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docs: Pinecone Sparse vectorstore example (#31066)
Description: Pinecone SparseVectorStore example cc @jamescalam --------- Co-authored-by: James Briggs <35938317+jamescalam@users.noreply.github.com> Co-authored-by: ccurme <chester.curme@gmail.com>
This commit is contained in:
Simonas Jakubonis
GitHub
parent
c51eadd54f
commit
0b79fc1733
@ -27,6 +27,15 @@ from langchain_pinecone import PineconeVectorStore
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For a more detailed walkthrough of the Pinecone vectorstore, see [this notebook](/docs/integrations/vectorstores/pinecone)
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### Sparse Vector store
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```python
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from langchain_pinecone import PineconeSparseVectorStore
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```
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For a more detailed walkthrough of the Pinecone vectorstore, see [this notebook](/docs/integrations/vectorstores/pinecone_sparse)
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## Retrievers
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### Pinecone Hybrid Search
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@ -34,7 +34,7 @@
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"id": "1917d123",
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"metadata": {},
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"source": [
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"Migration note: if you are migrating from the `langchain_community.vectorstores` implementation of Pinecone, you may need to remove your `pinecone-client` v2 dependency before installing `langchain-pinecone`, which relies on `pinecone-client` v3."
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"Migration note: if you are migrating from the `langchain_community.vectorstores` implementation of Pinecone, you may need to remove your `pinecone-client` v2 dependency before installing `langchain-pinecone`, which relies on `pinecone-client` v6."
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]
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},
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{
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576
docs/docs/integrations/vectorstores/pinecone_sparse.ipynb
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576
docs/docs/integrations/vectorstores/pinecone_sparse.ipynb
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@ -0,0 +1,576 @@
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{
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"cells": [
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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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"# Pinecone (sparse)\n",
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"\n",
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">[Pinecone](https://docs.pinecone.io/docs/overview) is a vector database with broad functionality.\n",
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"\n",
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"This notebook shows how to use functionality related to the `Pinecone` vector database.\n",
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"\n",
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"## Setup\n",
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"\n",
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"To use the `PineconeSparseVectorStore` you first need to install the partner package, as well as the other packages used throughout this notebook."
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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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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "5g1-ZqcEONGD",
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"outputId": "2d49c259-683b-46c2-994f-642f35e30357"
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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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"\u001b[33mWARNING: pinecone 6.0.2 does not provide the extra 'async'\u001b[0m\u001b[33m\n",
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"\u001b[0m"
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]
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}
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],
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"source": [
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"%pip install -qU \"langchain-pinecone==0.2.5\""
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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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"id": "-1vfBVLhONGE"
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},
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"source": [
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"### Credentials\n",
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"Create a new Pinecone account, or sign into your existing one, and create an API key to use in this notebook."
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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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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "k_Dp_DIlONGF",
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"outputId": "01728754-8708-4f05-e53d-2e251541370e"
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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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"Enter your Pinecone API key: ··········\n"
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]
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}
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],
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"source": [
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"import os\n",
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"from getpass import getpass\n",
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"\n",
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"from pinecone import Pinecone\n",
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"\n",
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"# get API key at app.pinecone.io\n",
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"os.environ[\"PINECONE_API_KEY\"] = os.getenv(\"PINECONE_API_KEY\") or getpass(\n",
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" \"Enter your Pinecone API key: \"\n",
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")\n",
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"\n",
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"# initialize client\n",
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"pc = Pinecone()"
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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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"id": "OFqeT2xHONGF"
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},
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"source": [
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"## Initialization\n",
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"Before initializing our vector store, let's connect to a Pinecone index. If one named index_name doesn't exist, it will be created."
