forked from Archives/langchain
harrison/use_output_parser
Harrison Chase
2 years ago
commit
bfe50949f5
@ -0,0 +1,200 @@
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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": "efc5be67",
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"metadata": {},
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"source": [
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"# Question-Answering with Sources\n",
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"\n",
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"This notebook goes over how to do question-answering with sources. It does this in a few different ways - first showing how you can use the `QAWithSourcesChain` to take in documents and use those, and next showing the `VectorDBQAWithSourcesChain`, which also does the lookup of the documents from a vector database. "
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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": "1c613960",
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"metadata": {},
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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.embeddings.cohere import CohereEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
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"from langchain.vectorstores.faiss import FAISS"
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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": "17d1306e",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('../state_of_the_union.txt') as f:\n",
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" state_of_the_union = f.read()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"texts = text_splitter.split_text(state_of_the_union)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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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": "0e745d99",
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"metadata": {},
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"outputs": [],
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"source": [
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"docsearch = FAISS.from_texts(texts, embeddings)"
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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": "f42d79dc",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Add in a fake source information\n",
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"for i, d in enumerate(docsearch.docstore._dict.values()):\n",
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" d.metadata = {'source': f\"{i}-pl\"}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aa1c1b60",
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"metadata": {},
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"source": [
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"### QAWithSourcesChain\n",
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"This shows how to use the `QAWithSourcesChain`, which takes in document objects and uses them directly."
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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": "61bce191",
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Justice Breyer\"\n",
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"docs = docsearch.similarity_search(query)"
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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": "57ddf8c7",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import QAWithSourcesChain\n",
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"from langchain.llms import OpenAI, Cohere\n",
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"from langchain.docstore.document import Document"
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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": "f908a92a",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = QAWithSourcesChain.from_llm(OpenAI(temperature=0))"
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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": "a505ac89",
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"metadata": {
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"scrolled": true
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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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"{'answer': ' The president thanked Justice Breyer for his service.',\n",
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" 'sources': '27-pl'}"
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]
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},
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"execution_count": 8,
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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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"chain({\"docs\": docs, \"question\": query}, return_only_outputs=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e6fc81de",
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"metadata": {},
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"source": [
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"### VectorDBQAWithSourcesChain\n",
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"\n",
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"This shows how to use the `VectorDBQAWithSourcesChain`, which uses a vector database to look up relevant documents."
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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": "8aa571ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import VectorDBQAWithSourcesChain"
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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": 10,
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"id": "aa859d4c",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = VectorDBQAWithSourcesChain.from_llm(OpenAI(temperature=0), vectorstore=docsearch)"
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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": "8ba36fa7",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
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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": "980fae3b",
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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": "3.8.7"
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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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@ -0,0 +1,180 @@
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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": "32e022a2",
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"metadata": {},
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"source": [
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"# PAL\n",
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"\n",
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"Implements Program-Aided Language Models, as in https://arxiv.org/pdf/2211.10435.pdf.\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": 1,
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"id": "1370e40f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import PALChain\n",
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"from langchain import OpenAI"
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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": "beddcac7",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)\n",
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"pal_chain = PALChain.from_math_prompt(llm, verbose=True)"
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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": "e2eab9d4",
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"metadata": {},
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"outputs": [],
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"source": [
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"question = \"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\""
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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": "3ef64b27",
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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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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mdef solution():\n",
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" \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"\n",
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" cindy_pets = 4\n",
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" marcia_pets = cindy_pets + 2\n",
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" jan_pets = marcia_pets * 3\n",
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" total_pets = cindy_pets + marcia_pets + jan_pets\n",
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" result = total_pets\n",
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" return result\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'28'"
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]
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},
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"execution_count": 4,
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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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"pal_chain.run(question)"
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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": "e524f81f",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)\n",
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"pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True)"
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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": "03a237b8",
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"metadata": {},
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"outputs": [],
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"source": [
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"question = \"On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses. If I remove all the pairs of sunglasses from the desk, how many purple items remain on it?\""
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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": "a84a4352",
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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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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
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"objects = []\n",
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"objects += [('booklet', 'blue')] * 2\n",
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"objects += [('booklet', 'purple')] * 2\n",
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"objects += [('sunglasses', 'yellow')] * 2\n",
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"\n",
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"# Remove all pairs of sunglasses\n",
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"objects = [object for object in objects if object[0] != 'sunglasses']\n",
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"\n",
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"# Count number of purple objects\n",
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"num_purple = len([object for object in objects if object[1] == 'purple'])\n",
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"answer = num_purple\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'2'"
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]
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},
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"execution_count": 7,
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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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"pal_chain.run(question)"
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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": "4ab20fec",
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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": "3.8.7"
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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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@ -0,0 +1,39 @@
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# Cool Demos
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Lots of people have built some pretty awesome stuff with LangChain.
