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https://github.com/hwchase17/langchain.git
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Add embeddings cache (#8976)
This PR adds the ability to temporarily cache or persistently store embeddings. A notebook has been included showing how to set up the cache and how to use it with a vectorstore.
This commit is contained in:
Eugene Yurtsev
GitHub
467
docs/extras/modules/data_connection/caching_embeddings.ipynb
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467
docs/extras/modules/data_connection/caching_embeddings.ipynb
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@@ -0,0 +1,467 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bf4061ce",
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"metadata": {},
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"source": [
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"# Caching Embeddings\n",
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"\n",
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"Embeddings can be stored or temporarily cached to avoid needing to recompute them.\n",
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"\n",
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"Caching embeddings can be done using a `CacheBackedEmbedder`.\n",
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"\n",
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"The cache backed embedder is a wrapper around an embedder that caches\n",
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"embeddings in a key-value store. \n",
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"\n",
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"The text is hashed and the hash is used as the key in the cache.\n",
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"\n",
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"\n",
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"The main supported way to initialized a `CacheBackedEmbedder` is `from_bytes_store`. This takes in the following parameters:\n",
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"\n",
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"- underlying_embedder: The embedder to use for embedding.\n",
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"- document_embedding_cache: The cache to use for storing document embeddings.\n",
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"- namespace: (optional, defaults to `\"\"`) The namespace to use for document cache. This namespace is used to avoid collisions with other caches. For example, set it to the name of the embedding model used.\n",
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"\n",
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"**Attention**: Be sure to set the `namespace` parameter to avoid collisions of the same text embedded using different embeddings models."
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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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"id": "a463c3c2-749b-40d1-a433-84f68a1cd1c7",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings import CacheBackedEmbedder\n",
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"from langchain.storage import InMemoryStore\n",
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"from langchain.storage import LocalFileStore\n",
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"from langchain.embeddings import OpenAIEmbeddings"
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]
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},
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{
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"cell_type": "markdown",
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"id": "564c9801-29f0-4452-aeac-527382e2c0e8",
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"metadata": {},
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"source": [
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"## In Memory\n",
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"\n",
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"This section shows how to set up an in memory cache for embeddings. This type of cache is primarily \n",
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"useful for unit tests or prototyping. Do **not** use this cache if you need to actually store the embeddings."
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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": "13bd1c5b-b7ba-4394-957c-7d5b5a841972",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"store = InMemoryStore()"
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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": 3,
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"id": "9d99885f-99e1-498c-904d-6db539ac9466",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"underlying_embedder = OpenAIEmbeddings()\n",
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"embedder = CacheBackedEmbedder.from_bytes_store(\n",
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" underlying_embedder, store, namespace=underlying_embedder.model\n",
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")"
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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": "682eb5d4-0b7a-4dac-b8fb-3de4ca6e421c",
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"metadata": {
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"tags": []
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},
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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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"CPU times: user 405 ms, sys: 32.9 ms, total: 438 ms\n",
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"Wall time: 715 ms\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"embeddings = embedder.embed_documents([\"hello\", \"goodbye\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "95233026-147f-49d1-bd87-e1e8b88ebdbc",
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"metadata": {},
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"source": [
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"The second time we try to embed the embedding time is only 2 ms because the embeddings are looked up in the cache."
