feat: RAG server changes for consistency

presets/ragengine/inference/inference.py

@@ -6,19 +6,49 @@
 from llama_index.llms.openai import OpenAI
 from llama_index.core.llms.callbacks import llm_completion_callback
 import requests
+from urllib.parse import urlparse, urljoin
 from ragengine.config import LLM_INFERENCE_URL, LLM_ACCESS_SECRET #, LLM_RESPONSE_FIELD
 
 OPENAI_URL_PREFIX = "https://api.openai.com"
 HUGGINGFACE_URL_PREFIX = "https://api-inference.huggingface.co"
 
 class Inference(CustomLLM):
     params: dict = {}
+    model: str = ""
 
     def set_params(self, params: dict) -> None:
         self.params = params
 
     def get_param(self, key, default=None):
         return self.params.get(key, default)
+    # Get base URL 
+    def _get_base_url(self) -> str:
+        parsed = urlparse(LLM_INFERENCE_URL)
+        base_url = f"{parsed.scheme}://{parsed.netloc}"
+        return urljoin(base_url, "/v1/models")
+
+    #Fetch and set the model from the inference endpoint
+    def set_model(self) -> None:
+
+        try:
+            models_url = self._get_base_url()
+            headers = {"Authorization": f"Bearer {LLM_ACCESS_SECRET}"}
+            response = requests.get(models_url, headers=headers)
+
+            if response.status_code == 404:
+                self.model = None
+                return
+
+            response.raise_for_status()
+
+            data = response.json()
+            if data.get("data") and len(data["data"]) > 0:
+                self.model = data["data"][0]["id"]
+            else:
+                raise ValueError("No model found in response")
+
+        except requests.RequestException as e:
+            raise Exception(f"Failed to fetch model information: {str(e)}")
 
     @llm_completion_callback()
     def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
@@ -53,8 +83,14 @@ def _huggingface_remote_complete(self, prompt: str, **kwargs: Any) -> Completion
 
     def _custom_api_complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
         headers = {"Authorization": f"Bearer {LLM_ACCESS_SECRET}"}
-        data = {"prompt": prompt, **kwargs}
-
+        if self.model != None:
+            data = {"prompt": prompt, "model":self.model}
+        else:
+            data = {"prompt": prompt}
+
+        for param in self.params:
+            data[param] = self.params[param]
+
         response = requests.post(LLM_INFERENCE_URL, json=data, headers=headers)
         response_data = response.json()
 

presets/ragengine/main.py

@@ -60,7 +60,9 @@ async def index_documents(request: IndexRequest): # TODO: Research async/sync wh
 @app.post("/query", response_model=QueryResponse)
 async def query_index(request: QueryRequest):
     try:
-        llm_params = request.llm_params or {} # Default to empty dict if no params provided
+        llm_params = {}        
+        for key, value in request.model_extra.items():
+            llm_params[key] = value  
         rerank_params = request.rerank_params or {} # Default to empty dict if no params provided
         return rag_ops.query(request.index_name, request.query, request.top_k, llm_params, rerank_params)
     except Exception as e:

presets/ragengine/models.py

@@ -3,7 +3,7 @@
 
 from typing import Dict, List, Optional
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 class Document(BaseModel):
     text: str
@@ -22,7 +22,7 @@ class QueryRequest(BaseModel):
     index_name: str
     query: str
     top_k: int = 10
-    llm_params: Optional[Dict] = None  # Accept a dictionary for parameters
+    model_config = ConfigDict(extra='allow')
     rerank_params: Optional[Dict] = None # Accept a dictionary for parameters
 
 class ListDocumentsResponse(BaseModel):

presets/ragengine/tests/api/test_main.py

@@ -1,7 +1,7 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
-from unittest.mock import patch
+from unittest.mock import patch, MagicMock
 
 from llama_index.core.storage.index_store import SimpleIndexStore
 
@@ -39,7 +39,15 @@ def test_index_documents_success():
     assert not doc2["metadata"]
 
