mirror of
https://github.com/hwchase17/langchain
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377 lines
10 KiB
Plaintext
377 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "134a0785",
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"metadata": {},
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"source": [
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"# Chat Vector DB\n",
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"\n",
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"This notebook goes over how to set up a chat model to chat with a vector database.\n",
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"\n",
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"This notebook is very similar to the example of using an LLM in the ChatVectorDBChain. The only differences here are (1) using a ChatModel, and (2) passing in a ChatPromptTemplate (optimized for chat models)."
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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": "70c4e529",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.vectorstores import Chroma\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.chains import ChatVectorDBChain"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cdff94be",
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"metadata": {},
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"source": [
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"Load in documents. You can replace this with a loader for whatever type of data you want"
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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": "01c46e92",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"loader = TextLoader('../../state_of_the_union.txt')\n",
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"documents = loader.load()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e9be4779",
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"metadata": {},
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"source": [
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"If you had multiple loaders that you wanted to combine, you do something like:"
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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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"id": "433363a5",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# loaders = [....]\n",
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"# docs = []\n",
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"# for loader in loaders:\n",
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"# docs.extend(loader.load())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "239475d2",
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"metadata": {},
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"source": [
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"We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over them."
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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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"id": "a8930cf7",
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"metadata": {
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"tags": []
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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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"Running Chroma using direct local API.\n",
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"Using DuckDB in-memory for database. Data will be transient.\n"
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]
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}
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],
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"source": [
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"documents = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()\n",
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"vectorstore = Chroma.from_documents(documents, embeddings)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "18415aca",
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"metadata": {},
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"source": [
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"We are now going to construct a prompt specifically designed for chat models."
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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": "c8805230",
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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.prompts.chat import (\n",
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" ChatPromptTemplate,\n",
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" SystemMessagePromptTemplate,\n",
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" AIMessagePromptTemplate,\n",
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" HumanMessagePromptTemplate,\n",
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")\n",
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"from langchain.schema import (\n",
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" AIMessage,\n",
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" HumanMessage,\n",
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" SystemMessage\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": 6,
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"id": "cc86c30e",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_template=\"\"\"Use the following pieces of context to answer the users question. \n",
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"If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
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"----------------\n",
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"{context}\"\"\"\n",
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"messages = [\n",
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" SystemMessagePromptTemplate.from_template(system_template),\n",
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" HumanMessagePromptTemplate.from_template(\"{question}\")\n",
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"]\n",
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"prompt = ChatPromptTemplate.from_messages(messages)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3c96b118",
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"metadata": {},
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"source": [
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"We now initialize the ChatVectorDBChain"
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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": "7b4110f3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"qa = ChatVectorDBChain.from_llm(ChatOpenAI(temperature=0), vectorstore,qa_prompt=prompt)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3872432d",
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"metadata": {},
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"source": [
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"Here's an example of asking a question with no chat history"
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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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"id": "7fe3e730",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat_history = []\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"result = qa({\"question\": query, \"chat_history\": chat_history})"
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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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"id": "bfff9cc8",
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"metadata": {
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"tags": []
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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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"\"The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. He described her as one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and a consensus builder. He also mentioned that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
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]
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},
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"execution_count": 9,
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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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"result[\"answer\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9e46edf7",
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"metadata": {},
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"source": [
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"Here's an example of asking a question with some chat history"
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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": 12,
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"id": "00b4cf00",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat_history = [(query, result[\"answer\"])]\n",
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"query = \"Did he mention who came before her\"\n",
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"result = qa({\"question\": query, \"chat_history\": chat_history})"
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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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"id": "f01828d1",
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"metadata": {
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"tags": []
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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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"'The context does not provide information about the predecessor of Ketanji Brown Jackson.'"
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]
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},
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"execution_count": 13,
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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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"result['answer']"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2324cdc6-98bf-4708-b8cd-02a98b1e5b67",
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"metadata": {},
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"source": [
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"## Chat Vector DB with streaming to `stdout`\n",
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"\n",
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"Output from the chain will be streamed to `stdout` token by token in this example."
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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": 15,
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"id": "2efacec3-2690-4b05-8de3-a32fd2ac3911",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.chains.llm import LLMChain\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.callbacks.base import CallbackManager\n",
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
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"from langchain.chains.chat_vector_db.prompts import CONDENSE_QUESTION_PROMPT\n",
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"from langchain.chains.question_answering import load_qa_chain\n",
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"\n",
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"# Construct a ChatVectorDBChain with a streaming llm for combine docs\n",
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"# and a separate, non-streaming llm for question generation\n",
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"llm = OpenAI(temperature=0)\n",
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"streaming_llm = ChatOpenAI(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
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"\n",
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"question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
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"doc_chain = load_qa_chain(streaming_llm, chain_type=\"stuff\", prompt=prompt)\n",
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"\n",
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"qa = ChatVectorDBChain(vectorstore=vectorstore, combine_docs_chain=doc_chain, question_generator=question_generator)"
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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": 16,
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"id": "fd6d43f4-7428-44a4-81bc-26fe88a98762",
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"metadata": {
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"tags": []
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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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"The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. He described her as one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and a consensus builder. He also mentioned that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
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]
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}
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],
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"source": [
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"chat_history = []\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"result = qa({\"question\": query, \"chat_history\": chat_history})"
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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": 17,
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"id": "5ab38978-f3e8-4fa7-808c-c79dec48379a",
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"metadata": {
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"tags": []
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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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"The context does not provide information on who Ketanji Brown Jackson succeeded on the United States Supreme Court."
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]
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}
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],
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"source": [
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"chat_history = [(query, result[\"answer\"])]\n",
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"query = \"Did he mention who she suceeded\"\n",
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"result = qa({\"question\": query, \"chat_history\": chat_history})\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": null,
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"id": "8e8d0055",
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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": "3.9.1"
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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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