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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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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "9xNxmZsRONGF",
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"outputId": "b661d0af-26bd-43b2-f277-5366efd1d865"
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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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"Index `langchain-sparse-vector-search` host: https://langchain-sparse-vector-search-yrrgefy.svc.aped-4627-b74a.pinecone.io\n"
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]
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}
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],
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"source": [
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"from pinecone import AwsRegion, CloudProvider, Metric, ServerlessSpec\n",
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"\n",
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"index_name = \"langchain-sparse-vector-search\" # change if desired\n",
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"model_name = \"pinecone-sparse-english-v0\"\n",
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"\n",
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"if not pc.has_index(index_name):\n",
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" pc.create_index_for_model(\n",
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" name=index_name,\n",
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" cloud=CloudProvider.AWS,\n",
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" region=AwsRegion.US_EAST_1,\n",
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" embed={\n",
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" \"model\": model_name,\n",
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" \"field_map\": {\"text\": \"chunk_text\"},\n",
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" \"metric\": Metric.DOTPRODUCT,\n",
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" },\n",
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" )\n",
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"\n",
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"index = pc.Index(index_name)\n",
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"print(f\"Index `{index_name}` host: {index.config.host}\")"
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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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"id": "E0fDNTF9ONGF"
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},
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"source": [
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"For our sparse embedding model we use [`pinecone-sparse-english-v0`](https://docs.pinecone.io/models/pinecone-sparse-english-v0), we initialize it like so:"
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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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"metadata": {
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"id": "swM_SFhOONGF"
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},
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"outputs": [],
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"source": [
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"from langchain_pinecone.embeddings import PineconeSparseEmbeddings\n",
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"\n",
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"sparse_embeddings = PineconeSparseEmbeddings(model=model_name)"
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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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"id": "ZDL58ZRZONGF"
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},
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"source": [
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"Now that our Pinecone index and embedding model are both ready, we can initialize our sparse vector store in LangChain:"
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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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"id": "GrBUli1VONGF"
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},
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"outputs": [],
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"source": [
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"from langchain_pinecone import PineconeSparseVectorStore\n",
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"\n",
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"vector_store = PineconeSparseVectorStore(index=index, embedding=sparse_embeddings)"
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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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"id": "Bp8aeN4SONGG"
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},
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"source": [
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"## Manage vector store\n",
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"Once you have created your vector store, we can interact with it by adding and deleting different items.\n",
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"\n",
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"### Add items to vector store\n",
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"We can add items to our vector store by using the `add_documents` function."
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Ef0s0ovdONGG",
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"outputId": "bd61d82f-902d-4010-9701-b0e2ecb02d17"
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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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"['95b598af-c3dc-4a8a-bdb7-5d21283e5a86',\n",
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" '838614a5-5635-4efd-9ac3-5237a37a542b',\n",
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" '093fd11f-c85b-4c83-83f0-117df64ff442',\n",
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" 'fb3ba32f-f802-410a-ad79-56f7bce938fe',\n",
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" '75cde9bf-7e91-4f06-8bae-c824dab16a08',\n",
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" '9de8f769-d604-4e56-b677-ee333cbc8e34',\n",
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" 'f5f4ae97-88e6-4669-bcf7-87072bb08550',\n",
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" 'f9f82811-187c-4b25-85b5-7a42b4da3bff',\n",
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" 'ce45957c-e8fc-41ef-819b-1bd52b6fc815',\n",
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" '66cacc6f-b8e2-441b-9f7f-468788aad88f']"
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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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"from uuid import uuid4\n",
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"\n",
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"from langchain_core.documents import Document\n",
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"\n",
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"documents = [\n",
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" Document(\n",
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" page_content=\"I had chocolate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
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" metadata={\"source\": \"social\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.\",\n",
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" metadata={\"source\": \"news\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Building an exciting new project with LangChain - come check it out!\",\n",
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" metadata={\"source\": \"social\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Robbers broke into the city bank and stole $1 million in cash.\",\n",
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" metadata={\"source\": \"news\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Wow! That was an amazing movie. I can't wait to see it again.\",\n",
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" metadata={\"source\": \"social\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Is the new iPhone worth the price? Read this review to find out.\",\n",
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" metadata={\"source\": \"website\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"The top 10 soccer players in the world right now.\",\n",
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" metadata={\"source\": \"website\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"LangGraph is the best framework for building stateful, agentic applications!\",\n",
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" metadata={\"source\": \"social\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"The stock market is down 500 points today due to fears of a recession.\",\n",
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" metadata={\"source\": \"news\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"I have a bad feeling I am going to get deleted :(\",\n",
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" metadata={\"source\": \"social\"},\n",
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" ),\n",
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"]\n",
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"\n",
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"uuids = [str(uuid4()) for _ in range(len(documents))]\n",
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"\n",
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"vector_store.add_documents(documents=documents, ids=uuids)"
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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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"id": "KUIIEuYxONGG"
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},
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"source": [
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"### Delete items from vector store"
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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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"id": "pLFI8xEJONGG"
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},
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"source": [
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"We can delete records from our vector store using the `delete` method, providing it with a list of document IDs to delete."
