donb-hf commited on
Commit
dfe28dc
1 Parent(s): 0463856

update design and notebook

Browse files
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"
11
+ ]
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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)"
86
+ ]
87
+ },
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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",
101
+ " 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": [
109
+ "## Retrieve Information using Metadata in Vector Store"
110
+ ]
111
+ },
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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",
119
+ " embedding=embedding,\n",
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+ " collection_name=COLLECTION_NAME,\n",
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+ " url=QDRANT_API_URL,\n",
122
+ " api_key=QDRANT_API_KEY,\n",
123
+ " prefer_grpc=True, \n",
124
+ ")\n",
125
+ "\n",
126
+ "retriever = qdrant.as_retriever(\n",
127
+ " search_type=\"similarity_score_threshold\",\n",
128
+ " search_kwargs={\"score_threshold\": 0.5, \"k\": 5}\n",
129
+ ")"
130
+ ]
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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",
139
+ "from operator import itemgetter\n",
140
+ "from langchain.schema.runnable import RunnablePassthrough\n",
141
+ "\n",
142
+ "llm = ChatGroq(model=\"llama3-70b-8192\", temperature=0.3)\n",
143
+ "\n",
144
+ "rag_chain = (\n",
145
+ " {\"context\": itemgetter(\"question\") | retriever, \"question\": itemgetter(\"question\")}\n",
146
+ " | RunnablePassthrough.assign(context=itemgetter(\"context\"))\n",
147
+ " | {\"response\": prompt | llm, \"context\": itemgetter(\"context\")}\n",
148
+ ")"
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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"
177
+ ]
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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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+ }
config.py CHANGED
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  # File: config.py
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  import os
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- DATASET_NAME = "dwb2023/arxiv-papers-dataset"
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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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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