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update design and notebook

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Files changed (4) hide show
  1. arxiv_metadata_loader-retriever.ipynb +210 -0
  2. config.py +12 -1
  3. diagrams/langchain-rag-loader.excalidraw +662 -0
  4. diagrams/langchain-rag-retriever.excalidraw +1433 -0
arxiv_metadata_loader-retriever.ipynb ADDED
@@ -0,0 +1,210 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "metadata": {},
6
+ "source": [
7
+ "# Arxiv Metadata Dataset - Loader and Retriever\n",
8
+ "\n",
9
+ "- Load Arxiv Metadata from Hugging Face DataSet and Load in to Qdrant\n",
10
+ "- Use LangGraph to store trace info"
11
+ ]
12
+ },
13
+ {
14
+ "cell_type": "markdown",
15
+ "metadata": {},
16
+ "source": []
17
+ },
18
+ {
19
+ "cell_type": "code",
20
+ "execution_count": null,
21
+ "metadata": {},
22
+ "outputs": [],
23
+ "source": [
24
+ "%pip install -qU pymupdf \n",
25
+ "%pip install -qU langchain langchain-core langchain-community langchain-text-splitters \n",
26
+ "%pip install -qU langchain-openai\n",
27
+ "%pip install -qU langchain-groq\n",
28
+ "%pip install -qU langchain-qdrant"
29
+ ]
30
+ },
31
+ {
32
+ "cell_type": "code",
33
+ "execution_count": null,
34
+ "metadata": {},
35
+ "outputs": [],
36
+ "source": [
37
+ "# Parameterize some stuff\n",
38
+ "\n",
39
+ "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?\""
40
+ ]
41
+ },
42
+ {
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+ "cell_type": "code",
44
+ "execution_count": null,
45
+ "metadata": {},
46
+ "outputs": [],
47
+ "source": [
48
+ "import os\n",
49
+ "from langchain import hub\n",
50
+ "from langchain_groq import ChatGroq\n",
51
+ "from config import COLLECTION_NAME, DATASET_NAME, OPENAI_API_KEY, QDRANT_API_KEY, QDRANT_API_URL, LANGCHAIN_HUB_PROMPT\n",
52
+ "from langchain_community.document_loaders import PyMuPDFLoader\n",
53
+ "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
54
+ "from langchain_qdrant import Qdrant\n",
55
+ "# idenify data loader for html documents"
56
+ ]
57
+ },
58
+ {
59
+ "cell_type": "code",
60
+ "execution_count": null,
61
+ "metadata": {},
62
+ "outputs": [],
63
+ "source": [
64
+ "from langchain_openai import OpenAIEmbeddings\n",
65
+ "\n",
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+ "embedding = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
67
+ "prompt = hub.pull(LANGCHAIN_HUB_PROMPT)"
68
+ ]
69
+ },
70
+ {
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+ "cell_type": "code",
72
+ "execution_count": null,
73
+ "metadata": {},
74
+ "outputs": [],
75
+ "source": [
76
+ "# URL Path is retrieved from the dataset\n",
77
+ "# 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",
80
+ "URL_PATH = # need to retrieve the URL path from the dataset\n",
81
+ "loader = PyMuPDFLoader(URL_PATH, extract_images=True)\n",
82
+ "docs = loader.load()\n",
83
+ "\n",
84
+ "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
85
+ "splits = text_splitter.split_documents(docs)"
86
+ ]
87
+ },
88
+ {
89
+ "cell_type": "code",
90
+ "execution_count": null,
91
+ "metadata": {},
92
+ "outputs": [],
93
+ "source": [
94
+ "# Store the chunks in Qdrant\n",
95
+ "from_splits = Qdrant.from_documents(\n",
96
+ " embedding=embedding,\n",
97
+ " collection_name=COLLECTION_NAME,\n",
98
+ " url=QDRANT_API_URL,\n",
99
+ " api_key=QDRANT_API_KEY,\n",
100
+ " prefer_grpc=True, \n",
101
+ " documents=splits,\n",
102
+ ")"
103
+ ]
104
+ },
105
+ {
106
+ "cell_type": "markdown",
107
+ "metadata": {},
108
+ "source": [
109
+ "## Retrieve Information using Metadata in Vector Store"
110
+ ]
111
+ },
112
+ {
113
+ "cell_type": "code",
114
+ "execution_count": null,
115
+ "metadata": {},
116
+ "outputs": [],
117
+ "source": [
118
+ "qdrant = Qdrant.from_existing_collection(\n",
119
+ " embedding=embedding,\n",
120
+ " collection_name=COLLECTION_NAME,\n",
121
+ " 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
+ ]
131
+ },
132
+ {
133
+ "cell_type": "code",
134
+ "execution_count": null,
135
+ "metadata": {},
136
+ "outputs": [],
137
+ "source": [
138
+ "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
+ ")"
149
+ ]
150
+ },
151
+ {
152
+ "cell_type": "code",
153
+ "execution_count": null,
154
+ "metadata": {},
155
+ "outputs": [],
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+ "source": [
157
+ "print(rag_chain.get_graph().draw_ascii())"
158
+ ]
159
+ },
160
+ {
161
+ "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": [
166
+ "response = rag_chain.invoke({\"question\" : QUESTION})"
167
+ ]
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+ },
169
+ {
170
+ "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": [
175
+ "# return the response. filter on the response key AIMessage content element\n",
176
+ "print(response[\"response\"].content)\n"
177
+ ]
178
+ },
179
+ {
180
+ "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
@@ -1,4 +1,15 @@
1
  # File: config.py
2
  import os
3
 
4
- DATASET_NAME = "dwb2023/arxiv-papers-dataset"
 
 
 
 
 
 
 
 
 
 
 
 
1
  # File: config.py
2
  import os
3
 
4
+ OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
5
+ QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
6
+ QDRANT_API_URL = os.getenv("QDRANT_API_URL")
7
+ GROQ_API_KEY = os.getenv("GROQ_API_KEY")
8
+ COLLECTION_NAME = "retrieval_metadata_arxiv_dataset"
9
+ DATASET_NAME = "dwb2023/arxiv-papers-dataset"
10
+
11
+ LANGCHAIN_PROJECT="retrieval_metadata_arxiv_dataset"
12
+ LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
13
+ LANGCHAIN_TRACING_V2="true"
14
+ LANGCHAIN_HUB_PROMPT="rlm/rag-prompt-llama3"
15
+ LANGCHAIN_API_KEY=os.getenv("LANGCHAIN_API_KEY")
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