feat:Add use cases to draw knowledge maps using streamlit+pne+networkx #804
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Hizeros
requested review from
ruanrongman,
Undertone0809,
Luffy2004-c and
longsihua2003
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Warning Rate limit exceeded@Hizeros has exceeded the limit for the number of commits or files that can be reviewed per hour. Please wait 3 minutes and 7 seconds before requesting another review. How to resolve this issue?After the wait time has elapsed, a review can be triggered using the We recommend that you space out your commits to avoid hitting the rate limit. How do rate limits work?CodeRabbit enforces hourly rate limits for each developer per organization. Our paid plans have higher rate limits than the trial, open-source and free plans. In all cases, we re-allow further reviews after a brief timeout. Please see our FAQ for further information. WalkthroughThe recent updates introduce LLMapper, an innovative tool designed to generate concept maps using large language models in conjunction with Wikipedia. Key features include an interactive Streamlit application for user engagement, backend functionalities for knowledge graph creation, and comprehensive documentation to assist users. This project emphasizes educational exploration of generative AI concepts, allowing users to visualize relationships and easily access relevant information for analysis. Changes
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Actionable comments posted: 8
Outside diff range, codebase verification and nitpick comments (5)
example/llmapper/README.md (4)
3-5: Clarify the project description.The description could be more precise about the purpose and functionality of the tool.
- An experiment in using LLMs, wikipedia and promptulate to draw simple concept maps. + An experiment in using large language models (LLMs), Wikipedia, and Promptulate to draw simple concept maps.
5-7: Improve the project disclaimer.The disclaimer could be more professional and clear about the current state of the project.
- LLMapper is a crude prototype for refining the prompts. Which is to say, this isn't (yet) a serious tool; it's a toy for learning about generative AI. + LLMapper is an early-stage prototype focused on refining prompts. It is currently a learning tool for exploring generative AI concepts and not intended for production use.
7-7: Highlight the lack of error handling more clearly.The statement about error handling could be more prominent.
- It's very early days. Among other things, there's no error detection or graceful failures. Use at your own risk. + Note: This is an early-stage prototype with no error detection or graceful failure mechanisms. Use at your own risk.
14-16: Correct the repository URL.The URL "https://www.github.com/Undertone0809/promptulate" redirects to "https://github.com/Undertone0809/promptulate". Update the URL to "https://github.com/Undertone0809/promptulate" to avoid the redirect.
- example/llmapper/README.md: Line 14
Analysis chain
Correct the repository URL.
Ensure the repository URL is correct and accessible.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the repository URL. # Test: Check if the repository URL is accessible. Expect: The repository should be accessible. curl -I https://www.github.com/Undertone0809/promptulateLength of output: 464
example/llmapper/core.py (1)
4-6: Group related imports together.Group the
matplotlibandnetworkximports together for better readability.- import matplotlib - import matplotlib.pyplot as plt - import networkx as nx + import matplotlib + import matplotlib.pyplot as plt + import networkx as nx
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files ignored due to path filters (1)
example/llmapper/img/a-knowledge-graph-of-Musk.pngis excluded by!**/*.png
Files selected for processing (5)
- example/llmapper/README.md (1 hunks)
- example/llmapper/app.py (1 hunks)
- example/llmapper/core.py (1 hunks)
- example/llmapper/prompt-summarize.md (1 hunks)
- example/llmapper/requirements.txt (1 hunks)
Additional context used
LanguageTool
example/llmapper/README.md
[uncategorized] ~46-~46: You might be missing the article “the” here.
Context: ...//modelor.ai). ## How It Works 1. Use wikipedia_search tool to retrieve relevant content based...(AI_EN_LECTOR_MISSING_DETERMINER_THE)
example/llmapper/prompt-summarize.md
[formatting] ~25-~25: These punctuation marks differ from each other. Use only one if you are ending a sentence.
Context: ...rite it in a section called WHAT THIS IS:. - Speculate about why this subject mat...(DOUBLE_PUNCTUATION_PREMIUM)
[formatting] ~27-~27: These punctuation marks differ from each other. Use only one if you are ending a sentence.
Context: ...ns it in a section called WHY IT MATTERS:. - Choose the 10 MOST IMPORTANT concept...(DOUBLE_PUNCTUATION_PREMIUM)
[formatting] ~29-~29: These punctuation marks differ from each other. Use only one if you are ending a sentence.
Context: ...e list in a section called MAIN CONCEPTS:. - Write a list of how each concept in ...(DOUBLE_PUNCTUATION_PREMIUM)
[formatting] ~31-~31: These punctuation marks differ from each other. Use only one if you are ending a sentence.
