erlangchain

DeepWiki

Minimal building blocks for talking to LLMs from Erlang, without third-party dependencies.

ModuleRole
llmone-call chat client for OpenAI (Responses API) and Anthropic, with tool-use and multimodal support
llm_datasourcecreate and manage provider-backed vector-store datasources
json_utildependency-free JSON encode/decode

Install

%% rebar.config
{deps, [
{erlangchain, "~> 0.2.0"}
]}.

Set OPENAI_API_KEY and/or ANTHROPIC_API_KEY in the environment (a .env file in the working directory is loaded automatically if present).

llm

%% Simple completion (defaults to openai/small):
{ok, #{content := Text}} = llm:chat([#{role => user, content => <<"hello">>}]),
%% Pick provider + size:
{ok, Resp} = llm:chat(openai, big, Messages),
%% Tool use — pass tool specs, get back tool_calls to run and feed back:
{ok, #{tool_calls := Calls}} = llm:chat(openai, big, Messages, Tools),
%% OpenAI file search — pass a vector store id before Opts:
{ok, Resp} = llm:chat(openai, big, Messages, Tools, <<"vs_product_docs">>, #{}).
%% Datasource lifecycle — file paths are uploaded and attached as one batch:
{ok, DatasourceId} = llm_datasource:create(openai, <<"Product docs">>),
{ok, #{file_ids := FileIds}} =
llm_datasource:files_add(
openai, DatasourceId, ["docs/guide.pdf", "docs/api.md"]
),
ok = llm_datasource:files_remove(openai, DatasourceId, FileIds),
ok = llm_datasource:delete(openai, DatasourceId).

Messages are maps like #{role => system|user|assistant, content => binary()}, plus #{role => tool_result, tool_use_id => Id, content => Bin} to return tool output. Datasource is none or an OpenAI vector store id. Anthropic does not support managed vector-store datasources. See the header of src/llm.erl for the full message/response shapes. Deleting datasource files detaches them from that vector store; it does not permanently delete the uploaded OpenAI files.

json_util

<<"{\"a\":1}">> = json_util:encode(#{<<"a">> => 1}),
#{<<"a">> := 1} = json_util:decode(<<"{\"a\":1}">>).

License

MIT — see LICENSE.