mirror of
https://github.com/qdrant/fastembed.git
synced 2026-07-23 11:20:51 -05:00
Make 0.2.1 Release + Update docs (#116)
* Update version from 0.2.0 (yanked) to 0.2.1 * Update text embedding to include prefix for passages and queries * Update supported models to use the latest API * * fix(text_embedding_base.py): remove unnecessary prefix from texts in embed method * feat(text_embedding_base.py): update query_embed method to updated instruction for the v1.5 model * Remove comparison, since the ranking is identical even with varying embedding * Refactor text embedding query handling
This commit is contained in:
Nirant
@@ -39,11 +39,19 @@
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stderr",
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"text": [
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"\u001b[32m2024-02-07 22:20:57.013\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfastembed.embedding\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m7\u001b[0m - \u001b[33m\u001b[1mDefaultEmbedding, FlagEmbedding, JinaEmbedding are deprecated. Use TextEmbedding instead.\u001b[0m\n"
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]
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}
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],
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"source": [
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"from typing import List\n",
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"import numpy as np\n",
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"from fastembed.embedding import FlagEmbedding as Embedding"
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"from fastembed import TextEmbedding"
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]
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},
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{
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@@ -84,7 +92,7 @@
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" \"His life has been depicted in various films, TV shows, and books\",\n",
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"]\n",
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"# Initialize the DefaultEmbedding class with the desired parameters\n",
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"embedding_model = Embedding(model_name=\"BAAI/bge-small-en\", max_length=512)\n",
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"embedding_model = TextEmbedding(model_name=\"BAAI/bge-small-en\", max_length=512)\n",
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"\n",
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"# We'll use the passage_embed method to get the embeddings for the documents\n",
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"embeddings: List[np.ndarray] = list(\n",
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@@ -124,65 +132,27 @@
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" print(f\"Rank {i+1}: {documents[sorted_scores[i]]}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Running and Comparing Queries\n",
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"Finally, we run our sample query using the `print_top_k` function.\n",
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"\n",
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"The differences between using query embeddings and plain embeddings can be observed in the retrieved ranks:\n",
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"\n",
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"Using query embeddings (from `query_embed` method):"
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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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"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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"Rank 1: Maharana Pratap was a Rajput warrior king from Mewar\n",
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"Rank 2: Maharana Pratap is considered a symbol of Rajput resistance against foreign rule\n",
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"Rank 3: His legacy is celebrated in Rajasthan through festivals and monuments\n",
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"Rank 4: He had 11 wives and 17 sons, including Amar Singh I who succeeded him as ruler of Mewar\n",
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"Rank 5: He fought against the Mughal Empire led by Akbar\n"
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]
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"data": {
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"text/plain": [
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"(array([-0.04393955, 0.04452892, -0.00760788, -0.03399807, 0.01951348],\n",
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" dtype=float32),\n",
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" array([-0.06002192, 0.04322132, -0.00545516, -0.04419701, -0.00542277],\n",
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" dtype=float32))"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"print_top_k(query_embedding, embeddings, documents)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Using plain embeddings (from `embed` method):"
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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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"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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"Rank 1: Maharana Pratap was a Rajput warrior king from Mewar\n",
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"Rank 2: Maharana Pratap is considered a symbol of Rajput resistance against foreign rule\n",
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"Rank 3: His legacy is celebrated in Rajasthan through festivals and monuments\n",
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"Rank 4: He had 11 wives and 17 sons, including Amar Singh I who succeeded him as ruler of Mewar\n",
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"Rank 5: He fought against the Mughal Empire led by Akbar\n"
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]
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}
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],
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"source": [
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"print_top_k(plain_query_embedding, embeddings, documents)"
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"query_embedding[:5], plain_query_embedding[:5]"
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]
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},
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{
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@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -12,7 +12,7 @@
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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": 4,
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"metadata": {},
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"outputs": [
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{
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@@ -40,6 +40,7 @@
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" <th>dim</th>\n",
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" <th>description</th>\n",
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" <th>size_in_GB</th>\n",
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" <th>sources</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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@@ -49,6 +50,7 @@
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" <td>768</td>\n",
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" <td>Base English model</td>\n",
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" <td>0.50</td>\n",
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" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gz'}</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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@@ -56,136 +58,161 @@
