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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.vectorstores import FAISS\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain import OpenAI\n",
"from langchain.chains import RetrievalQA\n",
"from langchain.document_loaders import DirectoryLoader\n",
"import magic\n",
"import os\n",
"import nltk\n",
"\n",
"openai_api_key = os.getenv(\"OPENAI_API_KEY\")\n",
"data_location= os.getenv(\"VECTOR_DATA_DIR\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chroma"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from modules.vector_stores.vector_stores.chroma_manager import get_default_chroma_mgr\n",
"\n",
"chroma_mgr = get_default_chroma_mgr(persisted=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chroma_mgr.persist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from modules.vector_stores.retrieval.basic_qa import get_default_qa\n",
"\n",
"qa = get_default_qa(chroma_mgr.db)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## Cite sources\n",
"def process_llm_response(llm_response):\n",
" print(llm_response['result'])\n",
" print('\\n\\nSources:')\n",
" for source in llm_response[\"source_documents\"]:\n",
" print(source.metadata['source'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# full example\n",
"query = \"What is a date table?\"\n",
"resp = qa.ask(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## FAISS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from modules.vector_stores.loaders.pypdf_load_strategy import PyPDFLoadStrategy, PyPDFConfig, get_default_pypdf_loader\n",
"from modules.vector_stores.embedding.openai import OpenAIEmbeddings, OpenAIEmbedConfig, get_default_openai_embeddings\n",
"def get_example_pdf_embedding():\n",
" dir_location = \"../data\"\n",
" loader = get_default_pypdf_loader(dir_location)\n",
" documents = loader.load()\n",
" embeddings = get_default_openai_embeddings()\n",
" index = FAISS.from_documents(documents, embeddings)\n",
" return index\n",
"index = get_example_pdf_embedding()\n",
"llm = OpenAI(openai_api_key=openai_api_key)\n",
"qa = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=index.as_retriever())\n",
"qa = RetrievalQA.from_chain_type(llm=llm,\n",
" chain_type=\"stuff\",\n",
" retriever=index.as_retriever(),\n",
" return_source_documents=True)\n",
"query = \"What is a date table?\"\n",
"result = qa({\"query\": query})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"docsearch = FAISS.from_documents(documents, embeddings)\n",
"llm = OpenAI(openai_api_key=openai_api_key)\n",
"qa = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=docsearch.as_retriever())\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"qa = RetrievalQA.from_chain_type(llm=llm,\n",
" chain_type=\"stuff\",\n",
" retriever=docsearch.as_retriever(),\n",
" return_source_documents=True)\n",
"query = \"What is a date table?\"\n",
"result = qa({\"query\": query})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.10.6"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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