├── .gitignore
├── .streamlit
    └── secrets.toml.example
├── Dockerfile
├── Pipfile
├── Pipfile.lock
├── README.md
├── app.yaml
├── poetry.lock
├── pyproject.toml
├── rag_demo
    ├── __init__.py
    ├── __pycache__
    │   └── neo4j_driver.cpython-311.pyc
    ├── analytics.py
    ├── constants.py
    ├── free_use_manager.py
    ├── graph_cypher_chain.py
    ├── graph_cypher_tool.py
    ├── graph_tool.py
    ├── main.py
    ├── rag_agent.py
    ├── sidebar.py
    ├── vector_chain.py
    ├── vector_graph_chain.py
    ├── vector_graph_tool.py
    └── vector_tool.py
└── requirements.txt


/.gitignore:
--------------------------------------------------------------------------------
 1 | # Notebook output
 2 | failed_files.json
 3 | 
 4 | # Jupyter Notebook
 5 | .ipynb_checkpoints
 6 | __pycache__
 7 | 
 8 | # Mac System file
 9 | .DS_STORE
10 | 
11 | # UI file
12 | .env
13 | # secrets.toml
14 | 
15 | # Dependency managers
16 | *.lock


--------------------------------------------------------------------------------
/.streamlit/secrets.toml.example:
--------------------------------------------------------------------------------
1 | NEO4J_URI=""
2 | NEO4J_USERNAME="neo4j"
3 | NEO4J_PASSWORD=""
4 | SEGMENT_WRITE_KEY=""
5 | OPENAI_API_KEY=""


--------------------------------------------------------------------------------
/Dockerfile:
--------------------------------------------------------------------------------
 1 | FROM python:3.11-slim
 2 | 
 3 | WORKDIR /app
 4 | 
 5 | COPY requirements.txt .
 6 | COPY secrets.toml app/.streamlit/secrets.toml
 7 | RUN pip install -r requirements.txt
 8 | 
 9 | COPY . .
10 | 
11 | CMD ["streamlit", "run", "rag_demo/main.py", "--server.enableCORS", "false", "--browser.serverAddress", "0.0.0.0", "--browser.gatherUsageStats", "false", "--server.port", "8080"]


--------------------------------------------------------------------------------
/Pipfile:
--------------------------------------------------------------------------------
 1 | [[source]]
 2 | url = "https://pypi.org/simple"
 3 | verify_ssl = true
 4 | name = "pypi"
 5 | 
 6 | [packages]
 7 | langchain = "~=0.1.3"
 8 | langchain-openai = "~=0.0.5"
 9 | neo4j = "==5.16"
10 | segment-analytics-python = "==2.2.3"
11 | streamlit = "==1.30"
12 | streamlit-feedback = "==0.1.3"
13 | retry = "==0.9.2"
14 | langchain-community = "~=0.0.27"
15 | langchainhub = "~=0.1.15"
16 | 
17 | [dev-packages]
18 | 
19 | [requires]
20 | python_version = "3.11"
21 | 


--------------------------------------------------------------------------------
/Pipfile.lock:
--------------------------------------------------------------------------------
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1792 |             ],
1793 |             "markers": "python_version >= '3.7'",
1794 |             "version": "==1.9.4"
1795 |         },
1796 |         "zipp": {
1797 |             "hashes": [
1798 |                 "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31",
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1800 |             ],
1801 |             "markers": "python_version >= '3.8'",
1802 |             "version": "==3.17.0"
1803 |         }
1804 |     },
1805 |     "develop": {}
1806 | }
1807 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Neo4j RAG Demo
 2 | This sample application demonstrates how to implement a Large Language Model (LLM) and Retrieval Augmented Generation (RAG) system with a Neo4j Graph Database. This application uses Streamlit, LangChain, [Neo4jVector](https://python.langchain.com/docs/integrations/vectorstores/neo4jvector) vectorstore and [Neo4j DB QA Chain](https://python.langchain.com/docs/use_cases/graph/graph_cypher_qa)
 3 | 
 4 | ![Alt Text](https://res.cloudinary.com/dk0tizgdn/image/upload/v1707842287/rag-demo-short_vwezew.gif)
 5 | 
 6 | ## Requirements
 7 | - [Poetry](https://python-poetry.org) for dependency managament.
 8 | - Duplicate the `secrets.toml.example` file to `secrets.toml` and populate with appropriate keys.
 9 | 
10 | ## Usage
11 | ```
12 | poetry update
13 | poetry run streamlit run rag_demo/main.py --server.port=80
14 | 
15 | OR
16 | 
17 | pipenv shell
18 | pipenv install
19 | pipenv run streamlit run rag_demo/main.py
20 | ```
21 | 
22 | ## GCloud Update
23 | A hosted example of the rag-demo can be found at https://dev.neo4j.com/rag-demo. To create and run your own hosted version of this app on Google Cloud:
24 | 
25 | 1. First Install [gcloud CLI](https://cloud.google.com/sdk/docs/install), then:
26 | 2. Update the requirements.txt file with `poetry export --without-hashes --format=requirements.txt > requirements.txt`
27 | 3. Make sure a copy of the streamlit secrets.toml file is in the root folder path (may have to temp comment out from .gitignore for the gcloud build to properly find the secrets.toml file)
28 | 4. Run the following terminal commands
29 | ```
30 | gcloud auth login
31 | gcloud init
32 | gcloud builds submit --tag gcr.io/<gcloud_project_id>/streamlit-app
33 | gcloud run deploy --image gcr.io/<gcloud_project_id>/streamlit-app --platform managed --allow-unauthenticated
34 | ```


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/app.yaml:
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1 | runtime: python311
2 | 
3 | entrypoint: streamlit run rag_demo/main.py --server.enableCORS false --browser.serverAddress 0.0.0.0 --browser.gatherUsageStats false --server.port $PORT


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/pyproject.toml:
--------------------------------------------------------------------------------
 1 | [tool.poetry]
 2 | name = "rag-demo"
 3 | version = "0.1.1"
 4 | description = ""
 5 | authors = ["Andreas Kollegger <akollegger@gmail.com>", "Jennifer Reif<jennifer@thehecklers.org>", "Jason Koo <jalakoo@gmail.com>", "Alison Cossette <alisoncossette@gmail.com>"]
 6 | readme = "README.md"
 7 | packages = [{include = "rag_demo"}]
 8 | 
 9 | [tool.poetry.dependencies]
10 | python = "^3.11"
11 | langchain = "^0.2.5"
12 | langchain-openai = "^0.1.8"
13 | langchain-community = "^0.2.5"
14 | langchainhub = "^0.1.20"
15 | neo4j = "^5.21.0"
16 | retry = "^0.9.2"
17 | segment-analytics-python = "^2.3.2"
18 | streamlit = "^1.35.0"
19 | streamlit-feedback = "^0.1.3"
20 | 
21 | [build-system]
22 | requires = ["poetry-core"]
23 | build-backend = "poetry.core.masonry.api"
24 | 


