tuneapi documentation

Welcome to the documentation for tuneapi package. This combines the most commonly used python utilities across Tune AI into a single MIT-licensed package. It contains 3 major submodules: - tuneapi.apis: Contains all the APIs that are used to interact with the Tune AI services and LLM providers - tuneapi.types: Contains all the types that are used generally - tuneapi.utils: Contains all the utility functions that are used across the Tune AI codebase. This is pretty interesting - tuneapi.endpoints: Contains all the API endpoints for the Tune Studio

Prompt for Chat

Since tuneapi is a new and unpopular package most LLMs will not be able to generate code based on it. However you can paste the following code snippet in the prompt to generate the code for LLM API calls.

You are going to use `tuneapi` package to use the LLM endpoint. Use structured generation for logical parts of
the process. Here's an example on how to use `tuneapi`:

```python
from tuneapi import tt, ta

# define a thread which is a collection of messages with system, user and assistant messages
thread = tt.Thread(
    tt.system(...),   # add optional system message here
    tt.human(...),    # add user message here
    tt.assistant(...) # for assistant response
)

# define a model
model = ta.Gemini() # other LLMs: Openai, Anthropic, Groq, TuneModel, Mistral

# get the response
resp: str = model.chat(thread)

# You can also generate structured response for better control
from pydantic import BaseModel, Field
from typing import List, Optional

class MathProblem(BaseModel):
    a: int = Field(..., description="First number")
    b: int = Field(..., description="Second number")
    operator: str = Field(..., description="Operator")
    hint: Optional[str] = Field(None, description="Hint for the problem")

class MathTest(BaseModel):
    title: str = Field(..., description="Title of the test")
    problems: List[MathProblem] = ... # only list of other BaseModel is allowed

# define a thread which is a collection of messages
thread = tt.Thread(
    tt.human("Give me 5 problems for KG-1 class"),
    schema=MathTest
)

# get structured output
resp: MathTest = model.chat(thread)
```

Structured generation

Types and Logic is the two parts of programming.

With structured generation you can get pydantic.BaseModel objects from tt.ModelInterface.chat and tt.ModelInterface.chat_async methods. The currect limitation is that keys cannot have another BaseModel as value only List[BaseModel] is allowed.

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