Running pre-commit checks manually

Author: ajosh0504

apps/graph_rag_demo/.gitignore

@@ -16,4 +16,3 @@ node_modules
 #Ignore PDF Files
 PDF_KG
 .sass-cache
-

apps/graph_rag_demo/README.md

@@ -1,5 +1,5 @@
 # kg_graph__rag_mongo
-This guide explores how to leverage MongoDB's capabilities to create and manipulate graph representations using both Python and Node.js. By utilizing these two popular programming languages, we can demonstrate the versatility of MongoDB in different development environments, showcasing how to perform essential Graph RAG to represent and analyze graphs can provide valuable insights into complex relationships and interactions within data combining the Graph and RAG data in MongoDB. 
+This guide explores how to leverage MongoDB's capabilities to create and manipulate graph representations using both Python and Node.js. By utilizing these two popular programming languages, we can demonstrate the versatility of MongoDB in different development environments, showcasing how to perform essential Graph RAG to represent and analyze graphs can provide valuable insights into complex relationships and interactions within data combining the Graph and RAG data in MongoDB.
 
 
 ## Prep Steps
@@ -8,21 +8,21 @@ This guide explores how to leverage MongoDB's capabilities to create and manipul
     Set up a Atlas a [cloud-based MongoDB instance of MongoDB.](https://www.mongodb.com/docs/atlas/tutorial/create-new-cluster/)
 
 2. Install Required Libraries:
-    For Python: 
-    Please do a regular installation of Anaconda for your Operating System using the [doc](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) 
+    For Python:
+    Please do a regular installation of Anaconda for your Operating System using the [doc](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html)
     Once conda is installed, open up a shell with the Conda CLI, change to this directory and create a new environment with the packages listed in the requirements.txt by executing the following statement:
-    
+
  ```bash
     conda env create -f environment.yaml
     conda activate kg-demo
 ```
-    For Node.js: 
+    For Node.js:
     For the Node JS files we have a package.json and you will be able to use npm install to install all the packages needed. Our Code was tested on Node version v20.15.0 and npm version 10.7.0
 
-3.  Now open the .env file in the directory and populate the following three variables. Please note that the .env file may  be empty when you clone this repository   
+3.  Now open the .env file in the directory and populate the following three variables. Please note that the .env file may  be empty when you clone this repository
     OPENAI_API_KEY1=
     ATLAS_CONNECTION_STRING=
-    PDF = 
+    PDF =
 
 4. Create the MongoDB Atlas Database:
     Please note that ATLAS_CONNECTION_STRING and OPENAI_API_KEY1 should already be created in the environment file.
@@ -32,7 +32,7 @@ This guide explores how to leverage MongoDB's capabilities to create and manipul
 5. Download and install **MongoDB Compass** for your platform by following the steps mentioned [here](https://www.mongodb.com/docs/compass/current/install/). Please ensure you install a compass version 1.40.0 or higher. Once installed, connect to your Atlas cluster by following the link [here](https://www.mongodb.com/docs/compass/current/connect/).
 
 6. Create vector index on the Compass UI for the <code>embedding</code> field for <code>knowledge_graph</code> collection. [Please refer to this document](https://www.mongodb.com/docs/compass/current/indexes/create-vector-search-index/). You can use the following json document for the index defination. Please name the vector index as <code>vector_index</code>
-    Sample: 
+    Sample:
     ```json
     {
         "fields": [ {
@@ -44,8 +44,8 @@ This guide explores how to leverage MongoDB's capabilities to create and manipul
     }
     ```
     After this create an Atlas Search Index on the <code>knowledge_graph</code> collection. [Please refer this document](https://www.mongodb.com/docs/compass/current/indexes/create-search-index/). Please name the Atlas Search Index as <code>default</code>
-    
-    
+
+
 
 
 ## Running the application
@@ -97,4 +97,4 @@ It will then ask about the Spanning Tree depth. You can give it a value of 2 or
 The answer for this question should be something as below:
 
