id stringlengths 14 15 | text stringlengths 23 2.21k | source stringlengths 52 97 |
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322323d21da1-2 | ZERO_SHOT_REACT_DESCRIPTION​This shows how to initialize the agent using the ZERO_SHOT_REACT_DESCRIPTION agent type. Note that this is an alternative to the above.agent_executor = create_sql_agent( llm=OpenAI(temperature=0), toolkit=toolkit, verbose=True, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,)... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-3 | datawarehouse oriented databases support user-level quota, for limiting resource usage.Example: describing a table​agent_executor.run("Describe the playlisttrack table") > Entering new chain... Invoking: `list_tables_sql_db` with `{}` Album, Artist, Track, PlaylistTrack, InvoiceLine, sal... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-4 | "PlaylistId" INTEGER NOT NULL, "TrackId" INTEGER NOT NULL, PRIMARY KEY ("PlaylistId", "TrackId"), FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"), FOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId") ) ``` Here are three sample rows from the `PlaylistTrack` t... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-5 | table:\n\n```\nPlaylistId TrackId\n1 3402\n1 3389\n1 3390\n```\n\nPlease let me know if there is anything else I can help you with.'Example: describing a table, recovering from an error​In this example, the agent tries to search for a table that doesn't exist, but finds the next bes... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-6 | PRIMARY KEY ("PlaylistId", "TrackId"), FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"), FOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId") ) SELECT * FROM 'PlaylistTrack' LIMIT 3; PlaylistId TrackId 1 3402 1 3389 1 3390 Thought: I now know the final answer F... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-7 | NOT NULL, "LastName" NVARCHAR(20) NOT NULL, "Company" NVARCHAR(80), "Address" NVARCHAR(70), "City" NVARCHAR(40), "State" NVARCHAR(40), "Country" NVARCHAR(40), "PostalCode" NVARCHAR(10), "Phone" NVARCHAR(24), "Fax" NVARCHAR(24), "Email" NVAR... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-8 | 34 Stuttgart None Germany 70174 +49 0711 2842222 None leonekohler@surfeu.de 5 3 François Tremblay None 1498 rue Bélanger Montréal QC Canada H2G 1A7 +1 (514) 721-4711 None ftremblay@gmail.com 3 CREATE TABLE "Invoice" ( "InvoiceId" INTEGER NOT NULL, "CustomerId" INTEGER NOT NULL, "... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-9 | 2 4 2009-01-02 00:00:00 Ullevålsveien 14 Oslo None Norway 0171 3.96 3 8 2009-01-03 00:00:00 Grétrystraat 63 Brussels None Belgium 1000 5.94 Thought: I should query the Invoice and Customer tables to get the total sales per country. Action: query_sql_db Action Input: SELECT c.Country, SUM(i.Total) AS Total... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-10 | Entering new AgentExecutor chain... Action: list_tables_sql_db Action Input: "" Observation: Invoice, MediaType, Artist, InvoiceLine, Genre, Playlist, Employee, Album, PlaylistTrack, Track, Customer Thought: I should look at the schema of the Playlist and PlaylistTrack tables to see what columns I can use. ... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-11 | the total number of tracks in each playlist. Action: query_checker_sql_db Action Input: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name Observation: SELECT Playlist.Name, CO... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-12 | 'Music' (6580), 'Music Videos' (1). > Finished chain. "The total number of tracks in each playlist are: '90’s Music' (1477), 'Brazilian Music' (39), 'Classical' (75), 'Classical 101 - Deep Cuts' (25), 'Classical 101 - Next Steps' (25), 'Classical 101 - The Basics' (25), 'Grunge' (15), 'Heavy Metal Classic' ... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-13 | ArtistId Name 1 AC/DC 2 Accept 3 Aerosmith CREATE TABLE "Track" ( "TrackId" INTEGER NOT NULL, "Name" NVARCHAR(200) NOT NULL, "AlbumId" INTEGER, "MediaTypeId" INTEGER NOT NULL, "GenreId" INTEGER, "Composer" NVARCHAR(220), "Milliseconds" INTEGER N... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-14 | 3 Fast As a Shark 3 2 1 F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman 230619 3990994 0.99 CREATE TABLE "InvoiceLine" ( "InvoiceLineId" INTEGER NOT NULL, "InvoiceId" INTEGER NOT NULL, "TrackId" INTEGER NOT NULL, "UnitPrice" NUMERIC(10, 2) NOT NULL, "Quantity" INTEG... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-15 | (sqlite3.OperationalError) no such column: Track.ArtistId [SQL: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3] (B... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
322323d21da1-16 | 107), ('Metallica', 91)] Thought: I now know the final answer. Final Answer: The top 3 best selling artists are Iron Maiden, U2, and Metallica. > Finished chain. 'The top 3 best selling artists are Iron Maiden, U2, and Metallica.'PreviousSpark SQL AgentNextVectorstore AgentInitializationUsing ZERO_SHOT_... | https://python.langchain.com/docs/integrations/toolkits/sql_database |
b8e6958ed147-0 | Xorbits Agent | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/toolkits/xorbits |
b8e6958ed147-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsAmadeus ToolkitAzure Cognitive Services ToolkitCSV AgentDocument ComparisonGitHubGmail ToolkitJiraJSON ... | https://python.langchain.com/docs/integrations/toolkits/xorbits |
b8e6958ed147-2 | (891, 12) Thought: I now know the final answer Final Answer: There are 891 rows and 12 columns. > Finished chain. 'There are 891 rows and 12 columns.'agent.run("How many people are in pclass 1?") > Entering new chain... 0%| | 0.00/100 [00:00<?, ?it/s] Thought: I need to co... | https://python.langchain.com/docs/integrations/toolkits/xorbits |
b8e6958ed147-3 | > Finished chain. 'The mean age is 29.69911764705882.'agent.run("Group the data by sex and find the average age for each group") > Entering new chain... Thought: I need to group the data by sex and then find the average age for each group Action: python_repl_ast Action Input: data.groupby('Sex')... | https://python.langchain.com/docs/integrations/toolkits/xorbits |
b8e6958ed147-4 | to get the desired result Action: python_repl_ast Action Input: data[(data['Age'] > 30) & (data['Fare'] > 30) & (data['Fare'] < 50) & ((data['Pclass'] == 1) | (data['Pclass'] == 2))].shape[0] Observation: 20 Thought: I now know the final answer Final Answer: 20 > Finished chain. '20'Numpy examp... | https://python.langchain.com/docs/integrations/toolkits/xorbits |
b8e6958ed147-5 | Thought: I need to access the 2nd element of the array Action: python_repl_ast Action Input: data[1] 0%| | 0.00/100 [00:00<?, ?it/s] Observation: 2 Thought: I now know the final answer Final Answer: 2 > Finished chain. '2'agent.run( "Reshape the array into a 2-dimensional... | https://python.langchain.com/docs/integrations/toolkits/xorbits |
b8e6958ed147-6 | and sum the array along the first axis") > Entering new chain... Thought: I need to reshape the array and then sum it Action: python_repl_ast Action Input: np.sum(np.reshape(data, (3,2)), axis=0) 0%| | 0.00/100 [00:00<?, ?it/s] Observation: [ 9 12] Thought: I now know the ... | https://python.langchain.com/docs/integrations/toolkits/xorbits |
b8e6958ed147-7 | 1. 1.] [1. 1. 1.] [1. 1. 1.]] Thought: I now know the final answer Final Answer: The covariance matrix is [[1. 1. 1.], [1. 1. 1.], [1. 1. 1.]]. > Finished chain. 'The covariance matrix is [[1. 1. 1.], [1. 1. 1.], [1. 1. 1.]].'agent.run("compute the U of Singular Value Decomposition of the matrix... | https://python.langchain.com/docs/integrations/toolkits/xorbits |
66b1754cd434-0 | Document Comparison | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsAmadeus ToolkitAzure Cognitive Services ToolkitCSV AgentDocument ComparisonGitHubGmail ToolkitJiraJSON ... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-2 | }, # https://digitalassets.tesla.com/tesla-contents/image/upload/IR/TSLA-Q1-2023-Update { "name": "tesla-earnings", "path": "/Users/harrisonchase/Downloads/TSLA-Q1-2023-Update.pdf", },]for file in files: loader = PyPDFLoader(file["path"]) pages = loader.load_and_split() text_splitter = C... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-3 | tools=tools, llm=llm, verbose=True,)agent({"input": "did alphabet or tesla have more revenue?"}) > Entering new chain... Invoking: `alphabet-earnings` with `{'question': 'revenue'}` {'query': 'revenue', 'result': 'The revenue for Alphabet Inc. for the quarter ended March 31, 2023, wa... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-4 | = Truellm = ChatOpenAI( temperature=0, model="gpt-3.5-turbo-0613",)agent = initialize_agent( agent=AgentType.OPENAI_MULTI_FUNCTIONS, tools=tools, llm=llm, verbose=True,)agent({"input": "did alphabet or tesla have more revenue?"}) [chain/start] [1:chain:AgentExecutor] Entering Chain run with input: ... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-5 | "function_call": { "name": "tool_selection", "arguments": "{\n \"actions\": [\n {\n \"action_name\": \"alphabet-earnings\",\n \"action\": {\n \"question\": \"What was Alphabet's revenue?\"\n }\n },\n {\n \"action_name\": \"tesla-earnings\",\n \... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-6 | }, "run": null } [tool/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings] Entering Tool run with input: "{'question': "What was Alphabet's revenue?"}" [chain/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA] Entering Chain run with input: { "query": "What wa... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-7 | and administrative 3,374 3,759 \nTotal costs and expenses 47,917 52,372 \nIncome from operations 20,094 17,415 \nOther income (expense), net (1,160) 790 \nIncome before income taxes 18,934 18,205 \nProvision for income taxes 2,498 3,154 \nNet income $ 16,436 $ 15,051 \nBasic earnings per share of Class A, C... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-8 | Google, said: “Resilience in Search and momentum in Cloud resulted in Q1 \nconsolidated revenues of $69.8 billion, up 3% year over year, or up 6% in constant currency. We remain committed \nto delivering long-term growth and creating capacity to invest in our most compelling growth areas by re-engineering \nour cost ... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-9 | % 25 %\nOther income (expense), net $ (1,160) $ 790 \nNet income $ 16,436 $ 15,051 \nDiluted EPS $ 1.23 $ 1.17 \n(1) Non-GAAP measure. See the table captioned “Reconciliation from GAAP revenues to non-GAAP constant currency \nrevenues and GAAP percentage change in revenues to non-GAAP percentage change in constant c... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-10 | expect most of those affected will no longer be reflected in our headcount by the end of the second quarter \nof 2023, subject to local law and consultation requirements.\nSegment Operating Results\nReflecting DeepMind’s increasing collaboration with Google Services, Google Cloud, and Other Bets, beginning in \nthe f... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-11 | , respectively. For the three months ended March 31, 2023, unallocated \ncorporate costs include charges related to the reductions in our workforce and office space totaling $2.5 billion . \n2\n\nSegment results\nThe following table presents our segment revenues and operating income (loss) (in millions; unaudited):\nQu... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-12 | Bets is a combination of multiple operating segments that are not individually material. Revenues \nfrom Other Bets are generated primarily from the sale of health technology and internet services.\nAfter the segment reporting changes discussed above, unallocated corporate costs primarily include AI-focused \nshared R&... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-13 | $ 74,842 \n(1)Includes hedging gains (losses); in fiscal years 2021 and 2022 hedging gains of $149 million and $2.0 billion, respectively.\n8" } [llm/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA > 5:chain:StuffDocumentsChain > 6:chain:LLMChain > 7:llm:ChatOpenAI] Entering LLM run... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-14 | 2,498 3,154 \nNet income $ 16,436 $ 15,051 \nBasic earnings per share of Class A, Class B, and Class C stock $ 1.24 $ 1.18 \nDiluted earnings per share of Class A, Class B, and Class C stock $ 1.23 $ 1.17 \nNumber of shares used in basic earnings per share calculation 13,203 12,781 \nNumber of shares used in diluted... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-15 | 2023 financial highlights (unaudited)\nOur first quarter 2023 results reflect:\ni.$2.6 billion in charges related to reductions in our workforce and office space; \nii.a $988 million reduction in depreciation expense from the change in estimated useful life of our servers and \ncertain network equipment; and\niii.... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-16 | GAAP percentage change in revenues to non-GAAP percentage change in constant currency revenues� for \nmore details.\n\nQ1 2023 supplemental information (in millions, except for number of employees; unaudited)\nRevenues, T raffic Acquisition Costs (TAC), and number of employees\nQuarter Ended March 31,\n2022 2023\n... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-17 | corporate costs instead of within \nOther Bets. Additionally, beginning in the first quarter of 2023, we updated and simplified our cost allocation \nmethodologies to provide our business leaders with increased transparency for decision-making . Prior periods have \nbeen recast to reflect the revised presentation and a... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-18 | 2023\n(recast)\nRevenues:\nGoogle Services $ 61,472 $ 61,961 \nGoogle Cloud 5,821 7,454 \nOther Bets 440 288 \nHedging gains (losses) 278 84 \nTotal revenues $ 68,011 $ 69,787 \nOperating income (loss):\nGoogle Services $ 21,973 $ 21,737 \nGoogle Cloud (706) 191 \nOther Bets (835) (1,225) \nCorporate costs, u... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-19 | certain human resource costs, and legal, including certain fines and settlements. In the first quarter of 2023, \nunallocated corporate costs also include charges associated with reductions in our workforce and office space. \nAdditionally, hedging gains (losses) related to revenue are included in unallocated corporate... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-20 | [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA > 5:chain:StuffDocumentsChain > 6:chain:LLMChain > 7:llm:ChatOpenAI] [1.61s] Exiting LLM run with output: { "generations": [ [ { "text": "Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 millio... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-21 | }, "run": null } [chain/end] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA > 5:chain:StuffDocumentsChain > 6:chain:LLMChain] [1.61s] Exiting Chain run with output: { "text": "Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million." } [chain/end] [... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-22 | 2023, was $69,787 million."}" [tool/start] [1:chain:AgentExecutor > 8:tool:tesla-earnings] Entering Tool run with input: "{'question': "What was Tesla's revenue?"}" [chain/start] [1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA] Entering Chain run with input: { "query": "What was Te... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-23 | net income1in Q1In the current macroeconomic environment, we see this year as a unique \nopportunity for Tesla. As many carmakers are working through challenges with the \nunit economics of their EV programs, we aim to leverage our position as a cost \nleader. We are focused on rapidly growing production, investments i... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-24 | Cybertruck factory tooling on track; producing Alpha versions\nModel Y was the best -selling vehicle in Europe in Q1\nModel Y was the best -selling vehicle in the US in Q1 (ex -pickups)\n\n01234O T H E R H I G H L I G H T S\n9Services & Other gross margin\nEnergy Storage deployments (GWh)Energy Storage\nEnergy storag... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-25 | except per share data Q1-2022 Q2-2022 Q3-2022 Q4-2022 Q1-2023\nREVENUES\nAutomotive sales 15,514 13,670 17,785 20,241 18,878 \nAutomotive regulatory credits 679 344 286 467 521 \nAutomotive leasing 668 588 621 599 564 \nTotal automotive revenues 16,861 14,602 18,692 21,307 19,963 \nEnergy generation and storage 616 866... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-26 | general and administrative 992 961 961 1,032 1,076 \nRestructuring and other — 142 — 34 —\nTotal operating expenses 1,857 1,770 1,694 1,876 1,847 \nINCOME FROM OPERATIONS 3,603 2,464 3,688 3,901 2,664 \nInterest income 28 26 86 157 213 \nInterest expense (61) (44) (53) (33) (29)\nOther income (expense), net 56 28... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-27 | $ 0.65 $ 0.95 $ 1.07 $ 0.73 \nWeighted average shares used in computing net income per share of common stock(1)\nBasic 3,103 3,111 3,146 3,160 3,166\nDiluted 3,472 3,464 3,468 3,471 3,468\nS T A T E M E N T O F O P E R A T I O N S\n(Unaudited)\n23 (1) Prior period results have been retroa... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-28 | to operating lease accounting 12,167 9,227 11,004 15,184 22,357 84%\nTotal end of quarter operating lease vehicle count 128,402 131,756 135,054 140,667 153,988 20%\nGlobal vehicle inventory (days of supply )(1)3 4 8 13 15 400%\nSolar deployed (MW) 48 106 94 100 67 40%\nStorage deployed (MWh) 846 1,133 2,100 2,462 3,889... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-29 | > 10:chain:StuffDocumentsChain > 11:chain:LLMChain > 12:llm:ChatOpenAI] Entering LLM run with input: { "prompts": [ "System: Use the following pieces of context to answer the users question. \nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\n----------------\nS... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-30 | operating margins reduced at a manageable rate. We \nexpect ongoing cost reduction of our vehicles, including improved production \nefficiency at our newest factories and lower logistics costs, and remain focused on \noperating leverage as we scale.\nWe are rapidly growing energy storage production capacity at our Mega... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-31 | first of many. We recently announced our second 40 GWh Megafactory, this time in Shanghai, with construction starting later this year. \nSolar\nSolar deployments increased by 40% YoY in Q1 to 67 MW, but declined sequentially in \nthe quarter, predominantly due to volatile weather and other factors. In addition, the sol... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-32 | \nTotal revenues 18,756 16,934 21,454 24,318 23,329 \nCOST OF REVENUES\nAutomotive sales 10,914 10,153 13,099 15,433 15,422 \nAutomotive leasing 408 368 381 352 333 \nTotal automotive cost of revenues 11,322 10,521 13,480 15,785 15,755 \nEnergy generation and storage 688 769 1,013 1,151 1,361 \nServices and other 1,286... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-33 | 3,636 3,983 2,800 \nProvision for income taxes 346 205 305 276 261 \nNET INCOME 3,280 2,269 3,331 3,707 2,539 \nNet (loss) income attributable to noncontrolling interests and redeemable noncontrolling interests in \nsubsidiaries(38) 10 39 20 26 \nNET INCOME ATTRIBUTABLE TO COMMON STOCKHOLDERS 3,318 2,259 3,292 3,687 2,... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-34 | reflect the three -for-one stock split effected in the form of a stock d ividend in August 2022.\n\nQ1-2022 Q2-2022 Q3-2022 Q4-2022 Q1-2023 YoY\nModel S/X production 14,218 16,411 19,935 20,613 19,437 37%\nModel 3/Y production 291,189 242,169 345,988 419,088 421,371 45%\nTotal production 305,407 258,580 365,923 439,701... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-35 | 1,372 1,453 1,532 1,584 1,692 23%\nSupercharger stations 3,724 3,971 4,283 4,678 4,947 33%\nSupercharger connectors 33,657 36,165 38,883 42,419 45,169 34%\n(1)Days of supply is calculated by dividing new car ending inventory by the relevant quarter’s deliveries and using 75 trading days (aligned with Automotive News ... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-36 | "content": "Tesla's revenue for Q1-2023 was $23.329 billion.", "additional_kwargs": {}, "example": false } } ] ], "llm_output": { "token_usage": { "prompt_tokens": 2246, "completion_tokens": 16, "total_tokens": 2262 ... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-37 | { "output_text": "Tesla's revenue for Q1-2023 was $23.329 billion." } [chain/end] [1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA] [1.61s] Exiting Chain run with output: { "result": "Tesla's revenue for Q1-2023 was $23.329 billion." } [tool/end] [1:chain:AgentExecutor > 8:t... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-38 | \"action\": {\\n \"question\": \"What was Tesla\\'s revenue?\"\\n }\\n }\\n ]\\n}'}\nFunction: {\"query\": \"What was Alphabet's revenue?\", \"result\": \"Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million.\"}\nAI: {'name': 'tool_selection', 'arguments': '{\\n \"actions\": [\\... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-39 | { "text": "Alphabet had a revenue of $69,787 million, while Tesla had a revenue of $23.329 billion. Therefore, Alphabet had more revenue than Tesla.", "generation_info": null, "message": { "content": "Alphabet had a revenue of $69,787 million, while Tesla had a revenue of $... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
66b1754cd434-40 | while Tesla had a revenue of $23.329 billion. Therefore, Alphabet had more revenue than Tesla." } {'input': 'did alphabet or tesla have more revenue?', 'output': 'Alphabet had a revenue of $69,787 million, while Tesla had a revenue of $23.329 billion. Therefore, Alphabet had more revenue than Tesla.'}Previous... | https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit |
88986f689ca0-0 | JSON Agent | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/toolkits/json |
88986f689ca0-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsAmadeus ToolkitAzure Cognitive Services ToolkitCSV AgentDocument ComparisonGitHubGmail ToolkitJiraJSON ... | https://python.langchain.com/docs/integrations/toolkits/json |
88986f689ca0-2 | toolkit=json_toolkit, verbose=True)Example: getting the required POST parameters for a request​json_agent_executor.run( "What are the required parameters in the request body to the /completions endpoint?") > Entering new AgentExecutor chain... Action: json_spec_list_keys Action Input: data Obse... | https://python.langchain.com/docs/integrations/toolkits/json |
88986f689ca0-3 | Observation: ['operationId', 'tags', 'summary', 'requestBody', 'responses', 'x-oaiMeta'] Thought: I should look at the requestBody key to see what parameters are required Action: json_spec_list_keys Action Input: data["paths"]["/completions"]["post"]["requestBody"] Observation: ['required', 'content'] Th... | https://python.langchain.com/docs/integrations/toolkits/json |
88986f689ca0-4 | Observation: #/components/schemas/CreateCompletionRequest Thought: I should look at the CreateCompletionRequest schema to see what parameters are required Action: json_spec_list_keys Action Input: data["components"]["schemas"]["CreateCompletionRequest"] Observation: ['type', 'properties', 'required'] Tho... | https://python.langchain.com/docs/integrations/toolkits/json |
09139d1007c5-0 | PlayWright Browser Toolkit | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/toolkits/playwright |
09139d1007c5-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsAmadeus ToolkitAzure Cognitive Services ToolkitCSV AgentDocument ComparisonGitHubGmail ToolkitJiraJSON ... | https://python.langchain.com/docs/integrations/toolkits/playwright |
09139d1007c5-2 | create_async_playwright_browser, create_sync_playwright_browser, # A synchronous browser is available, though it isn't compatible with jupyter.)# This import is required only for jupyter notebooks, since they have their own eventloopimport nest_asyncionest_asyncio.apply()Instantiating a Browser Toolkit​It's alway... | https://python.langchain.com/docs/integrations/toolkits/playwright |
09139d1007c5-3 | return_direct=False, verbose=False, callbacks=None, callback_manager=None, sync_browser=None, async_browser=<Browser type=<BrowserType name=chromium executable_path=/Users/wfh/Library/Caches/ms-playwright/chromium-1055/chrome-mac/Chromium.app/Contents/MacOS/Chromium> version=112.0.5615.29>), ExtractTextTool(name='e... | https://python.langchain.com/docs/integrations/toolkits/playwright |
09139d1007c5-4 | callback_manager=None, sync_browser=None, async_browser=<Browser type=<BrowserType name=chromium executable_path=/Users/wfh/Library/Caches/ms-playwright/chromium-1055/chrome-mac/Chromium.app/Contents/MacOS/Chromium> version=112.0.5615.29>), CurrentWebPageTool(name='current_webpage', description='Returns the URL of ... | https://python.langchain.com/docs/integrations/toolkits/playwright |
09139d1007c5-5 | crisis"}, {"innerText": "Clashes renew in West Darfur as food and water shortages worsen in Sudan violence"}, {"innerText": "Thai policeman\\u2019s wife investigated over alleged murder and a dozen other poison cases"}, {"innerText": "American teacher escaped Sudan on French evacuation plane, with no help offered back ... | https://python.langchain.com/docs/integrations/toolkits/playwright |
09139d1007c5-6 | "Two sides agree to extend Sudan ceasefire"}, {"innerText": "Spanish Leopard 2 tanks are on their way to Ukraine, defense minister confirms"}, {"innerText": "Flamb\\u00e9ed pizza thought to have sparked deadly Madrid restaurant fire"}, {"innerText": "Another bomb found in Belgorod just days after Russia accidentally st... | https://python.langchain.com/docs/integrations/toolkits/playwright |
09139d1007c5-7 | pushed millions into crisis made 100 times more likely by climate change, analysis finds"}, {"innerText": "For years, a UK mining giant was untouchable in Zambia for pollution until a former miner\\u2019s son took them on"}, {"innerText": "Former Sudanese minister Ahmed Haroun wanted on war crimes charges freed from Kh... | https://python.langchain.com/docs/integrations/toolkits/playwright |
09139d1007c5-8 | from using their phones while eating"}, {"innerText": "South African opera star will perform at coronation of King Charles III"}, {"innerText": "Luxury loot under the hammer: France auctions goods seized from drug dealers"}, {"innerText": "Judy Blume\\u2019s books were formative for generations of readers. Here\\u2019s... | https://python.langchain.com/docs/integrations/toolkits/playwright |
09139d1007c5-9 | year"}, {"innerText": "Ocean census aims to discover 100,000 previously unknown marine species"}, {"innerText": "Wall Street Journal editor discusses reporter\\u2019s arrest in Moscow"}, {"innerText": "Can Tunisia\\u2019s democracy be saved?"}, {"innerText": "Yasmeen Lari, \\u2018starchitect\\u2019 turned social engine... | https://python.langchain.com/docs/integrations/toolkits/playwright |
09139d1007c5-10 | the agent wants to remember the current webpage, it can use the `current_webpage` toolawait tools_by_name["current_webpage"].arun({}) 'https://web.archive.org/web/20230428133211/https://cnn.com/world'Use within an Agent​Several of the browser tools are StructuredTool's, meaning they expect multiple arguments. Thes... | https://python.langchain.com/docs/integrations/toolkits/playwright |
09139d1007c5-11 | h3, h4, h5, h6" } } ``` Observation: [] Thought: Thought: The page has loaded, I can now extract the headers Action: ``` { "action": "get_elements", "action_input": { "selector": "h1, h2, h3, h4, h5, h6" } } ``` Observation: [] Thought: Thought: I nee... | https://python.langchain.com/docs/integrations/toolkits/playwright |
09139d1007c5-12 | h3: Follow us on social media h3: Langchain Foundation Ltd. All rights reserved. PreviousPandas Dataframe AgentNextPowerBI Dataset AgentInstantiating a Browser ToolkitUse within an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/toolkits/playwright |
7878c9725cb9-0 | CSV Agent | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/toolkits/csv |
7878c9725cb9-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsAmadeus ToolkitAzure Cognitive Services ToolkitCSV AgentDocument ComparisonGitHubGmail ToolkitJiraJSON ... | https://python.langchain.com/docs/integrations/toolkits/csv |
7878c9725cb9-2 | model="gpt-3.5-turbo-0613"), "titanic.csv", verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS,)agent.run("how many rows are there?") Error in on_chain_start callback: 'name' Invoking: `python_repl_ast` with `df.shape[0]` 891There are 891 rows in the dataframe. > Finished chain. ... | https://python.langchain.com/docs/integrations/toolkits/csv |
7878c9725cb9-3 | # Calculate the square root of the average age square_root = math.sqrt(average_age) square_root` 5.585696017507576The square root of the average age is approximately 5.59. > Finished chain. 'The square root of the average age is approximately 5.59.'Multi CSV Example​This next part shows... | https://python.langchain.com/docs/integrations/toolkits/csv |
455a500677fd-0 | Office365 Toolkit | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/toolkits/office365 |
455a500677fd-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsAmadeus ToolkitAzure Cognitive Services ToolkitCSV AgentDocument ComparisonGitHubGmail ToolkitJiraJSON ... | https://python.langchain.com/docs/integrations/toolkits/office365 |
455a500677fd-2 | is a JSON list of all the events in the user's calendar between the start and end times. You can assume that the user can not schedule any meeting over existing meetings, and that the user is busy during meetings. Any times without events are free for the user. ", args_schema=<class 'langchain.tools.office365.events_s... | https://python.langchain.com/docs/integrations/toolkits/office365 |
455a500677fd-3 | 'langchain.tools.office365.send_event.SendEventSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, account=Account Client Id: f32a022c-3c4c-4d10-a9d8-f6a9a9055302), O365SendMessage(name='send_email', description='Use this tool to send an email with the provi... | https://python.langchain.com/docs/integrations/toolkits/office365 |
455a500677fd-4 | and the subject was 'Collaboration Request'."agent.run( "Can you schedule a 30 minute meeting with a sentient parrot to discuss research collaborations on October 3, 2023 at 2 pm Easter Time?") /home/vscode/langchain-py-env/lib/python3.11/site-packages/O365/utils/windows_tz.py:639: PytzUsageWarning: The zone attr... | https://python.langchain.com/docs/integrations/toolkits/office365 |
455a500677fd-5 | event is titled 'Meeting with sentient parrot' and is scheduled from 6:00 PM to 6:30 PM."PreviousMultion ToolkitNextOpenAPI agentsAssign Environmental VariablesCreate the Toolkit and Get ToolsUse within an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/toolkits/office365 |
aae4b800acc8-0 | PowerBI Dataset Agent | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/toolkits/powerbi |
aae4b800acc8-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsAmadeus ToolkitAzure Cognitive Services ToolkitCSV AgentDocument ComparisonGitHubGmail ToolkitJiraJSON ... | https://python.langchain.com/docs/integrations/toolkits/powerbi |
aae4b800acc8-2 | import AgentExecutorfrom azure.identity import DefaultAzureCredentialfast_llm = ChatOpenAI( temperature=0.5, max_tokens=1000, model_name="gpt-3.5-turbo", verbose=True)smart_llm = ChatOpenAI(temperature=0, max_tokens=100, model_name="gpt-4", verbose=True)toolkit = PowerBIToolkit( powerbi=PowerBIDataset( dat... | https://python.langchain.com/docs/integrations/toolkits/powerbi |
aae4b800acc8-3 | <> "")))----Question: What was the average of value in revenue in dollars?DAX: EVALUATE ROW("Average", AVERAGE(revenue_details[dollar_value]))----"""toolkit = PowerBIToolkit( powerbi=PowerBIDataset( dataset_id="<dataset_id>", table_names=["table1", "table2"], credential=DefaultAzureCredential(),... | https://python.langchain.com/docs/integrations/toolkits/powerbi |
9ac991f0ae11-0 | GitHub | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/toolkits/github |
9ac991f0ae11-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsAmadeus ToolkitAzure Cognitive Services ToolkitCSV AgentDocument ComparisonGitHubGmail ToolkitJiraJSON ... | https://python.langchain.com/docs/integrations/toolkits/github |
9ac991f0ae11-2 | The GitHub tool allows agents to interact with a given GitHub repository. It implements CRUD operations for modifying files and can read/comment on Issues. The tool wraps the PyGitHub library.In order to interact with the GitHub API you must create a GitHub app. Next, you must set the following environment variables:GI... | https://python.langchain.com/docs/integrations/toolkits/github |
9ac991f0ae11-3 | 'number': 1}] Thought: I need to get more information about this issue. Action: Get Issue Action Input: 1 Observation: {'title': 'Change the main script to print Hello AI!', 'body': None, 'comments': '[]'} Thought: I need to update the main script to print Hello AI! Action: Update File Action Input... | https://python.langchain.com/docs/integrations/toolkits/github |
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