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/rag_demo/__init__.py:
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https://raw.githubusercontent.com/neo4j-examples/rag-demo/738e96c4c6db75badcbc816ec5d97b0bacbcd234/rag_demo/__init__.py


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/rag_demo/__pycache__/neo4j_driver.cpython-311.pyc:
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https://raw.githubusercontent.com/neo4j-examples/rag-demo/738e96c4c6db75badcbc816ec5d97b0bacbcd234/rag_demo/__pycache__/neo4j_driver.cpython-311.pyc


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/rag_demo/analytics.py:
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 1 | from segment import analytics
 2 | import streamlit as st
 3 | import uuid
 4 | 
 5 | SESSION_ID = "SESSION_ID"
 6 | 
 7 | try:
 8 |     segment_key = st.secrets["SEGMENT_WRITE_KEY"]
 9 |     if segment_key == "" or segment_key is None:
10 |         ANALYTICS_ENABLED = False
11 |     else:
12 |         ANALYTICS_ENABLED = True
13 |         analytics.write_key = segment_key
14 | except:
15 |     ANALYTICS_ENABLED = False
16 | 
17 | def track(
18 |         user_id: str, 
19 |         event_name: str, 
20 |         properties: dict):
21 |     """Simple wrapper for Segment's analytics.track()
22 | 
23 |     Args:
24 |         user_id (str): Unique identifier for session/user
25 |         event_name (str): Name of tracking event
26 |         properties (dict): Any optional additional properties
27 |     """
28 | 
29 |     if ANALYTICS_ENABLED is False:
30 |         return
31 |     
32 |     if SESSION_ID not in st.session_state:
33 |         st.session_state[SESSION_ID] = str(uuid.uuid4())
34 | 
35 |     properties["session_id"] = st.session_state[SESSION_ID]
36 | 
37 |     analytics.track(
38 |         user_id = user_id, 
39 |         event = event_name, 
40 |         properties =  properties)


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/rag_demo/constants.py:
--------------------------------------------------------------------------------
 1 | SCHEMA_IMG_PATH = "https://res.cloudinary.com/dk0tizgdn/image/upload/v1709044753/rag-demo-data-model_j4dvfk.png"
 2 | LANGCHAIN_IMG_PATH = "https://res.cloudinary.com/dk0tizgdn/image/upload/v1704991084/langchain-neo4j_cy2mky.png"
 3 | 
 4 | # Has a white border around logo
 5 | TITLE = f"""
 6 | <style>
 7 |    @import url('https://fonts.googleapis.com/css2?family=Roboto&display=swap');
 8 | </style>
 9 | <div style='text-align: center; font-size: 2.5rem; font-weight: 600; font-family: "Roboto"; color: #018BFF; line-height:1; '>SEC EDGAR Filings</div>
10 | <div style='text-align: center; font-size: 1.5rem; font-weight: 300; font-family: "Roboto"; color: rgb(179 185 182); line-height:0; '>
11 |    Powered by <svg id="svg" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="160" height="120" viewBox="70, 0, 400,400"><g id="svgg"><path id="path0" d="M344.267 145.851 C 334.360 149.092,332.574 161.687,341.129 167.988 C 342.484 168.986,342.541 169.100,341.802 169.334 C 339.583 170.039,339.666 168.851,339.453 203.107 C 339.221 240.507,339.729 237.815,332.764 238.543 C 328.754 238.962,328.516 239.387,328.516 246.094 C 328.516 253.398,328.611 253.516,334.490 253.516 C 345.924 253.516,353.134 248.151,355.274 238.052 C 355.749 235.808,355.847 229.943,355.853 203.366 L 355.859 171.381 354.508 170.107 L 353.156 168.832 354.308 168.355 C 360.599 165.749,361.852 153.969,356.394 148.750 C 353.407 145.893,348.053 144.613,344.267 145.851 M350.496 148.586 C 360.359 151.554,358.990 166.377,348.784 167.115 C 342.944 167.537,338.672 163.512,338.672 157.585 C 338.672 151.138,344.425 146.759,350.496 148.586 M307.129 159.588 C 304.274 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12 | </div>
13 | """


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/rag_demo/free_use_manager.py:
--------------------------------------------------------------------------------
 1 | # Manages number of free questions permitted before forcing user to supply their own OpenAI Key
 2 | 
 3 | import streamlit as st
 4 | 
 5 | def _setup_free_questions_count():
 6 |     if "FREE_QUESTIONS_REMAINING" not in st.session_state:
 7 |         try:
 8 |             st.session_state["FREE_QUESTIONS_REMAINING"] = st.secrets["FREE_QUESTIONS_PER_SESSION"]
 9 |         except:
10 |             st.session_state["FREE_QUESTIONS_REMAINING"] = 100
11 | 
12 | def free_questions_exhausted()-> bool:
13 | 
14 |     _setup_free_questions_count()
15 |     
16 |     remaining = st.session_state["FREE_QUESTIONS_REMAINING"]
17 |     return remaining <= 0
18 | 
19 | def user_supplied_openai_key_unavailable()-> bool:
20 |     if "USER_OPENAI_KEY" not in st.session_state:
21 |         return True
22 |     uok = st.session_state["USER_OPENAI_KEY"]
23 |     if uok is None or uok == "":
24 |         return True
25 |     return False
26 | 
27 | def decrement_free_questions():
28 |     
29 |     _setup_free_questions_count()
30 | 
31 |     remaining = st.session_state["FREE_QUESTIONS_REMAINING"]
32 |     if remaining > 0:
33 |         st.session_state["FREE_QUESTIONS_REMAINING"] = remaining - 1
34 | 


