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langchain/docs/integrations/vectara/vectara_chat.ipynb
Ofer Mendelevitch c81fb88035
Vectara (#5069) 
# 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>
2023-05-24 01:24:58 -07:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "134a0785",
"metadata": {},
"source": [
"# Chat Over Documents with Vectara\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "70c4e529",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"from langchain.vectorstores import Vectara\n",
"from langchain.vectorstores.vectara import VectaraRetriever\n",
"from langchain.llms import OpenAI\n",
"from langchain.chains import ConversationalRetrievalChain"
]
},
{
"cell_type": "markdown",
"id": "cdff94be",
"metadata": {},
"source": [
"Load in documents. You can replace this with a loader for whatever type of data you want"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "01c46e92",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.document_loaders import TextLoader\n",
"loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
"documents = loader.load()"
]
},
{
"cell_type": "markdown",
"id": "239475d2",
"metadata": {},
"source": [
"We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over them."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a8930cf7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"vectorstore = Vectara.from_documents(documents, embedding=None)"
]
},
{
"cell_type": "markdown",
"id": "898b574b",
"metadata": {},
"source": [
"We can now create a memory object, which is neccessary to track the inputs/outputs and hold a conversation."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "af803fee",
"metadata": {},
"outputs": [],
"source": [
"from langchain.memory import ConversationBufferMemory\n",
"memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)"
]
},
{
"cell_type": "markdown",
"id": "3c96b118",
"metadata": {},
"source": [
"We now initialize the `ConversationalRetrievalChain`"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7b4110f3",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'langchain.vectorstores.vectara.Vectara'>\n"
]
}
],
"source": [
"openai_api_key = os.environ['OPENAI_API_KEY']\n",
"llm = OpenAI(openai_api_key=openai_api_key, temperature=0)\n",
"retriever = VectaraRetriever(vectorstore, alpha=0.025, k=5, filter=None)\n",
"\n",
"print(type(vectorstore))\n",
"d = retriever.get_relevant_documents('What did the president say about Ketanji Brown Jackson')\n",
"\n",
"qa = ConversationalRetrievalChain.from_llm(llm, retriever, memory=memory)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e8ce4fe9",
"metadata": {},
"outputs": [],
"source": [
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
"result = qa({\"question\": query})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4c79862b",
"metadata": {},
"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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result[\"answer\"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c697d9d1",
"metadata": {},
"outputs": [],
"source": [
"query = \"Did he mention who she suceeded\"\n",
"result = qa({\"question\": query})"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ba0678f3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' Justice Stephen Breyer.'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result['answer']"
]
},
{
"cell_type": "markdown",
"id": "b3308b01-5300-4999-8cd3-22f16dae757e",
"metadata": {},
"source": [
"## Pass in chat history\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "1b41a10b-bf68-4689-8f00-9aed7675e2ab",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever())"
]
},
{
"cell_type": "markdown",
"id": "83f38c18-ac82-45f4-a79e-8b37ce1ae115",
"metadata": {},
"source": [
"Here's an example of asking a question with no chat history"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "bc672290-8a8b-4828-a90c-f1bbdd6b3920",
"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": 12,
"id": "6b62d758-c069-4062-88f0-21e7ea4710bf",
"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": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result[\"answer\"]"
]
},
{
"cell_type": "markdown",
"id": "8c26a83d-c945-4458-b54a-c6bd7f391303",
"metadata": {},
"source": [
"Here's an example of asking a question with some chat history"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9c95460b-7116-4155-a9d2-c0fb027ee592",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"chat_history = [(query, result[\"answer\"])]\n",
"query = \"Did he mention who she suceeded\"\n",
"result = qa({\"question\": query, \"chat_history\": chat_history})"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "698ac00c-cadc-407f-9423-226b2d9258d0",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"' Justice Stephen Breyer.'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result['answer']"
]
},
{
"cell_type": "markdown",
"id": "0eaadf0f",
"metadata": {},
"source": [
"## Return Source Documents\n",
"You can also easily return source documents from the ConversationalRetrievalChain. This is useful for when you want to inspect what documents were returned."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "562769c6",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "ea478300",
"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": 17,
"id": "4cb75b4e",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"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'})"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result['source_documents'][0]"
]
},
{
"cell_type": "markdown",
"id": "669ede2f-d69f-4960-8468-8a768ce1a55f",
"metadata": {},
"source": [
"## ConversationalRetrievalChain with `search_distance`\n",
"If you are using a vector store that supports filtering by search distance, you can add a threshold value parameter."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f4f32c6f-8e49-44af-9116-8830b1fcc5f2",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"vectordbkwargs = {\"search_distance\": 0.9}"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "1e251775-31e7-4679-b744-d4a57937f93a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)\n",
"chat_history = []\n",
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
"result = qa({\"question\": query, \"chat_history\": chat_history, \"vectordbkwargs\": vectordbkwargs})"
]
},
{
"cell_type": "markdown",
"id": "99b96dae",
"metadata": {},
"source": [
"## ConversationalRetrievalChain with `map_reduce`\n",
"We can also use different types of combine document chains with the ConversationalRetrievalChain chain."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "e53a9d66",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chains import LLMChain\n",
"from langchain.chains.question_answering import load_qa_chain\n",
"from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "bf205e35",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
"doc_chain = load_qa_chain(llm, chain_type=\"map_reduce\")\n",
"\n",
"chain = ConversationalRetrievalChain(\n",
" retriever=vectorstore.as_retriever(),\n",
" question_generator=question_generator,\n",
" combine_docs_chain=doc_chain,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "78155887",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"chat_history = []\n",
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
"result = chain({\"question\": query, \"chat_history\": chat_history})"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "e54b5fa2",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"' The president did not mention Ketanji Brown Jackson.'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result['answer']"
]
},
{
"cell_type": "markdown",
"id": "a2fe6b14",
"metadata": {},
"source": [
"## ConversationalRetrievalChain with Question Answering with sources\n",
"\n",
"You can also use this chain with the question answering with sources chain."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "d1058fd2",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chains.qa_with_sources import load_qa_with_sources_chain"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "a6594482",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
"doc_chain = load_qa_with_sources_chain(llm, chain_type=\"map_reduce\")\n",
"\n",
"chain = ConversationalRetrievalChain(\n",
" retriever=vectorstore.as_retriever(),\n",
" question_generator=question_generator,\n",
" combine_docs_chain=doc_chain,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "e2badd21",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"chat_history = []\n",
"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
}
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