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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"id": "2c591a6a42ac7f0", | ||
"metadata": {}, | ||
"source": [ | ||
"# Bookend AI\n", | ||
"\n", | ||
"Let's load the Bookend AI Embeddings class." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "d94c62b4", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"from langchain.embeddings import BookendEmbeddings" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "523a09e3", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"embeddings = BookendEmbeddings(\n", | ||
" domain=\"your_domain\",\n", | ||
" api_token=\"your_api_token\",\n", | ||
" model_id=\"your_embeddings_model_id\",\n", | ||
")" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "b212bd5a", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"text = \"This is a test document.\"" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "57db66bd", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"query_result = embeddings.embed_query(text)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "b790fd09", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"doc_result = embeddings.embed_documents([text])" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3 (ipykernel)", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.10.1" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 5 | ||
} |
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"""Wrapper around Bookend AI embedding models.""" | ||
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import json | ||
from typing import Any, List | ||
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import requests | ||
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from langchain.pydantic_v1 import BaseModel, Field | ||
from langchain.schema.embeddings import Embeddings | ||
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API_URL = "https://api.bookend.ai/" | ||
DEFAULT_TASK = "embeddings" | ||
PATH = "/models/predict" | ||
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class BookendEmbeddings(BaseModel, Embeddings): | ||
"""Bookend AI sentence_transformers embedding models. | ||
Example: | ||
.. code-block:: python | ||
from langchain.embeddings import BookendEmbeddings | ||
bookend = BookendEmbeddings( | ||
domain={domain} | ||
api_token={api_token} | ||
model_id={model_id} | ||
) | ||
bookend.embed_documents([ | ||
"Please put on these earmuffs because I can't you hear.", | ||
"Baby wipes are made of chocolate stardust.", | ||
]) | ||
bookend.embed_query( | ||
"She only paints with bold colors; she does not like pastels." | ||
) | ||
""" | ||
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domain: str | ||
"""Request for a domain at https://bookend.ai/ to use this embeddings module.""" | ||
api_token: str | ||
"""Request for an API token at https://bookend.ai/ to use this embeddings module.""" | ||
model_id: str | ||
"""Embeddings model ID to use.""" | ||
auth_header: dict = Field(default_factory=dict) | ||
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def __init__(self, **kwargs: Any): | ||
super().__init__(**kwargs) | ||
self.auth_header = {"Authorization": "Basic {}".format(self.api_token)} | ||
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def embed_documents(self, texts: List[str]) -> List[List[float]]: | ||
"""Embed documents using a Bookend deployed embeddings model. | ||
Args: | ||
texts: The list of texts to embed. | ||
Returns: | ||
List of embeddings, one for each text. | ||
""" | ||
result = [] | ||
headers = self.auth_header | ||
headers["Content-Type"] = "application/json; charset=utf-8" | ||
params = { | ||
"model_id": self.model_id, | ||
"task": DEFAULT_TASK, | ||
} | ||
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for text in texts: | ||
data = json.dumps( | ||
{"text": text, "question": None, "context": None, "instruction": None} | ||
) | ||
r = requests.request( | ||
"POST", | ||
API_URL + self.domain + PATH, | ||
headers=headers, | ||
params=params, | ||
data=data, | ||
) | ||
result.append(r.json()[0]["data"]) | ||
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return result | ||
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def embed_query(self, text: str) -> List[float]: | ||
"""Embed a query using a Bookend deployed embeddings model. | ||
Args: | ||
text: The text to embed. | ||
Returns: | ||
Embeddings for the text. | ||
""" | ||
return self.embed_documents([text])[0] |
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27
libs/langchain/tests/integration_tests/embeddings/test_bookend.py
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"""Test Bookend AI embeddings.""" | ||
from langchain.embeddings.bookend import BookendEmbeddings | ||
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def test_bookend_embedding_documents() -> None: | ||
"""Test Bookend AI embeddings for documents.""" | ||
documents = ["foo bar", "bar foo"] | ||
embedding = BookendEmbeddings( | ||
domain="<bookend_domain>", | ||
api_token="<bookend_api_token>", | ||
model_id="<bookend_embeddings_model_id>", | ||
) | ||
output = embedding.embed_documents(documents) | ||
assert len(output) == 2 | ||
assert len(output[0]) == 768 | ||
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def test_bookend_embedding_query() -> None: | ||
"""Test Bookend AI embeddings for query.""" | ||
document = "foo bar" | ||
embedding = BookendEmbeddings( | ||
domain="<bookend_domain>", | ||
api_token="<bookend_api_token>", | ||
model_id="<bookend_embeddings_model_id>", | ||
) | ||
output = embedding.embed_query(document) | ||
assert len(output) == 768 |
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@@ -53,6 +53,7 @@ | |
"QianfanEmbeddingsEndpoint", | ||
"JohnSnowLabsEmbeddings", | ||
"VoyageEmbeddings", | ||
"BookendEmbeddings", | ||
] | ||
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