--------------------------------------------------------------------------------
/rag_demo/graph_cypher_chain.py:
--------------------------------------------------------------------------------
  1 | from langchain.chains import GraphCypherQAChain
  2 | from langchain.chains.conversation.memory import ConversationBufferMemory
  3 | from langchain_community.graphs import Neo4jGraph
  4 | from langchain.prompts.prompt import PromptTemplate
  5 | from langchain_openai import ChatOpenAI
  6 | from retry import retry
  7 | import logging
  8 | import streamlit as st
  9 | 
 10 | CYPHER_GENERATION_TEMPLATE = """Task: Generate Cypher statement to query a graph database strictly based on the schema and instructions provided.
 11 | Instructions:
 12 | 1. Use only nodes, relationships, and properties mentioned in the schema.
 13 | 2. Always enclose the Cypher output inside 3 backticks. Do not add 'cypher' after the backticks.
 14 | 3. Always do a case-insensitive and fuzzy search for any properties related search. Eg: to search for a Company name use `toLower(c.name) contains 'neo4j'`
 15 | 4. Always use aliases to refer the node in the query
 16 | 5. Always return count(DISTINCT n) for aggregations to avoid duplicates
 17 | 6. `OWNS_STOCK_IN` relationship is syonymous with `OWNS` and `OWNER`
 18 | 7. Use examples of questions and accurate Cypher statements below to guide you.
 19 | 
 20 | Schema:
 21 | {schema}
 22 | 
 23 | Examples: Here are a few examples of generated Cypher statements for particular questions:
 24 | 
 25 | # How many Managers own Companies?
 26 | MATCH (m:Manager)-[:OWNS_STOCK_IN]->(c:Company)
 27 | RETURN count(DISTINCT m)
 28 | 
 29 | # How many companies are in filings?
 30 | MATCH (c:Company) 
 31 | RETURN count(DISTINCT c)
 32 | 
 33 | # Which companies are vulnerable to material shortage?
 34 | MATCH (co:Company)-[fi]-(f:Form)-[po]-(c:Chunk)
 35 | WHERE toLower(c.text) CONTAINS "material"
 36 | RETURN DISTINCT count(c) as chunks, co.name ORDER BY chunks desc
 37 | 
 38 | # Which companies are in a specific industry?
 39 | MATCH (co:Company)-[fi]-(f:Form)-[po]-(c:Chunk)
 40 | WHERE toLower(c.text) CONTAINS "industryName"
 41 | RETURN DISTINCT count(c) as chunks, co.name ORDER BY chunks desc
 42 | 
 43 | The question is:
 44 | {question}"""
 45 | 
 46 | CYPHER_GENERATION_PROMPT = PromptTemplate(
 47 |     input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE
 48 | )
 49 | 
 50 | MEMORY = ConversationBufferMemory(
 51 |     memory_key="chat_history", 
 52 |     input_key='question', 
 53 |     output_key='answer', 
 54 |     return_messages=True)
 55 | 
 56 | url = st.secrets["NEO4J_URI"]
 57 | username = st.secrets["NEO4J_USERNAME"]
 58 | password = st.secrets["NEO4J_PASSWORD"]
 59 | 
 60 | if "USER_OPENAI_API_KEY" in st.session_state:
 61 |     openai_key = st.session_state["USER_OPENAI_API_KEY"]
 62 | else:
 63 |     openai_key = st.secrets["OPENAI_API_KEY"]
 64 | 
 65 | graph = Neo4jGraph(
 66 |     url=url,
 67 |     username=username,
 68 |     password=password,
 69 |     sanitize = True
 70 | )
 71 | 
 72 | # Official API doc for GraphCypherQAChain at: https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html#
 73 | graph_chain = GraphCypherQAChain.from_llm(
 74 |     cypher_llm=ChatOpenAI(
 75 |         openai_api_key=openai_key, 
 76 |         temperature=0, 
 77 |         model_name="gpt-4"
 78 |     ),
 79 |     qa_llm=ChatOpenAI(
 80 |         openai_api_key=openai_key, 
 81 |         temperature=0, 
 82 |         model_name="gpt-4"
 83 |     ),
 84 |     validate_cypher= True,
 85 |     graph=graph,
 86 |     verbose=True, 
 87 |     # return_intermediate_steps = True,
 88 |     return_direct = True
 89 | )
 90 | 
 91 | @retry(tries=2, delay=12)
 92 | def get_results(question) -> str:
 93 |     """Generate a response from a GraphCypherQAChain targeted at generating answered related to relationships. 
 94 | 
 95 |     Args:
 96 |         question (str): User query
 97 | 
 98 |     Returns:
 99 |         str: Answer from chain
100 |     """
101 | 
102 |     logging.info(f'Using Neo4j database at url: {url}')
103 | 
104 |     graph.refresh_schema()
105 | 
106 |     prompt=CYPHER_GENERATION_PROMPT.format(schema=graph.get_schema, question=question)
107 |     print('Prompt:', prompt)
108 | 
109 |     chain_result = None
110 | 
111 |     try:
112 |         chain_result = graph_chain.invoke({
113 |             "query": question},
114 |             prompt=prompt,
115 |             return_only_outputs = True,
116 |         )
117 |     except Exception as e:
118 |         # Occurs when the chain can not generate a cypher statement
119 |         # for the question with the given database schema
120 |         logging.warning(f'Handled exception running graphCypher chain: {e}')
121 | 
122 |     logging.debug(f'chain_result: {chain_result}')
123 | 
124 |     if chain_result is None:
125 |         return "Sorry, I couldn't find an answer to your question"
126 |     
127 |     result = chain_result.get("result", None)
128 | 
129 |     return result


--------------------------------------------------------------------------------
/rag_demo/graph_cypher_tool.py:
--------------------------------------------------------------------------------
 1 | from langchain.tools import tool
 2 | from graph_cypher_chain import get_results
 3 | 
 4 | @tool("graph-cypher-tool")
 5 | def graph_cypher_tool(query:str) -> str:
 6 |     """
 7 |     Useful when answer requires calculating numerical answers like aggregations.
 8 |     Use when question asks for a count or how many.
 9 |     Use full question as input.
10 |     Do not call this tool more than once.
11 |     Do not call another tool if this returns results.
12 |     """
13 |     return get_results(query)


--------------------------------------------------------------------------------
/rag_demo/graph_tool.py:
--------------------------------------------------------------------------------
1 | from langchain.tools import tool
2 | from graph_chain import get_results
3 | 
4 | @tool("graph-tool")
5 | def graph_tool(query:str) -> str:
6 |     """Tool for returning aggregations of Manager or Company or Industry data or if answer is dependent on relationships between a Company and other objects. Use this tool second and to verify results of vector-graph-tool.
7 |     """
8 |     return get_results(query)