Context: ...t list in a section called RELATIONSHIPS:. This is the format for the RELATIONSHI...(DOUBLE_PUNCTUATION_PREMIUM)
[grammar] ~46-~46: Probably a preposition is missing after ‘references’.
Context: ...ion the article itself - Do not mention references - Write the summary in Markdown format(ATD_VERBS_TO_COLLOCATION)
Additional comments not posted (17)
example/llmapper/requirements.txt (1)
2-6: Verify the necessity of each dependency.Ensure that each of the following dependencies is necessary for the project:
promptulatewikipediamatplotlibnetworkxpydanticRemove any unnecessary dependencies to keep the project lightweight.
Verification successful
All dependencies are necessary.
The following dependencies are used in the codebase and are necessary for the project:
promptulatewikipediamatplotlibnetworkxpydanticNo unnecessary dependencies were found.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the necessity of each dependency. # Test: Search for imports in the codebase. Expect: Each dependency should be imported in the codebase. rg --type python 'import promptulate|import wikipedia|import matplotlib|import networkx|import pydantic'Length of output: 137
Script:
#!/bin/bash # Description: Verify the necessity of each dependency. # Find all Python files and search for the specified imports. fd -e py -x rg 'import promptulate|import wikipedia|import matplotlib|import networkx|import pydantic' {}Length of output: 1064
example/llmapper/core.py (5)
13-22: LGTM!The class definitions for
NodeandEdgelook good.
24-27: LGTM!The class definition for
LLMResponselooks good.
1-2: Optimize imports.Consider importing only the necessary components from
ioandtyping.- from io import BytesIO - from typing import List + from io import BytesIO + from typing import ListLikely invalid or redundant comment.
9-10: Ensure compatibility with matplotlib backend.Ensure that setting the matplotlib backend to "Agg" is compatible with the rest of the application.
Verification successful
Compatibility with matplotlib backend verified.
Setting the matplotlib backend to "Agg" is compatible with the rest of the application. The usage of
plt.subplotsin the code is general and does not depend on a specific backend.
example/llmapper/core.py:plt.subplotsScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the compatibility of the matplotlib backend. # Test: Search for matplotlib usage in the codebase. Expect: No compatibility issues with the "Agg" backend. rg --type python 'import matplotlib'Length of output: 69
Script:
#!/bin/bash # Description: Verify the compatibility of the matplotlib backend. # Test: Search for matplotlib usage in the codebase. Expect: No compatibility issues with the "Agg" backend. rg --type py 'import matplotlib'Length of output: 134
Script:
#!/bin/bash # Description: Extract relevant portions of the example/llmapper/core.py file where matplotlib is used. # Test: Print lines around the matplotlib import statements to understand their usage. rg --type py 'import matplotlib' -A 10 -B 2 example/llmapper/core.pyLength of output: 375
Script:
#!/bin/bash # Description: Search for matplotlib usage in the example/llmapper/core.py file. # Test: Print lines where matplotlib functions are used to understand their usage. rg 'plt\.' example/llmapper/core.pyLength of output: 81
42-68: Ensure the graph drawing logic is efficient.The graph drawing logic looks good. Ensure that the
spring_layoutand other drawing functions are efficient for large graphs.example/llmapper/app.py (6)
1-6: Imports look good!The necessary libraries are imported correctly.
7-23: Sidebar setup is clear and user-friendly.The sidebar allows users to select the language model, input API key, and optional proxy URL. The help link is useful.
25-37: Title and initial message setup is appropriate.The title and initial messages provide a clear introduction to the chatbot.
38-49: Message handling and user input are correctly implemented.The code correctly handles user input and appends it to the session state messages.
51-60: Wikipedia search and prompt generation are correctly implemented.The code correctly performs a Wikipedia search and generates a prompt for the language model.
62-82: Language model response handling and knowledge graph generation are correctly implemented.The code correctly handles the language model response and generates a knowledge graph.
example/llmapper/prompt-summarize.md (5)
1-7: MISSION and INPUT sections are clear and well-defined.The instructions for summarizing an article and the input requirements are clear and well-defined.
9-23: CONTEXT and METHODOLOGY sections are clear and detailed.The instructions for summarizing the article and listing concepts and relationships are clear and detailed.
24-32: OUTPUT section is clear and detailed.The instructions for generating the output are clear and detailed.
