gemini-cli

README

gemini-cli

gemini-cli is a simple yet versatile command-line interface for Google's Gemini LLMs, written in Go. It includes tools for chatting with these models and generating / comparing embeddings, with powerful SQLite storage and analysis capabilities.

Installing

Install gemini-cli on your machine with:

$ go install github.com/eliben/gemini-cli@latest

You can then invoke gemini-cli help to verify it's properly installed and found.

Usage

All gemini-cli invocations require an API key for https://ai.google.dev/ to be provided, either via the --key flag or an environment variable called GEMINI_API_KEY. You can visit that page to obtain a key - there's a generous free tier!

From here on, all examples assume the environment variable was set earlier to a valid key.

gemini-cli has a nested tree of subcommands to perform various tasks. You can always run gemini-cli help <command> [subcommand]... to get usage; e.g. gemini-cli help chat or gemini-cli help embed similar. The printed help information will describe every subcommand and its flags.

This guide will discuss some of the more common use cases.

Models

The list of Gemini models supported by the backend is available on this page. You can run gemini-cli models to ask the tool to print a list of model names it's familiar with. These are the names you can pass in with the --model flag (see the default model name by running gemini-cli help), and you can always omit the models/ prefix.

prompt - single prompts

The prompt command allows one to send queries consisting of text or images to the LLM. This is a single-shot interaction; the LLM will have no memory of previous prompts (see the chat command for with-memory interactions).

$ gemini-cli prompt <prompt or '-'>... [flags]

The prompt can be provided as a sequence of parts, each one a command-line argument.

The arguments are sent as a sequence to the model in the order provided. If --system is provided, it's prepended to the other arguments. An argument can be some quoted text, a name of an image file on the local filesystem or a URL pointing directly to an image file online. A special argument with the value - instructs the tool to read this prompt part from standard input. It can only appear once in a single invocation.

Some examples:

# Simple single prompt
$ gemini-cli prompt "why is the sky blue?"

# Multi-modal prompt with image file. Note that we have to ask for a
# vision-capable model explicitly
$ gemini-cli prompt --model gemini-pro-vision "describe this image:" test/datafiles/puppies.png

chat - in-terminal chat with a model

Running gemini-cli chat starts an interactive terminal chat with a model. You write prompts following the > character and the model prints its replies. In this mode, the model has a memory of your previous prompts and its own replies (within the model's context length limit). Example:

$ gemini-cli chat
Chatting with gemini-1.0-pro
Type 'exit' or 'quit' to exit
> name 3 dog breeds
1. Golden Retriever
2. Labrador Retriever
3. German Shepherd
> which of these is the heaviest?
German Shepherd

German Shepherds are typically the heaviest of the three breeds, with males
[...]
> and which are considered best for kids?
**Golden Retrievers** and **Labrador Retrievers** are both considered excellent
[...]
> 

counttok - counting tokens

We can ask the Gemini API to count the number of tokens in a given prompt or list of prompts. gemini-cli supports this with the counttok command. Examples:

$ gemini-cli counttok "why is the sky blue?"

$ cat textfile.txt | gemini-cli counttok -

Embeddings

Some of gemini-cli's most advanced capabilities are in interacting with Gemini's embedding models. gemini-cli uses SQLite to store embeddings for a potentially large number of inputs and query these embeddings for similarity. This is all done through subcommands of the embed command.

embed content - embedding a single piece of content

Useful for kicking the tires of embeddings, this subcommand embeds a single prompt taken from the command-line or a file, and prints out its embedding in various formats (controlled with the --format flag). Examples:

$ gemini-cli embed content "why is the sky blue?"

$ cat textfile.txt | gemini-cli embed content -

embed db - embedding multiple contents, storing results in a DB

embed db is a swiss-army knife subcommand for embedding multiple pieces of text and storing the results in a SQLite DB. It supports different kinds of inputs: a textual table, the file system or the DB itself.

All variations of embed db take the path of a DB file to use as output. If the file exists, it's expected to be a valid SQLite DB; otherwise, a new DB is created in that path. gemini-cli will store the results of embedding calculations in this DB in the embeddings table (this name can be configured with the --table flag), with this SQL schema:

id TEXT PRIMARY KEY
embedding BLOB

The id is taken from the input, based on its type. We'll go through the different variants of input next.

Filesystem input: when passed the --files or --files-list flag, gemini-cli takes inputs as files from the filesystem. Each file is one input: its path is the ID, and its contents are passed to the embedding model.

With --files, the flag value is a comma-separated pair of <root directory>,<glob pattern>; the root directory is walked recursively and every file matching the glob pattern is included in the input. For example:

$ gemini-cli embed db out.db --files somedir,*.txt

Embeds every .txt file found in somedir or any of its sub-directories. The ID for each file will be its path starting with somedir/.

With --files-list, the flag value is a comma-separated pair of filenames. Each name becomes an ID and the file's contents are passed to the embedding model. This can be useful for more sophisticated patterns that are difficult to express using a simple glob; for example, using pss and the paste command, this embeds any file that looks like a C++ file (i.e. ending with .h, .hpp, .cpp, .cxx and so on) in the current directory:

$ gemini-cli embed db out-db --files-list $(pss -f --cpp | paste -sd,)

SQLite DB input: when passed the --sql flag, gemini-cli takes inputs from the SQLite DB itself, or any other SQLite DB file. The flag value is a SQL select statement that should select at least two columns; the first one will be taken as the ID, and the others are concatenated to become the value passed to the embedding model.

For example, if out.db already has a table named docs with the column names id and content, this call will embed the contents of each row and place the output in the embeddings table:

$ gemini-cli embed db out.db --sql "select id, content from docs"

With the --attach flag, we can also ask gemini-cli to read inputs from other SQLite DB files. For example:

$ gemini-cli embed db out.db --attach inp,input.db --sql "select id, content from inp.docs"

Will read the inputs from input.db and write embedding outputs to out.db.

Tabular input: without additional flags, gemini-cli will expect a filename or - following the output DB name. This file (or data piped from standard input in case of -) is expected to be in either CSV, TSV (tab-separated values), JSON or JSONLines format and include a list of records that has an ID field and some arbitrary number of other fields that are all concatenated to create the content for the record. The content is embedded and the result is associated with the ID in the output SQLite DB.

For example:

$ cat input.csv
id,name,age
3,luci,23
4,merene,29
5,pat,52
$ cat input.csv | gemini-cli embed db out.db -

Will embed each record from the input file and create 3 rows in the embeddings table associated with the IDs 3, 4 and 5. In this mode, gemini-cli auto-detects the format of the file passed into it without relying on its extension (note that it's unaware of the extension when the input is piped through standard input).

Other flags: embed db has some additional flags that affect its behavior for all input modes. Run gemini help embed db details.

embed similar - finding similar items from an embeddings table

Once an embeddings table was computed with embed db, we can use the embed similar command to find values that are most similar (in terms of distance in embedding vector space) to some content. For example:

$ gemini-cli embed similar out.db somefile.txt

Will embed the contents of somefile.txt, then compare its embedding vector with the embeddings stored in the embeddings table of out.db, and print out the 5 closest entries (this number can be controlled with the --topk flag).

By default, embed similar will emit the ID of the similar entry and the similarity score for each record. The --show flag can be used to control which columns from the DB are printed out.

Acknowledgements

gemini-cli is inspired by Simon Willison's llm tool, but aimed at the Go ecosystem. Simon's website is a treasure trove of information about LLMs, embeddings and building tools that use them - check it out!