--------------------------------------------------------------------------------
/rag_demo/main.py:
--------------------------------------------------------------------------------
  1 | from analytics import track
  2 | from free_use_manager import (
  3 |     free_questions_exhausted,
  4 |     user_supplied_openai_key_unavailable,
  5 |     decrement_free_questions,
  6 | )
  7 | from langchain.globals import set_llm_cache
  8 | from langchain_community.cache import InMemoryCache
  9 | from streamlit_feedback import streamlit_feedback
 10 | from constants import TITLE
 11 | import logging
 12 | import rag_agent
 13 | import streamlit as st
 14 | from sidebar import sidebar
 15 | 
 16 | # Logging options
 17 | logger = logging.getLogger(__name__)
 18 | logger.setLevel(logging.INFO)
 19 | 
 20 | # Anonymous Session Analytics
 21 | if "SESSION_ID" not in st.session_state:
 22 |     # Track method will create and add session id to state on first run
 23 |     track("rag_demo", "appStarted", {})
 24 | 
 25 | # LangChain caching to reduce API calls
 26 | set_llm_cache(InMemoryCache())
 27 | 
 28 | # App title
 29 | st.markdown(TITLE, unsafe_allow_html=True)
 30 | sidebar()
 31 | 
 32 | # Define message placeholder and emoji feedback placeholder
 33 | placeholder = st.empty()
 34 | emoji_feedback = st.empty()
 35 | user_placeholder = st.empty()
 36 | 
 37 | # Initialize message history
 38 | if "messages" not in st.session_state:
 39 |     st.session_state.messages = [
 40 |         {
 41 |             "role": "ai",
 42 |             "content": f"This is a Proof of Concept application which shows how GenAI can be used with Neo4j to build and consume Knowledge Graphs using both vectors and structured data.\nSee the sidebar for more information!",
 43 |         },
 44 |         # {"role": "ai", "content": f"""This the schema in which the EDGAR filings are stored in Neo4j: \n <img style="width: 70%; height: auto;" src="{SCHEMA_IMG_PATH}"/>"""},
 45 |         # {"role": "ai", "content": f"""This is how the Chatbot flow goes: \n <img style="width: 70%; height: auto;" src="{LANGCHAIN_IMG_PATH}"/>"""}
 46 |     ]
 47 | 
 48 | # Display chat messages from history on app rerun
 49 | with placeholder.container():
 50 |     for message in st.session_state.messages:
 51 |         with st.chat_message(message["role"]):
 52 |             st.markdown(message["content"], unsafe_allow_html=True)
 53 | 
 54 | # User input - switch between sidebar sample quick select or actual user input. Clunky but works.
 55 | if free_questions_exhausted() and user_supplied_openai_key_unavailable():
 56 |     st.warning(
 57 |         "Thank you for trying out the Neo4j Rag Demo. Please input your OpenAI Key in the sidebar to continue asking questions."
 58 |     )
 59 |     st.stop()
 60 | 
 61 | if "sample" in st.session_state and st.session_state["sample"] is not None:
 62 |     user_input = st.session_state["sample"]
 63 | else:
 64 |     user_input = st.chat_input(
 65 |         placeholder="Ask question on the SEC Filings", key="user_input"
 66 |     )
 67 | 
 68 | if user_input:
 69 |     with user_placeholder.container():
 70 |         track("rag_demo", "question_submitted", {"question": user_input})
 71 |         st.session_state.messages.append({"role": "user", "content": user_input})
 72 |         with st.chat_message("user"):
 73 |             st.markdown(user_input)
 74 | 
 75 |         with st.chat_message("ai"):
 76 | 
 77 |             # Agent response
 78 |             with st.spinner("..."):
 79 | 
 80 |                 message_placeholder = st.empty()
 81 |                 thought_container = st.container()
 82 | 
 83 |                 # For displaying chain of thought - this callback handler appears to only works with the deprecated initialize_agent option (see rag_agent.py)
 84 |                 # st_callback = StreamlitCallbackHandler(
 85 |                 #   parent_container= thought_container,
 86 |                 #   expand_new_thoughts=False
 87 |                 # )
 88 |                 # StreamlitCcallbackHandler api doc: https://api.python.langchain.com/en/latest/callbacks/langchain_community.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html
 89 | 
 90 |                 agent_response = rag_agent.get_results(
 91 |                     question=user_input, callbacks=[]
 92 |                 )
 93 | 
 94 |                 if isinstance(agent_response, dict) is False:
 95 |                     logging.warning(
 96 |                         f"Agent response was not the expected dict type: {agent_response}"
 97 |                     )
 98 |                     agent_response = str(agent_response)
 99 | 
100 |                 content = agent_response["output"]
101 | 
102 |                 track(
103 |                     "rag_demo", "ai_response", {"type": "rag_agent", "answer": content}
104 |                 )
105 |                 new_message = {"role": "ai", "content": content}
106 |                 st.session_state.messages.append(new_message)
107 | 
108 |                 decrement_free_questions()
109 | 
110 |             message_placeholder.markdown(content)
111 | 
112 |     # Reinsert user chat input if sample quick select was previously used.
113 |     if "sample" in st.session_state and st.session_state["sample"] is not None:
114 |         st.session_state["sample"] = None
115 |         user_input = st.chat_input(
116 |             placeholder="Ask question on the SEC Filings", key="user_input"
117 |         )
118 | 
119 |     emoji_feedback = st.empty()
120 | 
121 | # Emoji feedback
122 | with emoji_feedback.container():
123 |     feedback = streamlit_feedback(feedback_type="thumbs")
124 |     if feedback:
125 |         score = feedback["score"]
126 |         last_bot_message = st.session_state["messages"][-1]["content"]
127 |         track(
128 |             "rag_demo",
129 |             "feedback_submitted",
130 |             {"score": score, "bot_message": last_bot_message},
131 |         )
132 | 


--------------------------------------------------------------------------------
/rag_demo/rag_agent.py:
--------------------------------------------------------------------------------
 1 | from langchain.agents import AgentExecutor, create_react_agent
 2 | from langchain_community.agent_toolkits.load_tools import load_tools
 3 | from langchain import hub  # requires langchainhub package
 4 | from langchain_openai import OpenAI
 5 | from vector_graph_tool import vector_graph_tool
 6 | from graph_cypher_tool import graph_cypher_tool
 7 | from vector_tool import vector_tool
 8 | from retry import retry
 9 | 
10 | 
11 | # Setup tools the agent will use
12 | llm = OpenAI(temperature=0)
13 | tools = load_tools([], llm=llm)
14 | tools = tools + [vector_graph_tool, graph_cypher_tool, vector_tool]
15 | 
16 | 
17 | # REACT AGENT EXECUTOR
18 | 
19 | prompt = hub.pull("hwchase17/react")
20 | 
21 | # More on agent types: https://python.langchain.com/docs/modules/agents/agent_types/
22 | agent = create_react_agent(llm, tools, prompt)
23 | 
24 | # NOTE: early_stopping_method generate option ONLY available for multi-action agents
25 | agent_executor = AgentExecutor(
26 |     agent=agent,
27 |     tools=tools,
28 |     verbose=True,
29 | )
30 | 
31 | 
32 | @retry(tries=2, delay=20)
33 | def get_results(question, callbacks) -> dict:
34 |     """Starts a LangChain agent to generate an answer using one of several Neo4j RAG tools.
35 | 
36 |     Args:
37 |         question (str): User query
38 |         callbacks (list): List of optional LangChain callback handlers
39 | 
40 |     Returns:
41 |         dict: Final answer as a dict with the keys: input, output, intermediate_steps
42 |     """
43 | 
44 |     response = agent_executor.invoke({"input": question}, callbacks=callbacks)
45 |     return response
46 | 


