文件列表:

Cargo.toml (754, 2023-07-30)
LICENSE (11357, 2023-07-30)
src (0, 2023-07-30)
src\exemplars.rs (102, 2023-07-30)
src\exemplars (0, 2023-07-30)
src\exemplars\generative_agents.rs (4909, 2023-07-30)
src\filler.rs (1902, 2023-07-30)
src\lib.rs (4436, 2023-07-30)
src\prompt.rs (13118, 2023-07-30)
src\utils.rs (381, 2023-07-30)
src\utils (0, 2023-07-30)
src\utils\embedding.rs (4081, 2023-07-30)
src\utils\llm.rs (15, 2023-07-30)
src\utils\llm (0, 2023-07-30)
src\utils\llm\openai.rs (6349, 2023-07-30)
src\utils\postprocess.rs (13, 2023-07-30)
src\utils\postprocess (0, 2023-07-30)
src\utils\postprocess\json.rs (2523, 2023-07-30)
src\utils\prompt_processing.rs (2732, 2023-07-30)
src\utils\timing.rs (652, 2023-07-30)
src\utils\token.rs (6332, 2023-07-30)
src\utils\token (0, 2023-07-30)
src\utils\token\tiktoken.rs (765, 2023-07-30)
src\utils\vec_stores.rs (3815, 2023-07-30)

# transprompt Prompt-centric framework for developing LLM applications in Rust **Note: `transprompt` is now a WIP, so the APIs are subject to change.** ## Usage As for now, `transprompt` is beta, so it's not released on crates.io. To use it, add a dependency in `Cargo.toml` ```toml transprompt = { git = "https://github.com/ifsheldon/transprompt.git", branch = "main" } ``` ## Documentation Run the below command to build the documentation and open it in the browser. ```shell cargo doc --open ``` ## Why `transprompt` 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). ## Concepts and Design 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. ### Prompt Template and Placeholder As straightforward as its name, it's a template of prompts. For example, a template looks like ```text 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. ### Partial Prompt 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`. ### Filler 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` and `FillWithMut`. 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. ### Endpoint or LLM 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! ### Application or Agent or Whatever A LLM application is just a ordered collection of: * Prompt templates * Fillers (and intermediate partial prompts) * Post-processing stages ## TODOs Sorted from top to down by importance: - [x] Vector database connection: simplest Qdrant DB for now - [x] LLM integration: basics for OpenAI ChatGPT - [ ] Other LLM support - [x] Documentation: basic documentation for now - [ ] ~~Integration of [guidance](https://github.com/microsoft/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 - [x] 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 - [x] 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 Contribution are always welcome. Please see TODOs. ## License `transprompt` will always remain free under Apache license. ## Attribution * `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.

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