Spaces:
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update design and notebook
Browse files- arxiv_metadata_loader-retriever.ipynb +210 -0
- config.py +12 -1
- diagrams/langchain-rag-loader.excalidraw +662 -0
- diagrams/langchain-rag-retriever.excalidraw +1433 -0
arxiv_metadata_loader-retriever.ipynb
ADDED
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Arxiv Metadata Dataset - Loader and Retriever\n",
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"\n",
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"- Load Arxiv Metadata from Hugging Face DataSet and Load in to Qdrant\n",
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"- Use LangGraph to store trace info"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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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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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU pymupdf \n",
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"%pip install -qU langchain langchain-core langchain-community langchain-text-splitters \n",
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"%pip install -qU langchain-openai\n",
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"%pip install -qU langchain-groq\n",
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"%pip install -qU langchain-qdrant"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Parameterize some stuff\n",
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"\n",
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"QUESTION = \"What are the emerging patterns for building Systems of Agents that could provide the system the ability to evolve and improve its own processes through learning?\""
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from langchain import hub\n",
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"from langchain_groq import ChatGroq\n",
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"from config import COLLECTION_NAME, DATASET_NAME, OPENAI_API_KEY, QDRANT_API_KEY, QDRANT_API_URL, LANGCHAIN_HUB_PROMPT\n",
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"from langchain_community.document_loaders import PyMuPDFLoader\n",
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"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
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"from langchain_qdrant import Qdrant\n",
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"# idenify data loader for html 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_openai import OpenAIEmbeddings\n",
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"\n",
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"embedding = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
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"prompt = hub.pull(LANGCHAIN_HUB_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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# URL Path is retrieved from the dataset\n",
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"# need to use another loader for HTML documents\n",
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"\n",
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"# iterate over retrieved records from the huggingface dataset\n",
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"URL_PATH = # need to retrieve the URL path from the dataset\n",
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"loader = PyMuPDFLoader(URL_PATH, extract_images=True)\n",
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"docs = loader.load()\n",
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"\n",
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"text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
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"splits = text_splitter.split_documents(docs)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Store the chunks in Qdrant\n",
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"from_splits = Qdrant.from_documents(\n",
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" embedding=embedding,\n",
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" collection_name=COLLECTION_NAME,\n",
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" url=QDRANT_API_URL,\n",
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" api_key=QDRANT_API_KEY,\n",
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" prefer_grpc=True, \n",
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" documents=splits,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Retrieve Information using Metadata in Vector Store"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"qdrant = Qdrant.from_existing_collection(\n",
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" embedding=embedding,\n",
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" collection_name=COLLECTION_NAME,\n",
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" url=QDRANT_API_URL,\n",
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" api_key=QDRANT_API_KEY,\n",
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" prefer_grpc=True, \n",
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")\n",
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"\n",
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"retriever = qdrant.as_retriever(\n",
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" search_type=\"similarity_score_threshold\",\n",
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" search_kwargs={\"score_threshold\": 0.5, \"k\": 5}\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_groq import ChatGroq\n",
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"from operator import itemgetter\n",
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"from langchain.schema.runnable import RunnablePassthrough\n",
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"\n",
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"llm = ChatGroq(model=\"llama3-70b-8192\", temperature=0.3)\n",
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"\n",
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"rag_chain = (\n",
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" {\"context\": itemgetter(\"question\") | retriever, \"question\": itemgetter(\"question\")}\n",
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" | RunnablePassthrough.assign(context=itemgetter(\"context\"))\n",
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" | {\"response\": prompt | llm, \"context\": itemgetter(\"context\")}\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(rag_chain.get_graph().draw_ascii())"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"response = rag_chain.invoke({\"question\" : QUESTION})"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# return the response. filter on the response key AIMessage content element\n",
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"print(response[\"response\"].content)\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"response[\"context\"]"
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]
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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",
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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": "3.10.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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config.py
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# File: config.py
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import os
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-
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# File: config.py
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import os
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
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QDRANT_API_URL = os.getenv("QDRANT_API_URL")
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GROQ_API_KEY = os.getenv("GROQ_API_KEY")
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COLLECTION_NAME = "retrieval_metadata_arxiv_dataset"
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DATASET_NAME = "dwb2023/arxiv-papers-dataset"
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LANGCHAIN_PROJECT="retrieval_metadata_arxiv_dataset"
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LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
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LANGCHAIN_TRACING_V2="true"
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LANGCHAIN_HUB_PROMPT="rlm/rag-prompt-llama3"
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LANGCHAIN_API_KEY=os.getenv("LANGCHAIN_API_KEY")
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diagrams/langchain-rag-loader.excalidraw
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