 @patch('requests.post')
-def test_query_index_success(mock_post):
+@patch('requests.get') 
+def test_query_index_success(mock_get, mock_post):
+    mock_get.return_value = MagicMock(
+        status_code=200,
+        json=lambda: {
+            "data": [{"id": "test-model"}]
+        }
+    )
+
     # Define Mock Response for Custom Inference API
     mock_response = {
         "result": "This is the completion from the API"
@@ -62,7 +70,7 @@ def test_query_index_success(mock_post):
         "index_name": "test_index",
         "query": "test query",
         "top_k": 1,
-        "llm_params": {"temperature": 0.7}
+        "temperature": 0.7
     }
 
     response = client.post("/query", json=request_data)
@@ -135,7 +143,7 @@ def test_reranker_and_query_with_index(mock_post):
         "index_name": "test_index",
         "query": "what is the capital of france?",
         "top_k": 5,
-        "llm_params": {"temperature": 0.7},
+        "temperature": 0.7,
         "rerank_params": {"top_n": top_n}
     }
 
@@ -171,14 +179,13 @@ def test_query_index_failure():
         "index_name": "non_existent_index",  # Use an index name that doesn't exist
         "query": "test query",
         "top_k": 1,
-        "llm_params": {"temperature": 0.7}
+        "temperature": 0.7
     }
 
     response = client.post("/query", json=request_data)
     assert response.status_code == 500
     assert response.json()["detail"] == "No such index: 'non_existent_index' exists."
 
-
 def test_list_all_indexed_documents_success():
     response = client.get("/indexed-documents")
     assert response.status_code == 200

presets/ragengine/tests/vector_store/test_base_store.py

@@ -2,7 +2,7 @@
 # Licensed under the MIT license.
 
 import os
-from unittest.mock import patch
+from unittest.mock import patch, MagicMock
 import pytest
 from abc import ABC, abstractmethod
 
@@ -66,7 +66,15 @@ def check_indexed_documents(self, vector_store_manager):
         pass
 
     @patch('requests.post')
-    def test_query_documents(self, mock_post, vector_store_manager):
+    @patch('requests.get') 
+    def test_query_documents(self, mock_get, mock_post, vector_store_manager):
+        mock_get.return_value = MagicMock(
+            status_code=200,
+            json=lambda: {
+                "data": [{"id": "test-model"}]
+            }
+        )
+
         mock_response = {
             "result": "This is the completion from the API"
         }
@@ -87,13 +95,23 @@ def test_query_documents(self, mock_post, vector_store_manager):
         assert query_result["source_nodes"][0]["text"] == "First document"
         assert query_result["source_nodes"][0]["score"] == pytest.approx(self.expected_query_score, rel=1e-6)
 
+        mock_get.assert_called_once()
+
         mock_post.assert_called_once_with(
             LLM_INFERENCE_URL,
-            json={"prompt": "Context information is below.\n---------------------\ntype: text\n\nFirst document\n---------------------\nGiven the context information and not prior knowledge, answer the query.\nQuery: First\nAnswer: ", "formatted": True, 'temperature': 0.7},
+            json={"prompt": "Context information is below.\n---------------------\ntype: text\n\nFirst document\n---------------------\nGiven the context information and not prior knowledge, answer the query.\nQuery: First\nAnswer: ", "model": "test-model", 'temperature': 0.7},
             headers={"Authorization": f"Bearer {LLM_ACCESS_SECRET}"}
         )
 
-    def test_add_document(self, vector_store_manager):
+    @patch('requests.get')  
+    def test_add_document(self, mock_get, vector_store_manager):
+        mock_get.return_value = MagicMock(
+            status_code=200,
+            json=lambda: {
+                "data": [{"id": "test-model"}]
+            }
+        )
+
         documents = [Document(text="Third document", metadata={"type": "text"})]
         vector_store_manager.index_documents("test_index", documents)
 

presets/ragengine/vector_store/base.py

@@ -111,6 +111,9 @@ def query(self,
         """
         if index_name not in self.index_map:
             raise ValueError(f"No such index: '{index_name}' exists.")
+        if self.llm.model == "":
+            self.llm.set_model()
+
         self.llm.set_params(llm_params)
 
         node_postprocessors = []