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Harrison/combine documents chain (#212)
combine documents chain powering vector db qa with sources chain
This commit is contained in:
Harrison Chase
GitHub
200
docs/examples/chains/combine documents.ipynb
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200
docs/examples/chains/combine documents.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "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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@@ -39,7 +39,7 @@
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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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"execution_count": 2,
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"id": "99bbe19b",
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"metadata": {},
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"outputs": [
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@@ -49,7 +49,7 @@
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"\"\\n\\nThe President discusses the recent aggression by Russia, and the response by the United States and its allies. He announces new sanctions against Russia, and says that the free world is united in holding Putin accountable. The President also discusses the American Rescue Plan, the Bipartisan Infrastructure Law, and the Bipartisan Innovation Act. Finally, the President addresses the need for women's rights and equality for LGBTQ+ Americans.\""
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]
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},
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"execution_count": 3,
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -63,7 +63,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b581501e",
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"id": "baa6e808",
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"metadata": {},
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"outputs": [],
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"source": []
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