fix: Dockerfile of llmsearch and docker-comopse.yml for renaming

docker/Dockerfile

@@ -1,4 +1,4 @@
-FROM semantic-search-base:latest
+FROM llmsearch-base:latest
 
 WORKDIR /app
 

docker/Dockerfile.base

@@ -5,7 +5,8 @@ WORKDIR /app
 COPY requirements.txt requirements_production.txt ./
 
 # Install dependencies
-RUN pip install --no-cache-dir -r requirements.txt
+RUN pip install --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu124
 RUN pip install --no-cache-dir -r requirements_production.txt
 
 # Copy the rest of your application code to the container

docker/docker-compose.yml

@@ -1,35 +1,35 @@
 services:
   postgres:
-    container_name: ss-postgres
+    container_name: llmsearch-postgres
     image: postgres:latest
     environment:
       - POSTGRES_USER=postgres
       - POSTGRES_PASSWORD=postgres
-      - POSTGRES_DB=ssdb
+      - POSTGRES_DB=llmsearchdb
     # networks:
-    #   - semantic-search
+    #   - llmsearch
     volumes:
       - pgdata:/var/lib/postgresql/data
     ports:
       - 35432:5432
     restart: always
 
   pgadmin:
-    container_name: ss-pgadmin
+    container_name: llmsearch-pgadmin
     image: dpage/pgadmin4:latest
     environment:
       - [email protected]
       - PGADMIN_DEFAULT_PASSWORD=123456
     # networks:
-    #   - semantic-search
+    #   - llmsearch
     volumes:
       - pgadmin-data:/var/lib/pgadmin
     ports:
       - 35050:80
     restart: always
 
   elasticsearch:
-    container_name: ss-elasticsearch
+    container_name: llmsearch-elasticsearch
     image: docker.elastic.co/elasticsearch/elasticsearch:8.15.0
     environment:
       - discovery.type=single-node
@@ -41,12 +41,12 @@ services:
       - "39200:9200"
       - "39300:9300"
     # networks:
-    #   - semantic-search
+    #   - llmsearch
     volumes:
       - esdata:/usr/share/elasticsearch/data
 
   # opensearch-node1:
-  #   container_name: ss-opensearch-node1
+  #   container_name: llmsearch-opensearch-node1
   #   image: opensearchproject/opensearch:2.5.0 # You can choose the version you need
   #   environment:
   #     - cluster.name=opensearch-cluster
@@ -70,26 +70,26 @@ services:
   #     - "39200:9200" # OpenSearch REST API
   #     - "39600:9600" # Performance Analyzer
   #   # networks:
-  #   #   - semantic-search
+  #   #   - llmsearch
   #   healthcheck:
   #     test: ["CMD", "curl", "-f", "http://localhost:9200"]
   #     interval: 30s
   #     timeout: 10s
   #     retries: 5
 
   # opensearch-dashboards:
-  #   container_name: ss-opensearch-dashboards
+  #   container_name: llmsearch-opensearch-dashboards
   #   image: opensearchproject/opensearch-dashboards:2.5.0
   #   ports:
   #     - "35601:5601" # OpenSearch Dashboards
   #   environment:
   #     OPENSEARCH_HOSTS: '["http://opensearch-node1:9200"]'
   #     OPENSEARCH_SECURITY_ADMIN_PASSWORD: admin_password # Set the admin password here for dashboards
   #   # networks:
-  #   #   - semantic-search
+  #   #   - llmsearch
 
 # networks:
-#   semantic-search:
+#   llmsearch:
 #     driver: bridge
 
 volumes:

src/clients/openai.py

@@ -1,7 +1,7 @@
-
 import openai
 from typing import Optional, List
 
+
 class OpenAIClient:
     def __init__(self, api_key: str, model: str):
         """
@@ -14,7 +14,13 @@ def __init__(self, api_key: str, model: str):
         openai.api_key = api_key
         self.model = model
 
-    def generate(self, prompt: str, model: str, max_tokens: Optional[int] = 150, temperature: Optional[float] = 1.0) -> Optional[str]:
+    def generate(
+        self,
+        prompt: str,
+        model: str,
+        max_tokens: Optional[int] = 150,
+        temperature: Optional[float] = 1.0,
+    ) -> Optional[str]:
         """
         Sends a request to OpenAI's API to generate text based on the input prompt.
 
@@ -31,9 +37,13 @@ def generate(self, prompt: str, model: str, max_tokens: Optional[int] = 150, tem
                 model=self.model,
                 prompt=prompt,
                 max_tokens=max_tokens,
-                temperature=temperature
+                temperature=temperature,
+            )
+            return (
+                response.choices[0].text.strip()
+                if response and len(response.choices) > 0
+                else None
             )
-            return response.choices[0].text.strip() if response and len(response.choices) > 0 else None
         except Exception as e:
             print(f"Error during the request to OpenAI: {e}")
             return None
@@ -49,11 +59,12 @@ def get_embedding(self, text: str) -> Optional[List[float]]:
             list: A list of floats representing the embedding vector, or None if the request fails.
         """
         try:
-            response = openai.Embedding.create(
-                model=self.model,
-                input=text
+            response = openai.Embedding.create(model=self.model, input=text)
+            return (
+                response.data[0]["embedding"]
+                if response and "data" in response and len(response.data) > 0
+                else None
             )
-            return response.data[0]['embedding'] if response and 'data' in response and len(response.data) > 0 else None
         except Exception as e:
             print(f"Error during the request to OpenAI for embedding: {e}")
             return None
@@ -68,6 +79,7 @@ def update_model(self, new_model: str) -> None:
         self.model = new_model
         print(f"Updated model to: {self.model}")
 
+
 # Example usage:
 if __name__ == "__main__":
     # Initialize the client
@@ -80,11 +92,12 @@ def update_model(self, new_model: str) -> None:
         print(f"Generated Text: {generated_text}")
 
     # Example 2: Get embeddings for input text
-    input_text = "Quantum mechanics and general relativity are fundamental theories in physics."
+    input_text = (
+        "Quantum mechanics and general relativity are fundamental theories in physics."
+    )
     embedding = client.get_embedding(input_text)
     if embedding:
         print(f"Embedding: {embedding}")
 
     # Example 3: Update the model used by the client
     client.update_model("text-embedding-ada-002")
-