Add basic langchain demo

examples/langchain_custom_llm.py

@@ -0,0 +1,29 @@
+from llama_cpp import Llama
+
+from langchain.llms.base import LLM
+from typing import Optional, List, Mapping, Any
+
+class LlamaLLM(LLM):
+    model_path: str
+    llm: Llama
+
+    @property
+    def _llm_type(self) -> str:
+        return "llama-cpp-python"
+
+    def __init__(self, model_path: str, **kwargs: Any):
+        model_path = model_path
+        llm = Llama(model_path=model_path)
+        super().__init__(model_path=model_path, llm=llm,  **kwargs)
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        response = self.llm(prompt, stop=stop or [])
+        return response["choices"][0]["text"]
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        return {"model_path": self.model_path}
+
+llm = LlamaLLM(model_path="models/...")
+
+print(llm("Question: What is the capital of France? Answer: ", stop=["Question:", "\n"]))