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This is a collection of our favorites.
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If you see any other demos that you think we should highlight, be sure to let us know!
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## Open Source
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### [ThoughtSource](https://github.com/OpenBioLink/ThoughtSource)
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A central, open resource and community around data and tools related to chain-of-thought reasoning in large language models.
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### [Notion Database Question-Answering Bot](https://github.com/hwchase17/notion-qa)
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Open source GitHub project shows how to use LangChain to create a
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chatbot that can answer questions about an arbitrary Notion database.
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### [GPT Index](https://github.com/jerryjliu/gpt_index)
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GPT Index is a project consisting of a set of data structures that are created using GPT-3 and can be traversed using GPT-3 in order to answer queries.
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### [Grover's Algorithm](https://github.com/JavaFXpert/llm-grovers-search-party)
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Leveraging Qiskit, OpenAI and LangChain to demonstrate Grover's algorithm
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## Not Open Source
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### [Daimon](https://twitter.com/sjwhitmore/status/1580593217153531908?s=20&t=neQvtZZTlp623U3LZwz3bQ)
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A chat-based AI personal assistant with long-term memory about you.
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### [Clerkie](https://twitter.com/krrish_dh/status/1581028925618106368?s=20&t=neQvtZZTlp623U3LZwz3bQ)
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Stack Tracing QA Bot to help debug complex stack tracing (especially the ones that go multi-function/file deep).
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### [Sales Email Writer](https://twitter.com/Raza_Habib496/status/1596880140490838017?s=20&t=6MqEQYWfSqmJwsKahjCVOA)
|
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By Raza Habib, this demo utilizes LangChain + SerpAPI + HumanLoop to write sales emails.
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||||
Give it a company name and a person, this application will use Google Search (via SerpAPI) to get
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more information on the company and the person, and then write them a sales message.
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### [Question-Answering on a Web Browser](https://twitter.com/chillzaza_/status/1592961099384905730?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ)
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By Zahid Khawaja, this demo utilizes question answering to answer questions about a given website.
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A followup added this for [Youtube videos](https://twitter.com/chillzaza_/status/1593739682013220865?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ),
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and then another followup added it for [Wikipedia](https://twitter.com/chillzaza_/status/1594847151238037505?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ).
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@ -1 +1 @@
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0.0.22
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0.0.26
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@ -0,0 +1,94 @@
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"""Document combining chain."""
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from typing import Any, Dict, List
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from pydantic import BaseModel, Extra, Field, root_validator
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain.prompts.base import BasePromptTemplate
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from langchain.prompts.prompt import Prompt
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def _get_default_document_prompt() -> Prompt:
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return Prompt(input_variables=["page_content"], template="{page_content}")
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class CombineDocumentsChain(Chain, BaseModel):
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"""Combine documents."""
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llm_chain: LLMChain
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"""LLM wrapper to use after formatting documents."""
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document_prompt: BasePromptTemplate = Field(
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default_factory=_get_default_document_prompt
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)
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"""Prompt to use to format each document."""
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document_variable_name: str
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"""The variable name in the llm_chain to put the documents in.
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If only one variable in the llm_chain, this need not be provided."""
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input_key: str = "input_documents" #: :meta private:
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output_key: str = "output_text" #: :meta private:
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@property
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||||
def input_keys(self) -> List[str]:
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"""Expect input key.
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||||
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||||
:meta private:
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||||
"""
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||||
return [self.input_key]
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||||
|
||||
@property
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||||
def output_keys(self) -> List[str]:
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||||
"""Return output key.
