Added support for structured output in the API and added a reference implementation for meta-reference.
A few notes:
* Two formats are specified in the API: Json schema and EBNF based grammar
* Implementation only supports Json for now
We use lm-format-enhancer to provide the implementation right now but may change this especially because BNF grammars aren't supported by that library.
Fireworks has support for structured output and Together has limited supported for it too. Subsequent PRs will add these changes. We would like all our inference providers to provide structured output for llama models since it is an extremely important and highly sought-after need by the developers.
* docker compose ollama
* comment
* update compose file
* readme for distributions
* readme
* move distribution folders
* move distribution/templates to distributions/
* rename
* kill distribution/templates
* readme
* readme
* build/developer cookbook/new api provider
* developer cookbook
* readme
* readme
* [bugfix] fix case for agent when memory bank registered without specifying provider_id (#264)
* fix case where memory bank is registered without provider_id
* memory test
* agents unit test
* Add an option to not use elastic agents for meta-reference inference (#269)
* Allow overridding checkpoint_dir via config
* Small rename
* Make all methods `async def` again; add completion() for meta-reference (#270)
PR #201 had made several changes while trying to fix issues with getting the stream=False branches of inference and agents API working. As part of this, it made a change which was slightly gratuitous. Namely, making chat_completion() and brethren "def" instead of "async def".
The rationale was that this allowed the user (within llama-stack) of this to use it as:
```
async for chunk in api.chat_completion(params)
```
However, it causes unnecessary confusion for several folks. Given that clients (e.g., llama-stack-apps) anyway use the SDK methods (which are completely isolated) this choice was not ideal. Let's revert back so the call now looks like:
```
async for chunk in await api.chat_completion(params)
```
Bonus: Added a completion() implementation for the meta-reference provider. Technically should have been another PR :)
* Improve an important error message
* update ollama for llama-guard3
* Add vLLM inference provider for OpenAI compatible vLLM server (#178)
This PR adds vLLM inference provider for OpenAI compatible vLLM server.
* Create .readthedocs.yaml
Trying out readthedocs
* Update event_logger.py (#275)
spelling error
* vllm
* build templates
* delete templates
* tmp add back build to avoid merge conflicts
* vllm
* vllm
---------
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
Co-authored-by: Yuan Tang <terrytangyuan@gmail.com>
Co-authored-by: raghotham <rsm@meta.com>
Co-authored-by: nehal-a2z <nehal@coderabbit.ai>
This is just like `local` using `meta-reference` for everything except
it uses `vllm` for inference.
Docker works, but So far, `conda` is a bit easier to use with the vllm
provider. The default container base image does not include all the
necessary libraries for all vllm features. More cuda dependencies are
necessary.
I started changing this base image used in this template, but it also
required changes to the Dockerfile, so it was getting too involved to
include in the first PR.
Working so far:
* `python -m llama_stack.apis.inference.client localhost 5000 --model Llama3.2-1B-Instruct --stream True`
* `python -m llama_stack.apis.inference.client localhost 5000 --model Llama3.2-1B-Instruct --stream False`
Example:
```
$ python -m llama_stack.apis.inference.client localhost 5000 --model Llama3.2-1B-Instruct --stream False
User>hello world, write me a 2 sentence poem about the moon
Assistant>
The moon glows bright in the midnight sky
A beacon of light,
```
I have only tested these models:
* `Llama3.1-8B-Instruct` - across 4 GPUs (tensor_parallel_size = 4)
* `Llama3.2-1B-Instruct` - on a single GPU (tensor_parallel_size = 1)
* fixing safety inference and safety adapter for new API spec. Pinned the llama_models version to 0.0.24 as the latest version 0.0.35 has the model descriptor name changed. I was getting the missing package error during runtime as well, hence added the dependency to requirements.txt
* support Llama 3.2 models in Together inference adapter and cleanup Together safety adapter
* fixing model names
* adding vision guard to Together safety
This is yet another of those large PRs (hopefully we will have less and less of them as things mature fast). This one introduces substantial improvements and some simplifications to the stack.
Most important bits:
* Agents reference implementation now has support for session / turn persistence. The default implementation uses sqlite but there's also support for using Redis.
* We have re-architected the structure of the Stack APIs to allow for more flexible routing. The motivating use cases are:
- routing model A to ollama and model B to a remote provider like Together
- routing shield A to local impl while shield B to a remote provider like Bedrock
- routing a vector memory bank to Weaviate while routing a keyvalue memory bank to Redis
* Support for provider specific parameters to be passed from the clients. A client can pass data using `x_llamastack_provider_data` parameter which can be type-checked and provided to the Adapter implementations.
* 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>
2024-09-17 19:51:35 -07:00
Renamed from llama_toolchain/inference/providers.py (Browse further)