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" <td>768</td>\n",
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" <td>Base English model, v1.5</td>\n",
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" <td>0.44</td>\n",
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" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en-v1.5.tar.gz', 'hf': 'qdrant/bge-base-en-v1.5-onnx-q'}</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>BAAI/bge-large-en-v1.5-quantized</td>\n",
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" <td>1024</td>\n",
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" <td>Large English model, v1.5</td>\n",
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" <td>1.34</td>\n",
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" <td>{'hf': 'qdrant/bge-large-en-v1.5-onnx-q'}</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>BAAI/bge-large-en-v1.5</td>\n",
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" <td>1024</td>\n",
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" <td>Large English model, v1.5</td>\n",
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" <td>1.34</td>\n",
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" <td>{'hf': 'qdrant/bge-large-en-v1.5-onnx'}</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <th>4</th>\n",
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" <td>BAAI/bge-small-en</td>\n",
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" <td>384</td>\n",
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" <td>Fast English model</td>\n",
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" <td>0.20</td>\n",
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" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gz'}</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <th>5</th>\n",
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" <td>BAAI/bge-small-en-v1.5</td>\n",
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" <td>384</td>\n",
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" <td>Fast and Default English model</td>\n",
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" <td>0.13</td>\n",
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" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-en-v1.5.tar.gz', 'hf': 'qdrant/bge-small-en-v1.5-onnx-q'}</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <th>6</th>\n",
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" <td>BAAI/bge-small-zh-v1.5</td>\n",
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" <td>512</td>\n",
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" <td>Fast and recommended Chinese model</td>\n",
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" <td>0.10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>intfloat/multilingual-e5-large</td>\n",
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" <td>1024</td>\n",
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" <td>Multilingual model, e5-large. Recommend using this model for non-English languages</td>\n",
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" <td>2.24</td>\n",
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" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-zh-v1.5.tar.gz'}</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>jinaai/jina-embeddings-v2-base-en</td>\n",
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" <td>768</td>\n",
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" <td>English embedding model supporting 8192 sequence length</td>\n",
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" <td>0.55</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>jinaai/jina-embeddings-v2-small-en</td>\n",
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" <td>512</td>\n",
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" <td>English embedding model supporting 8192 sequence length</td>\n",
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" <td>0.13</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
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" <td>384</td>\n",
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" <td>Sentence Transformer model, MiniLM-L6-v2</td>\n",
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" <td>0.09</td>\n",
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" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz', 'hf': 'qdrant/all-MiniLM-L6-v2-onnx'}</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>xenova/multilingual-e5-large</td>\n",
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" <th>8</th>\n",
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" <td>intfloat/multilingual-e5-large</td>\n",
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" <td>1024</td>\n",
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" <td>Multilingual model. Recommended for non-English languages</td>\n",
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" <td>Multilingual model, e5-large. Recommend using this model for non-English languages</td>\n",
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" <td>2.24</td>\n",
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" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-multilingual-e5-large.tar.gz', 'hf': 'qdrant/multilingual-e5-large-onnx'}</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>xenova/paraphrase-multilingual-mpnet-base-v2</td>\n",
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" <th>9</th>\n",
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" <td>sentence-transformers/paraphrase-multilingual-mpnet-base-v2</td>\n",
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" <td>768</td>\n",
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" <td>Sentence-transformers model for tasks like clustering or semantic search</td>\n",
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" <td>1.11</td>\n",
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" <td>{'hf': 'xenova/paraphrase-multilingual-mpnet-base-v2'}</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>jinaai/jina-embeddings-v2-base-en</td>\n",
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" <td>768</td>\n",
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" <td>English embedding model supporting 8192 sequence length</td>\n",
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" <td>0.55</td>\n",
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" <td>{'hf': 'xenova/jina-embeddings-v2-base-en'}</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>jinaai/jina-embeddings-v2-small-en</td>\n",
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" <td>512</td>\n",
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" <td>English embedding model supporting 8192 sequence length</td>\n",
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" <td>0.13</td>\n",
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" <td>{'hf': 'xenova/jina-embeddings-v2-small-en'}</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" model dim \\\n",
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"0 BAAI/bge-base-en 768 \n",
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"1 BAAI/bge-base-en-v1.5 768 \n",
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"2 BAAI/bge-large-en-v1.5 1024 \n",
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"3 BAAI/bge-small-en 384 \n",
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"4 BAAI/bge-small-en-v1.5 384 \n",
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"5 BAAI/bge-small-zh-v1.5 512 \n",
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"6 intfloat/multilingual-e5-large 1024 \n",
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"7 jinaai/jina-embeddings-v2-base-en 768 \n",
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"8 jinaai/jina-embeddings-v2-small-en 512 \n",
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"9 sentence-transformers/all-MiniLM-L6-v2 384 \n",
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"10 xenova/multilingual-e5-large 1024 \n",
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"11 xenova/paraphrase-multilingual-mpnet-base-v2 768 \n",
