feat: Part 2 - Add custom LLM inference class (#630)

**Reason for Change**:
This series of PR will integrate llamaindex RAG service for Kaito.

This PR contains the custom LLM inference class for llamaindex. We need
this class because we use custom HTTP endpoint or OpenAI API for
handling LLM requests so we need a custom LLM inference class.


https://docs.llamaindex.ai/en/stable/module_guides/models/llms/usage_custom/#example-using-a-custom-llm-model-advanced

ragengine/config.py

@@ -0,0 +1,20 @@
+# config.py
+
+# Variables are set via environment variables from the RAGEngine CR
+# and exposed to the pod. For example, InferenceURL is specified in the CR and 
+# passed to the pod via env variables.
+
+import os
+
+EMBEDDING_TYPE = os.getenv("EMBEDDING_TYPE", "local")
+EMBEDDING_URL = os.getenv("EMBEDDING_URL")
+
+INFERENCE_URL = os.getenv("INFERENCE_URL", "http://localhost:5000/chat")
+INFERENCE_ACCESS_SECRET = os.getenv("AccessSecret", "default-inference-secret")
+# RESPONSE_FIELD = os.getenv("RESPONSE_FIELD", "result")
+
+MODEL_ID = os.getenv("MODEL_ID", "BAAI/bge-small-en-v1.5")
+VECTOR_DB_TYPE = os.getenv("VECTOR_DB_TYPE", "faiss")
+INDEX_SERVICE_NAME = os.getenv("INDEX_SERVICE_NAME", "default-index-service")
+ACCESS_SECRET = os.getenv("ACCESS_SECRET", "default-access-secret")
+PERSIST_DIR = "storage"

ragengine/inference/inference.py

@@ -0,0 +1,53 @@
+from typing import Any
+from llama_index.core.llms import CustomLLM, CompletionResponse, LLMMetadata, CompletionResponseGen
+from llama_index.llms.openai import OpenAI
+from llama_index.core.llms.callbacks import llm_completion_callback
+import requests
+from config import INFERENCE_URL, INFERENCE_ACCESS_SECRET #, RESPONSE_FIELD
+
+class Inference(CustomLLM):
+    params: dict = {}
+
+    def set_params(self, params: dict) -> None:
+        self.params = params
+
+    def get_param(self, key, default=None):
+        return self.params.get(key, default)
+
+    @llm_completion_callback()
+    def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
+        pass
+
+    @llm_completion_callback()
+    def complete(self, prompt: str, **kwargs) -> CompletionResponse:
+        try:
+            if "openai" in INFERENCE_URL:
+                return self._openai_complete(prompt, **kwargs, **self.params)
+            else:
+                return self._custom_api_complete(prompt, **kwargs, **self.params)
+        finally:
+            # Clear params after the completion is done
+            self.params = {}
+
+    def _openai_complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
+        llm = OpenAI(
+            api_key=INFERENCE_ACCESS_SECRET,
+            **kwargs  # Pass all kwargs directly; kwargs may include model, temperature, max_tokens, etc.
+        )
+        return llm.complete(prompt)
+
+    def _custom_api_complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
+        headers = {"Authorization": f"Bearer {INFERENCE_ACCESS_SECRET}"}
+        data = {"prompt": prompt, **kwargs}
+
+        response = requests.post(INFERENCE_URL, json=data, headers=headers)
+        response_data = response.json()
+
+        # Dynamically extract the field from the response based on the specified response_field
+        # completion_text = response_data.get(RESPONSE_FIELD, "No response field found") # not necessary for now
+        return CompletionResponse(text=str(response_data))
+
+    @property
+    def metadata(self) -> LLMMetadata:
+        """Get LLM metadata."""
+        return LLMMetadata()