Tools
LanguageTool
[formatting] ~25-~25: These punctuation marks differ from each other. Use only one if you are ending a sentence.
Context: ...rite it in a section called WHAT THIS IS:. - Speculate about why this subject mat...(DOUBLE_PUNCTUATION_PREMIUM)
[formatting] ~27-~27: These punctuation marks differ from each other. Use only one if you are ending a sentence.
Context: ...ns it in a section called WHY IT MATTERS:. - Choose the 10 MOST IMPORTANT concept...(DOUBLE_PUNCTUATION_PREMIUM)
[formatting] ~29-~29: These punctuation marks differ from each other. Use only one if you are ending a sentence.
Context: ...e list in a section called MAIN CONCEPTS:. - Write a list of how each concept in ...(DOUBLE_PUNCTUATION_PREMIUM)
[formatting] ~31-~31: These punctuation marks differ from each other. Use only one if you are ending a sentence.
Context: ...t list in a section called RELATIONSHIPS:. This is the format for the RELATIONSHI...(DOUBLE_PUNCTUATION_PREMIUM)
43-47: RULES section is clear and well-defined.The instructions for writing the summary are clear and well-defined.
Tools
LanguageTool
[grammar] ~46-~46: Probably a preposition is missing after ‘references’.
Context: ...ion the article itself - Do not mention references - Write the summary in Markdown format(ATD_VERBS_TO_COLLOCATION)
46-46: Fix grammar issue.Add "to" after "references".
- Do not mention references + Do not mention references toLikely invalid or redundant comment.
Tools
LanguageTool
[grammar] ~46-~46: Probably a preposition is missing after ‘references’.
Context: ...ion the article itself - Do not mention references - Write the summary in Markdown format(ATD_VERBS_TO_COLLOCATION)
| # OUTPUT | ||
| - Combine all of your understanding of the subject being summarized into a single, 20-word sentence. Do NOT mention the summary itself; focus only on the subject. Write it in a section called WHAT THIS IS:. | ||
|
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| - Speculate about why this subject matters and write a single 20-word sentence that explains it in a section called WHY IT MATTERS:. |
There was a problem hiding this comment.
Fix double punctuation.
Remove the extra colon at the end of the sentence.
- Write it in a section called WHY IT MATTERS:.
+ Write it in a section called WHY IT MATTERS:Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| - Speculate about why this subject matters and write a single 20-word sentence that explains it in a section called WHY IT MATTERS:. | |
| - Speculate about why this subject matters and write a single 20-word sentence that explains it in a section called WHY IT MATTERS: |
Tools
LanguageTool
[formatting] ~27-~27: These punctuation marks differ from each other. Use only one if you are ending a sentence.
Context: ...ns it in a section called WHY IT MATTERS:. - Choose the 10 MOST IMPORTANT concept...(DOUBLE_PUNCTUATION_PREMIUM)
| @@ -0,0 +1,6 @@ | |||
| streamlit>=1.28 | |||
There was a problem hiding this comment.
Specify a more precise version range for Streamlit.
Instead of >=1.28, consider using a more specific version range to avoid potential breaking changes in future releases.
- streamlit>=1.28
+ streamlit>=1.28,<2.0Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| streamlit>=1.28 | |
| streamlit>=1.28,<2.0 |
| 6. Do the same thing for the second concept, and then every remaining concept in the list. | ||
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| # OUTPUT | ||
| - Combine all of your understanding of the subject being summarized into a single, 20-word sentence. Do NOT mention the summary itself; focus only on the subject. Write it in a section called WHAT THIS IS:. |
There was a problem hiding this comment.
Fix double punctuation.
Remove the extra colon at the end of the sentence.
- Write it in a section called WHAT THIS IS:.
+ Write it in a section called WHAT THIS IS:Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| - Combine all of your understanding of the subject being summarized into a single, 20-word sentence. Do NOT mention the summary itself; focus only on the subject. Write it in a section called WHAT THIS IS:. | |
| - Combine all of your understanding of the subject being summarized into a single, 20-word sentence. Do NOT mention the summary itself; focus only on the subject. Write it in a section called WHAT THIS IS: |
Tools
LanguageTool
[formatting] ~25-~25: These punctuation marks differ from each other. Use only one if you are ending a sentence.
