_call_211_api(city: str, keyword: str | list[str]) -> str:
     if isinstance(keyword, str):
         return get_services_from_211(city, keyword)
     if isinstance(keyword, list):
@@ -32,19 +35,19 @@ def directly_call_211_api(city:str, keyword:str | list[str]) -> str:
     raise ValueError(f"Invalid keyword type: {type(keyword)}")
 
 
-def get_services_from_211(city:str, keyword:str | list[str]):
+def get_services_from_211(city: str, keyword: str | list[str]):
     location_endpoint = f"{TWO_ONE_ONE_BASE_SEARCH_ENDPOINT}/Search/Keyword?Keyword={keyword}&Location={city}&Top=10&OrderBy=Relevance&SearchMode=Any&IncludeStateNationalRecords=true&ReturnTaxonomyTermsIfNoResults=false"
 
-    TWO_ONE_ONE_API_KEY = os.environ.get('TWO_ONE_ONE_API_KEY')
+    TWO_ONE_ONE_API_KEY = os.environ.get("TWO_ONE_ONE_API_KEY")
 
     headers = {
-        'Accept': 'application/json',
-        'Api-Key': TWO_ONE_ONE_API_KEY,
+        "Accept": "application/json",
+        "Api-Key": TWO_ONE_ONE_API_KEY,
     }
 
     location_search = requests.get(location_endpoint, headers=headers)
     # From Search: /api/Filters/ServiceAreas?StateProvince=MI, returns []
-    try:        
+    try:
         first_result = location_search.json()["results"][0]["document"]
         # difficult to find param {location_id}, location_id returns dataowner
         location_id = first_result["idLocation"]
@@ -60,12 +63,16 @@ def get_services_from_211(city:str, keyword:str | list[str]):
         print("Failed to get services at location")
     return "[]"
 
+
 # Check for csv file
 csv_file = "nyc_referral_csv.csv"
 if not os.path.exists(csv_file):
-    print(f"Optionally download {csv_file} from google drive: https://drive.google.com/file/d/1YHgJvZCDF5VtTO-AQ4-I3_1jGzrcOHjY/view?usp=sharing")
+    print(
+        f"Optionally download {csv_file} from google drive: https://drive.google.com/file/d/1YHgJvZCDF5VtTO-AQ4-I3_1jGzrcOHjY/view?usp=sharing"
+    )
     input(f"Press Enter to continue without {csv_file}...")
 
+
 @tool
 def query_spreadsheet(city: str, service_type: str | list[str]) -> str:
     """Search spreadsheet for support resources given the city and service type, such as 'Food Assistance'."""
@@ -76,20 +83,24 @@ def query_spreadsheet(city: str, service_type: str | list[str]) -> str:
 
     # base implementation
     df = pandas.read_csv(csv_file)
-    separated_locations = city.split(',')
+    separated_locations = city.split(",")
     city_to_search = separated_locations[0]
-    query = service_type if isinstance(service_type, str) else '|'.join(service_type)
+    query = service_type if isinstance(service_type, str) else "|".join(service_type)
     print(query)
 
-    results = df.query(f'needs.str.contains("{query}", case=False) & counties_served.str.contains("{city_to_search}", case=False)', engine='python')
+    results = df.query(
+        f'needs.str.contains("{query}", case=False) & counties_served.str.contains("{city_to_search}", case=False)',
+        engine="python",
+    )
     if results.to_numpy().size == 0:
         return "[]"
-    
-    csv_with_header_to_json = results.replace(np.nan, None).to_dict('records')
+
+    csv_with_header_to_json = results.replace(np.nan, None).to_dict("records")
     dict_json = json.dumps(csv_with_header_to_json, indent=2)
-    
+
     return dict_json
 