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" model dim \\\n",
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"0 BAAI/bge-base-en 768 \n",
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"1 BAAI/bge-base-en-v1.5 768 \n",
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"2 BAAI/bge-large-en-v1.5-quantized 1024 \n",
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"3 BAAI/bge-large-en-v1.5 1024 \n",
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"4 BAAI/bge-small-en 384 \n",
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"5 BAAI/bge-small-en-v1.5 384 \n",
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"6 BAAI/bge-small-zh-v1.5 512 \n",
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"7 sentence-transformers/all-MiniLM-L6-v2 384 \n",
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"8 intfloat/multilingual-e5-large 1024 \n",
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"9 sentence-transformers/paraphrase-multilingual-mpnet-base-v2 768 \n",
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"10 jinaai/jina-embeddings-v2-base-en 768 \n",
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"11 jinaai/jina-embeddings-v2-small-en 512 \n",
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"\n",
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" description \\\n",
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"0 Base English model \n",
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"1 Base English model, v1.5 \n",
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"2 Large English model, v1.5 \n",
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"3 Fast English model \n",
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"4 Fast and Default English model \n",
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"5 Fast and recommended Chinese model \n",
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"6 Multilingual model, e5-large. Recommend using this model for non-English languages \n",
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"7 English embedding model supporting 8192 sequence length \n",
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"8 English embedding model supporting 8192 sequence length \n",
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"9 Sentence Transformer model, MiniLM-L6-v2 \n",
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"10 Multilingual model. Recommended for non-English languages \n",
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"11 Sentence-transformers model for tasks like clustering or semantic search \n",
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"3 Large English model, v1.5 \n",
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"4 Fast English model \n",
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"5 Fast and Default English model \n",
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"6 Fast and recommended Chinese model \n",
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"7 Sentence Transformer model, MiniLM-L6-v2 \n",
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"8 Multilingual model, e5-large. Recommend using this model for non-English languages \n",
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"9 Sentence-transformers model for tasks like clustering or semantic search \n",
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"10 English embedding model supporting 8192 sequence length \n",
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"11 English embedding model supporting 8192 sequence length \n",
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"\n",
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" size_in_GB \n",
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"0 0.50 \n",
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"1 0.44 \n",
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"2 1.34 \n",
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"3 0.20 \n",
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"4 0.13 \n",
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"5 0.10 \n",
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"6 2.24 \n",
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"7 0.55 \n",
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"8 0.13 \n",
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"9 0.09 \n",
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"10 2.24 \n",
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"11 1.11 "
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" size_in_GB \\\n",
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"0 0.50 \n",
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"1 0.44 \n",
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"2 1.34 \n",
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"3 1.34 \n",
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"4 0.20 \n",
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"5 0.13 \n",
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"6 0.10 \n",
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"7 0.09 \n",
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"8 2.24 \n",
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"9 1.11 \n",
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"10 0.55 \n",
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"11 0.13 \n",
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"\n",
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" sources \n",
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"0 {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gz'} \n",
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"1 {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en-v1.5.tar.gz', 'hf': 'qdrant/bge-base-en-v1.5-onnx-q'} \n",
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"2 {'hf': 'qdrant/bge-large-en-v1.5-onnx-q'} \n",
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"3 {'hf': 'qdrant/bge-large-en-v1.5-onnx'} \n",
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"4 {'url': 'https://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gz'} \n",
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"5 {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-en-v1.5.tar.gz', 'hf': 'qdrant/bge-small-en-v1.5-onnx-q'} \n",
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"6 {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-zh-v1.5.tar.gz'} \n",
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"7 {'url': 'https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz', 'hf': 'qdrant/all-MiniLM-L6-v2-onnx'} \n",
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"8 {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-multilingual-e5-large.tar.gz', 'hf': 'qdrant/multilingual-e5-large-onnx'} \n",
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"9 {'hf': 'xenova/paraphrase-multilingual-mpnet-base-v2'} \n",
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"10 {'hf': 'xenova/jina-embeddings-v2-base-en'} \n",
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"11 {'hf': 'xenova/jina-embeddings-v2-small-en'} "
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]
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},
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"execution_count": 6,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
||||
"from fastembed.embedding import Embedding\n",
|
||||
"from fastembed import TextEmbedding\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"pd.set_option(\"display.max_colwidth\", None)\n",
|
||||
"pd.DataFrame(Embedding.list_supported_models())"
|
||||
"pd.DataFrame(TextEmbedding.list_supported_models())"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -205,7 +232,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.7"
|
||||
"version": "3.11.5"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "fastembed"
|
||||
version = "0.2.0"
|
||||
version = "0.2.1"
|
||||
description = "Fast, light, accurate library built for retrieval embedding generation"
|
||||
authors = ["NirantK <nirant.bits@gmail.com>"]
|
||||
license = "Apache License"
|
||||
|
||||
Reference in New Issue
Block a user