Re-organize examples folder

examples/high_level_api/fastapi_server.py

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+"""Example FastAPI server for llama.cpp.
+
+To run this example:
+
+```bash
+pip install fastapi uvicorn sse-starlette
+export MODEL=../models/7B/...
+uvicorn fastapi_server_chat:app --reload
+```
+
+Then visit http://localhost:8000/docs to see the interactive API docs.
+
+"""
+import os
+import json
+from typing import List, Optional, Literal, Union, Iterator
+
+import llama_cpp
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, BaseSettings, Field, create_model_from_typeddict
+from sse_starlette.sse import EventSourceResponse
+
+
+class Settings(BaseSettings):
+    model: str
+    n_ctx: int = 2048
+    n_batch: int = 2048
+    n_threads: int = os.cpu_count() or 1
+    f16_kv: bool = True
+    use_mlock: bool = True
+    embedding: bool = True
+    last_n_tokens_size: int = 64
+
+
+app = FastAPI(
+    title="🦙 llama.cpp Python API",
+    version="0.0.1",
+)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+settings = Settings()
+llama = llama_cpp.Llama(
+    settings.model,
+    f16_kv=settings.f16_kv,
+    use_mlock=settings.use_mlock,
+    embedding=settings.embedding,
+    n_threads=settings.n_threads,
+    n_batch=settings.n_batch,
+    n_ctx=settings.n_ctx,
+    last_n_tokens_size=settings.last_n_tokens_size,
+)
+
+
+class CreateCompletionRequest(BaseModel):
+    prompt: str
+    suffix: Optional[str] = Field(None)
+    max_tokens: int = 16
+    temperature: float = 0.8
+    top_p: float = 0.95
+    logprobs: Optional[int] = Field(None)
+    echo: bool = False
+    stop: List[str] = []
+    repeat_penalty: float = 1.1
+    top_k: int = 40
+    stream: bool = False
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "prompt": "\n\n### Instructions:\nWhat is the capital of France?\n\n### Response:\n",
+                "stop": ["\n", "###"],
+            }
+        }
+
+
+CreateCompletionResponse = create_model_from_typeddict(llama_cpp.Completion)
+
+
+@app.post(
+    "/v1/completions",
+    response_model=CreateCompletionResponse,
+)
+def create_completion(request: CreateCompletionRequest):
+    if request.stream:
+        chunks: Iterator[llama_cpp.CompletionChunk] = llama(**request.dict())  # type: ignore
+        return EventSourceResponse(dict(data=json.dumps(chunk)) for chunk in chunks)
+    return llama(**request.dict())
+
+
+class CreateEmbeddingRequest(BaseModel):
+    model: Optional[str]
+    input: str
+    user: Optional[str]
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "input": "The food was delicious and the waiter...",
+            }
+        }
+
+
+CreateEmbeddingResponse = create_model_from_typeddict(llama_cpp.Embedding)
+
+
+@app.post(
+    "/v1/embeddings",
+    response_model=CreateEmbeddingResponse,
+)
+def create_embedding(request: CreateEmbeddingRequest):
+    return llama.create_embedding(**request.dict(exclude={"model", "user"}))
+
+
+class ChatCompletionRequestMessage(BaseModel):
+    role: Union[Literal["system"], Literal["user"], Literal["assistant"]]
+    content: str
+    user: Optional[str] = None
+
+
+class CreateChatCompletionRequest(BaseModel):
+    model: Optional[str]
+    messages: List[ChatCompletionRequestMessage]
+    temperature: float = 0.8
+    top_p: float = 0.95
+    stream: bool = False
+    stop: List[str] = []
+    max_tokens: int = 128
+    repeat_penalty: float = 1.1
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "messages": [
+                    ChatCompletionRequestMessage(
+                        role="system", content="You are a helpful assistant."
+                    ),
+                    ChatCompletionRequestMessage(
+                        role="user", content="What is the capital of France?"
+                    ),
+                ]
+            }
+        }
+
+
+CreateChatCompletionResponse = create_model_from_typeddict(llama_cpp.ChatCompletion)
+
+
+@app.post(
+    "/v1/chat/completions",
+    response_model=CreateChatCompletionResponse,
+)
+async def create_chat_completion(
+    request: CreateChatCompletionRequest,
+) -> Union[llama_cpp.ChatCompletion, EventSourceResponse]:
+    completion_or_chunks = llama.create_chat_completion(
+        **request.dict(exclude={"model"}),
+    )
+
+    if request.stream:
+
+        async def server_sent_events(
+            chat_chunks: Iterator[llama_cpp.ChatCompletionChunk],
+        ):
+            for chat_chunk in chat_chunks:
+                yield dict(data=json.dumps(chat_chunk))
+            yield dict(data="[DONE]")
+
+        chunks: Iterator[llama_cpp.ChatCompletionChunk] = completion_or_chunks  # type: ignore
+
+        return EventSourceResponse(
+            server_sent_events(chunks),
+        )
+    completion: llama_cpp.ChatCompletion = completion_or_chunks  # type: ignore
+    return completion

examples/high_level_api/high_level_api_embedding.py

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+import argparse
+
+from llama_cpp import Llama
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-m", "--model", type=str, default=".//models/...")
+args = parser.parse_args()
+
+llm = Llama(model_path=args.model, embedding=True)
+
+print(llm.create_embedding("Hello world!"))

examples/high_level_api/high_level_api_inference.py

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+import json
+import argparse
+
+from llama_cpp import Llama
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-m", "--model", type=str, default="./models/...")
+args = parser.parse_args()
+
+llm = Llama(model_path=args.model)
+
+output = llm(
+    "Question: What are the names of the planets in the solar system? Answer: ",
+    max_tokens=48,
+    stop=["Q:", "\n"],
+    echo=True,
+)
+
+print(json.dumps(output, indent=2))

examples/high_level_api/high_level_api_streaming.py

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+import json
+import argparse
+
+from llama_cpp import Llama
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-m", "--model", type=str, default="./models/...")
+args = parser.parse_args()
+
+llm = Llama(model_path=args.model)
+
+stream = llm(
+    "Question: What are the names of the planets in the solar system? Answer: ",
+    max_tokens=48,
+    stop=["Q:", "\n"],
+    stream=True,
+)
+
+for output in stream:
+    print(json.dumps(output, indent=2))

examples/high_level_api/langchain_custom_llm.py

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+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"))