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Start cookbook and move stuff from use cases (#11636)
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Bagatur
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cookbook/myscale_vector_sql.ipynb
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cookbook/myscale_vector_sql.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": "245065c6",
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"metadata": {},
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"source": [
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"# Vector SQL Retriever with MyScale\n",
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"\n",
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">[MyScale](https://docs.myscale.com/en/) is an integrated vector database. You can access your database in SQL and also from here, LangChain. MyScale can make a use of [various data types and functions for filters](https://blog.myscale.com/2023/06/06/why-integrated-database-solution-can-boost-your-llm-apps/#filter-on-anything-without-constraints). It will boost up your LLM app no matter if you are scaling up your data or expand your system to broader application."
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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": "0246c5bf",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip3 install clickhouse-sqlalchemy InstructorEmbedding sentence_transformers openai langchain-experimental"
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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": "7585d2c3",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"from os import environ\n",
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"import getpass\n",
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"from typing import Dict, Any\n",
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"from langchain.llms import OpenAI\nfrom langchain.utilities import SQLDatabase\nfrom langchain.chains import LLMChain\n",
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"from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
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"from sqlalchemy import create_engine, Column, MetaData\n",
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"from langchain.prompts import PromptTemplate\n",
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"\n",
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"\n",
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"from sqlalchemy import create_engine\n",
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"\n",
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"MYSCALE_HOST = \"msc-1decbcc9.us-east-1.aws.staging.myscale.cloud\"\n",
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"MYSCALE_PORT = 443\n",
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"MYSCALE_USER = \"chatdata\"\n",
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"MYSCALE_PASSWORD = \"myscale_rocks\"\n",
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"OPENAI_API_KEY = getpass.getpass(\"OpenAI API Key:\")\n",
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"\n",
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"engine = create_engine(\n",
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" f\"clickhouse://{MYSCALE_USER}:{MYSCALE_PASSWORD}@{MYSCALE_HOST}:{MYSCALE_PORT}/default?protocol=https\"\n",
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")\n",
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"metadata = MetaData(bind=engine)\n",
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"environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
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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": "e08d9ddc",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
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"from langchain_experimental.sql.vector_sql import VectorSQLOutputParser\n",
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"\n",
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"output_parser = VectorSQLOutputParser.from_embeddings(\n",
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" model=HuggingFaceInstructEmbeddings(\n",
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" model_name=\"hkunlp/instructor-xl\", model_kwargs={\"device\": \"cpu\"}\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "84b705b2",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.callbacks import StdOutCallbackHandler\n",
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"\n",
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"from langchain.utilities.sql_database import SQLDatabase\n",
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"from langchain_experimental.sql.prompt import MYSCALE_PROMPT\n",
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"from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
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"\n",
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"chain = VectorSQLDatabaseChain(\n",
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" llm_chain=LLMChain(\n",
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" llm=OpenAI(openai_api_key=OPENAI_API_KEY, temperature=0),\n",
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" prompt=MYSCALE_PROMPT,\n",
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" ),\n",
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" top_k=10,\n",
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" return_direct=True,\n",
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" sql_cmd_parser=output_parser,\n",
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" database=SQLDatabase(engine, None, metadata),\n",
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")\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"pd.DataFrame(\n",
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" chain.run(\n",
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" \"Please give me 10 papers to ask what is PageRank?\",\n",
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" callbacks=[StdOutCallbackHandler()],\n",
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" )\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": "6c09cda0",
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"metadata": {},
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"source": [
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"## SQL Database as Retriever"
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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": "734d7ff5",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain\n",
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"\n",
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"from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
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"from langchain_experimental.retrievers.vector_sql_database \\\n",
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" import VectorSQLDatabaseChainRetriever\n",
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"from langchain_experimental.sql.prompt import MYSCALE_PROMPT\n",
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"from langchain_experimental.sql.vector_sql import VectorSQLRetrieveAllOutputParser\n",
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"\n",
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"output_parser_retrieve_all = VectorSQLRetrieveAllOutputParser.from_embeddings(\n",
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" output_parser.model\n",
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")\n",
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"\n",
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"chain = VectorSQLDatabaseChain.from_llm(\n",
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" llm=OpenAI(openai_api_key=OPENAI_API_KEY, temperature=0),\n",
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" prompt=MYSCALE_PROMPT,\n",
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" top_k=10,\n",
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" return_direct=True,\n",
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" db=SQLDatabase(engine, None, metadata),\n",
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" sql_cmd_parser=output_parser_retrieve_all,\n",
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" native_format=True,\n",
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")\n",
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"\n",
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"# You need all those keys to get docs\n",
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"retriever = VectorSQLDatabaseChainRetriever(sql_db_chain=chain, page_content_key=\"abstract\")\n",
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"\n",
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"document_with_metadata_prompt = PromptTemplate(\n",
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" input_variables=[\"page_content\", \"id\", \"title\", \"authors\", \"pubdate\", \"categories\"],\n",
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" template=\"Content:\\n\\tTitle: {title}\\n\\tAbstract: {page_content}\\n\\tAuthors: {authors}\\n\\tDate of Publication: {pubdate}\\n\\tCategories: {categories}\\nSOURCE: {id}\",\n",
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")\n",
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"\n",
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"chain = RetrievalQAWithSourcesChain.from_chain_type(\n",
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" ChatOpenAI(\n",
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" model_name=\"gpt-3.5-turbo-16k\", openai_api_key=OPENAI_API_KEY, temperature=0.6\n",
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" ),\n",
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" retriever=retriever,\n",
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" chain_type=\"stuff\",\n",
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" chain_type_kwargs={\n",
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" \"document_prompt\": document_with_metadata_prompt,\n",
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" },\n",
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" return_source_documents=True,\n",
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")\n",
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"ans = chain(\"Please give me 10 papers to ask what is PageRank?\",\n",
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" callbacks=[StdOutCallbackHandler()])\n",
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"print(ans[\"answer\"])"
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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": "4948ff25",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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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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"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": "3.11.3"
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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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