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docs: updated pinecone example notebook (#30993)
- **Description:** Update Pinecone notebook example - **Issue:** N\A - **Dependencies:** N\A - **Twitter handle:** N\A - [ x ] **Add tests and docs**: Just notebook updates If no one reviews your PR within a few days, please @-mention one of baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
This commit is contained in:
Simonas Jakubonis
GitHub
parent
1204fb8010
commit
b8d0403671
@ -26,7 +26,7 @@
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},
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"outputs": [],
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"source": [
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"pip install -qU langchain-pinecone pinecone-notebooks"
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"pip install -qU langchain langchain-pinecone langchain-openai"
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]
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},
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{
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@ -49,7 +49,7 @@
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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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"execution_count": null,
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"id": "eb554814",
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"metadata": {},
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"outputs": [],
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@ -57,7 +57,7 @@
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"import getpass\n",
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"import os\n",
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"\n",
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"from pinecone import Pinecone, ServerlessSpec\n",
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"from pinecone import Pinecone\n",
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"\n",
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"if not os.getenv(\"PINECONE_API_KEY\"):\n",
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" os.environ[\"PINECONE_API_KEY\"] = getpass.getpass(\"Enter your Pinecone API key: \")\n",
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@ -98,59 +98,41 @@
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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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"execution_count": 4,
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"id": "276a06dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"import time\n",
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"from pinecone import ServerlessSpec\n",
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"\n",
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"index_name = \"langchain-test-index\" # change if desired\n",
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"\n",
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"existing_indexes = [index_info[\"name\"] for index_info in pc.list_indexes()]\n",
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"\n",
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"if index_name not in existing_indexes:\n",
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"if not pc.has_index(index_name):\n",
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" pc.create_index(\n",
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" name=index_name,\n",
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" dimension=3072,\n",
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" dimension=1536,\n",
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" metric=\"cosine\",\n",
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" spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\"),\n",
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" )\n",
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" while not pc.describe_index(index_name).status[\"ready\"]:\n",
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" time.sleep(1)\n",
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"\n",
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"index = pc.Index(index_name)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3a4d377f",
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"metadata": {},
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"source": [
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"Now that our Pinecone index is setup, we can initialize our vector store. \n",
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"\n",
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"import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
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"\n",
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"<EmbeddingTabs/>\n"
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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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"execution_count": 5,
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"id": "1485db56",
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"metadata": {},
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"outputs": [],
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"source": [
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"# | output: false\n",
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"# | echo: false\n",
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"from langchain_openai import OpenAIEmbeddings\n",
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"\n",
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"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")"
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"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")"
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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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"execution_count": 6,
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"id": "6e104aee",
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"metadata": {},
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"outputs": [],
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@ -176,30 +158,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": null,
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"id": "70e688f4",
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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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"['167b8681-5974-467f-adcb-6e987a18df01',\n",
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" 'd16010fd-41f8-4d49-9c22-c66d5555a3fe',\n",
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" 'ffcacfb3-2bc2-44c3-a039-c2256a905c0e',\n",
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" 'cf3bfc9f-5dc7-4f5e-bb41-edb957394126',\n",
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" 'e99b07eb-fdff-4cb9-baa8-619fd8efeed3',\n",
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" '68c93033-a24f-40bd-8492-92fa26b631a4',\n",
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" 'b27a4ecb-b505-4c5d-89ff-526e3d103558',\n",
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" '4868a9e6-e6fb-4079-b400-4a1dfbf0d4c4',\n",
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" '921c0e9c-0550-4eb5-9a6c-ed44410788b2',\n",
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" 'c446fc23-64e8-47e7-8c19-ecf985e9411e']"
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]
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},
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"execution_count": 15,
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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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"outputs": [],
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"source": [
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"from uuid import uuid4\n",
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"\n",
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@ -268,7 +230,6 @@
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" document_10,\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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@ -282,7 +243,7 @@
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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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"execution_count": 8,
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"id": "5b8437cd",
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"metadata": {},
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"outputs": [],
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@ -306,19 +267,10 @@
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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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"execution_count": 9,
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"id": "ffbcb3fb",
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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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"* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]\n",
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"* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"results = vector_store.similarity_search(\n",
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" \"LangChain provides abstractions to make working with LLMs easy\",\n",
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@ -341,18 +293,10 @@
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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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"execution_count": null,
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"id": "5fb24583",
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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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"* [SIM=0.553187] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"results = vector_store.similarity_search_with_score(\n",
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" \"Will it be hot tomorrow?\", k=1, filter={\"source\": \"news\"}\n",
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@ -377,25 +321,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": null,
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"id": "78140e87",
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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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"[Document(metadata={'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]"
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]
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},
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"execution_count": 19,
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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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"outputs": [],
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"source": [
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"retriever = vector_store.as_retriever(\n",
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" search_type=\"similarity_score_threshold\",\n",
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" search_kwargs={\"k\": 1, \"score_threshold\": 0.5},\n",
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" search_kwargs={\"k\": 1, \"score_threshold\": 0.4},\n",
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")\n",
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"retriever.invoke(\"Stealing from the bank is a crime\", filter={\"source\": \"news\"})"
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]
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@ -427,7 +360,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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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@ -441,7 +374,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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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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