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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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"metadata": {
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"id": "eboEnsbRONGG"
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},
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"outputs": [],
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"source": [
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"vector_store.delete(ids=[uuids[-1]])"
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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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"id": "ggaQ5g4bONGG"
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},
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"source": [
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"## Query vector store\n",
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"\n",
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"Once we have loaded our documents into the vector store we're most likely ready to begin querying. There are various method for doing this in LangChain.\n",
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"\n",
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"First, we'll see how to perform a simple vector search by querying our `vector_store` directly via the `similarity_search` 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": 8,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "QcIS_P8CONGG",
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"outputId": "774da46e-b919-4128-bc77-6c392e77f9f3"
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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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"* Building an exciting new project with LangChain - come check it out! [{'source': 'social'}]\n",
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"* Building an exciting new project with LangChain - come check it out! [{'source': 'social'}]\n",
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"* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'social'}]\n"
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]
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}
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],
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"source": [
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"results = vector_store.similarity_search(\"I'm building a new LangChain project!\", k=3)\n",
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"\n",
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"for res in results:\n",
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" print(f\"* {res.page_content} [{res.metadata}]\")"
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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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"id": "n-JlbidpONGG"
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},
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"source": [
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"We can also add [metadata filtering](https://docs.pinecone.io/guides/data/understanding-metadata#metadata-query-language) to our query to limit our search based on various criteria. Let's try a simple filter to limit our search to include only records with `source==\"social\"`:"
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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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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "N19HBLNCONGG",
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"outputId": "9c5e96c2-0b4e-4083-cd6a-dd6d8662df09"
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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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"* Building an exciting new project with LangChain - come check it out! [{'source': 'social'}]\n",
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"* Building an exciting new project with LangChain - come check it out! [{'source': 'social'}]\n",
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"* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'social'}]\n"
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]
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}
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],
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"source": [
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"results = vector_store.similarity_search(\n",
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" \"I'm building a new LangChain project!\",\n",
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" k=3,\n",
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" filter={\"source\": \"social\"},\n",
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")\n",
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"for res in results:\n",
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" print(f\"* {res.page_content} [{res.metadata}]\")"
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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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"id": "yEs4xmESONGG"
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},
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"source": [
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"When comparing these results, we can see that our first query returned a different record from the `\"website\"` source. In our latter, filtered, query — this is no longer the case."
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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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"id": "vWGkIfzCONGH"
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},
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"source": [
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"### Similarity Search and Scores\n",
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"\n",
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"We can also search while returning the similarity score in a list of `(document, score)` tuples. Where the `document` is a LangChain `Document` object containing our text content and metadata."