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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": "f819c3ff-a212-4d06-a5f7-5eb1435c1feb",
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"metadata": {
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"tags": []
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},
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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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"CPU times: user 1.55 ms, sys: 436 µs, total: 1.99 ms\n",
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"Wall time: 1.99 ms\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"embeddings_from_cache = embedder.embed_documents([\"hello\", \"goodbye\"])"
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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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"id": "ec38fb72-90a9-4687-a483-c62c87d1f4dd",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 6,
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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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"embeddings == embeddings_from_cache"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f6cbe100-8587-4830-b207-fb8b524a9854",
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"metadata": {},
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"source": [
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"## File system\n",
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"\n",
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"This section covers how to use a file system store"
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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": "a0070271-0809-4528-97e0-2a88216846f3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"fs = LocalFileStore(\"./cache/\")"
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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": 8,
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"id": "0b20e9fe-f57f-4d7c-9f81-105c5f8726f4",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"embedder2 = CacheBackedEmbedder.from_bytes_store(\n",
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" underlying_embedder, fs, namespace=underlying_embedder.model\n",
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")"
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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": 9,
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"id": "630515fd-bf5c-4d9c-a404-9705308f3a2c",
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"metadata": {
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"tags": []
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},
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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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"CPU times: user 10.5 ms, sys: 988 µs, total: 11.5 ms\n",
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"Wall time: 220 ms\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"embeddings = embedder2.embed_documents([\"hello\", \"goodbye\"])"
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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": 10,
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"id": "30e6bb87-42c9-4d08-88ac-0d22c9c449a1",
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"metadata": {
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"tags": []
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},
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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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"CPU times: user 3.49 ms, sys: 0 ns, total: 3.49 ms\n",
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"Wall time: 3.03 ms\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"embeddings = embedder2.embed_documents([\"hello\", \"goodbye\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "12ed5a45-8352-4e0f-8583-5537397f53c0",
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"metadata": {},
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"source": [
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"Here are the embeddings that have been persisted to the directory `./cache`. \n",
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"\n",
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"Notice that the embedder takes a namespace parameter."
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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": 11,
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"id": "658e2914-05e9-44a3-a8fe-3fe17ca84039",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['text-embedding-ada-002e885db5b-c0bd-5fbc-88b1-4d1da6020aa5',\n",
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" 'text-embedding-ada-0026ba52e44-59c9-5cc9-a084-284061b13c80']"
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]
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},
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"execution_count": 11,
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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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"list(fs.yield_keys())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c67f8e97-4851-4e26-ab6f-3418b0188dc4",
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"metadata": {},
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"source": [
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"## Using with a vectorstore\n",
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"\n",
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"Let's see this cache in action with the FAISS vectorstore."
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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": 12,
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"id": "9e4314d8-88ef-4f52-81ae-0be771168bb6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import FAISS"
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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": 13,
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"id": "30743664-38f5-425d-8216-772b64e7f348",
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"metadata": {},
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"outputs": [],
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"source": [
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"fs = LocalFileStore(\"./cache/\")\n",
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"\n",
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"cached_embedder = CacheBackedEmbedder.from_bytes_store(\n",
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" OpenAIEmbeddings(), fs, namespace=underlying_embedder.model\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "06a6f305-724f-4b71-adef-be0169f61381",
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"metadata": {},
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"source": [
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"The cache is empty prior to embedding"
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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": 14,
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"id": "f9ad627f-ced2-4277-b336-2434f22f2c8a",
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"metadata": {},
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"outputs": [
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{
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"data": {
|
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"text/plain": [
|
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"['text-embedding-ada-002e885db5b-c0bd-5fbc-88b1-4d1da6020aa5',\n",
|
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" 'text-embedding-ada-0026ba52e44-59c9-5cc9-a084-284061b13c80']"
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]
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},
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"execution_count": 14,
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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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"list(fs.yield_keys())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5814aa9c-e8e4-4079-accf-53c49615971e",
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"metadata": {},
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"source": [
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"Load the document, split it into chunks, embed each chunk and load it into the vector store."
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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": 16,
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"id": "cf958ac2-e60e-4668-b32c-8bb2d78b3c61",
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"metadata": {},
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"outputs": [],
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"source": [
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"raw_documents = TextLoader(\"../state_of_the_union.txt\").load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"documents = text_splitter.split_documents(raw_documents)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fc433fec-ab64-4f11-ae8b-fc3dd76cd79a",
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"metadata": {},
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"source": [
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"create the vectorstore"
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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": 17,
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"id": "3a1d7bb8-3b72-4bb5-9013-cf7729caca61",
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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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"CPU times: user 124 ms, sys: 22.6 ms, total: 146 ms\n",
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"Wall time: 832 ms\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"db = FAISS.from_documents(documents, cached_embedder)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c94a734c-fa66-40ce-8610-12b00b7df334",
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"metadata": {},
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"source": [
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"If we try to create the vectostore again, it'll be much faster since it does not need to re-compute any embeddings."
