Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://python.langchain.com/docs/integrations/text_embedding/infinity/ below:

Infinity | 🦜️🔗 LangChain

Infinity allows to create Embeddings using a MIT-licensed Embedding Server.

This notebook goes over how to use Langchain with Embeddings with the Infinity Github Project.

from langchain_community.embeddings import InfinityEmbeddings, InfinityEmbeddingsLocal

To install infinity use the following command. For further details check out the Docs on Github. Install the torch and onnx dependencies.

pip install infinity_emb[torch,optimum]

documents = [
    "Baguette is a dish.",
    "Paris is the capital of France.",
    "numpy is a lib for linear algebra",
    "You escaped what I've escaped - You'd be in Paris getting fucked up too",
]
query = "Where is Paris?"

embeddings = InfinityEmbeddingsLocal(
    model="sentence-transformers/all-MiniLM-L6-v2",
    
    revision=None,
    
    batch_size=32,
    
    device="cuda",
    
)


async def embed():
    

    
    async with embeddings:
        
        
        
        

        documents_embedded = await embeddings.aembed_documents(documents)
        query_result = await embeddings.aembed_query(query)
        print("embeddings created successful")
    return documents_embedded, query_result

/home/michael/langchain/libs/langchain/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
/home/michael/langchain/libs/langchain/.venv/lib/python3.10/site-packages/optimum/bettertransformer/models/encoder_models.py:301: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:177.)
  hidden_states = torch._nested_tensor_from_mask(hidden_states, ~attention_mask)

documents_embedded, query_result = await embed()

import numpy as np

scores = np.array(documents_embedded) @ np.array(query_result).T
dict(zip(documents, scores))

To install infinity use the following command. For further details check out the Docs on Github.

pip install infinity_emb[all]

%pip install --upgrade --quiet infinity_emb[all]

Start up the server - best to be done from a separate terminal, not inside Jupyter Notebook

model=sentence-transformers/all-MiniLM-L6-v2
port=7797
infinity_emb --port $port --model-name-or-path $model

or alternativley just use docker:

model=sentence-transformers/all-MiniLM-L6-v2
port=7797
docker run -it --gpus all -p $port:$port michaelf34/infinity:latest --model-name-or-path $model --port $port

documents = [
    "Baguette is a dish.",
    "Paris is the capital of France.",
    "numpy is a lib for linear algebra",
    "You escaped what I've escaped - You'd be in Paris getting fucked up too",
]
query = "Where is Paris?"

infinity_api_url = "http://localhost:7797/v1"

embeddings = InfinityEmbeddings(
    model="sentence-transformers/all-MiniLM-L6-v2", infinity_api_url=infinity_api_url
)
try:
    documents_embedded = embeddings.embed_documents(documents)
    query_result = embeddings.embed_query(query)
    print("embeddings created successful")
except Exception as ex:
    print(
        "Make sure the infinity instance is running. Verify by clicking on "
        f"{infinity_api_url.replace('v1', 'docs')} Exception: {ex}. "
    )

Make sure the infinity instance is running. Verify by clicking on http://localhost:7797/docs Exception: HTTPConnectionPool(host='localhost', port=7797): Max retries exceeded with url: /v1/embeddings (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f91c35dbd30>: Failed to establish a new connection: [Errno 111] Connection refused')).

import numpy as np

scores = np.array(documents_embedded) @ np.array(query_result).T
dict(zip(documents, scores))

{'Baguette is a dish.': 0.31344215908661155,
 'Paris is the capital of France.': 0.8148670296896388,
 'numpy is a lib for linear algebra': 0.004429399861302009,
 "You escaped what I've escaped - You'd be in Paris getting fucked up too": 0.5088476180154582}

• Embedding model conceptual guide
• Embedding model how-to guides

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4