MistralAI Codestral 烹饪手册¶

MistralAI 发布了 codestral-latest - 一个代码模型。

Codestral 是 MistralAI 新推出的代码模型，专为代码生成而设计，精通 80 多种编程语言。它通过完成函数、编写测试和填充代码片段来简化编码任务，提高开发人员效率并减少错误。Codestral 通过统一的 API 端点进行操作，使其成为软件开发的多功能工具。

本烹饪手册展示了如何使用 codestral-latest 模型与 llama-index。它指导您使用 Codestral 的“填充中间”(fill-in-the-middle) 和指令 (instruct) 端点。

设置 LLM¶

In [ ]

import os

os.environ["MISTRAL_API_KEY"] = "<YOUR MISTRAL API KEY>"

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(model="codestral-latest", temperature=0.1)

import os os.environ["MISTRAL_API_KEY"] = "" from llama_index.llms.mistralai import MistralAI llm = MistralAI(model="codestral-latest", temperature=0.1)

指令模式用法¶

编写一个斐波那契函数¶

In [ ]

from llama_index.core.llms import ChatMessage

messages = [ChatMessage(role="user", content="Write a function for fibonacci")]

response = llm.chat(messages)

print(response)

from llama_index.core.llms import ChatMessage messages = [ChatMessage(role="user", content="Write a function for fibonacci")] response = llm.chat(messages) print(response)

assistant: Sure, here is a simple Python function that calculates the nth number in the Fibonacci sequence:

```python
def fibonacci(n):
    if n <= 0:
        print("Input should be positive integer.")
    elif n == 1:
        return 0
    elif n == 2:
        return 1
    else:
        a, b = 0, 1
        for i in range(2, n):
            a, b = b, a + b
        return b
```

You can use this function to find the nth number in the Fibonacci sequence by calling `fibonacci(n)`, where `n` is the position of the number you want to find. For example, `fibonacci(10)` will return the 10th number in the Fibonacci sequence.

使用 LlamaIndex 构建 RAG 流水线的函数¶

注意：输出大部分准确，但基于旧版本的 LlamaIndex 包。

In [ ]

messages = [
    ChatMessage(
        role="user",
        content="Write a function to build RAG pipeline using LlamaIndex.",
    )
]

response = llm.chat(messages)

print(response)

messages = [ ChatMessage( role="user", content="Write a function to build RAG pipeline using LlamaIndex.", ) ] response = llm.chat(messages) print(response)

assistant: Sure, I can help you with that. Here's a basic example of how you can build a Retrieval Augmented Generation (RAG) pipeline using LlamaIndex. This example assumes that you have a list of documents.

```python
from llama_index import VectorStoreIndex, SimpleDirectoryReader

def build_rag_pipeline(documents_path):
    # Load documents
    documents = SimpleDirectoryReader(documents_path).load_data()

    # Create index
    index = VectorStoreIndex.from_documents(documents)

    # Create query engine
    query_engine = index.as_query_engine()

    return query_engine

# Usage
query_engine = build_rag_pipeline("path_to_your_documents")
response = query_engine.query("Your query here")
print(response)
```

In this code:

1. We first import the necessary classes from LlamaIndex.
2. We define a function `build_rag_pipeline` that takes a path to a directory of documents as input.
3. We load the documents using `SimpleDirectoryReader`.
4. We create an index from the documents using `VectorStoreIndex.from_documents`.
5. We create a query engine from the index using `index.as_query_engine`.
6. Finally, we return the query engine.

You can use the query engine to ask questions about the documents. The query engine will use the index to retrieve relevant documents and then generate a response based on those documents.

填充中间¶

此功能允许用户设置一个带有提示的起始点，以及一个带有后缀和停止符的可选结束点。然后 Codestral 模型会生成中间的代码，非常适合需要特定代码生成的任务。

用代码的开始和结束填充代码¶

In [ ]

prompt = "def multiply("
suffix = "return a*b"

response = llm.fill_in_middle(prompt, suffix)

print(
    f"""
{prompt}
{response.text}
{suffix}
"""
)

prompt = "def multiply(" suffix = "return a*b" response = llm.fill_in_middle(prompt, suffix) print( f""" {prompt} {response.text} {suffix} """ )

def multiply(
a, b):
  """
  This function multiplies two numbers
  """
  
return a*b

用代码的开始、结束和停止 token 填充代码¶

In [ ]

prompt = "def multiply(a,"
suffix = ""
stop = ["\n\n\n"]

response = llm.fill_in_middle(prompt, suffix, stop)

print(
    f"""
{prompt}
{response.text}
{suffix}
"""
)

prompt = "def multiply(a," suffix = "" stop = ["\n\n\n"] response = llm.fill_in_middle(prompt, suffix, stop) print( f""" {prompt} {response.text} {suffix} """ )

def multiply(a,
 b):

    return a * b

# test the function
print(multiply(2, 3))  # should print 6
print(multiply(-1, 5))  # should print -5
print(multiply(0, 99))  # should print 0

# we can also test the function with large numbers
print(multiply(123456789, 987654321))  # should print 121932631132635269

# the function should also work with floating point numbers
print(multiply(3.14, 2.71))  # should print approximately 8.5392

# the function should also work with negative floating point numbers
print(multiply(-3.14, 2.71))  # should print approximately -8.5392

# the function should also work with mixed types (integer and floating point)
print(multiply(2, 3.14))  # should print approximately 6.28