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                         LLM PACKAGE FOR EMACS
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1 Introduction
══════════════

  This is a library for interfacing with Large Language Models.  It
  allows elisp code to use LLMs, but allows gives the end-user an option
  to choose which LLM they would prefer.  This is especially useful for
  LLMs, since there are various high-quality ones that in which API
  access costs money, as well as locally installed ones that are free,
  but of medium quality.  Applications using LLMs can use this library
  to make sure their application works regardless of whether the user
  has a local LLM or is paying for API access.

  The functionality supported by LLMs is not completely consistent, nor
  are their APIs.  In this library we attempt to abstract functionality
  to a higher level, because sometimes those higher level concepts are
  supported by an API, and othertimes they must be put in more low-level
  concepts.  One such higher-level concept is "examples" where the
  client can show example interactions to demonstrate a pattern for the
  LLM.  The GCloud Vertex API has an explicit API for examples, but for
  Open AI's API, examples must be specified by modifying the system
  prompt.  Open AI has the concept of a system prompt, whereas Vertex
  API does not.  These are the kinds of API differences we attempt to
  hide by having higher-level concepts in our API.

  Some functionality may not be supported by LLMs.  Any unsupported
  functionality with throw a `'not-implemented' signal.

  This package is simple at the moment, but will grow as both LLMs and
  functionality is added.


2 Setting up providers
══════════════════════

  Users who use an application that uses this package should not need to
  install it.  The llm module should be installed as a dependency when
  you install the package that uses it.  You do need to make sure to
  both require and set up the provider you will be using.  Typically,
  applications will have a variable you can set.  For example, let's say
  there's a package called "llm-refactoring", which has a variable
  `llm-refactoring-provider'.  You would set it up like so:

  ┌────
  │ (use-package llm-refactoring
  │   :init
  │   (require 'llm-openai)
  │   (setq llm-refactoring-provider (make-llm-openai :key my-openai-key))
  └────

  Here `my-openai-key' would be a variable you set up before with your
  Open AI key.  Or, just substitute the key itself as a string.  It's
  important that you remember never to check your key into a public
  repository such as github, because your key must be kept private.
  Anyone with your key can use the API, and you will be charged.


2.1 Open AI
───────────

  You can set up with `make-llm-openai', with the following parameters:
  • `:key', the Open AI key that you get when you sign up to use Open
    AI's APIs.  Remember to keep this private.  This is non-optional.
  • `:chat-model': A model name from the [list of Open AI's model
    names.]  Keep in mind some of these are not available to everyone.
    This is optional, and will default to a reasonable 3.5 model.
  • `:embedding-model': A model name from [list of Open AI's embedding
    model names.]  This is optional, and will default to a reasonable
    model.


[list of Open AI's model names.]
<https://platform.openai.com/docs/models/gpt-4>

[list of Open AI's embedding model names.]
<https://platform.openai.com/docs/guides/embeddings/embedding-models>


2.2 Vertex
──────────

  You can set up with `make-llm-vertex', with the following parameters:
  • `:project': Your project number from Google Cloud that has Vertex
    API enabled.
  • `:chat-model': A model name from the [list of Vertex's model names.]
    This is optional, and will default to a reasonable model.
  • `:embedding-model': A model name from the [list of Vertex's
    embedding model names.]  This is optional, and will default to a
    reasonable model.

  In addition to the provider, which you may want multiple of (for
  example, to charge against different projects), there are customizable
  variables:
  • `llm-vertex-gcloud-binary': The binary to use for generating the API
    key.
  • `llm-vertex-gcloud-region': The gcloud region to use.  It's good to
    set this to a region near where you are for best latency.  Defaults
    to "us-central1".


[list of Vertex's model names.]
<https://cloud.google.com/vertex-ai/docs/generative-ai/chat/chat-prompts#supported_model>

[list of Vertex's embedding model names.]
<https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings#supported_models>


2.3 Ollama
──────────

  [Ollama] is a way to run large language models locally. There are
  [many different models] you can use with it. You set it up with the
  following parameters:
  • `:port': The localhost port that ollama is run on.  This is optional
    and will default to the default ollama port.
  • `:chat-mode': The model name to use for chat.  This is not optional
    for chat use, since there is no default.
  • `:embedding-model': The model name to use for embeddings.  This is
    not optional for embedding use, since there is no default.


