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cli[patch]: Update integration template for embedding models (#25248)

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Update integration template for embedding models
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Eugene Yurtsev 2024-08-09 14:28:57 -04:00 committed by GitHub
parent b6f0174bb9
commit dadb6f1445
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1 changed files with 7 additions and 35 deletions
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42
libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb
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@ -24,13 +24,9 @@
"## Overview\n",
"### Integration details\n",
"\n",
"- TODO: Fill in table features.\n",
"- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
"- TODO: Make sure API reference links are correct.\n",
"import { ItemTable } from \"@theme/FeatureTables\";\n",
"\n",
"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/text_embedding/__package_name_short_snake__) | Package downloads | Package latest |\n",
"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
"| [__ModuleName__Embeddings](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
"<ItemTable category=\"text_embedding\" item=\"__ModuleName__\" />\n",
"\n",
"## Setup\n",
"\n",
@ -157,10 +153,10 @@
"retriever = vectorstore.as_retriever()\n",
"\n",
"# Retrieve the most similar text\n",
"retrieved_document = retriever.invoke(\"What is LangChain?\")\n",
"retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
"\n",
"# show the retrieved document's content\n",
"retrieved_document.page_content"
"retrieved_documents[0].page_content"
]
},
{
@ -210,7 +206,7 @@
"text2 = (\n",
" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
")\n",
"two_vectors = embeddings.embed_queries([text, text2])\n",
"two_vectors = embeddings.embed_documents([text, text2])\n",
"for vector in two_vectors:\n",
" print(str(vector)[:100]) # Show the first 100 characters of the vector"
]
@ -220,34 +216,10 @@
"id": "98785c12",
"metadata": {},
"source": [
"### Async Usage\n",
"## API Reference\n",
"\n",
"You can also use `aembed_query` and `aembed_documents` for producing embeddings asynchronously:\n"
"For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html).\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c3bef91",
"metadata": {},
"outputs": [],
"source": [
"import asyncio\n",
"\n",
"async def async_example():\n",
" single_vector = await embeddings.embed_query(text)\n",
" print(str(single_vector)[:100]) # Show the first 100 characters of the vector\n",
"\n",
"asyncio.run(async_example())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1bd4396",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {