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API Updates (#73)

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* API Keys passed from Client instead of distro configuration

* delete distribution registry

* Rename the "package" word away

* Introduce a "Router" layer for providers

Some providers need to be factorized and considered as thin routing
layers on top of other providers. Consider two examples:

- The inference API should be a routing layer over inference providers,
  routed using the "model" key
- The memory banks API is another instance where various memory bank
  types will be provided by independent providers (e.g., a vector store
  is served by Chroma while a keyvalue memory can be served by Redis or
  PGVector)

This commit introduces a generalized routing layer for this purpose.

* update `apis_to_serve`

* llama_toolchain -> llama_stack

* Codemod from llama_toolchain -> llama_stack

- added providers/registry
- cleaned up api/ subdirectories and moved impls away
- restructured api/api.py
- from llama_stack.apis.<api> import foo should work now
- update imports to do llama_stack.apis.<api>
- update many other imports
- added __init__, fixed some registry imports
- updated registry imports
- create_agentic_system -> create_agent
- AgenticSystem -> Agent

* Moved some stuff out of common/; re-generated OpenAPI spec

* llama-toolchain -> llama-stack (hyphens)

* add control plane API

* add redis adapter + sqlite provider

* move core -> distribution

* Some more toolchain -> stack changes

* small naming shenanigans

* Removing custom tool and agent utilities and moving them client side

* Move control plane to distribution server for now

* Remove control plane from API list

* no codeshield dependency randomly plzzzzz

* Add "fire" as a dependency

* add back event loggers

* stack configure fixes

* use brave instead of bing in the example client

* add init file so it gets packaged

* add init files so it gets packaged

* Update MANIFEST

* bug fix

---------

Co-authored-by: Hardik Shah <hjshah@fb.com>
Co-authored-by: Xi Yan <xiyan@meta.com>
Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
This commit is contained in:
Ashwin Bharambe 2024-09-17 19:51:35 -07:00 • committed by GitHub
parent f294eac5f5
commit 9487ad8294
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213 changed files with 1725 additions and 1204 deletions
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24
docs/cli_reference.md
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@ -1,6 +1,6 @@
# Llama CLI Reference
The `llama` CLI tool helps you setup and use the Llama toolchain & agentic systems. It should be available on your path after installing the `llama-toolchain` package.
The `llama` CLI tool helps you setup and use the Llama toolchain & agentic systems. It should be available on your path after installing the `llama-stack` package.
### Subcommands
1. `download`: `llama` cli tools supports downloading the model from Meta or HuggingFace.
@ -276,16 +276,16 @@ The following command and specifications allows you to get started with building
```
llama stack build <path/to/config>
```
- You will be required to pass in a file path to the build.config file (e.g. `./llama_toolchain/configs/distributions/conda/local-conda-example-build.yaml`). We provide some example build config files for configuring different types of distributions in the `./llama_toolchain/configs/distributions/` folder.
- You will be required to pass in a file path to the build.config file (e.g. `./llama_stack/distribution/example_configs/conda/local-conda-example-build.yaml`). We provide some example build config files for configuring different types of distributions in the `./llama_stack/distribution/example_configs/` folder.
The file will be of the contents
```
$ cat ./llama_toolchain/configs/distributions/conda/local-conda-example-build.yaml
$ cat ./llama_stack/distribution/example_configs/conda/local-conda-example-build.yaml
name: 8b-instruct
distribution_spec:
distribution_type: local
description: Use code from `llama_toolchain` itself to serve all llama stack APIs
description: Use code from `llama_stack` itself to serve all llama stack APIs
docker_image: null
providers:
inference: meta-reference
@ -311,7 +311,7 @@ After this step is complete, a file named `8b-instruct-build.yaml` will be gener
To specify a different API provider, we can change the `distribution_spec` in our `<name>-build.yaml` config. For example, the following build spec allows you to build a distribution using TGI as the inference API provider.
```
$ cat ./llama_toolchain/configs/distributions/conda/local-tgi-conda-example-build.yaml
$ cat ./llama_stack/distribution/example_configs/conda/local-tgi-conda-example-build.yaml
name: local-tgi-conda-example
distribution_spec:
@ -328,7 +328,7 @@ image_type: conda
The following command allows you to build a distribution with TGI as the inference API provider, with the name `tgi`.
```
llama stack build --config ./llama_toolchain/configs/distributions/conda/local-tgi-conda-example-build.yaml --name tgi
llama stack build --config ./llama_stack/distribution/example_configs/conda/local-tgi-conda-example-build.yaml --name tgi
```
We provide some example build configs to help you get started with building with different API providers.
@ -337,11 +337,11 @@ We provide some example build configs to help you get started with building with
To build a docker image, simply change the `image_type` to `docker` in our `<name>-build.yaml` file, and run `llama stack build --config <name>-build.yaml`.
```
$ cat ./llama_toolchain/configs/distributions/docker/local-docker-example-build.yaml
$ cat ./llama_stack/distribution/example_configs/docker/local-docker-example-build.yaml
name: local-docker-example
distribution_spec:
description: Use code from `llama_toolchain` itself to serve all llama stack APIs
description: Use code from `llama_stack` itself to serve all llama stack APIs
docker_image: null
providers:
inference: meta-reference
@ -354,7 +354,7 @@ image_type: docker
The following command allows you to build a Docker image with the name `docker-local`
```
llama stack build --config ./llama_toolchain/configs/distributions/docker/local-docker-example-build.yaml --name docker-local
llama stack build --config ./llama_stack/distribution/example_configs/docker/local-docker-example-build.yaml --name docker-local
Dockerfile created successfully in /tmp/tmp.I0ifS2c46A/DockerfileFROM python:3.10-slim
WORKDIR /app
@ -480,9 +480,9 @@ This server is running a Llama model locally.
Once the server is setup, we can test it with a client to see the example outputs.
```
cd /path/to/llama-stack
conda activate <env> # any environment containing the llama-toolchain pip package will work
conda activate <env> # any environment containing the llama-stack pip package will work
python -m llama_toolchain.inference.client localhost 5000
python -m llama_stack.apis.inference.client localhost 5000
```
This will run the chat completion client and query the distribution’s /inference/chat_completion API.
@ -500,7 +500,7 @@ You know what's even more hilarious? People like you who think they can just Goo
Similarly you can test safety (if you configured llama-guard and/or prompt-guard shields) by:
```
python -m llama_toolchain.safety.client localhost 5000
python -m llama_stack.safety.client localhost 5000
```
You can find more example scripts with client SDKs to talk with the Llama Stack server in our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/sdk_examples) repo.