forked from Archives/langchain
c81fb88035
# Vectara Integration This PR provides integration with Vectara. Implemented here are: * langchain/vectorstore/vectara.py * tests/integration_tests/vectorstores/test_vectara.py * langchain/retrievers/vectara_retriever.py And two IPYNB notebooks to do more testing: * docs/modules/chains/index_examples/vectara_text_generation.ipynb * docs/modules/indexes/vectorstores/examples/vectara.ipynb --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
727 lines
18 KiB
Plaintext
727 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "134a0785",
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"metadata": {},
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"source": [
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"# Chat Over Documents with Vectara\n",
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"\n",
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"This notebook is based on the [chat_vector_db](https://github.com/hwchase17/langchain/blob/master/docs/modules/chains/index_examples/chat_vector_db.ipynb) notebook, but using Vectara as the 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": "70c4e529",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import os\n",
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"from langchain.vectorstores import Vectara\n",
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"from langchain.vectorstores.vectara import VectaraRetriever\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.chains import ConversationalRetrievalChain"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cdff94be",
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"metadata": {},
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"source": [
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"Load in documents. You can replace this with a loader for whatever type of data you want"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "01c46e92",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
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"documents = loader.load()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "239475d2",
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"metadata": {},
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"source": [
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"We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over them."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "a8930cf7",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"vectorstore = Vectara.from_documents(documents, embedding=None)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "898b574b",
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"metadata": {},
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"source": [
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"We can now create a memory object, which is neccessary to track the inputs/outputs and hold a conversation."
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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": "af803fee",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.memory import ConversationBufferMemory\n",
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"memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=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": "3c96b118",
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"metadata": {},
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"source": [
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"We now initialize the `ConversationalRetrievalChain`"
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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": "7b4110f3",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'langchain.vectorstores.vectara.Vectara'>\n"
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]
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}
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],
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"source": [
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"openai_api_key = os.environ['OPENAI_API_KEY']\n",
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"llm = OpenAI(openai_api_key=openai_api_key, temperature=0)\n",
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"retriever = VectaraRetriever(vectorstore, alpha=0.025, k=5, filter=None)\n",
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"\n",
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"print(type(vectorstore))\n",
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"d = retriever.get_relevant_documents('What did the president say about Ketanji Brown Jackson')\n",
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"\n",
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"qa = ConversationalRetrievalChain.from_llm(llm, retriever, memory=memory)"
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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": "e8ce4fe9",
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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 Ketanji Brown Jackson\"\n",
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"result = qa({\"question\": 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": 7,
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"id": "4c79862b",
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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 one of the nation's top legal minds, a former top litigator in private practice, and a former federal public defender.\""
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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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"result[\"answer\"]"
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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": "c697d9d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"Did he mention who she suceeded\"\n",
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"result = qa({\"question\": 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": 9,
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"id": "ba0678f3",
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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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"' Justice Stephen Breyer.'"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result['answer']"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b3308b01-5300-4999-8cd3-22f16dae757e",
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"metadata": {},
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"source": [
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"## Pass in chat history\n",
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"\n",
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"In the above example, we used a Memory object to track chat history. We can also just pass it in explicitly. In order to do this, we need to initialize a chain without any memory object."
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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": "1b41a10b-bf68-4689-8f00-9aed7675e2ab",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "83f38c18-ac82-45f4-a79e-8b37ce1ae115",
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"metadata": {},
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"source": [
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"Here's an example of asking a question with no chat history"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "bc672290-8a8b-4828-a90c-f1bbdd6b3920",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat_history = []\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"result = qa({\"question\": query, \"chat_history\": chat_history})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "6b62d758-c069-4062-88f0-21e7ea4710bf",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, and a former federal public defender.\""
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result[\"answer\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8c26a83d-c945-4458-b54a-c6bd7f391303",
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"metadata": {},
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"source": [
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"Here's an example of asking a question with some chat history"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "9c95460b-7116-4155-a9d2-c0fb027ee592",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat_history = [(query, result[\"answer\"])]\n",
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"query = \"Did he mention who she suceeded\"\n",
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"result = qa({\"question\": query, \"chat_history\": chat_history})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "698ac00c-cadc-407f-9423-226b2d9258d0",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"' Justice Stephen Breyer.'"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result['answer']"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0eaadf0f",
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"metadata": {},
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"source": [
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"## Return Source Documents\n",
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"You can also easily return source documents from the ConversationalRetrievalChain. This is useful for when you want to inspect what documents were returned."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "562769c6",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=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": 16,
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"id": "ea478300",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat_history = []\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"result = qa({\"question\": query, \"chat_history\": chat_history})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "4cb75b4e",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Document(page_content='Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice. A former federal public defender.', metadata={'source': '../../modules/state_of_the_union.txt'})"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result['source_documents'][0]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "669ede2f-d69f-4960-8468-8a768ce1a55f",
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"metadata": {},
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"source": [
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"## ConversationalRetrievalChain with `search_distance`\n",
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"If you are using a vector store that supports filtering by search distance, you can add a threshold value parameter."