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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": 18,
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"id": "714cb2e2-77ba-41a8-bb83-84e75342af2d",
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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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"CPU times: user 32.9 ms, sys: 286 µs, total: 33.2 ms\n",
|
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"Wall time: 32.5 ms\n"
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]
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}
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],
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"source": [
|
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"%%time\n",
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"db2 = FAISS.from_documents(documents, cached_embedder)"
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]
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},
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{
|
||||
"cell_type": "markdown",
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||||
"id": "93d37b2a-5406-4e2c-b786-869e2430d19d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"And here are some of the embeddings that got created:"
|
||||
]
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},
|
||||
{
|
||||
"cell_type": "code",
|
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"execution_count": 19,
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"id": "f2ca32dd-3712-4093-942b-4122f3dc8a8e",
|
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"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['text-embedding-ada-002614d7cf6-46f1-52fa-9d3a-740c39e7a20e',\n",
|
||||
" 'text-embedding-ada-0020fc1ede2-407a-5e14-8f8f-5642214263f5',\n",
|
||||
" 'text-embedding-ada-002e885db5b-c0bd-5fbc-88b1-4d1da6020aa5',\n",
|
||||
" 'text-embedding-ada-002e4ad20ef-dfaa-5916-9459-f90c6d8e8159',\n",
|
||||
" 'text-embedding-ada-002a5ef11e4-0474-5725-8d80-81c91943b37f']"
|
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]
|
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},
|
||||
"execution_count": 19,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
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}
|
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],
|
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"source": [
|
||||
"list(fs.yield_keys())[:5]"
|
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]
|
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}
|
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],
|
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"metadata": {
|
||||
"kernelspec": {
|
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"display_name": "Python 3 (ipykernel)",
|
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"language": "python",
|
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"name": "python3"
|
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},
|
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"language_info": {
|
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.9.10"
|
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}
|
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},
|
||||
"nbformat": 4,
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"nbformat_minor": 5
|
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}
|
||||
@@ -20,6 +20,7 @@ from langchain.embeddings.aleph_alpha import (
|
||||
)
|
||||
from langchain.embeddings.awa import AwaEmbeddings
|
||||
from langchain.embeddings.bedrock import BedrockEmbeddings
|
||||
from langchain.embeddings.cache import CacheBackedEmbeddings
|
||||
from langchain.embeddings.clarifai import ClarifaiEmbeddings
|
||||
from langchain.embeddings.cohere import CohereEmbeddings
|
||||
from langchain.embeddings.dashscope import DashScopeEmbeddings
|
||||
@@ -62,10 +63,11 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
__all__ = [
|
||||
"OpenAIEmbeddings",
|
||||
"HuggingFaceEmbeddings",
|
||||
"CohereEmbeddings",
|
||||
"CacheBackedEmbeddings",
|
||||
"ClarifaiEmbeddings",
|
||||
"CohereEmbeddings",
|
||||
"ElasticsearchEmbeddings",
|
||||
"HuggingFaceEmbeddings",
|
||||
"JinaEmbeddings",
|
||||
"LlamaCppEmbeddings",
|
||||
"HuggingFaceHubEmbeddings",
|
||||
|
||||
175
libs/langchain/langchain/embeddings/cache.py
Normal file
175
libs/langchain/langchain/embeddings/cache.py
Normal file
@@ -0,0 +1,175 @@
|
||||
"""Module contains code for a cache backed embedder.
|
||||
|
||||
The cache backed embedder is a wrapper around an embedder that caches
|
||||
embeddings in a key-value store. The cache is used to avoid recomputing
|
||||
embeddings for the same text.
|
||||
|
||||
The text is hashed and the hash is used as the key in the cache.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import uuid
|
||||
from functools import partial
|
||||
from typing import Callable, List, Sequence, Union, cast
|
||||
|
||||
from langchain.embeddings.base import Embeddings
|
||||
from langchain.schema import BaseStore
|
||||
from langchain.storage.encoder_backed import EncoderBackedStore
|
||||
|
||||
NAMESPACE_UUID = uuid.UUID(int=1985)
|
||||
|
||||
|
||||
def _hash_string_to_uuid(input_string: str) -> uuid.UUID:
|
||||
"""Hash a string and returns the corresponding UUID."""