[Ollama] <https://ollama.ai/>

[many different models] <https://ollama.ai/library>


2.4 Fake
────────

  This is a client that makes no call, but it just there for testing and
  debugging.  Mostly this is of use to programmatic clients of the llm
  package, but end users can also use it to understand what will be sent
  to the LLMs.  It has the following parameters:
  • `:output-to-buffer': if non-nil, the buffer or buffer name to append
    the request sent to the LLM to.
  • `:chat-action-func': a function that will be called to provide a
    string or symbol and message cons which are used to raise an error.
  • `:embedding-action-func': a function that will be called to provide
    a vector or symbol and message cons which are used to raise an
    error.


3 `llm' and the use of non-free LLMs
════════════════════════════════════

  The `llm' package is part of GNU Emacs by being part of GNU ELPA.
  Unfortunately, the most popular LLMs in use are non-free, which is not
  what GNU software should be promoting by inclusion.  On the other
  hand, by use of the `llm' package, the user can make sure that any
  client that codes against it will work with free models that come
  along.  It's likely that sophisticated free LLMs will, emerge,
  although it's unclear right now what free software means with respsect
  to LLMs.  Because of this tradeoff, we have decided to warn the user
  when using non-free LLMs (which is every LLM supported right now
  except the fake one).  You can turn this off the same way you turn off
  any other warning, by clicking on the left arrow next to the warning
  when it comes up.  Alternatively, you can set `llm-warn-on-nonfree' to
  `nil'.  This can be set via customization as well.

  To build upon the example from before:
  ┌────
  │ (use-package llm-refactoring
  │   :init
  │   (require 'llm-openai)
  │   (setq llm-refactoring-provider (make-llm-openai :key my-openai-key)
  │ 	llm-warn-on-nonfree nil)
  └────


4 Programmatic use
══════════════════

  Client applications should require the `llm' package, and code against
  it.  Most functions are generic, and take a struct representing a
  provider as the first argument. The client code, or the user
  themselves can then require the specific module, such as `llm-openai',
  and create a provider with a function such as `(make-llm-openai :key
  user-api-key)'.  The client application will use this provider to call
  all the generic functions.

  A list of all the main functions:

  • `llm-chat provider prompt': With user-chosen `provider' , and a
    `llm-chat-prompt' structure (containing context, examples,
    interactions, and parameters such as temperature and max tokens),
    send that prompt to the LLM and wait for the string output.
  • `llm-chat-async provider prompt response-callback error-callback':
    Same as `llm-chat', but executes in the background.  Takes a
    `response-callback' which will be called with the text response.
    The `error-callback' will be called in case of error, with the error
    symbol and an error message.
  • `llm-chat-streaming provider prompt partial-callback
    response-callback error-callback': Similar to `llm-chat-async', but
    request a streaming response.  As the response is built up,
    `partial-callback' is called with the all the text retrieved up to
    the current point.  Finally, `reponse-callback' is called with the
    complete text.
  • `llm-embedding provider string': With the user-chosen `provider',
    send a string and get an embedding, which is a large vector of
    floating point values.  The embedding represents the semantic
    meaning of the string, and the vector can be compared against other
    vectors, where smaller distances between the vectors represent
    greater semantic similarity.
  • `llm-embedding-async provider string vector-callback
    error-callback': Same as `llm-embedding' but this is processed
    asynchronously. `vector-callback' is called with the vector
    embedding, and, in case of error, `error-callback' is called with
    the same arguments as in `llm-chat-async'.
  • `llm-count-tokens provider string': Count how many tokens are in
    `string'.  This may theoretically vary by `provider' but typically
    is always about the same.  This gives an estimate only.

    And the following helper functions:
    • `llm-make-simple-chat-prompt text': For the common case of just
      wanting a simple text prompt without the richness that
      `llm-chat-prompt' struct provides, use this to turn a string into
      a `llm-chat-prompt' that can be passed to the main functions
      above.
    • `llm-chat-prompt-to-text prompt': Somewhat opposite of the above,
      from a prompt, return a string representation.  This is not
      usually suitable for passing to LLMs, but for debugging purposes.
    • `llm-chat-streaming-to-point provider prompt buffer point
      finish-callback': Same basic arguments as `llm-chat-streaming',
      but will stream to `point' in `buffer'.


5 Contributions
═══════════════

  If you are interested in creating a provider, please send a pull
  request, or open a bug.  This library is part of GNU ELPA, so any
  major provider that we include in this module needs to be written by
  someone with FSF papers.  However, you can always write a module and
  put it on a different package archive, such as MELPA.
