Added RAKE Keyword extraction and query search

MinuteMate/back/main.py

@@ -1,5 +1,22 @@
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
+from weaviate.classes.query import Rerank, MetadataQuery
+import os
+import weaviate
+from weaviate.classes.init import Auth
+from weaviate.classes.init import AdditionalConfig, Timeout
+from rake_nltk import Rake
+import nltk
+nltk.download('stopwords')
+nltk.download('punkt')
+
+
+weaviate_url = os.environ["WEAVIATE_URL"]
+weaviate_api_key = os.environ["WEAVIATE_API_KEY"]
+client = weaviate.connect_to_weaviate_cloud(
+    cluster_url=weaviate_url,
+    auth_credentials=Auth.api_key(weaviate_api_key),
+)
 
 # Initialize the FastAPI app
 app = FastAPI(
@@ -19,11 +36,10 @@ class PromptResponse(BaseModel):
 
 # Your Python processing logic
 def process_prompt(prompt: str) -> str:
-    response = ''
-
-    #Call whatever code we need to here
-
-    return response
+    rake = Rake()
+    rake.extract_keywords_from_text(prompt)
+    return rake.get_ranked_phrases()[:3] 
+
 
 # API endpoint
 @app.post("/process-prompt", response_model=PromptResponse)
@@ -36,3 +52,22 @@ async def process_prompt_endpoint(request: PromptRequest):
         return PromptResponse(result=result)
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
+
+
+
+# This is the code to get the collections based on top 3 keywords we are fetching from the RAKE code. 
+# You can add this code block below wherever you are configuring your API
+collection = client.collections.get("MeetingDocument")
+response = collection.query.bm25(
+    query=",".join(keywords),
+    limit=5,
+    # rerank=Rerank(
+    #     prop="content",
+    #     query="meeting"
+    # ),
+    # return_metadata=MetadataQuery(score=True)
+)
+
+for o in response.objects:
+    print(o.properties)
+    # print(o.metadata.rerank_score)