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||||
|
||||
:meta private:
|
||||
"""
|
||||
return [self.output_key]
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||||
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||||
@root_validator(pre=True)
|
||||
def get_default_document_variable_name(cls, values: Dict) -> Dict:
|
||||
"""Get default document variable name, if not provided."""
|
||||
if "document_variable_name" not in values:
|
||||
llm_chain_variables = values["llm_chain"].prompt.input_variables
|
||||
if len(llm_chain_variables) == 1:
|
||||
values["document_variable_name"] = llm_chain_variables[0]
|
||||
else:
|
||||
raise ValueError(
|
||||
"document_variable_name must be provided if there are "
|
||||
"multiple llm_chain_variables"
|
||||
)
|
||||
else:
|
||||
llm_chain_variables = values["llm_chain"].prompt.input_variables
|
||||
if values["document_variable_name"] not in llm_chain_variables:
|
||||
raise ValueError(
|
||||
f"document_variable_name {values['document_variable_name']} was "
|
||||
f"not found in llm_chain input_variables: {llm_chain_variables}"
|
||||
)
|
||||
return values
|
||||
|
||||
def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
|
||||
docs = inputs[self.input_key]
|
||||
# Other keys are assumed to be needed for LLM prediction
|
||||
other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
|
||||
# Get relevant information from each document.
|
||||
doc_dicts = []
|
||||
for doc in docs:
|
||||
base_info = {"page_content": doc.page_content}
|
||||
base_info.update(doc.metadata)
|
||||
document_info = {
|
||||
k: base_info[k] for k in self.document_prompt.input_variables
|
||||
}
|
||||
doc_dicts.append(document_info)
|
||||
# Format each document according to the prompt
|
||||
doc_strings = [self.document_prompt.format(**doc) for doc in doc_dicts]
|
||||
# Join the documents together to put them in the prompt.
|
||||
other_keys[self.document_variable_name] = "\n".join(doc_strings)
|
||||
# Call predict on the LLM.
|
||||
output = self.llm_chain.predict(**other_keys)
|
||||
return {self.output_key: output}
|
||||
@ -0,0 +1,4 @@
|
||||
"""Implements Program-Aided Language Models.
|
||||
|
||||
As in https://arxiv.org/pdf/2211.10435.pdf.
|
||||
"""
|
||||
@ -0,0 +1,81 @@
|
||||
"""Implements Program-Aided Language Models.
|
||||
|
||||
As in https://arxiv.org/pdf/2211.10435.pdf.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from pydantic import BaseModel, Extra
|
||||
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.chains.pal.colored_object_prompt import COLORED_OBJECT_PROMPT
|
||||
from langchain.chains.pal.math_prompt import MATH_PROMPT
|
||||
from langchain.chains.python import PythonChain
|
||||
from langchain.input import print_text
|
||||
from langchain.llms.base import LLM
|
||||
from langchain.prompts.base import BasePromptTemplate
|
||||
|
||||
|
||||
class PALChain(Chain, BaseModel):
|
||||
"""Implements Program-Aided Language Models."""
|
||||
|
||||
llm: LLM
|
||||
prompt: BasePromptTemplate
|
||||
stop: str = "\n\n"
|
||||
get_answer_expr: str = "print(solution())"
|
||||
output_key: str = "result" #: :meta private:
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@property
|
||||
def input_keys(self) -> List[str]:
|
||||
"""Return the singular input key.
|
||||
|
||||
:meta private:
|
||||
"""
|
||||
return self.prompt.input_variables
|
||||
|
||||
@property
|
||||
def output_keys(self) -> List[str]:
|
||||
"""Return the singular output key.
|
||||
|
||||
:meta private:
|
||||
"""
|
||||
return [self.output_key]
|
||||
|
||||
def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
|
||||
llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
|
||||
code = llm_chain.predict(stop=[self.stop], **inputs)
|
||||
if self.verbose:
|
||||
print_text(code, color="green", end="\n")
|
||||
repl = PythonChain()
|
||||
res = repl.run(code + f"\n{self.get_answer_expr}")
|
||||
return {self.output_key: res.strip()}
|
||||
|
||||
@classmethod
|
||||
def from_math_prompt(cls, llm: LLM, **kwargs: Any) -> PALChain:
|
||||
"""Load PAL from math prompt."""
