diff --git a/docs/docs/integrations.md b/docs/docs/integrations.md index 73266240..61f9a19c 100644 --- a/docs/docs/integrations.md +++ b/docs/docs/integrations.md @@ -1,5 +1,11 @@ # Python Integrations +## Cognita +https://github.com/truefoundry/cognita + +## RagFlow +https://github.com/infiniflow/ragflow + ## Langchain (from running server) Infinity has an official integration into `pip install langchain>=0.342`. You can find more documentation on that here: diff --git a/docs/docs/python_engine.md b/docs/docs/python_engine.md index 254b9191..3d198be6 100644 --- a/docs/docs/python_engine.md +++ b/docs/docs/python_engine.md @@ -6,22 +6,22 @@ Use asynchronous programming in Python using `asyncio` for flexible and efficien ```python import asyncio -from infinity_emb import AsyncEmbeddingEngine, EngineArgs +from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine from infinity_emb.log_handler import logger logger.setLevel(5) # Debug # Define sentences for embedding sentences = ["Embed this sentence via Infinity.", "Paris is in France."] # Initialize the embedding engine with model specifications -engine = AsyncEmbeddingEngine.from_args( +array = AsyncEngineArray.from_args([ EngineArgs( model_name_or_path="BAAI/bge-small-en-v1.5", engine="torch", lengths_via_tokenize=True - ) + )] ) -async def main(): +async def embed_image(engine: AsyncEmbeddingEngine): await engine.astart() # initializes the engine job1 = asyncio.create_task(engine.embed(sentences=sentences)) # submit a second job in parallel @@ -34,7 +34,7 @@ async def main(): await engine.astop() asyncio.run( - main() + embed_image(array["BAAI/bge-small-en-v1.5"]) ) ``` @@ -46,15 +46,16 @@ Please select a model from huggingface that is a AutoModelForSequenceClassificat ```python import asyncio -from infinity_emb import AsyncEmbeddingEngine, EngineArgs +from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine query = "What is the python package infinity_emb?" docs = ["This is a document not related to the python package infinity_emb, hence...", "Paris is in France!", "infinity_emb is a package for sentence embeddings and rerankings using transformer models in Python!"] -engine_args = EngineArgs(model_name_or_path = "mixedbread-ai/mxbai-rerank-xsmall-v1", engine="torch") +array = AsyncEmbeddingEngine.from_args( + [EngineArgs(model_name_or_path = "mixedbread-ai/mxbai-rerank-xsmall-v1", engine="torch")] +) -engine = AsyncEmbeddingEngine.from_args(engine_args) -async def main(): +async def rerank(engine: AsyncEmbeddingEngine): async with engine: ranking, usage = await engine.rerank(query=query, docs=docs) print(list(zip(ranking, docs))) @@ -63,7 +64,7 @@ async def main(): ranking, usage = await engine.rerank(query=query, docs=docs) await engine.astop() -asyncio.run(main()) +asyncio.run(rerank(array[0])) ``` When using the CLI, use this command to launch rerankers: @@ -71,26 +72,67 @@ When using the CLI, use this command to launch rerankers: infinity_emb v2 --model-id mixedbread-ai/mxbai-rerank-xsmall-v1 ``` +Example models: +- [mixedbread-ai/mxbai-rerank-xsmall-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1) +- [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) +- [jinaai/jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en) + +## CLIP models + +CLIP models are able to encode images and text at the same time. + +```python +import asyncio +from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine + +sentences = ["This is awesome.", "I am bored."] +images = ["http://images.cocodataset.org/val2017/000000039769.jpg"] +engine_args = EngineArgs( + model_name_or_path = "wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M", + engine="torch" +) +array = AsyncEngineArray.from_args([engine_args]) + +async def embed(engine: AsyncEmbeddingEngine): + await engine.astart() + embeddings, usage = await engine.embed(sentences=sentences) + embeddings_image, _ = await engine.image_embed(images=images) + await engine.astop() + +asyncio.run(embed(array["wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M"])) +``` + +Example models: +- [wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M](https://huggingface.co/wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M) +- [jinaai/jina-clip-v1](https://huggingface.co/jinaai/jina-clip-v1) (requires `pip install timm`) +- Currently no support for pure vision models: nomic-ai/nomic-embed-vision-v1.5, .. + + ## Text Classification Use text classification with Infinity's `classify` feature, which allows for sentiment analysis, emotion detection, and more classification tasks. ```python import asyncio -from infinity_emb import AsyncEmbeddingEngine, EngineArgs +from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine sentences = ["This is awesome.", "I am bored."] -engine_args = EngineArgs(model_name_or_path = "SamLowe/roberta-base-go_emotions", +engine_args = EngineArgs( + model_name_or_path = "SamLowe/roberta-base-go_emotions", engine="torch", model_warmup=True) -engine = AsyncEmbeddingEngine.from_args(engine_args) -async def main(): +array = AsyncEngineArray.from_args([engine_args]) + +async def classifier(): async with engine: predictions, usage = await engine.classify(sentences=sentences) # or handle the async start / stop yourself. await engine.astart() predictions, usage = await engine.classify(sentences=sentences) await engine.astop() -asyncio.run(main()) +asyncio.run(classifier(array["SamLowe/roberta-base-go_emotions"])) ``` -Running via CLI requires a new FastAPI schema and server integration - PR's are also welcome there. +Example models: +- [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) +- [SamLowe/roberta-base-go_emotions](SamLowe/roberta-base-go_emotions) + diff --git a/libs/infinity_emb/poetry.lock b/libs/infinity_emb/poetry.lock index 4b91d309..f41660c4 100644 --- a/libs/infinity_emb/poetry.lock +++ b/libs/infinity_emb/poetry.lock @@ -123,24 +123,25 @@ files = [ [[package]] name = "anyio" -version = "3.7.1" +version = "4.4.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "anyio-3.7.1-py3-none-any.whl", hash = "sha256:91dee416e570e92c64041bd18b900d1d6fa78dff7048769ce5ac5ddad004fbb5"}, - {file = "anyio-3.7.1.tar.gz", hash = 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