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Ensure you are using Python 3.11. It is best to work in a virtual environment:

# go to your repo root (which may be langroid-examples)
cd <your repo root>
python3 -m venv .venv
. ./.venv/bin/activate
To see how to use Langroid in your own repo, you can take a look at the langroid-examples repo, which can be a good starting point for your own repo. The langroid-examples repo already contains a pyproject.toml file so that you can use Poetry to manage your virtual environment and dependencies. For example you can do
poetry install # installs latest version of langroid
Alternatively, use pip to install langroid into your virtual environment:
pip install langroid

Optional Installs for using SQL Chat with a PostgreSQL DB

If you are using SQLChatAgent (e.g. the script examples/data-qa/sql-chat/, with a postgres db, you will need to:

  • Install PostgreSQL dev libraries for your platform, e.g.
    • sudo apt-get install libpq-dev on Ubuntu,
    • brew install postgresql on Mac, etc.
  • Install langroid with the postgres extra, e.g. pip install langroid[postgres] or poetry add langroid[postgres] or poetry install -E postgres. If this gives you an error, try pip install psycopg2-binary in your virtualenv.

Work in a nice terminal, such as Iterm2, rather than a notebook

All of the examples we will go through are command-line applications. For the best experience we recommend you work in a nice terminal that supports colored outputs, such as Iterm2.

OpenAI GPT4 is required

The various LLM prompts and instructions in Langroid have been tested to work well with GPT4. Switching to GPT3.5-Turbo is easy via a config flag, and may suffice for some applications, but in general you may see inferior results.

Set up tokens/keys

To get started, all you need is an OpenAI API Key. If you don't have one, see this OpenAI Page. Currently only OpenAI models are supported. Others will be added later (Pull Requests welcome!).

In the root of the repo, copy the .env-template file to a new file .env:

cp .env-template .env
Then insert your OpenAI API Key. Your .env file should look like this:

Alternatively, you can set this as an environment variable in your shell (you will need to do this every time you open a new shell):

export OPENAI_API_KEY=your-key-here-without-quotes

All of the following environment variable settings are optional, and some are only needed to use specific features (as noted below).

  • Qdrant Vector Store API Key, URL. This is only required if you want to use Qdrant cloud. Langroid uses LanceDB as the default vector store in its DocChatAgent class (for RAG). Alternatively Chroma is also currently supported. We use the local-storage version of Chroma, so there is no need for an API key.
  • Redis Password, host, port: This is optional, and only needed to cache LLM API responses using Redis Cloud. Redis offers a free 30MB Redis account which is more than sufficient to try out Langroid and even beyond. If you don't set up these, Langroid will use a pure-python Redis in-memory cache via the Fakeredis library.
  • Momento Serverless Caching of LLM API responses (as an alternative to Redis). To use Momento instead of Redis:
    • enter your Momento Token in the .env file, as the value of MOMENTO_AUTH_TOKEN (see example file below),
    • in the .env file set CACHE_TYPE=momento (instead of CACHE_TYPE=redis which is the default).
  • GitHub Personal Access Token (required for apps that need to analyze git repos; token-based API calls are less rate-limited). See this GitHub page.
  • Google Custom Search API Credentials: Only needed to enable an Agent to use the GoogleSearchTool. To use Google Search as an LLM Tool/Plugin/function-call, you'll need to set up a Google API key, then setup a Google Custom Search Engine (CSE) and get the CSE ID. (Documentation for these can be challenging, we suggest asking GPT4 for a step-by-step guide.) After obtaining these credentials, store them as values of GOOGLE_API_KEY and GOOGLE_CSE_ID in your .env file. Full documentation on using this (and other such "stateless" tools) is coming soon, but in the meantime take a peek at the test tests/main/ to see how to use it.

If you add all of these optional variables, your .env file should look like this:

CACHE_TYPE=redis # or momento
MOMENTO_AUTH_TOKEN=your-momento-token-no-quotes # instead of REDIS* variables
QDRANT_API_URL= # note port number must be included

Microsoft Azure OpenAI setup[Optional]

This section applies only if you are using Microsoft Azure OpenAI.

When using Azure OpenAI, additional environment variables are required in the .env file. This page Microsoft Azure OpenAI provides more information, and you can set each environment variable as follows:

  • AZURE_OPENAI_API_KEY, from the value of API_KEY
  • AZURE_OPENAI_API_BASE from the value of ENDPOINT, typically looks like
  • For AZURE_OPENAI_API_VERSION, you can use the default value in .env-template, and latest version can be found here
  • AZURE_OPENAI_DEPLOYMENT_NAME is the name of the deployed model, which is defined by the user during the model setup
  • AZURE_OPENAI_MODEL_NAME Azure OpenAI allows specific model names when you select the model for your deployment. You need to put precisly the exact model name that was selected. For example, GPT-3.5 (should be gpt-35-turbo-16k or gpt-35-turbo) or GPT-4 (should be gpt-4-32k or gpt-4).
  • AZURE_OPENAI_MODEL_VERSION is required if AZURE_OPENAI_MODEL_NAME = gpt=4, which will assist Langroid to determine the cost of the model

Next steps

Now you should be ready to use Langroid! As a next step, you may want to see how you can use Langroid to interact directly with the LLM (OpenAI GPT models only for now).