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docs: Pinecone Rerank example notebook (#31147)
Created an example notebook of how to use Pinecone Reranking service cc @jamescalam
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Simonas Jakubonis
GitHub
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docs/docs/integrations/retrievers/pinecone_rerank.ipynb
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docs/docs/integrations/retrievers/pinecone_rerank.ipynb
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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": "7fb27b941602401d91542211134fc71a",
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"metadata": {},
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"source": [
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"# Pinecone Rerank\n",
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"\n",
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"> This notebook shows how to use **PineconeRerank** for two-stage vector retrieval reranking using Pinecone's hosted reranking API as demonstrated in `langchain_pinecone/libs/pinecone/rerank.py`."
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]
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},
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{
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"cell_type": "markdown",
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"id": "acae54e37e7d407bbb7b55eff062a284",
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"metadata": {},
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"source": [
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"## Setup\n",
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"Install the `langchain-pinecone` package."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9a63283cbaf04dbcab1f6479b197f3a8",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU \"langchain-pinecone\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "8dd0d8092fe74a7c96281538738b07e2",
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"metadata": {},
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"source": [
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"## Credentials\n",
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"Set your Pinecone API key to use the reranking API."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "72eea5119410473aa328ad9291626812",
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"metadata": {},
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"outputs": [],
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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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"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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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8edb47106e1a46a883d545849b8ab81b",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"Use `PineconeRerank` to rerank a list of documents by relevance to a query."
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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": "10185d26023b46108eb7d9f57d49d2b3",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/jakit/customers/aurelio/langchain-pinecone/libs/pinecone/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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},
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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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"Score: 0.9998 | Content: Paris is the capital of France.\n",
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"Score: 0.1950 | Content: The Eiffel Tower is in Paris.\n",
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"Score: 0.0042 | Content: Berlin is the capital of Germany.\n"
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]
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}
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],
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"source": [
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"from langchain_core.documents import Document\n",
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"from langchain_pinecone import PineconeRerank\n",
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"\n",
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"# Initialize reranker\n",
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"reranker = PineconeRerank(model=\"bge-reranker-v2-m3\")\n",
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"\n",
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"# Sample documents\n",
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"documents = [\n",
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" Document(page_content=\"Paris is the capital of France.\"),\n",
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" Document(page_content=\"Berlin is the capital of Germany.\"),\n",
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" Document(page_content=\"The Eiffel Tower is in Paris.\"),\n",
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"]\n",
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"\n",
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"# Rerank documents\n",
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"query = \"What is the capital of France?\"\n",
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"reranked_docs = reranker.compress_documents(documents, query)\n",
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"\n",
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"# Print results\n",
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"for doc in reranked_docs:\n",
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" score = doc.metadata.get(\"relevance_score\")\n",
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" print(f\"Score: {score:.4f} | Content: {doc.page_content}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8763a12b2bbd4a93a75aff182afb95dc",
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"metadata": {},
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"source": [
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"## Usage\n",
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"### Reranking with Top-N\n",
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"Specify `top_n` to limit the number of returned documents."
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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": "7623eae2785240b9bd12b16a66d81610",
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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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"Top-1 Result:\n",
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"Score: 0.9998 | Content: Paris is the capital of France.\n"
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]
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}
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],
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"source": [
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"# Return only top-1 result\n",
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"reranker_top1 = PineconeRerank(model=\"bge-reranker-v2-m3\", top_n=1)\n",
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"top1_docs = reranker_top1.compress_documents(documents, query)\n",
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"print(\"Top-1 Result:\")\n",
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"for doc in top1_docs:\n",
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" print(f\"Score: {doc.metadata['relevance_score']:.4f} | Content: {doc.page_content}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7cdc8c89c7104fffa095e18ddfef8986",
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"metadata": {},
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"source": [
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"## Reranking with Custom Rank Fields\n",
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"If your documents are dictionaries or have custom fields, use `rank_fields` to specify the field to rank on."
