forked from Archives/langchain
168 lines
4.1 KiB
Plaintext
168 lines
4.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "25c90e9e",
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"metadata": {},
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"source": [
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"# Loading from LangChainHub\n",
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"\n",
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"This notebook covers how to load chains from [LangChainHub](https://github.com/hwchase17/langchain-hub)."
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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": "8b54479e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import load_chain\n",
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"\n",
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"chain = load_chain(\"lc://chains/llm-math/chain.json\")"
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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": "4828f31f",
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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 LLMMathChain chain...\u001b[0m\n",
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"whats 2 raised to .12\u001b[32;1m\u001b[1;3m\n",
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"Answer: 1.0791812460476249\u001b[0m\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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"'Answer: 1.0791812460476249'"
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]
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},
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"execution_count": 3,
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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.run(\"whats 2 raised to .12\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8db72cda",
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"metadata": {},
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"source": [
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"Sometimes chains will require extra arguments that were not serialized with the chain. For example, a chain that does question answering over a vector database will require 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": "aab39528",
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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.vectorstores import Chroma\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain import OpenAI, VectorDBQA"
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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": "16a85d5e",
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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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"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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"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()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"texts = 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(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": 6,
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"id": "6a82e91e",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = load_chain(\"lc://chains/vector-db-qa/stuff/chain.json\", vectorstore=vectorstore)"
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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": "efe9b25b",
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"metadata": {},
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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 said that Ketanji Brown Jackson is a Circuit Court of Appeals Judge, one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans, and will continue Justice Breyer's legacy of excellence.\""
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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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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"chain.run(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": null,
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"id": "f910a32f",
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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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