+
 @tool
 def merge_json_results(user_query, result_211_api, result_spreadsheet):
     """Merge JSON results from 211 API and spreadsheet"""
@@ -104,17 +115,32 @@ def merge_json_results(user_query, result_211_api, result_spreadsheet):
 
     relevance_agent = create_relevance_agent()
     # A long list of resources confuses the LLM, so sample only 40
-    sampled_resources : list[dict] = random.sample(list(deduplicated_dict.values()), min(40, len(deduplicated_dict)))
+    sampled_resources: list[dict] = random.sample(
+        list(deduplicated_dict.values()), min(40, len(deduplicated_dict))
+    )
     formatted_resources = _format_for_prompt(sampled_resources)
-    prioritized_list = relevance_agent.invoke({"resources": formatted_resources, "user_query": user_query})
+    prioritized_list = relevance_agent.invoke(
+        {"resources": formatted_resources, "user_query": user_query}
+    )
     return prioritized_list
 
+
 ALTERNATIVE_KEYS = [
     # from spreadsheet
-    "id", "alternate_name", "url", "website", "email", "tax_id", 
+    "id",
+    "alternate_name",
+    "url",
+    "website",
+    "email",
+    "tax_id",
     # from 211
-    "idService", "idOrganization", "name", "alternateName"
-    ]
+    "idService",
+    "idOrganization",
+    "name",
+    "alternateName",
+]
+
+
 def _merge_and_deduplicate(dict_listA, dict_listB):
     """Merge 2 list of objects, removing duplicates based on object's 'name'.
     If 'name' is not present, use one of ALTERNATIVE_KEYS to deduplicate."""
@@ -130,9 +156,12 @@ def _merge_and_deduplicate(dict_listA, dict_listB):
             deduplicated_dict[obj["name"]] = obj
             continue
 
-        deduplicated_dict[obj["name"]] = _merge_objects(deduplicated_dict[obj["name"]], obj)
+        deduplicated_dict[obj["name"]] = _merge_objects(
+            deduplicated_dict[obj["name"]], obj
+        )
     return deduplicated_dict
 
+
 def _merge_objects(objA: dict, objB: dict):
     """Merge 2 objects, concatenating values if same key are in both objects"""
     merged_obj = {}
@@ -141,7 +170,10 @@ def _merge_objects(objA: dict, objB: dict):
         merged_obj[key] = ";; ".join(set(values_list))
     return merged_obj
 
+
 APPROVED_RESOURCE_NAMES = ["Alpena CAO"]
+
+
 def _filter_approved(deduplicated_dict):
     """Filter collection to only include approved resource names"""
     for key in list(deduplicated_dict):
@@ -150,10 +182,16 @@ def _filter_approved(deduplicated_dict):
             del deduplicated_dict[key]
     return deduplicated_dict
 
+
 def _format_for_prompt(resources: dict):
     """Format resources for prompt"""
-    return "\n".join([f"- {resource['name']} ({resource['phone']}): provides {resource.get('needs')} for counties {resource.get('counties_served')}. {resource.get('description', '')}"
-                      for resource in resources])
+    return "\n".join(
+        [
+            f"- {resource['name']} ({resource['phone']}): provides {resource.get('needs')} for counties {resource.get('counties_served')}. {resource.get('description', '')}"
+            for resource in resources
+        ]
+    )
+
 
 def create_relevance_agent():
     return (
@@ -162,6 +200,7 @@ def create_relevance_agent():
         | create_llm(model_name="openhermes", settings={"temperature": 0, "top_p": 0.8})
     )
 
+
 def _agent_prompt_template():
     template = """You are a helpful automated agent that filters and prioritizes benefits services. \
 Downselect to less than 10 services total and prioritize the following list of services based on the user's query. \

02-household-queries/api_helpers.py

@@ -7,9 +7,10 @@
 # Fetches data from Guru, currently not used as we're pulling data from static json files
 # Eventually we would like to pull the latest updated data from the Guru API
 GURU_ENDPOINT = "https://api.getguru.com/api/v1/"
+
+
 def get_guru_data():
     url = f"{GURU_ENDPOINT}cards/3fbff9c4-56a8-4561-a7d1-09727f1b4703"
-    headers = {
-    'Authorization': os.environ.get('GURU_TOKEN')}
+    headers = {"Authorization": os.environ.get("GURU_TOKEN")}
     response = requests.request("GET", url, headers=headers)
-    return response.json()
+    return response.json()