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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": "b118ea5561624da68c537baed56e602f",
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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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"ID: doc3 | Score: 0.9892\n",
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"ID: doc1 | Score: 0.0006\n",
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"ID: doc2 | Score: 0.0000\n"
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]
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}
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],
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"source": [
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"# Sample dictionary documents with 'text' field\n",
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"docs_dict = [\n",
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" {\n",
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" \"id\": \"doc1\",\n",
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" \"text\": \"Article about renewable energy.\",\n",
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" \"title\": \"Renewable Energy\",\n",
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" },\n",
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" {\"id\": \"doc2\", \"text\": \"Report on economic growth.\", \"title\": \"Economic Growth\"},\n",
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" {\n",
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" \"id\": \"doc3\",\n",
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" \"text\": \"News on climate policy changes.\",\n",
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" \"title\": \"Climate Policy\",\n",
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" },\n",
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"]\n",
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"\n",
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"# Initialize reranker with rank_fields\n",
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"reranker_text = PineconeRerank(model=\"bge-reranker-v2-m3\", rank_fields=[\"text\"])\n",
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"climate_docs = reranker_text.rerank(docs_dict, \"Latest news on climate change.\")\n",
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"\n",
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"# Show IDs and scores\n",
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"for res in climate_docs:\n",
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" print(f\"ID: {res['id']} | Score: {res['score']:.4f}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a80bb6c3",
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"metadata": {},
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"source": [
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"We can rerank based on title field"
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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": "a6f2768e",
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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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"ID: doc2 | Score: 0.8918 | Title: Economic Growth\n",
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"ID: doc3 | Score: 0.0002 | Title: Climate Policy\n",
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"ID: doc1 | Score: 0.0000 | Title: Renewable Energy\n"
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]
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}
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],
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"source": [
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"economic_docs = reranker_text.rerank(docs_dict, \"Economic forecast.\")\n",
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"\n",
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"# Show IDs and scores\n",
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"for res in economic_docs:\n",
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" print(\n",
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" f\"ID: {res['id']} | Score: {res['score']:.4f} | Title: {res['document']['title']}\"\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": "938c804e27f84196a10c8828c723f798",
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"metadata": {},
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"source": [
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"## Reranking with Additional Parameters\n",
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"You can pass model-specific parameters (e.g., `truncate`) directly to `.rerank()`."
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]
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},
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{
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"cell_type": "markdown",
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"id": "a94c501c",
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"metadata": {},
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"source": [
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"How to handle inputs longer than those supported by the model. Accepted values: END or NONE.\n",
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"END truncates the input sequence at the input token limit. NONE returns an error when the input exceeds the input token limit."
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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": "504fb2a444614c0babb325280ed9130a",
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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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"ID: docA | Score: 0.6950\n",
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"ID: docB | Score: 0.0001\n"
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]
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}
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],
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"source": [
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"# Rerank with custom truncate parameter\n",
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"docs_simple = [\n",
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" {\"id\": \"docA\", \"text\": \"Quantum entanglement is a physical phenomenon...\"},\n",
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" {\"id\": \"docB\", \"text\": \"Classical mechanics describes motion...\"},\n",
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"]\n",
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"\n",
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"reranked = reranker.rerank(\n",
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" documents=docs_simple,\n",
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" query=\"Explain the concept of quantum entanglement.\",\n",
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" truncate=\"END\",\n",
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")\n",
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"# Print reranked IDs and scores\n",
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"for res in reranked:\n",
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" print(f\"ID: {res['id']} | Score: {res['score']:.4f}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ab78bcd8",
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"metadata": {},
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"source": [
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"## Use within a chain"
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]
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},
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{
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"cell_type": "markdown",
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"id": "59bbdb311c014d738909a11f9e486628",
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"metadata": {},
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"source": [
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"## API reference\n",
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"- `PineconeRerank(model, top_n, rank_fields, return_documents)`\n",
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"- `.rerank(documents, query, rank_fields=None, model=None, top_n=None, truncate=\"END\")`\n",
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"- `.compress_documents(documents, query)` (returns `Document` objects with `relevance_score` in metadata)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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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": "3.10.15"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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