--------------------------------------------------------------------------------
/rag_demo/sidebar.py:
--------------------------------------------------------------------------------
 1 | from constants import SCHEMA_IMG_PATH, LANGCHAIN_IMG_PATH
 2 | import streamlit as st
 3 | import streamlit.components.v1 as components
 4 | 
 5 | def ChangeButtonColour(wgt_txt, wch_hex_colour = '12px'):
 6 |     htmlstr = """<script>var elements = window.parent.document.querySelectorAll('*'), i;
 7 |                 for (i = 0; i < elements.length; ++i) 
 8 |                     { if (elements[i].innerText == |wgt_txt|) 
 9 |                         { elements[i].style.color ='""" + wch_hex_colour + """'; } }</script>  """
10 | 
11 |     htmlstr = htmlstr.replace('|wgt_txt|', "'" + wgt_txt + "'")
12 |     components.html(f"{htmlstr}", height=0, width=0)
13 | 
14 | def sidebar():
15 |     with st.sidebar:
16 | 
17 |         with st.expander("OpenAI Key"):
18 |             new_oak = st.text_input("Your OpenAI API Key")
19 |             # if "USER_OPENAI_KEY" not in st.session_state:
20 |             #     st.session_state["USER_OPENAI_KEY"] = new_oak
21 |             # else:
22 |             st.session_state["USER_OPENAI_KEY"] = new_oak
23 | 
24 |         st.markdown(f"""This the schema in which the EDGAR filings are stored in Neo4j: \n <img style="width: 70%; height: auto;" src="{SCHEMA_IMG_PATH}"/>""", unsafe_allow_html=True)
25 | 
26 |         st.markdown(f"""This is how the Chatbot flow goes: \n <img style="width: 70%; height: auto;" src="{LANGCHAIN_IMG_PATH}"/>""", unsafe_allow_html=True)
27 | 
28 |         st.markdown("""Questions you can ask of the dataset:""", unsafe_allow_html=True)
29 | 
30 |         # To style buttons closer together
31 |         st.markdown("""
32 |                     <style>
33 |                         div[data-testid="column"] {
34 |                             width: fit-content !important;
35 |                             flex: unset;
36 |                         }
37 |                         div[data-testid="column"] * {
38 |                             width: fit-content !important;
39 |                         }
40 |                     </style>
41 |                     """, unsafe_allow_html=True)
42 |         
43 |         sample_questions = "How many companies are in the filings?", "Which companies are in the healthcare industry?","Which companies are vulnerable to lithium shortage?", "Which managers own more than one company?", "List the top 3 managers by the number of companies they own.", "Which 5 companies have the most managers?"
44 | 
45 |         for text, col in zip(sample_questions, st.columns(len(sample_questions))):
46 |             if col.button(text, key=text):
47 |                 st.session_state["sample"] = text
48 | 


