Added create index api

app.py

@@ -1,6 +1,6 @@
 from dotenv import load_dotenv
 load_dotenv()
-
+import requests
 from flask import Flask, request, jsonify
 from flask_cors import CORS, cross_origin
 import os
@@ -14,13 +14,7 @@
 client = MongoClient(os.getenv("MONGODB_URI"), server_api=ServerApi('1'))
 
 # connect to Atlas as a vector store
-store = MongoDBAtlasVectorSearch(
-    client,
-    db_name=os.getenv('MONGODB_DATABASE'), # this is the database where you stored your embeddings
-    collection_name=os.getenv('MONGODB_VECTORS'), # this is where your embeddings were stored in 2_load_and_index.py
-    index_name=os.getenv('MONGODB_VECTOR_INDEX') # this is the name of the index you created after loading your data
-)
-index = VectorStoreIndex.from_vector_store(store)
+
 
 app = Flask(__name__)
 cors = CORS(app)
@@ -34,20 +28,121 @@ def hello_world():
         "message": "hello world"
     })
 
+@app.route('/create_index', methods=['POST'])
+@cross_origin()
+def create_index():
+    user_id = request.json['user_id']
+    database_name = "test"
+    collection_name = user_id.split('@')[0] + '_invoice'
+    vector_collection_name = user_id.split('@')[0] + '_invoice_vector'
+    vector_index_name = (user_id.split('@')[0] + '_invoice_vector_index').replace('.', '_')
+    uri = os.getenv("MONGODB_API_URI")
+    headers = {
+        'Content-Type': 'application/json',
+        'Accept': 'application/vnd.atlas.2023-02-01+json',
+    }
+    payload = {
+        "collectionName": vector_collection_name,
+        "database": database_name,
+        "name": vector_index_name,
+        "type": "search",
+        "mappings": {
+            "dynamic": True,
+            "fields": {
+                "embedding": {
+                    "dimensions": 1536,
+                    "similarity": "cosine",
+                    "type": "knnVector"
+                }
+            }
+        }
+    }
+    try:
+        response = requests.post(
+            f"{uri}/groups/{os.getenv('MONGODB_ATLAS_GROUP_ID')}/clusters/{os.getenv('MONGODB_ATLAS_CLUSTER_NAME')}/fts/indexes",
+            headers=headers,
+            auth=requests.auth.HTTPDigestAuth(os.getenv('MONGODB_ATLAS_USERNAME'), os.getenv('MONGODB_ATLAS_PASSWORD')),
+            json=payload
+        )
+        response.raise_for_status()
+        return jsonify({"status": "success", "data": response.json()})
+    except requests.exceptions.HTTPError as e:
+        return jsonify({"status": "fail", "error": e.response.json()}), e.response.status_code
+    except requests.exceptions.RequestException as e:
+        return jsonify({"status": "fail", "error": str(e)}), 500
+    except Exception as e:
+        return jsonify({"status": "fail", "error": str(e)}), 500
+
+@app.route('/list_indexes/<database_name>/<collection_name>', methods=['GET'])
+@cross_origin()
+def list_indexes(database_name, collection_name):
+    uri = os.getenv("MONGODB_API_URI")
+    headers = {
+        'Content-Type': 'application/json',
+        'Accept': 'application/vnd.atlas.2023-02-01+json',
+    }
+    try:
+        response = requests.get(
+            f"{uri}/groups/{os.getenv('MONGODB_ATLAS_GROUP_ID')}/clusters/{os.getenv('MONGODB_ATLAS_CLUSTER_NAME')}/fts/indexes/{database_name}/{collection_name}",
+            headers=headers,
+            auth=requests.auth.HTTPDigestAuth(os.getenv('MONGODB_ATLAS_USERNAME'), os.getenv('MONGODB_ATLAS_PASSWORD')),
+        )
+        response.raise_for_status()
+        return jsonify({"status": "success", "data": response.json()})
+    except requests.exceptions.HTTPError as e:
+        return jsonify({"status": "fail", "error": e.response.json()}), e.response.status_code
+    except requests.exceptions.RequestException as e:
+        return jsonify({"status": "fail", "error": str(e)}), 500
+    except Exception as e:
+        return jsonify({"status": "fail", "error": str(e)}), 500
+
+
+
+
 @app.route('/process', methods=['POST'])
 @cross_origin()
 def process():
-    is_processed = process_entries(client = client)
-    if not is_processed:
-        return jsonify({"status": "failed", "error": "process failed"}), 400
-    return jsonify({"status": "success", "message": "process successful"})
+    try:
+        user_id = request.json['user_id']
+        database_name = "test"
+        collection_name = user_id.split('@')[0] + '_invoice'
+        vector_collection_name = user_id.split('@')[0] + '_invoice_vector'
+        vector_index_name = (user_id.split('@')[0] + '_invoice_vector_index').replace('.', '_')
+        is_processed = process_entries(
+            client=client,
+            database_name=database_name,
+            collection_name=collection_name,
+            vector_collection_name=vector_collection_name,
+            vector_index_name=vector_index_name
+        )
+
+        if not is_processed:
+            return jsonify({"status": "fail", "error": "process failed"}), 400
+
+        return jsonify({"status": "success", "message": "process successful"}), 200
+    except KeyError:
+        return jsonify({"status": "fail", "error": "invalid request body"}), 400
+    except Exception as e:
+        return jsonify({"status": "fail", "error": f"{e}"}), 500
 