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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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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "aRiHj8ADONGH",
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"outputId": "5649d70a-0bd0-446d-8e60-2348170da706"
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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",
|
||||
"text": [
|
||||
"[SIM=12.959961] Building an exciting new project with LangChain - come check it out! [{'source': 'social'}]\n",
|
||||
"[SIM=12.959961] Building an exciting new project with LangChain - come check it out! [{'source': 'social'}]\n",
|
||||
"[SIM=1.942383] LangGraph is the best framework for building stateful, agentic applications! [{'source': 'social'}]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"results = vector_store.similarity_search_with_score(\n",
|
||||
" \"I'm building a new LangChain project!\", k=3, filter={\"source\": \"social\"}\n",
|
||||
")\n",
|
||||
"for doc, score in results:\n",
|
||||
" print(f\"[SIM={score:3f}] {doc.page_content} [{doc.metadata}]\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "aqGelo6sONGH"
|
||||
},
|
||||
"source": [
|
||||
"### As a Retriever\n",
|
||||
"\n",
|
||||
"In our chains and agents we'll often use the vector store as a `VectorStoreRetriever`. To create that, we use the `as_retriever` method:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "Tsn9--KsONGH",
|
||||
"outputId": "3da43258-49fb-4080-cbdd-f97101fb8099"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"VectorStoreRetriever(tags=['PineconeSparseVectorStore', 'PineconeSparseEmbeddings'], vectorstore=<langchain_pinecone.vectorstores_sparse.PineconeSparseVectorStore object at 0x7c8087b24290>, search_type='similarity_score_threshold', search_kwargs={'k': 3, 'score_threshold': 0.5})"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"retriever = vector_store.as_retriever(\n",
|
||||
" search_type=\"similarity_score_threshold\",\n",
|
||||
" search_kwargs={\"k\": 3, \"score_threshold\": 0.5},\n",
|
||||
")\n",
|
||||
"retriever"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "yDqb3q-VUY2t"
|
||||
},
|
||||
"source": [
|
||||
"We can now query our retriever using the `invoke` method:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "mGV0p0TDUJyx",
|
||||
"outputId": "89db72c3-4ff2-4900-d302-482e54549b39"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.11/dist-packages/langchain_core/vectorstores/base.py:1082: UserWarning: Relevance scores must be between 0 and 1, got [(Document(id='093fd11f-c85b-4c83-83f0-117df64ff442', metadata={'source': 'social'}, page_content='Building an exciting new project with LangChain - come check it out!'), 6.97998045), (Document(id='54f8f645-9f77-4aab-b9fa-709fd91ae3b3', metadata={'source': 'social'}, page_content='Building an exciting new project with LangChain - come check it out!'), 6.97998045), (Document(id='f9f82811-187c-4b25-85b5-7a42b4da3bff', metadata={'source': 'social'}, page_content='LangGraph is the best framework for building stateful, agentic applications!'), 1.471191405)]\n",
|
||||
" self.vectorstore.similarity_search_with_relevance_scores(\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(id='093fd11f-c85b-4c83-83f0-117df64ff442', metadata={'source': 'social'}, page_content='Building an exciting new project with LangChain - come check it out!'),\n",
|
||||
" Document(id='54f8f645-9f77-4aab-b9fa-709fd91ae3b3', metadata={'source': 'social'}, page_content='Building an exciting new project with LangChain - come check it out!'),\n",
|
||||
" Document(id='f9f82811-187c-4b25-85b5-7a42b4da3bff', metadata={'source': 'social'}, page_content='LangGraph is the best framework for building stateful, agentic applications!')]"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"retriever.invoke(\n",
|
||||
" input=\"I'm building a new LangChain project!\", filter={\"source\": \"social\"}\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Usage for retrieval-augmented generation\n",
|
||||
"\n",
|
||||
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
|
||||
"\n",
|
||||
"- [Tutorials](/docs/tutorials/)\n",
|
||||
"- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
|
||||
"- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## API reference\n",
|
||||
"\n",
|
||||
"For detailed documentation of all features and configurations head to the API reference: \n",
|
||||
"https://python.langchain.com/api_reference/pinecone/vectorstores_sparse/langchain_pinecone.vectorstores_sparse.PineconeSparseVectorStore.html#langchain_pinecone.vectorstores_sparse.PineconeSparseVectorStore\n",
|
||||
"\n",
|
||||
"Sparse Embeddings:\n",
|
||||
"https://python.langchain.com/api_reference/pinecone/embeddings/langchain_pinecone.embeddings.PineconeSparseEmbeddings.html"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"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": "3.12.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user