|
||||
return cls(
|
||||
llm=llm,
|
||||
prompt=MATH_PROMPT,
|
||||
stop="\n\n",
|
||||
get_answer_expr="print(solution())",
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_colored_object_prompt(cls, llm: LLM, **kwargs: Any) -> PALChain:
|
||||
"""Load PAL from colored object prompt."""
|
||||
return cls(
|
||||
llm=llm,
|
||||
prompt=COLORED_OBJECT_PROMPT,
|
||||
stop="\n\n\n",
|
||||
get_answer_expr="print(answer)",
|
||||
**kwargs,
|
||||
)
|
||||
@ -0,0 +1,77 @@
|
||||
# flake8: noqa
|
||||
from langchain.prompts.prompt import PromptTemplate
|
||||
|
||||
template = (
|
||||
"""
|
||||
# Generate Python3 Code to solve problems
|
||||
# Q: On the nightstand, there is a red pencil, a purple mug, a burgundy keychain, a fuchsia teddy bear, a black plate, and a blue stress ball. What color is the stress ball?
|
||||
# Put objects into a dictionary for quick look up
|
||||
objects = dict()
|
||||
objects['pencil'] = 'red'
|
||||
objects['mug'] = 'purple'
|
||||
objects['keychain'] = 'burgundy'
|
||||
objects['teddy bear'] = 'fuchsia'
|
||||
objects['plate'] = 'black'
|
||||
objects['stress ball'] = 'blue'
|
||||
|
||||
# Look up the color of stress ball
|
||||
stress_ball_color = objects['stress ball']
|
||||
answer = stress_ball_color
|
||||
|
||||
|
||||
# Q: On the table, you see a bunch of objects arranged in a row: a purple paperclip, a pink stress ball, a brown keychain, a green scrunchiephone charger, a mauve fidget spinner, and a burgundy pen. What is the color of the object directly to the right of the stress ball?
|
||||
# Put objects into a list to record ordering
|
||||
objects = []
|
||||
objects += [('paperclip', 'purple')] * 1
|
||||
objects += [('stress ball', 'pink')] * 1
|
||||
objects += [('keychain', 'brown')] * 1
|
||||
objects += [('scrunchiephone charger', 'green')] * 1
|
||||
objects += [('fidget spinner', 'mauve')] * 1
|
||||
objects += [('pen', 'burgundy')] * 1
|
||||
|
||||
# Find the index of the stress ball
|
||||
stress_ball_idx = None
|
||||
for i, object in enumerate(objects):
|
||||
if object[0] == 'stress ball':
|
||||
stress_ball_idx = i
|
||||
break
|
||||
|
||||
# Find the directly right object
|
||||
direct_right = objects[i+1]
|
||||
|
||||
# Check the directly right object's color
|
||||
direct_right_color = direct_right[1]
|
||||
answer = direct_right_color
|
||||
|
||||
|
||||
# Q: On the nightstand, you see the following items arranged in a row: a teal plate, a burgundy keychain, a yellow scrunchiephone charger, an orange mug, a pink notebook, and a grey cup. How many non-orange items do you see to the left of the teal item?
|
||||
# Put objects into a list to record ordering
|
||||
objects = []
|
||||
objects += [('plate', 'teal')] * 1
|
||||
objects += [('keychain', 'burgundy')] * 1
|
||||
objects += [('scrunchiephone charger', 'yellow')] * 1
|
||||
objects += [('mug', 'orange')] * 1
|
||||
objects += [('notebook', 'pink')] * 1
|
||||
objects += [('cup', 'grey')] * 1
|
||||
|
||||
# Find the index of the teal item
|
||||
teal_idx = None
|
||||
for i, object in enumerate(objects):
|
||||
if object[1] == 'teal':
|
||||
teal_idx = i
|
||||
break
|
||||
|
||||
# Find non-orange items to the left of the teal item
|
||||
non_orange = [object for object in objects[:i] if object[1] != 'orange']
|
||||
|
||||
# Count number of non-orange objects
|
||||
num_non_orange = len(non_orange)
|
||||
answer = num_non_orange
|
||||
|
||||
|
||||
# Q: {question}
|
||||
""".strip()
|
||||
+ "\n"
|
||||
)
|
||||
|
||||
COLORED_OBJECT_PROMPT = PromptTemplate(input_variables=["question"], template=template)
|
||||
@ -0,0 +1,157 @@
|
||||
# flake8: noqa
|
||||
from langchain.prompts.prompt import PromptTemplate
|
||||
|
||||
template = (
|
||||
'''
|
||||
Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""Olivia has $23. She bought five bagels for $3 each. How much money does she have left?"""
