> ## Documentation Index
> Fetch the complete documentation index at: https://nusaai-edit.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# DSPy

> > Using DSPy with Neosantara AI.

DSPy is a framework for programming language models rather than relying on static prompts. It enables you to build modular AI systems with code instead of hand-crafted prompting, and it offers methods to automatically optimize these systems.

Features

* Programmatic approach to LLM interactions through Python
* Modular components for building complex AI pipelines
* Self-improvement algorithms that optimize prompts and weights
* Support for various applications from simple classifiers to RAG systems and agent loops

## Installing Libraries

<CodeGroup>
  ```shell Shell theme={null}
  pip install -U dspy -q
  ```
</CodeGroup>

Set your Neosantara AI API key:

<CodeGroup>
  ```shell Shell theme={null}
  export NAI_API_KEY=***
  ```
</CodeGroup>

## Example

Setup and connect DSPy to LLMs on Neosantara AI

<CodeGroup>
  ```python Python icon="python" theme={null}
  import dspy

  #Configure dspy with a LLM from Neosantara AI
  lm = dspy.LM('openai/nusantara-nase',
               api_key=os.environ.get("NAI_API_KEY"),
               api_base="https://api.neosantara.xyz/v1")

  #now you can call the LLM directly as follows
  lm("Say this is a test!", temperature=0.7)  # => ['Ini adalah sebuah tes.']
  lm(messages=[{"role": "user", "content": "Say this is a test!"}])  # => ['Ini adalah sebuah tes.']
  ```
</CodeGroup>

Now we can set up a DSPy module, like `dspy.ReAct` with a task-specific signature. For example, `question -> answer: str` tells the module to take a question and to produce a string answer below.

<CodeGroup>
  ```python Python icon="python" theme={null}
  #Configure dspy to use the LLM
  import dspy

  #Configure dspy with a LLM from Neosantara AI
  lm = dspy.LM('openai/archipelago-70b',
               api_key=os.environ.get("NAI_API_KEY"),
               api_base="https://api.neosantara.xyz/v1")

  #Configure dspy to use the LLM
  dspy.configure(lm=lm)

  ## Gives the agent access to a wikipedia search tool
  def search_wikipedia(query: str):
      # Use a more general search or the specific page if known to be reliable
      # For a general search, you might use a different tool or API
      # For this specific case, we stick to the provided URL but simplify the output
      results = dspy.ColBERTv2(url='https://id.m.wikipedia.org/wiki/Prabowo_Subianto')(query, k=5) # Get 5 result
      # Return the raw text content for the LLM to process
      return results[0]['text'] if results else "No relevant information found."


  ## setup ReAct module with question and string answer signature
  # Changed the signature to expect a string answer
  react = dspy.ReAct("question -> answer: str", tools=[search_wikipedia])

  # Use the question that needs a text answer
  pred = react(question="when the birthday prabowo?")

  print(pred.answer)
  ```
</CodeGroup>

<Accordion title="See output">
  The output will return an string for answer below:

  ```bash output theme={null}
   Prabowo Subianto lahir pada tanggal 17 Oktober 1951.
  ```
</Accordion>