halfed resources

App/Embedding/utils/Initialize.py

@@ -50,24 +50,6 @@ def generateChunks(chunks, task_id, n=100):
 
 
 def search(query: str, task_id: str):
-    # get api key from app.pinecone.io
-    PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY")
-    # find your environment next to the api key in pinecone console
-    PINECONE_ENV = os.environ.get("PINECONE_ENVIRONMENT")
-
-
-    index_name = "transcript-bits"
-    model_name = "thenlper/gte-base"
-    embeddings = HuggingFaceEmbeddings(model_name=model_name)
-
-
-    pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENV)
-    vector_index = pinecone.Index(index_name=index_name)
-    docsearch = Pinecone.from_existing_index(index_name, embeddings)
-
-
-
-
     filtering_conditions = {
             "task_id": {"$eq": "task_id"},
         }
@@ -80,19 +62,5 @@ def search(query: str, task_id: str):
 
 
 def encode(temp: list[Document]):
-    PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY")
-    # find your environment next to the api key in pinecone console
-    PINECONE_ENV = os.environ.get("PINECONE_ENVIRONMENT")
-
-
-    index_name = "transcript-bits"
-    model_name = "thenlper/gte-base"
-    embeddings = HuggingFaceEmbeddings(model_name=model_name)
-
-
-    pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENV)
-    vector_index = pinecone.Index(index_name=index_name)
-    docsearch = Pinecone.from_existing_index(index_name, embeddings)
-
     docsearch.add_documents(temp)
     # return  embeddings.embed_documents(texts = [d.page_content for d in temp])

Dockerfile

@@ -22,7 +22,7 @@ USER admin
 COPY --chown=admin . /srv
 
 # Command to run the application
-CMD uvicorn App.app:app --host 0.0.0.0 --port 7860 --workers 8 & celery -A App.Worker.celery worker -c 8 --loglevel=info
+CMD uvicorn App.app:app --host 0.0.0.0 --port 7860 --workers 4 & celery -A App.Worker.celery worker -c 4 --loglevel=info
 
 # Expose the server port
 EXPOSE 7860