|
||||
money_initial = 23
|
||||
bagels = 5
|
||||
bagel_cost = 3
|
||||
money_spent = bagels * bagel_cost
|
||||
money_left = money_initial - money_spent
|
||||
result = money_left
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?"""
|
||||
golf_balls_initial = 58
|
||||
golf_balls_lost_tuesday = 23
|
||||
golf_balls_lost_wednesday = 2
|
||||
golf_balls_left = golf_balls_initial - golf_balls_lost_tuesday - golf_balls_lost_wednesday
|
||||
result = golf_balls_left
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?"""
|
||||
computers_initial = 9
|
||||
computers_per_day = 5
|
||||
num_days = 4 # 4 days between monday and thursday
|
||||
computers_added = computers_per_day * num_days
|
||||
computers_total = computers_initial + computers_added
|
||||
result = computers_total
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?"""
|
||||
toys_initial = 5
|
||||
mom_toys = 2
|
||||
dad_toys = 2
|
||||
total_received = mom_toys + dad_toys
|
||||
total_toys = toys_initial + total_received
|
||||
result = total_toys
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?"""
|
||||
jason_lollipops_initial = 20
|
||||
jason_lollipops_after = 12
|
||||
denny_lollipops = jason_lollipops_initial - jason_lollipops_after
|
||||
result = denny_lollipops
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?"""
|
||||
leah_chocolates = 32
|
||||
sister_chocolates = 42
|
||||
total_chocolates = leah_chocolates + sister_chocolates
|
||||
chocolates_eaten = 35
|
||||
chocolates_left = total_chocolates - chocolates_eaten
|
||||
result = chocolates_left
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?"""
|
||||
cars_initial = 3
|
||||
cars_arrived = 2
|
||||
total_cars = cars_initial + cars_arrived
|
||||
result = total_cars
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?"""
|
||||
trees_initial = 15
|
||||
trees_after = 21
|
||||
trees_added = trees_after - trees_initial
|
||||
result = trees_added
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: {question}
|
||||
|
||||
# solution in Python:
|
||||
'''.strip()
|
||||
+ "\n\n\n"
|
||||
)
|
||||
MATH_PROMPT = PromptTemplate(input_variables=["question"], template=template)
|
||||
@ -0,0 +1 @@
|
||||
"""Question answering with sources over documents."""
|
||||
@ -0,0 +1,145 @@
|
||||
"""Question answering with sources over documents."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from pydantic import BaseModel, Extra, root_validator
|
||||
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.chains.combine_documents import CombineDocumentsChain
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.chains.qa_with_sources.prompt import (
|
||||
COMBINE_PROMPT,
|
||||
EXAMPLE_PROMPT,
|
||||
QUESTION_PROMPT,
|
||||
)
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.llms.base import LLM
|
||||
from langchain.prompts.base import BasePromptTemplate
|
||||
|
||||
|
||||
class BaseQAWithSourcesChain(Chain, BaseModel, ABC):
|
||||
"""Question answering with sources over documents."""
|
||||
|
||||
llm_question_chain: LLMChain
|
||||
"""LLM wrapper to use for asking questions to each document."""
|
||||
combine_document_chain: CombineDocumentsChain
|
||||
"""Chain to use to combine documents."""
|
||||
doc_source_key: str = "source"
|
||||
"""Key in document.metadata to use as source information"""
|
||||
question_key: str = "question" #: :meta private:
|
||||
input_docs_key: str = "docs" #: :meta private:
|
||||
answer_key: str = "answer" #: :meta private:
|
||||
sources_answer_key: str = "sources" #: :meta private:
|
||||
|
||||
@classmethod
|
||||
def from_llm(
|
||||
cls,
|
||||
llm: LLM,
|
||||
combine_document_prompt: BasePromptTemplate = EXAMPLE_PROMPT,
|
||||
question_prompt: BasePromptTemplate = QUESTION_PROMPT,
|
||||
combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
|
||||
**kwargs: Any,
|
||||
) -> BaseQAWithSourcesChain:
|
||||
"""Construct the chain from an LLM."""