--------------------------------------------------------------------------------
/rag_demo/vector_chain.py:
--------------------------------------------------------------------------------
  1 | from json import loads, dumps
  2 | from langchain.prompts.prompt import PromptTemplate
  3 | 
  4 | from langchain_community.vectorstores import Neo4jVector
  5 | from langchain.chains import RetrievalQAWithSourcesChain
  6 | from langchain.chains.conversation.memory import ConversationBufferMemory
  7 | from langchain_openai import ChatOpenAI, OpenAIEmbeddings
  8 | from retry import retry
  9 | import logging
 10 | import streamlit as st
 11 | 
 12 | VECTOR_PROMPT_TEMPLATE = """Human: You are a Financial expert with SEC filings who can answer questions only based on the context below.
 13 | * Answer the question STRICTLY based on the context provided in JSON below.
 14 | * Do not assume or retrieve any information outside of the context 
 15 | * Use three sentences maximum and keep the answer concise
 16 | * Think step by step before answering.
 17 | * Do not return helpful or extra text or apologies
 18 | * Just return summary to the user. DO NOT start with Here is a summary
 19 | * List the results in rich text format if there are more than one results
 20 | * If the context is empty, just respond None
 21 | 
 22 | <question>
 23 | {input}
 24 | </question>
 25 | 
 26 | Here is the context:
 27 | <context>
 28 | {context}
 29 | </context>
 30 | 
 31 | Assistant:"""
 32 | VECTOR_PROMPT = PromptTemplate(
 33 |     input_variables=["input", "context"], template=VECTOR_PROMPT_TEMPLATE
 34 | )
 35 | 
 36 | if "USER_OPENAI_API_KEY" in st.session_state:
 37 |     openai_key = st.session_state["USER_OPENAI_API_KEY"]
 38 | else:
 39 |     openai_key = st.secrets["OPENAI_API_KEY"]
 40 | 
 41 | EMBEDDING_MODEL = OpenAIEmbeddings(openai_api_key=openai_key)
 42 | MEMORY = ConversationBufferMemory(
 43 |     memory_key="chat_history",
 44 |     input_key="question",
 45 |     output_key="answer",
 46 |     return_messages=True,
 47 | )
 48 | 
 49 | index_name = "form_10k_chunks"
 50 | node_property_name = "textopenaiembedding"
 51 | url = st.secrets["NEO4J_URI"]
 52 | username = st.secrets["NEO4J_USERNAME"]
 53 | password = st.secrets["NEO4J_PASSWORD"]
 54 | 
 55 | 
 56 | vector_store = None
 57 | try:
 58 |     logging.debug(f"Attempting to retrieve existing vector index: {index_name}...")
 59 |     vector_store = Neo4jVector.from_existing_index(
 60 |         embedding=EMBEDDING_MODEL,
 61 |         url=url,
 62 |         username=username,
 63 |         password=password,
 64 |         index_name=index_name,
 65 |         embedding_node_property=node_property_name,
 66 |     )
 67 |     logging.debug(f"Using existing index: {index_name}")
 68 | except:
 69 |     logging.debug(
 70 |         f"No existing index found. Attempting to create a new vector index named {index_name}..."
 71 |     )
 72 |     try:
 73 |         vector_store = Neo4jVector.from_existing_graph(
 74 |             embedding=EMBEDDING_MODEL,
 75 |             url=url,
 76 |             username=username,
 77 |             password=password,
 78 |             index_name=index_name,
 79 |             node_label="Chunk",
 80 |             text_node_properties=["text"],
 81 |             embedding_node_property=node_property_name,
 82 |         )
 83 |         logging.debug(f"Created new index: {index_name}")
 84 |     except Exception as e:
 85 |         logging.error(f"Failed to retrieve existing or to create a Neo4jVector: {e}")
 86 | 
 87 | if vector_store is None:
 88 |     logging.error(f"Failed to retrieve or create a Neo4jVector. Exiting.")
 89 |     exit()
 90 | 
 91 | vector_retriever = vector_store.as_retriever()
 92 | 
 93 | vector_chain = RetrievalQAWithSourcesChain.from_chain_type(
 94 |     ChatOpenAI(temperature=0, openai_api_key=openai_key),
 95 |     chain_type="stuff",
 96 |     retriever=vector_retriever,
 97 |     memory=MEMORY,
 98 |     reduce_k_below_max_tokens=True,
 99 |     max_tokens_limit=3000,
100 | )
101 | 
102 | 
103 | @retry(tries=2, delay=5)
104 | def get_results(question) -> str:
105 |     """Generate response using Neo4jVector using vector index only
106 | 
107 |     Args:
108 |         question (str): User query
109 | 
110 |     Returns:
111 |         str: Formatted string answer with citations, if available.
112 |     """
113 | 
114 |     logging.info(f"Using Neo4j url: {url}")
115 | 
116 |     # Returns a dict with keys: answer, sources
117 |     chain_result = vector_chain.invoke(
118 |         {"question": question},
119 |         prompt=VECTOR_PROMPT,
120 |         return_only_outputs=True,
121 |     )
122 | 
123 |     logging.debug(f"chain_result: {chain_result}")
124 | 
125 |     result = chain_result["answer"]
126 | 
127 |     # Cite sources, if any
128 |     sources = chain_result["sources"]
129 |     sources_split = sources.split(", ")
130 |     for source in sources_split:
131 |         if source != "" and source != "N/A" and source != "None":
132 |             result += f"\n - [{source}]({source})"
133 | 
134 |     return result
135 | 
136 | 
137 | # Using the vector store directly. But this could blow out the token count
138 | # @retry(tries=5, delay=5)
139 | # def get_results(question)-> str:
140 | #     """Generate response using Neo4jVector using vector index only
141 | 
142 | #     Args:
143 | #         question (str): User query
144 | 
145 | #     Returns:
146 | #         str: Formatted string answer with citations, if available.
147 | #     """
148 | 
149 | #     logging.info(f'Using Neo4j url: {url}')
150 | 
151 | #     # Returns a dict with keys: answer, sources
152 | #     vector_result = vector_store.similarity_search(question, k=3)
153 | 
154 | #     logging.debug(f'chain_result: {vector_result}')
155 | 
156 | #     result = vector_result
157 | 
158 | #     return result
159 | 


