forked from Archives/langchain
170 lines
4.7 KiB
Plaintext
170 lines
4.7 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "07c1e3b9",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Retrieval Question/Answering\n",
|
|
"\n",
|
|
"This example showcases using a chat model to do question answering over a vector database.\n",
|
|
"\n",
|
|
"This notebook is very similar to the example of using an LLM in the RetrievalQA. The only differences here are (1) using a ChatModel, and (2) passing in a ChatPromptTemplate (optimized for chat models)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "82525493",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
|
|
"from langchain.vectorstores import Chroma\n",
|
|
"from langchain.text_splitter import CharacterTextSplitter\n",
|
|
"from langchain.chains import RetrievalQA"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "5c7049db",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Running Chroma using direct local API.\n",
|
|
"Using DuckDB in-memory for database. Data will be transient.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from langchain.document_loaders import TextLoader\n",
|
|
"loader = TextLoader('../../state_of_the_union.txt')\n",
|
|
"documents = loader.load()\n",
|
|
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
|
|
"texts = text_splitter.split_documents(documents)\n",
|
|
"\n",
|
|
"embeddings = OpenAIEmbeddings()\n",
|
|
"docsearch = Chroma.from_documents(texts, embeddings)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "35f99145",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can now set up the chat model and chat model specific prompt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "32a49412",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.chat_models import ChatOpenAI\n",
|
|
"from langchain.prompts.chat import (\n",
|
|
" ChatPromptTemplate,\n",
|
|
" SystemMessagePromptTemplate,\n",
|
|
" AIMessagePromptTemplate,\n",
|
|
" HumanMessagePromptTemplate,\n",
|
|
")\n",
|
|
"from langchain.schema import (\n",
|
|
" AIMessage,\n",
|
|
" HumanMessage,\n",
|
|
" SystemMessage\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "f231fb9b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"system_template=\"\"\"Use the following pieces of context to answer the users question. \n",
|
|
"If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
|
|
"----------------\n",
|
|
"{context}\"\"\"\n",
|
|
"messages = [\n",
|
|
" SystemMessagePromptTemplate.from_template(system_template),\n",
|
|
" HumanMessagePromptTemplate.from_template(\"{question}\")\n",
|
|
"]\n",
|
|
"prompt = ChatPromptTemplate.from_messages(messages)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "3018f865",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"chain_type_kwargs = {\"prompt\": prompt}\n",
|
|
"qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "032a47f8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"\"The president nominated Ketanji Brown Jackson to serve on the United States Supreme Court. He referred to her as one of our nation's top legal minds, a former federal public defender, a consensus builder, and from a family of public school educators and police officers. Since she's been nominated, she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
|
|
"qa.run(query)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "8b403637",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.1"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|