Context: ...rite it in a section called WHAT THIS IS:. - Speculate about why this subject mat...(DOUBLE_PUNCTUATION_PREMIUM)
|
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| - Speculate about why this subject matters and write a single 20-word sentence that explains it in a section called WHY IT MATTERS:. | ||
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| - Choose the 10 MOST IMPORTANT concepts in the article in order of importance. A concept is a common or proper noun that is a key part of the article. The most important concepts are those that help explain what this is and why it matters. Only include one concept per bullet. Don't include descriptions of each concept; only the concepts themselves. Include concepts that explain why this subject matters. The first concept in the list is the main subject of the article. Output the list in a section called MAIN CONCEPTS:. |
There was a problem hiding this comment.
Fix double punctuation.
Remove the extra colon at the end of the sentence.
- Output the list in a section called MAIN CONCEPTS:.
+ Output the list in a section called MAIN CONCEPTS:Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| - Choose the 10 MOST IMPORTANT concepts in the article in order of importance. A concept is a common or proper noun that is a key part of the article. The most important concepts are those that help explain what this is and why it matters. Only include one concept per bullet. Don't include descriptions of each concept; only the concepts themselves. Include concepts that explain why this subject matters. The first concept in the list is the main subject of the article. Output the list in a section called MAIN CONCEPTS:. | |
| - Choose the 10 MOST IMPORTANT concepts in the article in order of importance. A concept is a common or proper noun that is a key part of the article. The most important concepts are those that help explain what this is and why it matters. Only include one concept per bullet. Don't include descriptions of each concept; only the concepts themselves. Include concepts that explain why this subject matters. The first concept in the list is the main subject of the article. Output the list in a section called MAIN CONCEPTS: |
Tools
LanguageTool
[formatting] ~29-~29: These punctuation marks differ from each other. Use only one if you are ending a sentence.
Context: ...e list in a section called MAIN CONCEPTS:. - Write a list of how each concept in ...(DOUBLE_PUNCTUATION_PREMIUM)
|
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| - Choose the 10 MOST IMPORTANT concepts in the article in order of importance. A concept is a common or proper noun that is a key part of the article. The most important concepts are those that help explain what this is and why it matters. Only include one concept per bullet. Don't include descriptions of each concept; only the concepts themselves. Include concepts that explain why this subject matters. The first concept in the list is the main subject of the article. Output the list in a section called MAIN CONCEPTS:. | ||
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| - Write a list of how each concept in the MAIN CONCEPTS list relates to each of the other concepts in that list and their hierarchy.The hierarchical relationship between each concept is represented by an Arabic numeral corresponding to the id= hierarchy. ONLY USE CONCEPTS FROM THE CONCEPTS LIST. Do not introduce new concepts. Add each relationship to a list in the format "noun verb noun." DO NOT WRITE SENTENCES, only noun-verb-noun. Only include one object and subject in each bullet point. Consider how this concept relates to the main subject. Include relationships that help explain why this subject matters. Output that list in a section called RELATIONSHIPS:. |
There was a problem hiding this comment.
Fix double punctuation.
Remove the extra colon at the end of the sentence.
- Output that list in a section called RELATIONSHIPS:.
+ Output that list in a section called RELATIONSHIPS:Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| - Write a list of how each concept in the MAIN CONCEPTS list relates to each of the other concepts in that list and their hierarchy.The hierarchical relationship between each concept is represented by an Arabic numeral corresponding to the id= hierarchy. ONLY USE CONCEPTS FROM THE CONCEPTS LIST. Do not introduce new concepts. Add each relationship to a list in the format "noun verb noun." DO NOT WRITE SENTENCES, only noun-verb-noun. Only include one object and subject in each bullet point. Consider how this concept relates to the main subject. Include relationships that help explain why this subject matters. Output that list in a section called RELATIONSHIPS:. | |
| - Write a list of how each concept in the MAIN CONCEPTS list relates to each of the other concepts in that list and their hierarchy.The hierarchical relationship between each concept is represented by an Arabic numeral corresponding to the id= hierarchy. ONLY USE CONCEPTS FROM THE CONCEPTS LIST. Do not introduce new concepts. Add each relationship to a list in the format "noun verb noun." DO NOT WRITE SENTENCES, only noun-verb-noun. Only include one object and subject in each bullet point. Consider how this concept relates to the main subject. Include relationships that help explain why this subject matters. Output that list in a section called RELATIONSHIPS: |
Tools
LanguageTool
[formatting] ~31-~31: These punctuation marks differ from each other. Use only one if you are ending a sentence.
Context: ...t list in a section called RELATIONSHIPS:. This is the format for the RELATIONSHI...(DOUBLE_PUNCTUATION_PREMIUM)
|
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| ## How It Works | ||
|
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| 1. Use wikipedia_search tool to retrieve relevant content based on the keywords entered by the user |
There was a problem hiding this comment.