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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": 18,
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"id": "f4f32c6f-8e49-44af-9116-8830b1fcc5f2",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"vectordbkwargs = {\"search_distance\": 0.9}"
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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": 19,
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"id": "1e251775-31e7-4679-b744-d4a57937f93a",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)\n",
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"chat_history = []\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"result = qa({\"question\": query, \"chat_history\": chat_history, \"vectordbkwargs\": vectordbkwargs})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "99b96dae",
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"metadata": {},
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"source": [
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"## ConversationalRetrievalChain with `map_reduce`\n",
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"We can also use different types of combine document chains with the ConversationalRetrievalChain chain."
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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": 20,
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"id": "e53a9d66",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.chains import LLMChain\n",
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"from langchain.chains.question_answering import load_qa_chain\n",
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"from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT"
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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": 21,
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"id": "bf205e35",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
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"doc_chain = load_qa_chain(llm, chain_type=\"map_reduce\")\n",
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"\n",
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"chain = ConversationalRetrievalChain(\n",
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" retriever=vectorstore.as_retriever(),\n",
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" question_generator=question_generator,\n",
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" combine_docs_chain=doc_chain,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "78155887",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat_history = []\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"result = chain({\"question\": query, \"chat_history\": chat_history})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "e54b5fa2",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"' The president did not mention Ketanji Brown Jackson.'"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result['answer']"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a2fe6b14",
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"metadata": {},
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"source": [
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"## ConversationalRetrievalChain with Question Answering with sources\n",
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"\n",
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"You can also use this chain with the question answering with sources chain."
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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": 24,
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"id": "d1058fd2",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.chains.qa_with_sources import load_qa_with_sources_chain"
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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": 25,
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"id": "a6594482",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"\n",
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"question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
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"doc_chain = load_qa_with_sources_chain(llm, chain_type=\"map_reduce\")\n",
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"\n",
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"chain = ConversationalRetrievalChain(\n",
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" retriever=vectorstore.as_retriever(),\n",
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" question_generator=question_generator,\n",
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" combine_docs_chain=doc_chain,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"id": "e2badd21",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat_history = []\n",
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||
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
|
||
"result = chain({\"question\": query, \"chat_history\": chat_history})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "edb31fe5",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"' The president did not mention Ketanji Brown Jackson.\\nSOURCES: ../../modules/state_of_the_union.txt'"
|
||
]
|
||
},
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"result['answer']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2324cdc6-98bf-4708-b8cd-02a98b1e5b67",
|
||
"metadata": {},
|
||
"source": [
|
||
"## ConversationalRetrievalChain with streaming to `stdout`\n",
|
||
"\n",
|
||
"Output from the chain will be streamed to `stdout` token by token in this example."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "2efacec3-2690-4b05-8de3-a32fd2ac3911",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from langchain.chains.llm import LLMChain\n",
|
||
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
|
||
"from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT\n",
|
||
"from langchain.chains.question_answering import load_qa_chain\n",
|
||
"\n",
|
||
"# Construct a ConversationalRetrievalChain with a streaming llm for combine docs\n",
|
||
"# and a separate, non-streaming llm for question generation\n",
|
||
"llm = OpenAI(temperature=0, openai_api_key=openai_api_key)\n",
|
||
"streaming_llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0, openai_api_key=openai_api_key)\n",
|
||
"\n",
|
||
"question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
|
||
"doc_chain = load_qa_chain(streaming_llm, chain_type=\"stuff\", prompt=QA_PROMPT)\n",
|
||
"\n",
|
||
"qa = ConversationalRetrievalChain(\n",
|
||
" retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "fd6d43f4-7428-44a4-81bc-26fe88a98762",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, and a former federal public defender."
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"chat_history = []\n",
|
||
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
|
||
"result = qa({\"question\": query, \"chat_history\": chat_history})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "5ab38978-f3e8-4fa7-808c-c79dec48379a",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" Justice Stephen Breyer."
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"chat_history = [(query, result[\"answer\"])]\n",
|
||
"query = \"Did he mention who she suceeded\"\n",
|
||
"result = qa({\"question\": query, \"chat_history\": chat_history})\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f793d56b",
|
||
"metadata": {},
|
||
"source": [
|
||
"## get_chat_history Function\n",
|
||
"You can also specify a `get_chat_history` function, which can be used to format the chat_history string."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "a7ba9d8c",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_chat_history(inputs) -> str:\n",
|
||
" res = []\n",
|
||
" for human, ai in inputs:\n",
|
||
" res.append(f\"Human:{human}\\nAI:{ai}\")\n",
|
||
" return \"\\n\".join(res)\n",
|
||
"qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), get_chat_history=get_chat_history)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "a3e33c0d",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"chat_history = []\n",
|
||
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
|
||
"result = qa({\"question\": query, \"chat_history\": chat_history})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"id": "936dc62f",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, and a former federal public defender.\""
|
||
]
|
||
},
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"result['answer']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "b8c26901",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.9"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|