|
||||
llm_question_chain = LLMChain(llm=llm, prompt=question_prompt)
|
||||
llm_combine_chain = LLMChain(llm=llm, prompt=combine_prompt)
|
||||
combine_document_chain = CombineDocumentsChain(
|
||||
llm_chain=llm_combine_chain,
|
||||
document_prompt=combine_document_prompt,
|
||||
document_variable_name="summaries",
|
||||
)
|
||||
return cls(
|
||||
llm_question_chain=llm_question_chain,
|
||||
combine_document_chain=combine_document_chain,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@property
|
||||
def input_keys(self) -> List[str]:
|
||||
"""Expect input key.
|
||||
|
||||
:meta private:
|
||||
"""
|
||||
return [self.question_key]
|
||||
|
||||
@property
|
||||
def output_keys(self) -> List[str]:
|
||||
"""Return output key.
|
||||
|
||||
:meta private:
|
||||
"""
|
||||
return [self.answer_key, self.sources_answer_key]
|
||||
|
||||
@root_validator(pre=True)
|
||||
def validate_question_chain(cls, values: Dict) -> Dict:
|
||||
"""Validate question chain."""
|
||||
llm_question_chain = values["llm_question_chain"]
|
||||
if len(llm_question_chain.input_keys) != 2:
|
||||
raise ValueError(
|
||||
f"The llm_question_chain should have two inputs: a content key "
|
||||
f"(the first one) and a question key (the second one). Got "
|
||||
f"{llm_question_chain.input_keys}."
|
||||
)
|
||||
return values
|
||||
|
||||
@root_validator()
|
||||
def validate_combine_chain_can_be_constructed(cls, values: Dict) -> Dict:
|
||||
"""Validate that the combine chain can be constructed."""
|
||||
# Try to construct the combine documents chains.
|
||||
|
||||
return values
|
||||
|
||||
@abstractmethod
|
||||
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
|
||||
"""Get docs to run questioning over."""
|
||||
|
||||
def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
|
||||
docs = self._get_docs(inputs)
|
||||
query = inputs[self.question_key]
|
||||
content_key, query_key = self.llm_question_chain.input_keys
|
||||
results = self.llm_question_chain.apply(
|
||||
[{content_key: d.page_content, query_key: query} for d in docs]
|
||||
)
|
||||
question_result_key = self.llm_question_chain.output_key
|
||||
result_docs = [
|
||||
Document(page_content=r[question_result_key], metadata=docs[i].metadata)
|
||||
for i, r in enumerate(results)
|
||||
]
|
||||
answer_dict = self.combine_document_chain(
|
||||
{
|
||||
self.combine_document_chain.input_key: result_docs,
|
||||
self.question_key: query,
|
||||
}
|
||||
)
|
||||
answer = answer_dict[self.combine_document_chain.output_key]
|
||||
if "\nSOURCES: " in answer:
|
||||
answer, sources = answer.split("\nSOURCES: ")
|
||||
else:
|
||||
sources = ""
|
||||
return {self.answer_key: answer, self.sources_answer_key: sources}
|
||||
|
||||
|
||||
class QAWithSourcesChain(BaseQAWithSourcesChain, BaseModel):
|
||||
"""Question answering with sources over documents."""
|
||||
|
||||
input_docs_key: str = "docs" #: :meta private:
|
||||
|
||||
@property
|
||||
def input_keys(self) -> List[str]:
|
||||
"""Expect input key.
|
||||
|
||||
:meta private:
|
||||
"""
|
||||
return [self.input_docs_key, self.question_key]
|
||||
|
||||
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
|
||||
return inputs[self.input_docs_key]
|
||||
@ -0,0 +1,20 @@
|
||||
"""Question-answering with sources over a vector database."""