--------------------------------------------------------------------------------
/rag_demo/vector_graph_chain.py:
--------------------------------------------------------------------------------
  1 | from langchain.prompts.prompt import PromptTemplate
  2 | 
  3 | from langchain_community.vectorstores import Neo4jVector
  4 | from langchain.chains import RetrievalQAWithSourcesChain
  5 | from langchain.chains.conversation.memory import ConversationBufferMemory
  6 | from langchain_openai import ChatOpenAI, OpenAIEmbeddings
  7 | from retry import retry
  8 | import logging
  9 | import streamlit as st
 10 | 
 11 | VECTOR_GRAPH_PROMPT_TEMPLATE = """Task: Provide names and related information financial filing data strictly based on the text and instructions provided.
 12 | Instructions:
 13 | 1. Answer the question STRICTLY based on the text.
 14 | 2. Do not assume or retrieve any information outside of the text provided.
 15 | 3. Use as much information from the text as possible, including sources and links if available.
 16 | 4. If the output is empty, just respond None.
 17 | 
 18 | Question:
 19 | {question}
 20 | """
 21 | VECTOR_GRAPH_PROMPT = PromptTemplate(
 22 |     input_variables=["question"], template=VECTOR_GRAPH_PROMPT_TEMPLATE
 23 | )
 24 | 
 25 | if "USER_OPENAI_API_KEY" in st.session_state:
 26 |     openai_key = st.session_state["USER_OPENAI_API_KEY"]
 27 | else:
 28 |     openai_key = st.secrets["OPENAI_API_KEY"]
 29 | 
 30 | EMBEDDING_MODEL = OpenAIEmbeddings(openai_api_key=openai_key)
 31 | MEMORY = ConversationBufferMemory(
 32 |     memory_key="chat_history",
 33 |     input_key="question",
 34 |     output_key="answer",
 35 |     return_messages=True,
 36 | )
 37 | 
 38 | index_name = "form_10k_chunks"
 39 | node_property_name = "textEmbedding"
 40 | url = st.secrets["NEO4J_URI"]
 41 | username = st.secrets["NEO4J_USERNAME"]
 42 | password = st.secrets["NEO4J_PASSWORD"]
 43 | retrieval_query = """
 44 |     WITH node AS doc, score as similarity
 45 |     ORDER BY similarity DESC LIMIT 5
 46 |     CALL { WITH doc
 47 |         OPTIONAL MATCH (prevDoc:Chunk)-[:NEXT]->(doc)
 48 |         OPTIONAL MATCH (doc)-[:NEXT]->(nextDoc:Chunk)
 49 |         RETURN prevDoc, doc AS result, nextDoc
 50 |     }
 51 |     WITH result, prevDoc, nextDoc, similarity
 52 |     CALL {
 53 |         WITH result
 54 |         OPTIONAL MATCH (result)-[:PART_OF]->(:Form)<-[:FILED]-(company:Company)
 55 |         OPTIONAL MATCH (company)<-[:OWNS_STOCK_IN]-(manager:Manager)
 56 |         WITH result, company.name as companyName, apoc.text.join(collect(manager.managerName),';') as managers
 57 |         WHERE companyName IS NOT NULL OR managers > ""
 58 |         WITH result, companyName, managers
 59 |         ORDER BY result.score DESC
 60 |         RETURN result as document, result.score as popularity, companyName, managers
 61 |     }
 62 |     RETURN coalesce(prevDoc.text,'') + coalesce(document.text,'') + coalesce(nextDoc.text,'') + '\n Company: ' + coalesce(companyName,'') + '\n Managers: ' + coalesce(managers,'') as text, 
 63 |         similarity as score,
 64 |         {companies: coalesce(companyName,''), managers: coalesce(managers,''), source: document.source} AS metadata
 65 | """
 66 | 
 67 | 
 68 | vector_store = None
 69 | try:
 70 |     logging.debug(f"Attempting to retrieve existing vector index: {index_name}...")
 71 |     vector_store = Neo4jVector.from_existing_index(
 72 |         embedding=EMBEDDING_MODEL,
 73 |         url=url,
 74 |         username=username,
 75 |         password=password,
 76 |         index_name=index_name,
 77 |         embedding_node_property=node_property_name,
 78 |         retrieval_query=retrieval_query,
 79 |     )
 80 |     logging.debug(f"Using existing index: {index_name}")
 81 | except:
 82 |     logging.debug(
 83 |         f"No existing index found. Attempting to create a new vector index named {index_name}..."
 84 |     )
 85 |     try:
 86 |         vector_store = Neo4jVector.from_existing_graph(
 87 |             embedding=EMBEDDING_MODEL,
 88 |             url=url,
 89 |             username=username,
 90 |             password=password,
 91 |             index_name=index_name,
 92 |             node_label="Chunk",
 93 |             text_node_properties=["text"],
 94 |             embedding_node_property=node_property_name,
 95 |             retrieval_query=retrieval_query,
 96 |         )
 97 |         logging.debug(f"Created new index: {index_name}")
 98 |     except Exception as e:
 99 |         logging.error(f"Failed to retrieve existing or to create a Neo4jVector: {e}")
100 | 
101 | if vector_store is None:
102 |     logging.error(f"Failed to retrieve or create a Neo4jVector. Exiting.")
103 |     exit()
104 | 
105 | vector_graph_retriever = vector_store.as_retriever()
106 | 
107 | vector_graph_chain = RetrievalQAWithSourcesChain.from_chain_type(
108 |     ChatOpenAI(temperature=0, openai_api_key=openai_key),
109 |     chain_type="stuff",
110 |     retriever=vector_graph_retriever,
111 |     memory=MEMORY,
112 |     reduce_k_below_max_tokens=True,
113 |     max_tokens_limit=3000,
114 | )
115 | 
116 | 
117 | @retry(tries=2, delay=5)
118 | def get_results(question) -> str:
119 |     """Generate response using Neo4jVector using vector index only
120 | 
121 |     Args:
122 |         question (str): User query
123 | 
124 |     Returns:
125 |         str: Formatted string answer with citations, if available.
126 |     """
127 | 
128 |     logging.info(f"Using Neo4j url: {url}")
129 | 
130 |     prompt = VECTOR_GRAPH_PROMPT.format(question=question)
131 | 
132 |     # Returns a dict with keys: answer, sources
133 |     chain_result = vector_graph_chain.invoke(
134 |         {"question": question},
135 |         prompt=prompt,
136 |         return_only_outputs=True,
137 |     )
138 | 
139 |     logging.debug(f"chain_result: {chain_result}")
140 |     result = chain_result["answer"]
141 | 
142 |     # Cite sources, if any
143 |     sources = chain_result["sources"]
144 |     sources_split = sources.split(", ")
145 |     for source in sources_split:
146 |         if source != "" and source != "N/A" and source != "None":
147 |             result += f"\n - [{source}]({source})"
148 | 
149 |     return result
150 | 


--------------------------------------------------------------------------------
/rag_demo/vector_graph_tool.py:
--------------------------------------------------------------------------------
 1 | from langchain.tools import tool
 2 | from vector_graph_chain import get_results
 3 | 
 4 | @tool("vector-graph-tool")
 5 | def vector_graph_tool(query:str) -> str:
 6 |     """
 7 |     Useful when answer requires specific information about companies, managers, or industries.
 8 |     Use when question asks for which companies, managers, or industries.
 9 |     Use full question as input.
10 |     Do not call this tool more than once.
11 |     Do not call another tool if this returns results.
12 |     """
13 |     return get_results(query)


--------------------------------------------------------------------------------
/rag_demo/vector_tool.py:
--------------------------------------------------------------------------------
 1 | from langchain.tools import tool
 2 | from vector_graph_chain import get_results
 3 | 
 4 | 
 5 | @tool("vector-tool")
 6 | def vector_tool(query: str) -> str:
 7 |     """
 8 |     For finding similar entities to the ones in the search query.
 9 |     Only use this tool if no answer from others.
10 |     Do not call another tool after this one.
11 |     """
12 |     return get_results(query)
13 | 


