Embeddings#

This notebook goes over how to use the Embedding class in LangChain.

The Embedding class is a class designed for interfacing with embeddings. There are lots of Embedding providers (OpenAI, Cohere, Hugging Face, etc) - this class is designed to provide a standard interface for all of them.

Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.

The base Embedding class in LangChain exposes two methods: embed_documents and embed_query. The largest difference is that these two methods have different interfaces: one works over multiple documents, while the other works over a single document. Besides this, another reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself).

OpenAI#

Let’s load the OpenAI Embedding class.

from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

text = "This is a test document."

query_result = embeddings.embed_query(text)

doc_result = embeddings.embed_documents([text])

Let’s load the OpenAI Embedding class with first generation models (e.g. text-search-ada-doc-001/text-search-ada-query-001). Note: These are not recommended models - see here

from langchain.embeddings.openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model_name="ada")

text = "This is a test document."

query_result = embeddings.embed_query(text)

doc_result = embeddings.embed_documents([text])

AzureOpenAI#

Let’s load the OpenAI Embedding class with environment variables set to indicate to use Azure endpoints.

# set the environment variables needed for openai package to know to reach out to azure
import os

os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"

embeddings = OpenAIEmbeddings(model="your-embeddings-deployment-name")

text = "This is a test document."

query_result = embeddings.embed_query(text)

doc_result = embeddings.embed_documents([text])

Cohere#

Let’s load the Cohere Embedding class.

from langchain.embeddings import CohereEmbeddings

embeddings = CohereEmbeddings(cohere_api_key=cohere_api_key)

text = "This is a test document."

query_result = embeddings.embed_query(text)

doc_result = embeddings.embed_documents([text])

Hugging Face Hub#

Let’s load the Hugging Face Embedding class.

from langchain.embeddings import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings()

text = "This is a test document."

query_result = embeddings.embed_query(text)

doc_result = embeddings.embed_documents([text])

TensorflowHub#

Let’s load the TensorflowHub Embedding class.

from langchain.embeddings import TensorflowHubEmbeddings

embeddings = TensorflowHubEmbeddings()

2023-01-30 23:53:01.652176: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-01-30 23:53:34.362802: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

text = "This is a test document."

query_result = embeddings.embed_query(text)

InstructEmbeddings#

Let’s load the HuggingFace instruct Embeddings class.

from langchain.embeddings import HuggingFaceInstructEmbeddings

embeddings = HuggingFaceInstructEmbeddings(
    query_instruction="Represent the query for retrieval: "
)

load INSTRUCTOR_Transformer
max_seq_length  512

text = "This is a test document."

query_result = embeddings.embed_query(text)

Self Hosted Embeddings#

Let’s load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes.

from langchain.embeddings import (
    SelfHostedEmbeddings,
    SelfHostedHuggingFaceEmbeddings,
    SelfHostedHuggingFaceInstructEmbeddings,
)
import runhouse as rh

# For an on-demand A100 with GCP, Azure, or Lambda
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)

# For an on-demand A10G with AWS (no single A100s on AWS)
# gpu = rh.cluster(name='rh-a10x', instance_type='g5.2xlarge', provider='aws')

# For an existing cluster
# gpu = rh.cluster(ips=['<ip of the cluster>'],
#                  ssh_creds={'ssh_user': '...', 'ssh_private_key':'<path_to_key>'},
#                  name='my-cluster')

embeddings = SelfHostedHuggingFaceEmbeddings(hardware=gpu)

text = "This is a test document."

query_result = embeddings.embed_query(text)

And similarly for SelfHostedHuggingFaceInstructEmbeddings:

embeddings = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)

Now let’s load an embedding model with a custom load function:

def get_pipeline():
    from transformers import (
        AutoModelForCausalLM,
        AutoTokenizer,
        pipeline,
    )  # Must be inside the function in notebooks

    model_id = "facebook/bart-base"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id)
    return pipeline("feature-extraction", model=model, tokenizer=tokenizer)


def inference_fn(pipeline, prompt):
    # Return last hidden state of the model
    if isinstance(prompt, list):
        return [emb[0][-1] for emb in pipeline(prompt)]
    return pipeline(prompt)[0][-1]

embeddings = SelfHostedEmbeddings(
    model_load_fn=get_pipeline,
    hardware=gpu,
    model_reqs=["./", "torch", "transformers"],
    inference_fn=inference_fn,
)

query_result = embeddings.embed_query(text)

Fake Embeddings#

LangChain also provides a fake embedding class. You can use this to test your pipelines.

from langchain.embeddings import FakeEmbeddings

embeddings = FakeEmbeddings(size=1352)

query_result = embeddings.embed_query("foo")

doc_results = embeddings.embed_documents(["foo"])

SageMaker Endpoint Embeddings#

Let’s load the SageMaker Endpoints Embeddings class. The class can be used if you host, e.g. your own Hugging Face model on SageMaker.

For instrucstions on how to do this, please see here

!pip3 install langchain boto3

from typing import Dict
from langchain.embeddings import SagemakerEndpointEmbeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
import json


class ContentHandler(ContentHandlerBase):
    content_type = "application/json"
    accepts = "application/json"

    def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
        input_str = json.dumps({"inputs": prompt, **model_kwargs})
        return input_str.encode('utf-8')
    
    def transform_output(self, output: bytes) -> str:
        response_json = json.loads(output.read().decode("utf-8"))
        return response_json["embeddings"]

content_handler = ContentHandler()


embeddings = SagemakerEndpointEmbeddings(
    # endpoint_name="endpoint-name", 
    # credentials_profile_name="credentials-profile-name", 
    endpoint_name="huggingface-pytorch-inference-2023-03-21-16-14-03-834", 
    region_name="us-east-1", 
    content_handler=content_handler
)

query_result = embeddings.embed_query("foo")

doc_results = embeddings.embed_documents(["foo"])

doc_results

Aleph Alpha#

There are two possible ways to use Aleph Alpha’s semantic embeddings. If you have texts with a dissimilar structure (e.g. a Document and a Query) you would want to use asymmetric embeddings. Conversely, for texts with comparable structures, symmetric embeddings are the suggested approach.

Asymmetric#

from langchain.embeddings import AlephAlphaAsymmetricSemanticEmbedding

document = "This is a content of the document"
query = "What is the contnt of the document?"

embeddings = AlephAlphaAsymmetricSemanticEmbedding()

doc_result = embeddings.embed_documents([document])

query_result = embeddings.embed_query(query)

Symmetric#

from langchain.embeddings import AlephAlphaSymmetricSemanticEmbedding

text = "This is a test text"

embeddings = AlephAlphaSymmetricSemanticEmbedding()

doc_result = embeddings.embed_documents([text])

query_result = embeddings.embed_query(text)

Embeddings

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