 
 @app.route('/query', methods=['POST'])
 @cross_origin()
 def process_form():
     # get the query
     query = request.json["query"]
+    user_id = request.json['user_id']
+    database_name = "test"
+    vector_collection_name = user_id.split('@')[0] + '_invoice_vector'
+    vector_index_name = (user_id.split('@')[0] + '_invoice_vector_index').replace('.', '_')
+    store = MongoDBAtlasVectorSearch(
+        client,
+        db_name=database_name, # this is the database where you stored your embeddings
+        collection_name=vector_collection_name, # this is where your embeddings were stored in 2_load_and_index.py
+        index_name=vector_index_name # this is the name of the index you created after loading your data
+    )
+    index = VectorStoreIndex.from_vector_store(store)
+
 
     if query is not None:
         # query your data!

process.py

@@ -28,24 +28,26 @@
 # llamaindex has a special class that does this for you
 # it pulls every object in a given collection
 
-def process_entries(client):
-    collection = client[os.getenv("MONGODB_DATABASE")][os.getenv("MONGODB_COLLECTION")]
+def process_entries(client, database_name, collection_name, vector_collection_name, vector_index_name):
+    # connect to Atlas as a vector store
+
     query_dict = {"processed": False}
+    collection = client[database_name][collection_name]
     unprocessed_entries = collection.find(query_dict)
     reader = SimpleMongoReader(uri=os.getenv("MONGODB_URI"))
     documents = reader.load_data(
-        os.getenv("MONGODB_DATABASE"),
-        os.getenv("MONGODB_COLLECTION"), # this is the collection where the objects you loaded in 1_import got stored
+        database_name,
+        collection_name, # this is the collection where the objects you loaded in 1_import got stored
         # field_names=["saleDate", "items", "storeLocation", "customer", "couponUsed", "purchaseMethod"], # these is a list of the top-level fields in your objects that will be indexed
         field_names=["text"],                               # make sure your objects have a field called "full_text" or that you change this value
         query_dict=query_dict # this is a mongo query dict that will filter your data if you don't want to index everything
     )
 
     store = MongoDBAtlasVectorSearch(
         client,
-        db_name=os.getenv('MONGODB_DATABASE'),
-        collection_name=os.getenv('MONGODB_VECTORS'), # this is where your embeddings will be stored
-        index_name=os.getenv('MONGODB_VECTOR_INDEX') # this is the name of the index you will need to create
+        db_name=database_name, # this is the database where you stored your embeddings
+        collection_name=vector_collection_name, # this is where your embeddings will be stored
+        index_name=vector_index_name # this is the name of the index you will need to create
     )
     # # create Atlas as a vector store
     # now create an index from all the Documents and store them in Atlas