Add missing article 'the'.
Add the article "the" before "wikipedia_search tool".
- Use wikipedia_search tool to retrieve relevant content based on the keywords entered by the user
+ Use the wikipedia_search tool to retrieve relevant content based on the keywords entered by the userCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| 1. Use wikipedia_search tool to retrieve relevant content based on the keywords entered by the user | |
| 1. Use the wikipedia_search tool to retrieve relevant content based on the keywords entered by the user |
Tools
LanguageTool
[uncategorized] ~46-~46: You might be missing the article “the” here.
Context: ...//modelor.ai). ## How It Works 1. Use wikipedia_search tool to retrieve relevant content based...(AI_EN_LECTOR_MISSING_DETERMINER_THE)
| def read_summarize_prompt(file_path): | ||
| with open(file_path, "r") as file: | ||
| content = file.read() | ||
| return content |
There was a problem hiding this comment.
Handle file reading exceptions.
Add exception handling for file reading to ensure robustness.
- def read_summarize_prompt(file_path):
- with open(file_path, "r") as file:
- content = file.read()
- return content
+ def read_summarize_prompt(file_path):
+ try:
+ with open(file_path, "r") as file:
+ content = file.read()
+ return content
+ except FileNotFoundError:
+ return "File not found."
+ except Exception as e:
+ return f"An error occurred: {e}"Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def read_summarize_prompt(file_path): | |
| with open(file_path, "r") as file: | |
| content = file.read() | |
| return content | |
| def read_summarize_prompt(file_path): | |
| try: | |
| with open(file_path, "r") as file: | |
| content = file.read() | |
| return content | |
| except FileNotFoundError: | |
| return "File not found." | |
| except Exception as e: | |
| return f"An error occurred: {e}" |
| def create_knowledge_graph(data: dict): | ||
| G = nx.DiGraph() | ||
| # Add nodes and specify the layer attribute for each node | ||
| for node in data["nodes"]: | ||
| # The node ID is the number of tiers | ||
| layer = node["id"] | ||
| G.add_node(node["id"], title=node["title"], layer=layer) | ||
| # Add a directed edge | ||
| for edge in data["edges"]: | ||
| G.add_edge(edge["from_"], edge["to"], title=edge["title"]) | ||
| return G |
There was a problem hiding this comment.
Optimize the knowledge graph creation.
Consider using a list comprehension to add nodes and edges for better readability and performance.
- G = nx.DiGraph()
- # Add nodes and specify the layer attribute for each node
- for node in data["nodes"]:
- # The node ID is the number of tiers
- layer = node["id"]
- G.add_node(node["id"], title=node["title"], layer=layer)
- # Add a directed edge
- for edge in data["edges"]:
- G.add_edge(edge["from_"], edge["to"], title=edge["title"])
- return G
+ G = nx.DiGraph()
+ G.add_nodes_from((node["id"], {"title": node["title"], "layer": node["id"]}) for node in data["nodes"])
+ G.add_edges_from((edge["from_"], edge["to"], {"title": edge["title"]}) for edge in data["edges"])
+ return GCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def create_knowledge_graph(data: dict): | |
| G = nx.DiGraph() | |
| # Add nodes and specify the layer attribute for each node | |
| for node in data["nodes"]: | |
| # The node ID is the number of tiers | |
| layer = node["id"] | |
| G.add_node(node["id"], title=node["title"], layer=layer) | |
| # Add a directed edge | |
| for edge in data["edges"]: | |
| G.add_edge(edge["from_"], edge["to"], title=edge["title"]) | |
| return G | |
| def create_knowledge_graph(data: dict): | |
| G = nx.DiGraph() | |
| G.add_nodes_from((node["id"], {"title": node["title"], "layer": node["id"]}) for node in data["nodes"]) | |
| G.add_edges_from((edge["from_"], edge["to"], {"title": edge["title"]}) for edge in data["edges"]) | |
| return G |
There was a problem hiding this comment.
Actionable comments posted: 0
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (1)
- example/llmapper/app.py (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- example/llmapper/app.py
Undertone0809
requested review from
coderabbitai
and removed request for
coderabbitai
| # display search results in the chatbot panel | ||
| # st.chat_message("assistant").write(query_result) | ||
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| summarize_prompt_path = "prompt-summarize.md" |
There was a problem hiding this comment.
Simply use variables directly, do not take a separate markdown file to do reading.
Summary by CodeRabbit
New Features
Documentation
Chores