|
||||
hash_value = hashlib.sha1(input_string.encode("utf-8")).hexdigest()
|
||||
return uuid.uuid5(NAMESPACE_UUID, hash_value)
|
||||
|
||||
|
||||
def _key_encoder(key: str, namespace: str) -> str:
|
||||
"""Encode a key."""
|
||||
return namespace + str(_hash_string_to_uuid(key))
|
||||
|
||||
|
||||
def _create_key_encoder(namespace: str) -> Callable[[str], str]:
|
||||
"""Create an encoder for a key."""
|
||||
return partial(_key_encoder, namespace=namespace)
|
||||
|
||||
|
||||
def _value_serializer(value: Sequence[float]) -> bytes:
|
||||
"""Serialize a value."""
|
||||
return json.dumps(value).encode()
|
||||
|
||||
|
||||
def _value_deserializer(serialized_value: bytes) -> List[float]:
|
||||
"""Deserialize a value."""
|
||||
return cast(List[float], json.loads(serialized_value.decode()))
|
||||
|
||||
|
||||
class CacheBackedEmbeddings(Embeddings):
|
||||
"""Interface for caching results from embedding models.
|
||||
|
||||
The interface allows works with any store that implements
|
||||
the abstract store interface accepting keys of type str and values of list of
|
||||
floats.
|
||||
|
||||
If need be, the interface can be extended to accept other implementations
|
||||
of the value serializer and deserializer, as well as the key encoder.
|
||||
|
||||
Examples:
|
||||
|
||||
..code-block:: python
|
||||
|
||||
from langchain.embeddings import CacheBackedEmbeddings, OpenAIEmbeddings
|
||||
from langchain.storage import LocalFileStore
|
||||
|
||||
store = LocalFileStore('./my_cache')
|
||||
|
||||
underlying_embedder = OpenAIEmbeddings()
|
||||
embedder = CacheBackedEmbeddings.from_bytes_store(
|
||||
underlying_embedder, store, namespace=underlying_embedder.model
|
||||
)
|
||||
|
||||
# Embedding is computed and cached
|
||||
embeddings = embedder.embed_documents(["hello", "goodbye"])
|
||||
|
||||
# Embeddings are retrieved from the cache, no computation is done
|
||||
embeddings = embedder.embed_documents(["hello", "goodbye"])
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
underlying_embeddings: Embeddings,
|
||||
document_embedding_store: BaseStore[str, List[float]],
|
||||
) -> None:
|
||||
"""Initialize the embedder.
|
||||
|
||||
Args:
|
||||
underlying_embeddings: the embedder to use for computing embeddings.
|
||||
document_embedding_store: The store to use for caching document embeddings.
|
||||
"""
|
||||
super().__init__()
|
||||
self.document_embedding_store = document_embedding_store
|
||||
self.underlying_embeddings = underlying_embeddings
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Embed a list of texts.
|
||||
|
||||
The method first checks the cache for the embeddings.
|
||||
If the embeddings are not found, the method uses the underlying embedder
|
||||
to embed the documents and stores the results in the cache.
|
||||
|
||||
Args:
|
||||
texts: A list of texts to embed.
|
||||
|
||||
Returns:
|
||||
A list of embeddings for the given texts.
|
||||
"""
|
||||
vectors: List[Union[List[float], None]] = self.document_embedding_store.mget(
|
||||
texts
|
||||
)
|
||||
missing_indices: List[int] = [
|
||||
i for i, vector in enumerate(vectors) if vector is None
|
||||
]
|
||||
missing_texts = [texts[i] for i in missing_indices]
|
||||
|
||||
if missing_texts:
|
||||
missing_vectors = self.underlying_embeddings.embed_documents(missing_texts)
|
||||
self.document_embedding_store.mset(
|
||||
list(zip(missing_texts, missing_vectors))
|
||||
)
|
||||
for index, updated_vector in zip(missing_indices, missing_vectors):
|
||||
vectors[index] = updated_vector
|
||||
|
||||
return cast(
|
||||
List[List[float]], vectors
|
||||
) # Nones should have been resolved by now
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Embed query text.