 **Social Support:Social factor Stress:Condition Diet:Lifestyle factor Physical Health:Condition Work Environment:Environment Job Satisfaction:Emotional state Aging:Condition Job Satisfaction:Condition Productivity:Condition Social Support:Activity Immune System:Biological system Cortisol:Hormone Cognitive Function:Function Immune System:System Diet:Activity Burnout:Condition Work Performance:Condition Heart Disease:Disease Cognitive Function:Condition Sleep Quality:Condition Inflammation:Biological process Social Support:Condition Productivity:Outcome Employee Turnover:Outcome Work Environment:Factor Diabetes:Disease Genetics:Biological factor Physical Activity:Activity Diet:Condition Obesity:Condition Heart Disease:Condition Social Support:Factor Social Relationships:Condition Sleep Quality:Health aspect Inflammation:Condition Diet:Factor Memory:Condition Blood Pressure:Condition Exercise:Activity Depression:Condition Anxiety:Condition Mental Health:Aspect Learning:Condition Sleep Quality:Aspect Stress:Concept Diet:Behavior Physical Health:Health aspect Anxiety:Emotional state Anxiety:Mental condition Depression:Mental condition Depression:Emotional state Physical Activity:Behavior Job Satisfaction:Psychological factor Mental Health:Health aspect Mental Health:Condition -----------
-Social support is related to aging through its impact on stress. Social support reduces stress, and since stress accelerates aging, having social support can indirectly slow down the aging process by reducing the level of stress experienced by individuals.**
+Social support is related to aging through its impact on stress. Social support reduces stress, and since stress accelerates aging, having social support can indirectly slow down the aging process by reducing the level of stress experienced by individuals.**

apps/graph_rag_demo/addEmbeddings.js

@@ -24,8 +24,8 @@ async function run() {
         // Ensure that the collection is empty
         await collection.deleteMany({});
         const pdfArray = [
-            './PDF_KG/Diet, Stress and Mental Healt.pdf', 
-            './PDF_KG/Effect of Stress Management Interventions on Job Stress among nurses working in critical care units.pdf', 
+            './PDF_KG/Diet, Stress and Mental Healt.pdf',
+            './PDF_KG/Effect of Stress Management Interventions on Job Stress among nurses working in critical care units.pdf',
             './PDF_KG/Factors contributing to stress among parents of children with autism.pdf',
             './PDF_KG/Level of physical activity, well-being, stress and self rated health in persons with migraine and co existing tension-type headache and neck pain.pdf',
             './PDF_KG/Stress and Blood Pr ess and Blood Pressure During Pr e During Pregnancy Racial Diff egnancy Racial Differences.pdf',
@@ -52,5 +52,3 @@ async function run() {
     }
 }
 run().catch(console.dir);
-
-

apps/graph_rag_demo/addTags.js

@@ -47,8 +47,8 @@ async function findAllDocuments(dbName, collectionName1, collectionName2) {
                     },
                     {
                       $merge: {
-                        into: collectionName2, 
-                        whenMatched: "merge", 
+                        into: collectionName2,
+                        whenMatched: "merge",
                         whenNotMatched: "discard"
                       }
                     }

apps/graph_rag_demo/build_graph.py

@@ -1,31 +1,34 @@
-from collections import defaultdict 
-def addEdge(graph,u,v): 
-    graph[u].append(v) 
-def generate_edges(graph): 
-    edges = [] 
-  
-    # for each node in graph 
-    for node in graph: 
-          
-        # for each neighbour node of a single node 
-        for neighbour in graph[node]: 
-              
-            # if edge exists then append 
-            edges.append((node, neighbour)) 
+from collections import defaultdict
+
+
+def addEdge(graph, u, v):
+    graph[u].append(v)
+
+
+def generate_edges(graph):
+    edges = []
+
+    # for each node in graph
+    for node in graph:
+        # for each neighbour node of a single node
+        for neighbour in graph[node]:
+            # if edge exists then append
+            edges.append((node, neighbour))
     return edges
 
+
 def build_graph(level_dict):
     graph = defaultdict(list)
     relationship_names = defaultdict(set)
     for level in level_dict.keys():
-        #print(f'Level is {level}')
-        for source,targets in level_dict[level].items():
-            #print(f'Source is {source} and targets are {targets}')
-            for _,relationships in targets.items():
-                #print(f'Target Node {target_node} and relationships {relationships}')
+        # print(f'Level is {level}')
+        for source, targets in level_dict[level].items():
+            # print(f'Source is {source} and targets are {targets}')
+            for _, relationships in targets.items():
+                # print(f'Target Node {target_node} and relationships {relationships}')
                 for target_node, edges in relationships.items():
-                    addEdge(graph,source,target_node)
-                    relationship_key = (source,target_node)
+                    addEdge(graph, source, target_node)
+                    relationship_key = (source, target_node)
                     for edge in edges:
                         relationship_names[relationship_key].add(edge)
-    return graph,relationship_names
+    return graph, relationship_names