--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
 1 | aiohttp==3.9.3 ; python_version >= "3.11" and python_version < "4.0"
 2 | aiosignal==1.3.1 ; python_version >= "3.11" and python_version < "4.0"
 3 | altair==5.2.0 ; python_version >= "3.11" and python_version < "4.0"
 4 | annotated-types==0.6.0 ; python_version >= "3.11" and python_version < "4.0"
 5 | anyio==4.3.0 ; python_version >= "3.11" and python_version < "4.0"
 6 | attrs==23.2.0 ; python_version >= "3.11" and python_version < "4.0"
 7 | backoff==2.2.1 ; python_version >= "3.11" and python_version < "4.0"
 8 | blinker==1.7.0 ; python_version >= "3.11" and python_version < "4.0"
 9 | cachetools==5.3.3 ; python_version >= "3.11" and python_version < "4.0"
10 | certifi==2024.2.2 ; python_version >= "3.11" and python_version < "4.0"
11 | charset-normalizer==3.3.2 ; python_version >= "3.11" and python_version < "4.0"
12 | click==8.1.7 ; python_version >= "3.11" and python_version < "4.0"
13 | colorama==0.4.6 ; python_version >= "3.11" and python_version < "4.0" and platform_system == "Windows"
14 | dataclasses-json==0.6.4 ; python_version >= "3.11" and python_version < "4.0"
15 | decorator==5.1.1 ; python_version >= "3.11" and python_version < "4.0"
16 | distro==1.9.0 ; python_version >= "3.11" and python_version < "4.0"
17 | frozenlist==1.4.1 ; python_version >= "3.11" and python_version < "4.0"
18 | gitdb==4.0.11 ; python_version >= "3.11" and python_version < "4.0"
19 | gitpython==3.1.42 ; python_version >= "3.11" and python_version < "4.0"
20 | greenlet==3.0.3 ; python_version >= "3.11" and python_version < "4.0" and (platform_machine == "aarch64" or platform_machine == "ppc64le" or platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "AMD64" or platform_machine == "win32" or platform_machine == "WIN32")
21 | h11==0.14.0 ; python_version >= "3.11" and python_version < "4.0"
22 | httpcore==1.0.4 ; python_version >= "3.11" and python_version < "4.0"
23 | httpx==0.27.0 ; python_version >= "3.11" and python_version < "4.0"
24 | idna==3.6 ; python_version >= "3.11" and python_version < "4.0"
25 | importlib-metadata==6.11.0 ; python_version >= "3.11" and python_version < "4.0"
26 | jinja2==3.1.3 ; python_version >= "3.11" and python_version < "4.0"
27 | jsonpatch==1.33 ; python_version >= "3.11" and python_version < "4.0"
28 | jsonpointer==2.4 ; python_version >= "3.11" and python_version < "4.0"
29 | jsonschema-specifications==2023.12.1 ; python_version >= "3.11" and python_version < "4.0"
30 | jsonschema==4.21.1 ; python_version >= "3.11" and python_version < "4.0"
31 | langchain-community==0.0.19 ; python_version >= "3.11" and python_version < "4.0"
32 | langchain-core==0.1.23 ; python_version >= "3.11" and python_version < "4.0"
33 | langchain-openai==0.0.5 ; python_version >= "3.11" and python_version < "4.0"
34 | langchain==0.1.6 ; python_version >= "3.11" and python_version < "4.0"
35 | langchainhub==0.1.15 ; python_version >= "3.11" and python_version < "4.0"
36 | langsmith==0.0.87 ; python_version >= "3.11" and python_version < "4.0"
37 | markdown-it-py==3.0.0 ; python_version >= "3.11" and python_version < "4.0"
38 | markupsafe==2.1.5 ; python_version >= "3.11" and python_version < "4.0"
39 | marshmallow==3.21.1 ; python_version >= "3.11" and python_version < "4.0"
40 | mdurl==0.1.2 ; python_version >= "3.11" and python_version < "4.0"
41 | multidict==6.0.5 ; python_version >= "3.11" and python_version < "4.0"
42 | mypy-extensions==1.0.0 ; python_version >= "3.11" and python_version < "4.0"
43 | neo4j==5.18.0 ; python_version >= "3.11" and python_version < "4.0"
44 | numpy==1.26.4 ; python_version >= "3.11" and python_version < "4.0"
45 | openai==1.13.3 ; python_version >= "3.11" and python_version < "4.0"
46 | packaging==23.2 ; python_version >= "3.11" and python_version < "4.0"
47 | pandas==2.2.1 ; python_version >= "3.11" and python_version < "4.0"
48 | pillow==10.2.0 ; python_version >= "3.11" and python_version < "4.0"
49 | protobuf==4.25.3 ; python_version >= "3.11" and python_version < "4.0"
50 | py==1.11.0 ; python_version >= "3.11" and python_version < "4.0"
51 | pyarrow==15.0.1 ; python_version >= "3.11" and python_version < "4.0"
52 | pydantic-core==2.16.3 ; python_version >= "3.11" and python_version < "4.0"
53 | pydantic==2.6.3 ; python_version >= "3.11" and python_version < "4.0"
54 | pydeck==0.8.0 ; python_version >= "3.11" and python_version < "4.0"
55 | pygments==2.17.2 ; python_version >= "3.11" and python_version < "4.0"
56 | pyjwt==2.8.0 ; python_version >= "3.11" and python_version < "4.0"
57 | python-dateutil==2.9.0.post0 ; python_version >= "3.11" and python_version < "4.0"
58 | pytz==2024.1 ; python_version >= "3.11" and python_version < "4.0"
59 | pyyaml==6.0.1 ; python_version >= "3.11" and python_version < "4.0"
60 | referencing==0.33.0 ; python_version >= "3.11" and python_version < "4.0"
61 | regex==2023.12.25 ; python_version >= "3.11" and python_version < "4.0"
62 | requests==2.31.0 ; python_version >= "3.11" and python_version < "4.0"
63 | retry==0.9.2 ; python_version >= "3.11" and python_version < "4.0"
64 | rich==13.7.1 ; python_version >= "3.11" and python_version < "4.0"
65 | rpds-py==0.18.0 ; python_version >= "3.11" and python_version < "4.0"
66 | segment-analytics-python==2.3.2 ; python_version >= "3.11" and python_version < "4.0"
67 | six==1.16.0 ; python_version >= "3.11" and python_version < "4.0"
68 | smmap==5.0.1 ; python_version >= "3.11" and python_version < "4.0"
69 | sniffio==1.3.1 ; python_version >= "3.11" and python_version < "4.0"
70 | sqlalchemy==2.0.28 ; python_version >= "3.11" and python_version < "4.0"
71 | streamlit-feedback==0.1.3 ; python_version >= "3.11" and python_version < "4.0"
72 | streamlit==1.27.2 ; python_version >= "3.11" and python_version < "4.0"
73 | tenacity==8.2.3 ; python_version >= "3.11" and python_version < "4.0"
74 | tiktoken==0.5.2 ; python_version >= "3.11" and python_version < "4.0"
75 | toml==0.10.2 ; python_version >= "3.11" and python_version < "4.0"
76 | toolz==0.12.1 ; python_version >= "3.11" and python_version < "4.0"
77 | tornado==6.4 ; python_version >= "3.11" and python_version < "4.0"
78 | tqdm==4.66.2 ; python_version >= "3.11" and python_version < "4.0"
79 | types-requests==2.31.0.20240218 ; python_version >= "3.11" and python_version < "4.0"
80 | typing-extensions==4.10.0 ; python_version >= "3.11" and python_version < "4.0"
81 | typing-inspect==0.9.0 ; python_version >= "3.11" and python_version < "4.0"
82 | tzdata==2024.1 ; python_version >= "3.11" and python_version < "4.0"
83 | tzlocal==5.2 ; python_version >= "3.11" and python_version < "4.0"
84 | urllib3==2.2.1 ; python_version >= "3.11" and python_version < "4.0"
85 | validators==0.22.0 ; python_version >= "3.11" and python_version < "4.0"
86 | watchdog==4.0.0 ; python_version >= "3.11" and python_version < "4.0" and platform_system != "Darwin"
87 | yarl==1.9.4 ; python_version >= "3.11" and python_version < "4.0"
88 | zipp==3.17.0 ; python_version >= "3.11" and python_version < "4.0"
89 | 


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