|
||||
|
||||
This method does not support caching at the moment.
|
||||
|
||||
Support for caching queries is easily to implement, but might make
|
||||
sense to hold off to see the most common patterns.
|
||||
|
||||
If the cache has an eviction policy, we may need to be a bit more careful
|
||||
about sharing the cache between documents and queries. Generally,
|
||||
one is OK evicting query caches, but document caches should be kept.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
|
||||
Returns:
|
||||
The embedding for the given text.
|
||||
"""
|
||||
return self.underlying_embeddings.embed_query(text)
|
||||
|
||||
@classmethod
|
||||
def from_bytes_store(
|
||||
cls,
|
||||
underlying_embeddings: Embeddings,
|
||||
document_embedding_cache: BaseStore[str, bytes],
|
||||
*,
|
||||
namespace: str = "",
|
||||
) -> CacheBackedEmbeddings:
|
||||
"""On-ramp that adds the necessary serialization and encoding to the store.
|
||||
|
||||
Args:
|
||||
underlying_embeddings: The embedder to use for embedding.
|
||||
document_embedding_cache: The cache to use for storing document embeddings.
|
||||
*,
|
||||
namespace: The namespace to use for document cache.
|
||||
This namespace is used to avoid collisions with other caches.
|
||||
For example, set it to the name of the embedding model used.
|
||||
"""
|
||||
namespace = namespace
|
||||
key_encoder = _create_key_encoder(namespace)
|
||||
encoder_backed_store = EncoderBackedStore[str, List[float]](
|
||||
document_embedding_cache,
|
||||
key_encoder,
|
||||
_value_serializer,
|
||||
_value_deserializer,
|
||||
)
|
||||
return cls(underlying_embeddings, encoder_backed_store)
|
||||
49
libs/langchain/tests/unit_tests/embeddings/test_caching.py
Normal file
49
libs/langchain/tests/unit_tests/embeddings/test_caching.py
Normal file
@@ -0,0 +1,49 @@
|
||||
"""Embeddings tests."""
|
||||
from typing import List
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain.embeddings import CacheBackedEmbeddings
|
||||
from langchain.embeddings.base import Embeddings
|
||||
from langchain.storage.in_memory import InMemoryStore
|
||||
|
||||
|
||||
class MockEmbeddings(Embeddings):
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
# Simulate embedding documents
|
||||
embeddings: List[List[float]] = []
|
||||
for text in texts:
|
||||
embeddings.append([len(text), len(text) + 1])
|
||||
return embeddings
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
# Simulate embedding a query
|
||||
return [5.0, 6.0]
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def cache_embeddings() -> CacheBackedEmbeddings:
|
||||
"""Create a cache backed embeddings."""
|
||||
store = InMemoryStore()
|
||||
embeddings = MockEmbeddings()
|
||||
return CacheBackedEmbeddings.from_bytes_store(
|
||||
embeddings, store, namespace="test_namespace"
|
||||
)
|
||||
|
||||
|
||||
def test_embed_documents(cache_embeddings: CacheBackedEmbeddings) -> None:
|
||||
texts = ["1", "22", "a", "333"]
|
||||
vectors = cache_embeddings.embed_documents(texts)
|
||||
expected_vectors: List[List[float]] = [[1, 2.0], [2.0, 3.0], [1.0, 2.0], [3.0, 4.0]]
|
||||
assert vectors == expected_vectors
|
||||
keys = list(cache_embeddings.document_embedding_store.yield_keys())
|
||||
assert len(keys) == 4
|
||||
# UUID is expected to be the same for the same text
|
||||
assert keys[0] == "test_namespace812b86c1-8ebf-5483-95c6-c95cf2b52d12"
|
||||
|
||||
|
||||
def test_embed_query(cache_embeddings: CacheBackedEmbeddings) -> None:
|
||||
text = "query_text"
|
||||
vector = cache_embeddings.embed_query(text)
|
||||
expected_vector = [5.0, 6.0]
|
||||
assert vector == expected_vector
|
||||
Reference in New Issue
Block a user