apps/graph_rag_demo/depth_first_search.py

@@ -1,4 +1,4 @@
-def depth_first_search(graph,start,relationship_names,path):
+def depth_first_search(graph, start, relationship_names, path):
     visited = []
     stack = []
     parent = []
@@ -8,43 +8,53 @@ def depth_first_search(graph,start,relationship_names,path):
     visited.append(start)
     i = 0
     while stack:
-        s=stack.pop()
-        print(s,end=" ")
-        node = {'id':s,'group':0,'level':1,'label':s.split(":")[0]+' from '+s.split(":")[1]}
+        s = stack.pop()
+        print(s, end=" ")
+        node = {
+            "id": s,
+            "group": 0,
+            "level": 1,
+            "label": s.split(":")[0] + " from " + s.split(":")[1],
+        }
         nodes.append(node)
-        if i!=0:
+        if i != 0:
             parent_node = parent[-1]
-            parent_name=''
-            current_child_count=0
-            for x,y in parent_node.items():
-                parent_name=x
-                current_child_count=y
-            relationships = relationship_names[(parent_name,s)]
-            #print(f'{relationships}')
-            rel_names=''
-            j=0
+            parent_name = ""
+            current_child_count = 0
+            for x, y in parent_node.items():
+                parent_name = x
+                current_child_count = y
+            relationships = relationship_names[(parent_name, s)]
+            # print(f'{relationships}')
+            rel_names = ""
+            j = 0
             if relationships:
                 for rel in relationships:
-                    if j!=len(relationships)-1:
-                        rel_names+=rel+" / "
+                    if j != len(relationships) - 1:
+                        rel_names += rel + " / "
                     else:
-                        rel_names+=rel
-                    j+=1
-                path+= f'{parent_name.split(":")[0]} ({rel_names}) {s.split(":")[0]}, '
-                link = {'source':parent_name,'target':s,'strength':0.7,'linkName':rel_names}
+                        rel_names += rel
+                    j += 1
+                path += f'{parent_name.split(":")[0]} ({rel_names}) {s.split(":")[0]}, '
+                link = {
+                    "source": parent_name,
+                    "target": s,
+                    "strength": 0.7,
+                    "linkName": rel_names,
+                }
                 links.append(link)
             current_child_count -= 1
             if current_child_count == 0:
                 parent.pop()
             else:
-                parent[-1][parent_name]=current_child_count
-        k=0
+                parent[-1][parent_name] = current_child_count
+        k = 0
         for x in graph[s][::-1]:
             if x not in visited:
                 visited.append(x)
                 stack.append(x)
-                k+=1
+                k += 1
         if k > 0:
-            parent.append({s:k})
-        i+=1
-    return path,nodes,links
+            parent.append({s: k})
+        i += 1
+    return path, nodes, links

apps/graph_rag_demo/do_graphlookup.py

@@ -1,34 +1,33 @@
-from pymongo import MongoClient
-from pprint import pprint
 import os
+from pprint import pprint
+
 from dotenv import load_dotenv
+from pymongo import MongoClient
 
 load_dotenv()
-def graph_lookup(node_name,max_depth):
 
+
+def graph_lookup(node_name, max_depth):
     graph_lookup_docs = []
     try:
         uri = os.getenv("ATLAS_CONNECTION_STRING")
         client = MongoClient(uri, appname="devrel.showcase.apps.graph_rag_demo")
         database = client["langchain_db"]
         collection = database["nodes_relationships"]
         pipeline = [
-        {
-            '$match': {
-                '_id': node_name
-            }
-        }, {
-            '$graphLookup': {
-                'from': 'nodes_relationships', 
-                'startWith': '$_id', 
-                'connectFromField': 'relationships', 
-                'connectToField': '_id', 
-                'as': 'relates_to', 
-                'maxDepth': max_depth, 
-                'depthField': 'distance', 
-                'restrictSearchWithMatch': {}
-            }
-        }
+            {"$match": {"_id": node_name}},
+            {
+                "$graphLookup": {
+                    "from": "nodes_relationships",
+                    "startWith": "$_id",
+                    "connectFromField": "relationships",
+                    "connectToField": "_id",
+                    "as": "relates_to",
+                    "maxDepth": max_depth,
+                    "depthField": "distance",
+                    "restrictSearchWithMatch": {},
+                }
+            },
         ]
         cursor = collection.aggregate(pipeline)
         for doc in cursor: