search graph and cli tool #21
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WalkthroughRecent updates enhance the command-line interface with AI-driven search capabilities and introduce a dedicated search module within the graph package. The new Changes
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Actionable comments posted: 9
Outside diff range, codebase verification and nitpick comments (2)
packages/cli/src/commands/search.ts (2)
17-19: Consider enhancing model validation feedback.The error message for an invalid model could be more informative by suggesting valid model names.
- throw new Error(`Invalid model: ${modelName}`); + throw new Error(`Invalid model: ${modelName}. Please use a valid model name.`);
27-28: Log the entire response for debugging.Consider logging the entire response object for better debugging and understanding of the search results.
- console.log(response.messages[1].content); + console.log("Search response:", response);
Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Files ignored due to path filters (1)
packages/graph/src/search/search-graph.pngis excluded by!**/*.png
Files selected for processing (6)
- packages/cli/src/commands/search.ts (1 hunks)
- packages/graph/src/index.ts (1 hunks)
- packages/graph/src/search/README.md (1 hunks)
- packages/graph/src/search/graph.ts (1 hunks)
- packages/graph/src/search/index.ts (1 hunks)
- packages/graph/src/search/search.test.ts (1 hunks)
Files skipped from review due to trivial changes (2)
- packages/graph/src/index.ts
- packages/graph/src/search/index.ts
Additional comments not posted (5)
packages/cli/src/commands/search.ts (1)
5-12: Ensure comprehensive error handling.Consider adding error handling for the
performSearchfunction to gracefully handle potential issues during execution.packages/graph/src/search/README.md (1)
1-39: Excellent documentation!The README is comprehensive and well-structured, providing a clear example of the search functionality and expected output. It effectively communicates the module's capabilities.
packages/graph/src/search/graph.ts (3)
18-27: Well-defined search state interface.The
SearchStateinterface is well-defined, providing a clear structure for managing search state.
30-35: Effective state reducers.The
stringReducerandarrayReducerare well-implemented, effectively managing state updates. The use of a Set inarrayReducerensures uniqueness.
343-352: Well-structured search function.The
performSearchfunction is well-structured and effectively utilizes the search graph to perform searches and return the final state.
| test.skip("performSearchNode should filter and format search results", async (t) => { | ||
| // Mock the tavilyTool.invoke function | ||
| const mockResults = JSON.stringify([ | ||
| { | ||
| title: "Title 1", | ||
| content: "Content 1", | ||
| url: "http://example1.com", | ||
| score: 0.9, | ||
| }, | ||
| { | ||
| title: "Title 2", | ||
| content: "Content 2", | ||
| url: "http://example2.com", | ||
| score: 0.7, | ||
| }, | ||
| { | ||
| title: "Title 3", | ||
| content: "Content 3", | ||
| url: "http://example3.com", | ||
| score: 0.4, | ||
| }, // This should be filtered out | ||
| ]); | ||
| // const invokeStub = sinon.stub(tavilyTool, "invoke").resolves(mockResults); | ||
|
|
||
| const query = "what is the capital of the moon?"; | ||
| // Call the function | ||
| const result = await performSearchNode({ | ||
| query, | ||
| }); | ||
| // Assert the results | ||
| t.log(result); | ||
| t.pass(); | ||
| // t.deepEqual(result, { | ||
| // searchResults: ["Title 1\n\nContent 1", "Title 2\n\nContent 2"], | ||
| // sources: ["http://example1.com", "http://example2.com"], | ||
| // }); | ||
|
|
||
| // // Verify that the tavilyTool.invoke was called with the correct query | ||
| // t.true(invokeStub.calledOnceWith({ query })); | ||
|
|
||
| // // Restore the stub | ||
| // invokeStub.restore(); | ||
| }); |
There was a problem hiding this comment.
Complete and enable the skipped test.
This test is currently skipped and contains commented-out code. Consider completing the test and enabling it to verify the filtering and formatting of search results.
Uncomment the test code, ensure the stub is correctly set up, and add assertions to verify the results.
| test.skip("extractKeyPoints should extract key points from search results", async (t) => { | ||
| const searchResults = [ | ||
| "ESA - ESA/ESTEC Capital of the Moon - European Space Agency\n\nESA/ESTEC Capital of the Moon on 10-15 July From 10 to 15 July, Noordwijk (NL) was the 'Capital of the Moon' when ESA's establishment ESTEC hosted the 4th International Conference on Exploration and Utilisation of the Moon (ICEUM4). The Moon conference was organised by the International Lunar Exploration Working Group (ILEWG).[1]", | ||
| "Our Moon: the Moon | IAU - International Astronomical Union\n\nThe Moon is Earth's only natural satellite. It is the fifth largest natural satellite in the Solar System, and the largest natural satellite in the Milky Way.[2]", | ||
| ]; | ||
| const sources = [ | ||
| "https://www.esa.int/ESA/ESTEC/Capital_of_the_Moon", | ||
| "https://www.iau.org/publications/our_moon", | ||
| ]; | ||
| const result = await extractKeyPointsNode({ | ||
| searchResults, | ||
| sources, | ||
| }); | ||
| t.log(result); | ||
| t.pass(); | ||
| }); |
There was a problem hiding this comment.
Complete and enable the skipped test.
This test is currently skipped. Consider completing the test and enabling it to verify the extraction of key points from search results.
Add assertions to verify that the extracted key points match expected values.
| test.skip("extractRelatedQueries should extract related queries from search results", async (t) => { | ||
| const searchResults = [ | ||
| "ESA - ESA/ESTEC Capital of the Moon - European Space Agency\n\nESA/ESTEC Capital of the Moon on 10-15 July From 10 to 15 July, Noordwijk (NL) was the 'Capital of the Moon' when ESA's establishment ESTEC hosted the 4th International Conference on Exploration and Utilisation of the Moon (ICEUM4). The Moon conference was organised by the International Lunar Exploration Working Group (ILEWG).[1]", | ||
| "Our Moon: the Moon | IAU - International Astronomical Union\n\nThe Moon is Earth's only natural satellite. It is the fifth largest natural satellite in the Solar System, and the largest natural satellite in the Milky Way.[2]", | ||
| ]; | ||
| const query = "what is the capital of the moon?"; | ||
| const result = await generateRelatedQueriesNode({ | ||
| searchResults, | ||
| query, | ||
| }); | ||
| t.log(result); | ||
| t.pass(); | ||
| }); |
There was a problem hiding this comment.
Complete and enable the skipped test.
This test is currently skipped. Consider completing the test and enabling it to verify the extraction of related queries from search results.
Add assertions to verify that the extracted related queries match expected values.
| test.skip("generateResponse should generate a response from the search results", async (t) => { | ||
| const searchResults = [ | ||
| "ESA - ESA/ESTEC Capital of the Moon - European Space Agency\n\nESA/ESTEC Capital of the Moon on 10-15 July From 10 to 15 July, Noordwijk (NL) was the 'Capital of the Moon' when ESA's establishment ESTEC hosted the 4th International Conference on Exploration and Utilisation of the Moon (ICEUM4). The Moon conference was organised by the International Lunar Exploration Working Group (ILEWG).[1]", | ||
| ]; | ||
| const query = "what is the capital of the moon?"; | ||
| const sources = [ | ||
| "https://www.esa.int/ESA/ESTEC/Capital_of_the_Moon", | ||
| "https://www.iau.org/publications/our_moon", | ||
| ]; | ||
| const result = await generateResponseNode({ | ||
| searchResults, | ||
| sources, | ||
| query, | ||
| }); | ||
| t.log(result); | ||
| t.pass(); | ||
| }); |
There was a problem hiding this comment.
Complete and enable the skipped test.
This test is currently skipped. Consider completing the test and enabling it to verify the generation of responses from search results.
Add assertions to verify that the generated response matches expected values.
| test("should pass CI", async (t) => { | ||
| const result = await performSearch( | ||
| "What are effective ways to kill dandelions?", | ||
| { | ||
| configurable: { thread_id: "1" }, | ||
| } | ||
| ); | ||
| t.log(result); | ||
| t.pass(); |
There was a problem hiding this comment.
Add assertions to verify search results.
Currently, the test only logs the result and passes unconditionally. Consider adding assertions to verify the correctness of the search results.
- t.log(result);
- t.pass();
+ t.truthy(result, "Expected a valid search result");
+ t.is(typeof result, "object", "Expected result to be an object");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.
| test("should pass CI", async (t) => { | |
| const result = await performSearch( | |
| "What are effective ways to kill dandelions?", | |
| { | |
| configurable: { thread_id: "1" }, | |
| } | |
| ); | |
| t.log(result); | |
| t.pass(); | |
| test("should pass CI", async (t) => { | |
| const result = await performSearch( | |
| "What are effective ways to kill dandelions?", | |
| { | |
| configurable: { thread_id: "1" }, | |
| } | |
| ); | |
| t.truthy(result, "Expected a valid search result"); | |
| t.is(typeof result, "object", "Expected result to be an object"); |
| export const generateRelatedQueriesNode = async ( | ||
| state: Partial<SearchState>, | ||
| options?: RunnableConfig | ||
| ): Promise<Partial<SearchState>> => { | ||
| console.log("Generating related queries"); | ||
| const llm = await getModel({ | ||
| model: "gpt-4o-mini", | ||
| temperature: 0, | ||
| }); | ||
| const promptMessage = `You are tasked with generating related queries based on a user's initial query and the corresponding search results. This will help users explore the topic further and discover additional relevant information. | ||
|
|
||
| Here is the user's original query: | ||
| <user_query> | ||
| ${state.query || ""} | ||
| </user_query> | ||
|
|
||
| And here are the related search results: | ||
| <search_results> | ||
| ${state.searchResults?.join("\n\n") || ""} | ||
| </search_results> | ||
|
|
||
| Your task is to analyze the user query and search results, then generate a set of related queries that would allow the user to explore the topic more deeply or from different angles. | ||
|
|
||
| Follow these steps: | ||
|
|
||
| 1. Carefully read and understand the user's original query. | ||
| 2. Review the search results, noting key themes, concepts, and related topics that appear. | ||
| 3. Identify aspects of the topic that are not fully covered in the original query or search results but may be of interest to someone exploring this subject. | ||
| 4. Generate 3-5 related queries that: | ||
| a. Expand on different aspects of the original topic | ||
| b. Explore related concepts or themes | ||
| c. Address potential follow-up questions a user might have | ||
| d. Cover alternative perspectives or viewpoints on the topic | ||
|
|
||
| 5. Ensure that each related query is: | ||
| a. Clearly worded and easy to understand | ||
| b. Relevant to the original topic | ||
| c. Distinct from the other generated queries | ||
| d. Likely to provide valuable additional information | ||
|
|
||
| Format your response using the following XML structure: | ||
|
|
||
| <related_queries> | ||
| <query>First related query</query> | ||
| <query>Second related query</query> | ||
| <query>Third related query</query> | ||
| <query>Fourth related query (if applicable)</query> | ||
| <query>Fifth related query (if applicable)</query> | ||
| </related_queries> | ||
|
|
||
| Do not include any additional text, explanations, or commentary outside of the XML tags. Your entire response should be contained within the <related_queries> tags.`; | ||
|
|
||
| const prompt = ChatPromptTemplate.fromMessages([ | ||
| new HumanMessage(promptMessage), | ||
| ]); | ||
| const chain = prompt.pipe(llm); | ||
| const response = await chain.invoke({}); | ||
| if (typeof response.content === "string") { | ||
| const parsed = await parseXml(response.content); | ||
| return { | ||
| relatedQueries: parsed.related_queries.query, | ||
| }; | ||
| } else { | ||
| throw new Error("Response is not a string"); | ||
| } |
There was a problem hiding this comment.
Add error handling for language model invocation.
Consider adding error handling for the language model invocation to manage potential failures during related query generation.
- const response = await chain.invoke({});
+ let response;
+ try {
+ response = await chain.invoke({});
+ } catch (error) {
+ throw new Error(`Related query generation failed: ${error.message}`);
+ }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.
| export const generateRelatedQueriesNode = async ( | |
| state: Partial<SearchState>, | |
| options?: RunnableConfig | |
| ): Promise<Partial<SearchState>> => { | |
| console.log("Generating related queries"); | |
| const llm = await getModel({ | |
| model: "gpt-4o-mini", | |
| temperature: 0, | |
| }); | |
| const promptMessage = `You are tasked with generating related queries based on a user's initial query and the corresponding search results. This will help users explore the topic further and discover additional relevant information. | |
| Here is the user's original query: | |
| <user_query> | |
| ${state.query || ""} | |
| </user_query> | |
| And here are the related search results: | |
| <search_results> | |
| ${state.searchResults?.join("\n\n") || ""} | |
| </search_results> | |
| Your task is to analyze the user query and search results, then generate a set of related queries that would allow the user to explore the topic more deeply or from different angles. | |
| Follow these steps: | |
| 1. Carefully read and understand the user's original query. | |
| 2. Review the search results, noting key themes, concepts, and related topics that appear. | |
| 3. Identify aspects of the topic that are not fully covered in the original query or search results but may be of interest to someone exploring this subject. | |
| 4. Generate 3-5 related queries that: | |
| a. Expand on different aspects of the original topic | |
| b. Explore related concepts or themes | |
| c. Address potential follow-up questions a user might have | |
| d. Cover alternative perspectives or viewpoints on the topic | |
| 5. Ensure that each related query is: | |
| a. Clearly worded and easy to understand | |
| b. Relevant to the original topic | |
| c. Distinct from the other generated queries | |
| d. Likely to provide valuable additional information | |
| Format your response using the following XML structure: | |
| <related_queries> | |
| <query>First related query</query> | |
| <query>Second related query</query> | |
| <query>Third related query</query> | |
| <query>Fourth related query (if applicable)</query> | |
| <query>Fifth related query (if applicable)</query> | |
| </related_queries> | |
| Do not include any additional text, explanations, or commentary outside of the XML tags. Your entire response should be contained within the <related_queries> tags.`; | |
| const prompt = ChatPromptTemplate.fromMessages([ | |
| new HumanMessage(promptMessage), | |
| ]); | |
| const chain = prompt.pipe(llm); | |
| const response = await chain.invoke({}); | |
| if (typeof response.content === "string") { | |
| const parsed = await parseXml(response.content); | |
| return { | |
| relatedQueries: parsed.related_queries.query, | |
| }; | |
| } else { | |
| throw new Error("Response is not a string"); | |
| } | |
| const prompt = ChatPromptTemplate.fromMessages([ | |
| new HumanMessage(promptMessage), | |
| ]); | |
| const chain = prompt.pipe(llm); | |
| let response; | |
| try { | |
| response = await chain.invoke({}); | |
| } catch (error) { | |
| throw new Error(`Related query generation failed: ${error.message}`); | |
| } | |
| if (typeof response.content === "string") { | |
| const parsed = await parseXml(response.content); | |
| return { | |
| relatedQueries: parsed.related_queries.query, | |
| }; | |
| } else { | |
| throw new Error("Response is not a string"); | |
| } |
| export const performSearchNode = async ( | ||
| state: Partial<SearchState> | ||
| ): Promise<Partial<SearchState>> => { | ||
| console.log("Performing search for:", state.query); | ||
| if (!state.query) { | ||
| throw new Error("Query is required"); | ||
| } | ||
| const results = await tavilyTool.invoke(state.query); | ||
| const obj = JSON.parse(results); | ||
| const filteredResults = filterSearchResults(obj, 0.5); | ||
| const formattedResults = formatSearchResults(filteredResults); | ||
|
|
||
| return { | ||
| searchResults: formattedResults.searchResults, | ||
| sources: formattedResults.sources, | ||
| messages: [new HumanMessage({ content: state.query })], | ||
| }; | ||
| }; |
There was a problem hiding this comment.
Enhance error handling and input validation.
Consider adding error handling for the tavilyTool.invoke call to manage potential failures. Additionally, validate the input to ensure state.query is a non-empty string.
- const results = await tavilyTool.invoke(state.query);
+ let results;
+ try {
+ results = await tavilyTool.invoke(state.query);
+ } catch (error) {
+ throw new Error(`Search invocation failed: ${error.message}`);
+ }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.
| export const performSearchNode = async ( | |
| state: Partial<SearchState> | |
| ): Promise<Partial<SearchState>> => { | |
| console.log("Performing search for:", state.query); | |
| if (!state.query) { | |
| throw new Error("Query is required"); | |
| } | |
| const results = await tavilyTool.invoke(state.query); | |
| const obj = JSON.parse(results); | |
| const filteredResults = filterSearchResults(obj, 0.5); | |
| const formattedResults = formatSearchResults(filteredResults); | |
| return { | |
| searchResults: formattedResults.searchResults, | |
| sources: formattedResults.sources, | |
| messages: [new HumanMessage({ content: state.query })], | |
| }; | |
| }; | |
| export const performSearchNode = async ( | |
| state: Partial<SearchState> | |
| ): Promise<Partial<SearchState>> => { | |
| console.log("Performing search for:", state.query); | |
| if (!state.query) { | |
| throw new Error("Query is required"); | |
| } | |
| let results; | |
| try { | |
| results = await tavilyTool.invoke(state.query); | |
| } catch (error) { | |
| throw new Error(`Search invocation failed: ${error.message}`); | |
| } | |
| const obj = JSON.parse(results); | |
| const filteredResults = filterSearchResults(obj, 0.5); | |
| const formattedResults = formatSearchResults(filteredResults); | |
| return { | |
| searchResults: formattedResults.searchResults, | |
| sources: formattedResults.sources, | |
| messages: [new HumanMessage({ content: state.query })], | |
| }; | |
| }; |
| export const extractKeyPointsNode = async ( | ||
| state: Partial<SearchState> | ||
| ): Promise<Partial<SearchState>> => { | ||
| console.log("Extracting key points from search results"); | ||
| // map map the results to the sources | ||
| const llm = await getModel({ | ||
| model: "gpt-4o-mini", | ||
| temperature: 0, | ||
| }); | ||
| const promptMessage = `You will be given a user query and a piece of content that includes footnote references. Your task is to highlight key points from the content that are relevant to the user query, ensuring that you include the footnote number in any key highlights. | ||
| Here is the content with footnotes: | ||
| <content_with_footnotes> | ||
| ${state.searchResults?.join("\n\n") || ""} | ||
| </content_with_footnotes> | ||
|
|
||
| And here is the user query: | ||
| <user_query> | ||
| ${state.query || ""} | ||
| </user_query> | ||
|
|
||
| Your task is to: | ||
| 1. Read through the content carefully. | ||
| 2. Identify key points that are relevant to the user query. | ||
| 3. For each key point, create a key_point that includes the relevant footnote number. | ||
| 4. Format each key_point as follows: "This is a key_point[^X]" where X is the footnote number. | ||
|
|
||
| Here's an example of a properly formatted key_point: | ||
| <keyPoints>The Earth orbits around the Sun[^3]</keyPoints> | ||
|
|
||
| Please provide your key_points in the following format: | ||
| <key_points> | ||
| <key_point>First key_point[^X]</key_point> | ||
| <key_point>Second key_point[^Y]</key_point> | ||
| ... | ||
| </key_points> | ||
|
|
||
| Where X, Y, etc. are the relevant footnote numbers. | ||
|
|
||
| Remember to only include information that is present in the given content and relevant to the user query. Do not add any information from your own knowledge. | ||
|
|
||
| Please provide your highlights now.`; | ||
|
|
||
| const prompt = ChatPromptTemplate.fromMessages([ | ||
| new HumanMessage(promptMessage), | ||
| ]); | ||
| const chain = prompt.pipe(llm); | ||
| const response = await chain.invoke({}); | ||
| // type assert response is a string | ||
| if (typeof response.content === "string") { | ||
| const parsed = await parseXml(response.content); | ||
| const keyPoints = parsed.key_points; | ||
| return { | ||
| keyPoints: keyPoints.key_point, | ||
| }; | ||
| } else { | ||
| throw new Error("Response is not a string"); | ||
| } | ||
| }; |
There was a problem hiding this comment.
Add error handling for language model invocation.
Consider adding error handling for the language model invocation to manage potential failures during key point extraction.
- const response = await chain.invoke({});
+ let response;
+ try {
+ response = await chain.invoke({});
+ } catch (error) {
+ throw new Error(`Key point extraction failed: ${error.message}`);
+ }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.
| export const extractKeyPointsNode = async ( | |
| state: Partial<SearchState> | |
| ): Promise<Partial<SearchState>> => { | |
| console.log("Extracting key points from search results"); | |
| // map map the results to the sources | |
| const llm = await getModel({ | |
| model: "gpt-4o-mini", | |
| temperature: 0, | |
| }); | |
| const promptMessage = `You will be given a user query and a piece of content that includes footnote references. Your task is to highlight key points from the content that are relevant to the user query, ensuring that you include the footnote number in any key highlights. | |
| Here is the content with footnotes: | |
| <content_with_footnotes> | |
| ${state.searchResults?.join("\n\n") || ""} | |
| </content_with_footnotes> | |
| And here is the user query: | |
| <user_query> | |
| ${state.query || ""} | |
| </user_query> | |
| Your task is to: | |
| 1. Read through the content carefully. | |
| 2. Identify key points that are relevant to the user query. | |
| 3. For each key point, create a key_point that includes the relevant footnote number. | |
| 4. Format each key_point as follows: "This is a key_point[^X]" where X is the footnote number. | |
| Here's an example of a properly formatted key_point: | |
| <keyPoints>The Earth orbits around the Sun[^3]</keyPoints> | |
| Please provide your key_points in the following format: | |
| <key_points> | |
| <key_point>First key_point[^X]</key_point> | |
| <key_point>Second key_point[^Y]</key_point> | |
| ... | |
| </key_points> | |
| Where X, Y, etc. are the relevant footnote numbers. | |
| Remember to only include information that is present in the given content and relevant to the user query. Do not add any information from your own knowledge. | |
| Please provide your highlights now.`; | |
| const prompt = ChatPromptTemplate.fromMessages([ | |
| new HumanMessage(promptMessage), | |
| ]); | |
| const chain = prompt.pipe(llm); | |
| const response = await chain.invoke({}); | |
| // type assert response is a string | |
| if (typeof response.content === "string") { | |
| const parsed = await parseXml(response.content); | |
| const keyPoints = parsed.key_points; | |
| return { | |
| keyPoints: keyPoints.key_point, | |
| }; | |
| } else { | |
| throw new Error("Response is not a string"); | |
| } | |
| }; | |
| export const extractKeyPointsNode = async ( | |
| state: Partial<SearchState> | |
| ): Promise<Partial<SearchState>> => { | |
| console.log("Extracting key points from search results"); | |
| // map map the results to the sources | |
| const llm = await getModel({ | |
| model: "gpt-4o-mini", | |
| temperature: 0, | |
| }); | |
| const promptMessage = `You will be given a user query and a piece of content that includes footnote references. Your task is to highlight key points from the content that are relevant to the user query, ensuring that you include the footnote number in any key highlights. | |
| Here is the content with footnotes: | |
| <content_with_footnotes> | |
| ${state.searchResults?.join("\n\n") || ""} | |
| </content_with_footnotes> | |
| And here is the user query: | |
| <user_query> | |
| ${state.query || ""} | |
| </user_query> | |
| Your task is to: | |
| 1. Read through the content carefully. | |
| 2. Identify key points that are relevant to the user query. | |
| 3. For each key point, create a key_point that includes the relevant footnote number. | |
| 4. Format each key_point as follows: "This is a key_point[^X]" where X is the footnote number. | |
| Here's an example of a properly formatted key_point: | |
| <keyPoints>The Earth orbits around the Sun[^3]</keyPoints> | |
| Please provide your key_points in the following format: | |
| <key_points> | |
| <key_point>First key_point[^X]</key_point> | |
| <key_point>Second key_point[^Y]</key_point> | |
| ... | |
| </key_points> | |
| Where X, Y, etc. are the relevant footnote numbers. | |
| Remember to only include information that is present in the given content and relevant to the user query. Do not add any information from your own knowledge. | |
| Please provide your highlights now.`; | |
| const prompt = ChatPromptTemplate.fromMessages([ | |
| new HumanMessage(promptMessage), | |
| ]); | |
| const chain = prompt.pipe(llm); | |
| let response; | |
| try { | |
| response = await chain.invoke({}); | |
| } catch (error) { | |
| throw new Error(`Key point extraction failed: ${error.message}`); | |
| } | |
| // type assert response is a string | |
| if (typeof response.content === "string") { | |
| const parsed = await parseXml(response.content); | |
| const keyPoints = parsed.key_points; | |
| return { | |
| keyPoints: keyPoints.key_point, | |
| }; | |
| } else { | |
| throw new Error("Response is not a string"); | |
| } | |
| }; |
| export const generateResponseNode = async ( | ||
| state: Partial<SearchState>, | ||
| options?: RunnableConfig | ||
| ): Promise<Partial<SearchState>> => { | ||
| console.log("Generating response"); | ||
| const llm = await getModel({ | ||
| model: "gpt-4o-mini", | ||
| temperature: 0, | ||
| }); | ||
| const promptMessage = `You are an AI assistant tasked with responding to a user's query and creating a brief report based on provided search results. Follow these instructions carefully: | ||
|
|
||
| 1. You will be given a user's query and a set of search results. | ||
|
|
||
| 2. First, read the user's query: | ||
| <query> | ||
| ${state.query || ""} | ||
| </query> | ||
|
|
||
| 3. Respond to the user's query directly and concisely based on the information available in the search results. Your response should be helpful and informative. | ||
|
|
||
| 4. After responding to the query, create a small markdown-formatted report summarizing the key points from the search results. This report should: | ||
| - Be concise and informative | ||
| - Highlight the most relevant information related to the user's query | ||
| - Be structured with appropriate headings and subheadings | ||
| - Use bullet points or numbered lists where appropriate | ||
|
|
||
| 5. Throughout the report, include footnote annotations to reference the search results. Use superscript numbers (e.g., [^1], [^2]) to indicate these references within the text. | ||
|
|
||
| 6. At the end of the report, include a "References" section with the footnotes. Each footnote should correspond to a specific search result and provide a brief description or title of the source. | ||
|
|
||
| 7. Format your entire response as follows: | ||
| <response> | ||
| [Your direct response to the user's query] | ||
| </response> | ||
| <report> | ||
| [Your markdown-formatted report with footnote annotations] | ||
|
|
||
| ## References | ||
| [Footnotes with corresponding search result references] | ||
| </report> | ||
|
|
||
| 8. Ensure that your report is well-structured, easy to read, and provides valuable insights based on the search results. | ||
|
|
||
| Here are the search results to use for your response and report: | ||
| <search_results> | ||
| ${state.searchResults?.join("\n\n") || ""} | ||
| </search_results> | ||
| <sources> | ||
| ${ | ||
| state.sources | ||
| ?.map((source, index) => `[${index + 1}] ${source}`) | ||
| .join("\n\n") || "" | ||
| } | ||
| </sources> | ||
|
|
||
| Remember to maintain a professional and informative tone throughout your response and report.`; | ||
|
|
||
| const prompt = ChatPromptTemplate.fromMessages([ | ||
| new HumanMessage(promptMessage), | ||
| ]); | ||
| const chain = prompt.pipe(llm); | ||
| const response = await chain.invoke({}); | ||
| if (typeof response.content === "string") { | ||
| const { | ||
| response: responseContent, | ||
| report, | ||
| }: { response: string; report: string } = await parseXml(response.content); | ||
| return { | ||
| report, | ||
| messages: [new AIMessage({ content: responseContent })], | ||
| }; | ||
| } else { | ||
| throw new Error("Response is not a string"); | ||
| } |
There was a problem hiding this comment.
Add error handling for language model invocation.
Consider adding error handling for the language model invocation to manage potential failures during response generation.
- const response = await chain.invoke({});
+ let response;
+ try {
+ response = await chain.invoke({});
+ } catch (error) {
+ throw new Error(`Response generation failed: ${error.message}`);
+ }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.
| export const generateResponseNode = async ( | |
| state: Partial<SearchState>, | |
| options?: RunnableConfig | |
| ): Promise<Partial<SearchState>> => { | |
| console.log("Generating response"); | |
| const llm = await getModel({ | |
| model: "gpt-4o-mini", | |
| temperature: 0, | |
| }); | |
| const promptMessage = `You are an AI assistant tasked with responding to a user's query and creating a brief report based on provided search results. Follow these instructions carefully: | |
| 1. You will be given a user's query and a set of search results. | |
| 2. First, read the user's query: | |
| <query> | |
| ${state.query || ""} | |
| </query> | |
| 3. Respond to the user's query directly and concisely based on the information available in the search results. Your response should be helpful and informative. | |
| 4. After responding to the query, create a small markdown-formatted report summarizing the key points from the search results. This report should: | |
| - Be concise and informative | |
| - Highlight the most relevant information related to the user's query | |
| - Be structured with appropriate headings and subheadings | |
| - Use bullet points or numbered lists where appropriate | |
| 5. Throughout the report, include footnote annotations to reference the search results. Use superscript numbers (e.g., [^1], [^2]) to indicate these references within the text. | |
| 6. At the end of the report, include a "References" section with the footnotes. Each footnote should correspond to a specific search result and provide a brief description or title of the source. | |
| 7. Format your entire response as follows: | |
| <response> | |
| [Your direct response to the user's query] | |
| </response> | |
| <report> | |
| [Your markdown-formatted report with footnote annotations] | |
| ## References | |
| [Footnotes with corresponding search result references] | |
| </report> | |
| 8. Ensure that your report is well-structured, easy to read, and provides valuable insights based on the search results. | |
| Here are the search results to use for your response and report: | |
| <search_results> | |
| ${state.searchResults?.join("\n\n") || ""} | |
| </search_results> | |
| <sources> | |
| ${ | |
| state.sources | |
| ?.map((source, index) => `[${index + 1}] ${source}`) | |
| .join("\n\n") || "" | |
| } | |
| </sources> | |
| Remember to maintain a professional and informative tone throughout your response and report.`; | |
| const prompt = ChatPromptTemplate.fromMessages([ | |
| new HumanMessage(promptMessage), | |
| ]); | |
| const chain = prompt.pipe(llm); | |
| const response = await chain.invoke({}); | |
| if (typeof response.content === "string") { | |
| const { | |
| response: responseContent, | |
| report, | |
| }: { response: string; report: string } = await parseXml(response.content); | |
| return { | |
| report, | |
| messages: [new AIMessage({ content: responseContent })], | |
| }; | |
| } else { | |
| throw new Error("Response is not a string"); | |
| } | |
| export const generateResponseNode = async ( | |
| state: Partial<SearchState>, | |
| options?: RunnableConfig | |
| ): Promise<Partial<SearchState>> => { | |
| console.log("Generating response"); | |
| const llm = await getModel({ | |
| model: "gpt-4o-mini", | |
| temperature: 0, | |
| }); | |
| const promptMessage = `You are an AI assistant tasked with responding to a user's query and creating a brief report based on provided search results. Follow these instructions carefully: | |
| 1. You will be given a user's query and a set of search results. | |
| 2. First, read the user's query: | |
| <query> | |
| ${state.query || ""} | |
| </query> | |
| 3. Respond to the user's query directly and concisely based on the information available in the search results. Your response should be helpful and informative. | |
| 4. After responding to the query, create a small markdown-formatted report summarizing the key points from the search results. This report should: | |
| - Be concise and informative | |
| - Highlight the most relevant information related to the user's query | |
| - Be structured with appropriate headings and subheadings | |
| - Use bullet points or numbered lists where appropriate | |
| 5. Throughout the report, include footnote annotations to reference the search results. Use superscript numbers (e.g., [^1], [^2]) to indicate these references within the text. | |
| 6. At the end of the report, include a "References" section with the footnotes. Each footnote should correspond to a specific search result and provide a brief description or title of the source. | |
| 7. Format your entire response as follows: | |
| <response> | |
| [Your direct response to the user's query] | |
| </response> | |
| <report> | |
| [Your markdown-formatted report with footnote annotations] | |
| ## References | |
| [Footnotes with corresponding search result references] | |
| </report> | |
| 8. Ensure that your report is well-structured, easy to read, and provides valuable insights based on the search results. | |
| Here are the search results to use for your response and report: | |
| <search_results> | |
| ${state.searchResults?.join("\n\n") || ""} | |
| </search_results> | |
| <sources> | |
| ${ | |
| state.sources | |
| ?.map((source, index) => `[${index + 1}] ${source}`) | |
| .join("\n\n") || "" | |
| } | |
| </sources> | |
| Remember to maintain a professional and informative tone throughout your response and report.`; | |
| const prompt = ChatPromptTemplate.fromMessages([ | |
| new HumanMessage(promptMessage), | |
| ]); | |
| const chain = prompt.pipe(llm); | |
| let response; | |
| try { | |
| response = await chain.invoke({}); | |
| } catch (error) { | |
| throw new Error(`Response generation failed: ${error.message}`); | |
| } | |
| if (typeof response.content === "string") { | |
| const { | |
| response: responseContent, | |
| report, | |
| }: { response: string; report: string } = await parseXml(response.content); | |
| return { | |
| report, | |
| messages: [new AIMessage({ content: responseContent })], | |
| }; | |
| } else { | |
| throw new Error("Response is not a string"); | |
| } |
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Actionable comments posted: 0
Outside diff range, codebase verification and nitpick comments (2)
packages/graph/src/search/README.md (2)
9-25: Ensure consistent punctuation in list items.Consider adding colons or periods consistently at the end of each list item description for better readability.
9c9 < 1. **SearchState**: Defines the structure for storing search-related information. --- > 1. **SearchState**: Defines the structure for storing search-related information. 11c11 < 2. **StateGraph**: Utilizes LangChain's StateGraph to manage the flow of operations. --- > 2. **StateGraph**: Utilizes LangChain's StateGraph to manage the flow of operations. 13c13 < 3. **Main Nodes**: --- > 3. **Main Nodes**: 15,18c15,18 < - `performSearchNode`: Executes the web search using the Tavily API. < - `extractKeyPointsNode`: Identifies and extracts key points from search results. < - `generateRelatedQueriesNode`: Creates related queries based on the initial search. < - `generateResponseNode`: Produces a final response and report based on all gathered information. --- > - `performSearchNode`: Executes the web search using the Tavily API. > - `extractKeyPointsNode`: Identifies and extracts key points from search results. > - `generateRelatedQueriesNode`: Creates related queries based on the initial search. > - `generateResponseNode`: Produces a final response and report based on all gathered information. 20c20 < 4. **Utility Functions**: --- > 4. **Utility Functions**: 22,23c22,23 < - `filterSearchResults`: Filters search results based on a minimum score. < - `formatSearchResults`: Formats the search results for further processing. --- > - `filterSearchResults`: Filters search results based on a minimum score. > - `formatSearchResults`: Formats the search results for further processing. 25c25 < 5. **LLM Integration**: Utilizes GPT models for various natural language processing tasks. --- > 5. **LLM Integration**: Utilizes GPT models for various natural language processing tasks.Tools
LanguageTool
[uncategorized] ~15-~15: Loose punctuation mark.
Context: ...Main Nodes*: -performSearchNode: Executes the web search using the Tavil...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~16-~16: Loose punctuation mark.
Context: ... Tavily API. -extractKeyPointsNode: Identifies and extracts key points from...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~17-~17: Loose punctuation mark.
Context: ...sults. -generateRelatedQueriesNode: Creates related queries based on the in...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~18-~18: Loose punctuation mark.
Context: ...tial search. -generateResponseNode: Produces a final response and report ba...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~22-~22: Loose punctuation mark.
Context: ...Functions**: -filterSearchResults: Filters search results based on a minim...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~23-~23: Loose punctuation mark.
Context: ...inimum score. -formatSearchResults: Formats the search results for further ...(UNLIKELY_OPENING_PUNCTUATION)
37-41: Ensure consistent punctuation in function descriptions.Consider adding colons or periods consistently at the end of each function description for better readability.
37c37 < The module exports two main functions: --- > The module exports two main functions: 39,40c39,40 < 1. `performSearch`: Executes the entire search process and returns the final state. < 2. `streamSearchProcess`: Provides a stream of intermediate states during the search process. --- > 1. `performSearch`: Executes the entire search process and returns the final state. > 2. `streamSearchProcess`: Provides a stream of intermediate states during the search process.Tools
LanguageTool
[uncategorized] ~39-~39: Loose punctuation mark.
Context: ... two main functions: 1.performSearch: Executes the entire search process and ...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~40-~40: Loose punctuation mark.
Context: ...he final state. 2.streamSearchProcess: Provides a stream of intermediate state...(UNLIKELY_OPENING_PUNCTUATION)
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- packages/graph/src/search/README.md (1 hunks)
Additional context used
LanguageTool
packages/graph/src/search/README.md
[uncategorized] ~15-~15: Loose punctuation mark.
Context: ...Main Nodes*: -performSearchNode: Executes the web search using the Tavil...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~16-~16: Loose punctuation mark.
Context: ... Tavily API. -extractKeyPointsNode: Identifies and extracts key points from...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~17-~17: Loose punctuation mark.
Context: ...sults. -generateRelatedQueriesNode: Creates related queries based on the in...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~18-~18: Loose punctuation mark.
Context: ...tial search. -generateResponseNode: Produces a final response and report ba...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~22-~22: Loose punctuation mark.
Context: ...Functions**: -filterSearchResults: Filters search results based on a minim...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~23-~23: Loose punctuation mark.
Context: ...inimum score. -formatSearchResults: Formats the search results for further ...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~39-~39: Loose punctuation mark.
Context: ... two main functions: 1.performSearch: Executes the entire search process and ...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~40-~40: Loose punctuation mark.
Context: ...he final state. 2.streamSearchProcess: Provides a stream of intermediate state...(UNLIKELY_OPENING_PUNCTUATION)
Additional comments not posted (3)
packages/graph/src/search/README.md (3)
1-3: Clear Introduction.The introduction effectively summarizes the module's purpose.
27-34: Well-structured Workflow.The workflow section clearly outlines the search process steps.
42-87: Effective Examples.The example usage and response effectively demonstrate the module's functionality.
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Outside diff range, codebase verification and nitpick comments (3)
packages/graph/src/graph-creator/prompt/examples.ts (3)
2-2: Ensure clarity in example tasks.The task descriptions in the examples should be clear and concise. Consider adding more context or breaking down complex tasks into simpler steps if necessary.
- We need to build out a youtube graph that will parse a video and related data to return info to the user. + We need to build a YouTube graph to parse video data and return information to the user.
Line range hint
15-18: Add error handling for state updates.Consider adding error handling in reducers to manage unexpected state updates or invalid data.
const stringReducer = (prev: string = "", next?: string): string => { try { return next ?? prev; } catch (error) { console.error("Error updating string state:", error); return prev; } };
Line range hint
98-98: Clarify mock implementation comments.Ensure that comments indicating mock implementations are clear and distinguishable from actual logic to avoid confusion.
- // Mock implementation: Perform search using the query + // Mock implementation: Simulate search using the query
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- .github/workflows/build.yml (1 hunks)
- migrations/001_create_foo_table.sql (1 hunks)
- packages/graph/src/graph-creator/prompt/examples.ts (1 hunks)
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migrations/001_create_foo_table.sql (1)
18-18: Verify the impact of the index change.The index has been renamed and moved from the
acdoctable to thedocumentstable. Ensure that this change aligns with the application logic and does not adversely affect query performance..github/workflows/build.yml (1)
18-18: Verify the build timeout setting.The build job has a timeout of 15 minutes. Ensure this is sufficient for the build process under normal conditions.
packages/graph/src/graph-creator/prompt/examples.ts (1)
Line range hint
140-140: Verify the use of default URLs in examples.The use of a default YouTube URL in examples should be verified to ensure it doesn't mislead or cause confusion in real implementations.
Verification successful
Default YouTube URL Usage Verified in Examples
The default YouTube URL "https://www.youtube.com/watch?v=dQw4w9WgXcQ" is used appropriately within the
examples.tsfile as part of examples or mock data. There are no issues with its usage in this context.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the use of default URLs in example code. # Test: Check for the use of default YouTube URLs in the codebase. Expect: Default URLs should only be used in examples or mock data. rg 'https://www.youtube.com/watch?v=dQw4w9WgXcQ'Length of output: 50
Script:
#!/bin/bash # Description: Search for any YouTube URLs or placeholder URLs in the examples.ts file. # Test: Check for any YouTube URLs in the examples.ts file to ensure they are appropriately used. rg 'youtube\.com' packages/graph/src/graph-creator/prompt/examples.tsLength of output: 18103
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- packages/graph/package.json (1 hunks)
- packages/graph/src/examples/planning/planning-agent.ts (1 hunks)
- packages/graph/src/search/search.test.ts (1 hunks)
- packages/graph/src/youtube-parser/youtube-parser.ts (1 hunks)
- packages/tools/src/process/calculator.test.ts (1 hunks)
- packages/tools/src/process/calculator.ts (1 hunks)
- packages/tools/src/retrieve/web-search.ts (2 hunks)
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- packages/graph/package.json
Files skipped from review as they are similar to previous changes (1)
- packages/graph/src/search/search.test.ts
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packages/tools/src/process/calculator.test.ts (1)
4-11: Test case for addition operation looks good.The test case correctly verifies the addition functionality of the
calculatorTool. Consider adding more test cases for other operations like subtraction, multiplication, and division to ensure comprehensive test coverage.packages/tools/src/retrieve/web-search.ts (1)
2-2: Enhancement withtavilyToollooks good.The addition of
tavilyToolenhances the web search capabilities. Ensure that the integration oftavilyToolis thoroughly tested in the application.Also applies to: 18-18
packages/tools/src/process/calculator.ts (1)
1-36: Calculator tool implementation is well-structured.The implementation of
calculatorToolis clean and correctly useszodfor schema validation. The code covers basic arithmetic operations and follows best practices.packages/graph/src/youtube-parser/youtube-parser.ts (1)
85-106: Enhancements togetMetadatanode are robust.The changes improve error handling and ensure a structured return type. Verify that the error handling logic is consistent with the application's requirements.
packages/graph/src/examples/planning/planning-agent.ts (1)
194-194: Type assertion forpastStepsenhances type safety.The type assertion ensures that
pastStepsconforms to the expected tuple structure, improving type safety and robustness.
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packages/llm/package.json (1)
33-33: Ensure compatibility of@langchain/corewith existing dependencies.The addition of
@langchain/coreversion^0.2.18is noted. Ensure it is compatible with other dependencies in the project to prevent any potential version conflicts or runtime issues.
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Outside diff range, codebase verification and nitpick comments (9)
packages/graph/src/graph-creator/prompt/node-creator.ts (9)
1-1: Consider renaming the constant for clarity.The constant
nodeCreatorPromptcould be renamed to reflect its purpose more clearly, such asnodeCreationGuideorlangGraphNodeGuide.-export const nodeCreatorPrompt = +export const nodeCreationGuide =
Line range hint
5-5: Clarify the description of nodes.The description of nodes as "JavaScript/TypeScript functions" could be expanded to explain their role in the workflow more explicitly. Consider adding an example or diagram for better understanding.
Line range hint
13-13: Add examples for LLM-based and Tool-calling Nodes.Consider providing examples or use cases for LLM-based and Tool-calling Nodes to enhance understanding.
Line range hint
18-18: Clarify the distinction between START and END Nodes.The guide mentions START and END Nodes but does not elaborate on their specific roles or how they differ from other nodes. Consider adding more details.
Line range hint
32-32: Enhance the section on error handling.The best practices section mentions implementing error handling but does not provide examples. Consider adding a code snippet demonstrating error handling within a node.
Line range hint
42-42: Consider adding a section on testing nodes.Testing is crucial for node reliability. Consider adding a section on how to test nodes, including unit testing and integration testing strategies.
Line range hint
61-61: Provide more details on checkpointing.The section on using nodes with checkpointers could benefit from additional details or examples on how checkpointing works and its benefits.
Line range hint
74-74: Explain the concept of reflection in nodes.The advanced topics section introduces reflection in nodes but does not explain what it entails. Consider providing a brief explanation or example.
Line range hint
80-80: Clarify the concept of multi-agent nodes.The concept of multi-agent nodes is introduced without much context. Consider providing more details or examples to clarify how they function and their use cases.
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- packages/graph/src/graph-creator/graph.ts (1 hunks)
- packages/graph/src/graph-creator/prompt/node-creator.ts (1 hunks)
- packages/graph/src/search/search.test.ts (1 hunks)
- packages/llm/src/examples/fake-llm.ts (1 hunks)
- packages/llm/src/test.ts (1 hunks)
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- packages/graph/src/graph-creator/graph.test.ts
- packages/graph/src/graph-creator/graph.ts
- packages/llm/src/examples/fake-llm.ts
- packages/llm/src/test.ts
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packages/graph/src/graph-creator/prompt/node-creator.ts (1)
2-2: Ensure consistent use of terminology.The term "LangGraph" is used throughout the guide. Ensure that this term is consistently used elsewhere in the codebase and documentation.
Verification successful
Consistent Use of "LangGraph" Verified
The term "LangGraph" is consistently used across multiple files in the codebase, including TypeScript and Markdown files. This indicates a uniform application of the terminology throughout the documentation and code.
- Files with consistent usage:
packages/graph/src/graph-creator/graph.tspackages/graph/src/graph-creator/prompt/node-creator.tspackages/graph/src/youtube-parser/README.mdpackages/graph/src/graph-creator/prompt/build-prompt.tspackages/graph/src/graph-creator/prompt/langgraph-docs.ts- Additional documentation files
The usage aligns with the intent to maintain consistent terminology across the project.
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Script:
#!/bin/bash # Description: Verify consistent use of the term "LangGraph" across the codebase. # Test: Search for occurrences of "LangGraph". Expect: Consistent usage across files. rg --type markdown --type typescript 'LangGraph'Length of output: 83
Script:
#!/bin/bash # Description: Verify consistent use of the term "LangGraph" across the codebase. # Test: Search for occurrences of "LangGraph". Expect: Consistent usage across files. rg --glob '*.md' --glob '*.ts' 'LangGraph'Length of output: 42689
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Outside diff range, codebase verification and nitpick comments (2)
.changeset/cyan-radios-run.md (2)
16-18: Enhance README documentation details.Consider specifying whether the README includes usage examples, installation instructions, or other relevant details to improve user guidance further.
20-24: Specify the scope of the tests.Consider detailing the scope of the tests, such as whether they cover edge cases, performance, or integration aspects, to provide a clearer understanding of the testing coverage.
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.changeset/cyan-radios-run.md (1)
8-14: Clarity and completeness of the "New Features" section.The "New Features" section is clear and comprehensive, effectively detailing the new functionalities introduced in this update.
Summary by CodeRabbit
New Features
Documentation
Tests