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.vectorstores.base import VectorStore
|
||||
|
||||
|
||||
class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain, BaseModel):
|
||||
"""Question-answering with sources over a vector database."""
|
||||
|
||||
vectorstore: VectorStore
|
||||
"""Vector Database to connect to."""
|
||||
k: int = 4
|
||||
|
||||
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
|
||||
question = inputs[self.question_key]
|
||||
return self.vectorstore.similarity_search(question, k=self.k)
|
||||
@ -1,6 +1,12 @@
|
||||
"""Wrappers around embedding modules."""
|
||||
from langchain.embeddings.cohere import CohereEmbeddings
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings
|
||||
from langchain.embeddings.openai import OpenAIEmbeddings
|
||||
|
||||
__all__ = ["OpenAIEmbeddings", "HuggingFaceEmbeddings", "CohereEmbeddings"]
|
||||
__all__ = [
|
||||
"OpenAIEmbeddings",
|
||||
"HuggingFaceEmbeddings",
|
||||
"CohereEmbeddings",
|
||||
"HuggingFaceHubEmbeddings",
|
||||
]
|
||||
|
||||
@ -0,0 +1,105 @@
|
||||
"""Wrapper around HuggingFace Hub embedding models."""
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from pydantic import BaseModel, Extra, root_validator
|
||||
|
||||
from langchain.embeddings.base import Embeddings
|
||||
from langchain.utils import get_from_dict_or_env
|
||||
|
||||
DEFAULT_REPO_ID = "sentence-transformers/all-mpnet-base-v2"
|
||||
VALID_TASKS = ("feature-extraction",)
|
||||
|
||||
|
||||
class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
|
||||
"""Wrapper around HuggingFaceHub embedding models.
|
||||
|
||||
To use, you should have the ``huggingface_hub`` python package installed, and the
|
||||
environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
|
||||
it as a named parameter to the constructor.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.embeddings import HuggingFaceHubEmbeddings
|
||||
repo_id = "sentence-transformers/all-mpnet-base-v2"
|
||||
hf = HuggingFaceHubEmbeddings(
|
||||
repo_id=repo_id,
|
||||
task="feature-extraction",
|
||||
huggingfacehub_api_token="my-api-key",
|
||||
)
|
||||
"""
|
||||
|
||||
client: Any #: :meta private:
|
||||
repo_id: str = DEFAULT_REPO_ID
|
||||
"""Model name to use."""
|
||||
task: Optional[str] = "feature-extraction"
|
||||
"""Task to call the model with."""
|
||||
model_kwargs: Optional[dict] = None
|
||||
"""Key word arguments to pass to the model."""
|
||||
|
||||
huggingfacehub_api_token: Optional[str] = None
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
huggingfacehub_api_token = get_from_dict_or_env(
|
||||
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
|
||||
)
|
||||
try:
|
||||
from huggingface_hub.inference_api import InferenceApi
|
||||
|
||||
repo_id = values["repo_id"]
|
||||
if not repo_id.startswith("sentence-transformers"):
|
||||
raise ValueError(
|
||||
"Currently only 'sentence-transformers' embedding models "
|
||||
f"are supported. Got invalid 'repo_id' {repo_id}."
|
||||
)
|
||||
client = InferenceApi(
|
||||
repo_id=repo_id,
|
||||
token=huggingfacehub_api_token,
|
||||
task=values.get("task"),
|
||||
)
|
||||
if client.task not in VALID_TASKS:
|
||||
raise ValueError(
|
||||
f"Got invalid task {client.task}, "
|
||||
f"currently only {VALID_TASKS} are supported"
|
||||
)
|
||||
values["client"] = client
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import huggingface_hub python package. "
|
||||
"Please it install it with `pip install huggingface_hub`."
|
||||
)
|
||||
return values
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Call out to HuggingFaceHub's embedding endpoint for embedding search docs.
|
||||
|
||||
Args:
|
||||
texts: The list of texts to embed.
|
||||
|
||||
Returns:
|
||||
List of embeddings, one for each text.
|
||||
"""
|
||||
# replace newlines, which can negatively affect performance.
|
||||
texts = [text.replace("\n", " ") for text in texts]
|
||||
_model_kwargs = self.model_kwargs or {}
|
||||
responses = self.client(inputs=texts, params=_model_kwargs)
|
||||
return responses
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Call out to HuggingFaceHub's embedding endpoint for embedding query text.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
|
||||
Returns:
|
||||
Embeddings for the text.
