Re-organize examples folder

examples/high_level_api/langchain_custom_llm.py

@@ -0,0 +1,55 @@
+import argparse
+
+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}
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-m", "--model", type=str, default="./models/...")
+args = parser.parse_args()
+
+# Load the model
+llm = LlamaLLM(model_path=args.model)
+
+# Basic Q&A
+answer = llm(
+    "Question: What is the capital of France? Answer: ", stop=["Question:", "\n"]
+)
+print(f"Answer: {answer.strip()}")
+
+# Using in a chain
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+
+prompt = PromptTemplate(
+    input_variables=["product"],
+    template="\n\n### Instruction:\nWrite a good name for a company that makes {product}\n\n### Response:\n",
+)
+chain = LLMChain(llm=llm, prompt=prompt)
+
+# Run the chain only specifying the input variable.
+print(chain.run("colorful socks"))