Linting

.github/workflows/linting.yml

@@ -0,0 +1,42 @@
+name: Lint code and check formatting with black and isort
+
+on:
+  pull_request:
+
+jobs:
+    pre-commit:
+        runs-on: ubuntu-latest
+
+        steps:
+            - name: Checkout code
+              uses: actions/checkout@v4
+
+            - name: Set up Python
+              uses: actions/setup-python@v5
+              with:
+                  python-version: 3.11
+
+            - name: Cache pip dependencies
+              uses: actions/cache@v4
+              with:
+                path: ~/.cache/pip
+                key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
+                restore-keys: |
+                  ${{ runner.os }}-pip-
+
+            - name: Cache pre-commit dependencies
+              uses: actions/cache@v4
+              with:
+                path: ~/.cache/pre-commit
+                key: ${{ runner.os }}-pre-commit-${{ hashFiles('.pre-commit-config.yaml') }}
+                restore-keys: |
+                    ${{ runner.os }}-pre-commit-
+
+            - name: Install dependencies
+              run: |
+                python -m pip install --upgrade pip
+                pip install pre-commit
+
+            - name: Run pre-commit
+              run: |
+                pre-commit run --all-files

.pre-commit-config.yaml

@@ -0,0 +1,18 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+
+  - repo: https://github.com/pre-commit/mirrors-isort
+    rev: v5.10.1
+    hooks:
+      - id: isort
+        args: ["--profile=black"]
+
+  - repo: https://github.com/psf/black
+    rev: 24.10.0
+    hooks:
+      - id: black
+        args: ["--config=pyproject.toml"]

main.py

@@ -1,4 +1,4 @@
 from src.app import BitsGPT
 
 bitsgpt = BitsGPT()
-app = bitsgpt.app
+app = bitsgpt.app

pyproject.toml

@@ -13,6 +13,7 @@ langgraph = "^0.2.60"
 langsmith = "^0.2.7"
 langchain-groq = "^0.2.2"
 langgraph-cli = {extras = ["inmem"], version = "^0.1.65"}
+pre-commit = "^4.0.1"
 
 
 [build-system]

src/agents.py

@@ -1,13 +1,13 @@
 import os
 import textwrap
-from dotenv import load_dotenv
-
 
-from langchain_groq import ChatGroq
+from dotenv import load_dotenv
 from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
 
 load_dotenv()
 
+
 class Agents:
     def __init__(self):
 
@@ -25,8 +25,9 @@ def __init__(self):
                 agent_name = agent_name.split(".")[0]
                 self.prompts[agent_name] = f.read()
 
-
-    def get_prompt(self, agent_name: str, query: str, chat_history: str, agent_scratchpad=False) -> ChatPromptTemplate:
+    def get_prompt(
+        self, agent_name: str, query: str, chat_history: str, agent_scratchpad=False
+    ) -> ChatPromptTemplate:
 
         prompt = [
             (
@@ -35,21 +36,25 @@ def get_prompt(self, agent_name: str, query: str, chat_history: str, agent_scrat
             ),
             (
                 "user",
-                textwrap.dedent(f"<query>{query}</query>\n\n<history>{chat_history}</history>"),
+                textwrap.dedent(
+                    f"<query>{query}</query>\n\n<history>{chat_history}</history>"
+                ),
             ),
         ]
         if agent_scratchpad:
             prompt.append(("placeholder", "{agent_scratchpad}"))
         return ChatPromptTemplate.from_messages(prompt)
-    
+
     def intent_classifier(self, query: str, chat_history: str) -> str:
         prompt = self.get_prompt("INTENT_CLASSIFIER_AGENT", query, chat_history)
 
         chain = prompt | self.llm
 
-        result = chain.invoke({
-            "input": query,
-        })
+        result = chain.invoke(
+            {
+                "input": query,
+            }
+        )
 
         return result
 
@@ -58,11 +63,13 @@ def general_campus_query(self, query: str, chat_history: str) -> str:
 
         chain = prompt | self.llm
 
-        result = chain.invoke({
-            "input": query,
-        })
+        result = chain.invoke(
+            {
+                "input": query,
+            }
+        )
 
         return result
-    
+
     def course_query(self, query: str, chat_history: str) -> str:
         raise NotImplementedError("Course query not implemented yet")

src/app.py

@@ -1,6 +1,12 @@
-from langgraph.graph import StateGraph, END
+from langgraph.graph import END, StateGraph
+
+from .nodes import (
+    course_query,
+    general_campus_query,
+    intent_classifier,
+    not_related_query,
+)
 from .state import State
-from .nodes import intent_classifier, course_query, general_campus_query, not_related_query
 
 
 class BitsGPT:
@@ -25,12 +31,11 @@ def intent_router(state):
                 return "general_campus_query"
             else:
                 return "not_related_query"
-
-
+
         graph.add_conditional_edges("intent_classifer", intent_router)
 
         graph.add_edge("course_query", END)
         graph.add_edge("general_campus_query", END)
         graph.add_edge("not_related_query", END)
 
-        return graph
+        return graph

src/nodes.py

@@ -1,26 +1,33 @@
 from langchain_core.messages import AIMessage
+
 from .agents import Agents
 from .state import State
 
 agents = Agents()
 
+
 def intent_classifier(state: State):
     query = state["messages"][0].content
     result = agents.intent_classifier(query, state.get("chat_history", ""))
-    
+
     return {"messages": [result]}
 
+
 def course_query(state: State):
     query = state["messages"][0].content
     result = AIMessage("Course query not implemented yet")
     return {"messages": [result]}
 
+
 def general_campus_query(state: State):
     query = state["messages"][0].content
     result = agents.general_campus_query(query, state.get("chat_history", ""))
     return {"messages": [result]}
 
+
 def not_related_query(state: State):
     query = state["messages"][0].content
-    result = AIMessage("I'm sorry, I don't understand the question, if it relates to campus please rephrase.")
-    return {"messages": [result]}    
+    result = AIMessage(
+        "I'm sorry, I don't understand the question, if it relates to campus please rephrase."
+    )
+    return {"messages": [result]}