|
||||
"""
|
||||
response = self.embed_documents([text])[0]
|
||||
return response
|
||||
@ -0,0 +1,31 @@
|
||||
"""Test PAL chain."""
|
||||
|
||||
from langchain import OpenAI
|
||||
from langchain.chains.pal.base import PALChain
|
||||
|
||||
|
||||
def test_math_prompt() -> None:
|
||||
"""Test math prompt."""
|
||||
llm = OpenAI(model_name="code-davinci-002", temperature=0, max_tokens=512)
|
||||
pal_chain = PALChain.from_math_prompt(llm)
|
||||
question = (
|
||||
"Jan has three times the number of pets as Marcia. "
|
||||
"Marcia has two more pets than Cindy. "
|
||||
"If Cindy has four pets, how many total pets do the three have?"
|
||||
)
|
||||
output = pal_chain.run(question)
|
||||
assert output == "28"
|
||||
|
||||
|
||||
def test_colored_object_prompt() -> None:
|
||||
"""Test colored object prompt."""
|
||||
llm = OpenAI(model_name="code-davinci-002", temperature=0, max_tokens=512)
|
||||
pal_chain = PALChain.from_colored_object_prompt(llm)
|
||||
question = (
|
||||
"On the desk, you see two blue booklets, "
|
||||
"two purple booklets, and two yellow pairs of sunglasses. "
|
||||
"If I remove all the pairs of sunglasses from the desk, "
|
||||
"how many purple items remain on it?"
|
||||
)
|
||||
output = pal_chain.run(question)
|
||||
assert output == "2"
|
||||
@ -0,0 +1,28 @@
|
||||
"""Test HuggingFaceHub embeddings."""
|
||||
import pytest
|
||||
|
||||
from langchain.embeddings import HuggingFaceHubEmbeddings
|
||||
|
||||
|
||||
def test_huggingfacehub_embedding_documents() -> None:
|
||||
"""Test huggingfacehub embeddings."""
|
||||
documents = ["foo bar"]
|
||||
embedding = HuggingFaceHubEmbeddings()
|
||||
output = embedding.embed_documents(documents)
|
||||
assert len(output) == 1
|
||||
assert len(output[0]) == 768
|
||||
|
||||
|
||||
def test_huggingfacehub_embedding_query() -> None:
|
||||
"""Test huggingfacehub embeddings."""
|
||||
document = "foo bar"
|
||||
embedding = HuggingFaceHubEmbeddings()
|
||||
output = embedding.embed_query(document)
|
||||
assert len(output) == 768
|
||||
|
||||
|
||||
def test_huggingfacehub_embedding_invalid_repo() -> None:
|
||||
"""Test huggingfacehub embedding repo id validation."""
|
||||
# Only sentence-transformers models are currently supported.
|
||||
with pytest.raises(ValueError):
|
||||
HuggingFaceHubEmbeddings(repo_id="allenai/specter")
|
||||
@ -1,9 +1,9 @@
|
||||
"""Integration test for SerpAPI."""
|
||||
from langchain.chains.serpapi import SerpAPIChain
|
||||
from langchain.serpapi import SerpAPIWrapper
|
||||
|
||||
|
||||
def test_call() -> None:
|
||||
"""Test that call gives the correct answer."""
|
||||
chain = SerpAPIChain()
|
||||
chain = SerpAPIWrapper()
|
||||
output = chain.run("What was Obama's first name?")
|
||||
assert output == "Barack Hussein Obama II"
|
||||
@ -0,0 +1,9 @@
|
||||
"""Test functionality related to prompt utils."""
|
||||
from langchain.prompts.example_selector.semantic_similarity import sorted_values
|
||||
|
||||
|
||||
def test_sorted_vals() -> None:
|
||||
"""Test sorted values from dictionary."""
|
||||
test_dict = {"key2": "val2", "key1": "val1"}
|
||||
expected_response = ["val1", "val2"]
|
||||
assert sorted_values(test_dict) == expected_response
|
||||
Loading…
Reference in New Issue