Prompt-centric framework for developing LLM applications in Rust

Note: transprompt is not stable yet, so the APIs are subject to change.

As for now, transprompt is beta, so it's not released on crates.io. To use it, add a dependency in Cargo.toml

transprompt = { git = "https://github.com/ifsheldon/transprompt.git", branch = "main", version = "0.10" }

Run the below command to build the documentation and open it in the browser.

cargo doc --open

Because I'm done with layers of object-oriented abstraction that are mixed with inheritance hierarchies and methods that are overloaded and overridden from nowhere.

LLM programming, a fancy name of prompt engineering, starts with prompts, so it should be prompt-centric (or data-driven if you come from software engineering).

The overall of transprompt follows data-driven design. The APIs are designed to be as explicit as possible, so users should easily track every step that composes a prompt. The API hierarchy also aims to be as flat as possible. Cycle speed is NOT a top priority since LLM can take trillions of cycles to respond to a request.

As straightforward as its name, it's a template of prompts.

For example, a template looks like

You are a friendly and helpful assistant. Today is {{date}}.

Now, {{date}} is a placeholder, a slot to be filled, in this template, which has a name "date".

The format of a named placeholder is simply {{whatever name you like}}. The name can have any strings except those containing line breaks "\n"and "\r\n".

Why in this format?

Because of KISS and my limited regex proficiency.

While a prompt template is a blueprint, a partial prompt is an incomplete construction of the template, which means it has empty slots (AKA placeholders).

A PartialPrompt comes only from PromptTemplate::construct_prompt.

A PartialPrompt records which placeholder gets filled by what value and also unfilled placeholders.

When all placeholders in a PartialPrompt are filled, it's complete and thus ready to be transformed into a concrete prompt. This is simply done via PartialPrompt::complete.

Anything that fills one or more placeholders in a partial prompt.

In Rust, it means anything that implements FillPlaceholders and at least one of Fill, FillMut, FillWith<CTX> and FillWithMut<CTX>.

Fillers fill placeholders. Placeholders get filled via PartialPrompt::fill or PartialPrompt::try_fill.

A simple example is a date filler, which fills a placeholder name date that is represented in a template as {{date}}.

A filler can also be a composition of many fillers. Therefore, in a complex workflow, a PartialPrompt can be filled by concurrent fillers in multiple stages.

The endpoint of PromptTemplate -> PartialPrompt -> complete prompt (a String) pipeline is LLM, which consumes a prompt and produces a reply.

You can do any post-processing on the reply, but we will leave that in utilities.

Or, you can even kick off another pipeline that transforms a prompt template with fillers, so then the endpoint is a new start!

A LLM application is just a ordered collection of:

• Prompt templates
• Fillers (and intermediate partial prompts)
• Post-processing stages

Sorted from top to down by importance:

• Vector database connection: simplest Qdrant DB for now
• LLM integration: basics for OpenAI ChatGPT
  • Other LLM support
• Documentation: basic documentation for now
• Integration of guidance
  • I don't know how to do it yet, but the library is fxxking genius, despite its algorithmic simplicity.
  • No emergent need because of OpenAI function calling
• Utilities including
  • Simple JSON postprocessing: Only extracting out valid JSON content from a string for now
    • Add Support for Jsonformer No emergent need because of OpenAI function calling
  • Frequently used applications/agents
    • Generative Agents
  • Token counting utils: Now only basic tiktoken support
• Examples
• Future engineering improvements like advance compile time checking or type system dance
• Python counterpart?
  • I love Python's dynamism just like I like Rust's stasis, so I would love to see a prompt-centric counterpart in Python.
  • It seems Semantic Kernel is similar?

Contribution are always welcome. Please see TODOs.

transprompt will always remain free under Apache license.

• async_openai: The codebase of transprompt has copied content from this crate, which is transprompt::utils::llm::openai::ConversationConfig.
• tiktoken-rs: In transprompt::utils::token::tiktoken, we re-export the tiktoken-rs crate.