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https://github.com/hwchase17/langchain
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docs: fix hf embeddings install (#24577)
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@ -8,87 +8,68 @@
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"# Sentence Transformers on Hugging Face\n",
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"\n",
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">[Hugging Face sentence-transformers](https://huggingface.co/sentence-transformers) is a Python framework for state-of-the-art sentence, text and image embeddings.\n",
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">One of the embedding models is used in the `HuggingFaceEmbeddings` class.\n",
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">We have also added an alias for `SentenceTransformerEmbeddings` for users who are more familiar with directly using that package.\n",
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"\n",
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"`sentence_transformers` package models are originating from [Sentence-BERT](https://arxiv.org/abs/1908.10084)"
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">You can use these embedding models from the `HuggingFaceEmbeddings` class."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"id": "06c9f47d",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet langchain-huggingface"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "ff9be586",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
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"[-0.0383385568857193, 0.12346469610929489, -0.028642987832427025, 0.05365273728966713, 0.00884537026...\n"
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]
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}
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],
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"source": [
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"%pip install --upgrade --quiet sentence_transformers > /dev/null"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "861521a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_huggingface import HuggingFaceEmbeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ff9be586",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_huggingface import HuggingFaceEmbeddings\n",
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"\n",
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"embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
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"# Equivalent to SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
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"\n",
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"text = \"This is a test document.\"\n",
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"query_result = embeddings.embed_query(text)\n",
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"\n",
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"# show only the first 100 characters of the stringified vector\n",
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"print(str(query_result)[:100] + \"...\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "d0a98ae9",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "5d6c682b",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 9,
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"id": "bb5e74c0",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[-0.038338493555784225, 0.12346471846103668, -0.028642840683460236, 0.05365276336669922, 0.00884535...\n"
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]
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}
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],
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"source": [
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"doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])"
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"doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])\n",
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"print(str(doc_result)[:100] + \"...\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aaad49f8",
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"id": "d18544f5",
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"metadata": {},
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"outputs": [],
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"source": []
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@ -110,7 +91,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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"version": "3.11.4"
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},
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"vscode": {
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"interpreter": {
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