Update retriever.py

retriever.py

@@ -4,50 +4,29 @@ from langchain.docstore.document import Document
 import datasets
 
 
-class GuestInfoRetrieverTool(Tool):
-    name = "guest_info_retriever"
-    description = "Retrieves detailed information about gala guests based on their name or relation, in order to lauch the conversation."
-    inputs = {
-        "query": {
-            "type": "string",
-            "description": "The name or relation of the guest you want information about."
-        }
-    }
+class FrugalAI_methods(Tool):
+    name = "Frugal_AI_methods_retriever"
+    description = "Retrieves methods for model frugalization."
     output_type = "string"
 
-    def __init__(self, docs):
-        self.is_initialized = False
-        self.retriever = BM25Retriever.from_documents(docs)
-       
-
-    def forward(self, query: str):
-        results = self.retriever.invoke("Description")#get_relevant_documents(query)
-        if results:
-            return "\n\n" .join([doc.page_content for doc in results[:3]])
-        else:
-            return "No matching guest information found."
-
-
-def load_guest_dataset():
-    # Load the dataset
-    guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
-
-    # Convert dataset entries into Document objects
-    docs = [
-        Document(
-            page_content="\n".join([
-                f"Name: {guest['name']}",
-                f"Relation: {guest['relation']}",
-                f"Description: {guest['description']}",
-                f"Email: {guest['email']}"
-            ]),
-            metadata={"name": guest["name"]}
+    def pruning(self):
+        """
+        Optimizes models by removing unnecessary components, such as certain weights in a neural network. 
+        This function demonstrates how to apply pruning.
+        """
+        model = apply_pruning(model, amount=0.3)
+        code = "model = apply_pruning(model, amount=0.3)"  
+        return (
+            f"To apply pruning to a model, use the following code snippet: {code}. "
+            f"You should adapt it to your actual implementation. In particular, the 'amount' parameter "
+            f"can be increased or decreased depending on the initial number of weights and the complexity of your use case (minimu value: 0, maximum value: 1)."
         )
-        for guest in guest_dataset
-    ]
-
-    # Return the tool
-    return GuestInfoRetrieverTool(docs)
-
-
 
+    def quantization(self):
+        """
+        Converts high-precision weights into lower-precision one to reduce cost. 
+        """
+        code = "model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)"
+        return (
+            f"To apply quantization to a model, use the following code snippet: {code}."
+        )