langchain/docs/extras/integrations/vectorstores/pgvector.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PGVector\n",
    "\n",
    ">[PGVector](https://github.com/pgvector/pgvector) is an open-source vector similarity search for `Postgres`\n",
    "\n",
    "It supports:\n",
    "- exact and approximate nearest neighbor search\n",
    "- L2 distance, inner product, and cosine distance\n",
    "\n",
    "This notebook shows how to use the Postgres vector database (`PGVector`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the [installation instruction](https://github.com/pgvector/pgvector)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pgvector in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (0.1.8)\n",
      "Requirement already satisfied: numpy in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (from pgvector) (1.24.3)\n",
      "Requirement already satisfied: openai in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (0.27.7)\n",
      "Requirement already satisfied: requests>=2.20 in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (from openai) (2.28.2)\n",
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      "Requirement already satisfied: tiktoken in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (0.4.0)\n",
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     ]
    }
   ],
   "source": [
    "# Pip install necessary package\n",
    "!pip install pgvector\n",
    "!pip install openai\n",
    "!pip install psycopg2-binary\n",
    "!pip install tiktoken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenAI API Key:········\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import getpass\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Loading Environment Variables\n",
    "from typing import List, Tuple\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.vectorstores.pgvector import PGVector\n",
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.docstore.document import Document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
    "documents = loader.load()\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "docs = text_splitter.split_documents(documents)\n",
    "\n",
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PGVector needs the connection string to the database.\n",
    "CONNECTION_STRING = \"postgresql+psycopg2://harrisonchase@localhost:5432/test3\"\n",
    "\n",
    "# # Alternatively, you can create it from enviornment variables.\n",
    "# import os\n",
    "\n",
    "# CONNECTION_STRING = PGVector.connection_string_from_db_params(\n",
    "#     driver=os.environ.get(\"PGVECTOR_DRIVER\", \"psycopg2\"),\n",
    "#     host=os.environ.get(\"PGVECTOR_HOST\", \"localhost\"),\n",
    "#     port=int(os.environ.get(\"PGVECTOR_PORT\", \"5432\")),\n",
    "#     database=os.environ.get(\"PGVECTOR_DATABASE\", \"postgres\"),\n",
    "#     user=os.environ.get(\"PGVECTOR_USER\", \"postgres\"),\n",
    "#     password=os.environ.get(\"PGVECTOR_PASSWORD\", \"postgres\"),\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Similarity Search with Euclidean Distance (Default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The PGVector Module will try to create a table with the name of the collection.\n",
    "# So, make sure that the collection name is unique and the user has the permission to create a table.\n",
    "\n",
    "COLLECTION_NAME = \"state_of_the_union_test\"\n",
    "\n",
    "db = PGVector.from_documents(\n",
    "    embedding=embeddings,\n",
    "    documents=docs,\n",
    "    collection_name=COLLECTION_NAME,\n",
    "    connection_string=CONNECTION_STRING,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "docs_with_score = db.similarity_search_with_score(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "Score:  0.18460171628856903\n",
      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
      "\n",
      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
      "\n",
      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
      "\n",
      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Score:  0.18460171628856903\n",
      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
      "\n",
      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
      "\n",
      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
      "\n",
      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Score:  0.18470284560586236\n",
      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
      "\n",
      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
      "\n",
      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
      "\n",
      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Score:  0.21730864082247825\n",
      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
      "\n",
      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
      "\n",
      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
      "\n",
      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
      "\n",
      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
      "\n",
      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for doc, score in docs_with_score:\n",
    "    print(\"-\" * 80)\n",
    "    print(\"Score: \", score)\n",
    "    print(doc.page_content)\n",
    "    print(\"-\" * 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with vectorstore\n",
    "\n",
    "Above, we created a vectorstore from scratch. However, often times we want to work with an existing vectorstore.\n",
    "In order to do that, we can initialize it directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "store = PGVector(\n",
    "    collection_name=COLLECTION_NAME,\n",
    "    connection_string=CONNECTION_STRING,\n",
    "    embedding_function=embeddings,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add documents\n",
    "We can add documents to the existing vectorstore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['048c2e14-1cf3-11ee-8777-e65801318980']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store.add_documents([Document(page_content=\"foo\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs_with_score = db.similarity_search_with_score(\"foo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Document(page_content='foo', metadata={}), 3.3203430005457335e-09)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_with_score[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
       " 0.2404395365581814)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_with_score[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overriding a vectorstore\n",
    "\n",
    "If you have an existing collection, you override it by doing `from_documents` and setting `pre_delete_collection` = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = PGVector.from_documents(\n",
    "    documents=docs,\n",
    "    embedding=embeddings,\n",
    "    collection_name=COLLECTION_NAME,\n",
    "    connection_string=CONNECTION_STRING,\n",
    "    pre_delete_collection=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs_with_score = db.similarity_search_with_score(\"foo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
       " 0.2404115088144465)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_with_score[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using a VectorStore as a Retriever"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = store.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tags=None metadata=None vectorstore=<langchain.vectorstores.pgvector.PGVector object at 0x29f94f880> search_type='similarity' search_kwargs={}\n"
     ]
    }
   ],
   "source": [
    "print(retriever)"
   ]
  }
 ],
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