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c19072af2b23-0 | Human as a tool | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/human_tools |
c19072af2b23-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsHuman as a toolOn this pageHuman as a toolHuman are AGI so they can certainly be used as a tool to help out AI agent
when it is confused.from langchain.chat_models import ChatOpenAIfrom langchain.llms import OpenAIfrom langchain.agents import load_tools, initialize_agentfrom langchain.agents import AgentTypellm = ChatOpenAI(temperature=0.0)math_llm = OpenAI(temperature=0.0)tools = load_tools( ["human", "llm-math"], llm=math_llm,)agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)In the above code you can see the tool takes input directly from command line. | https://python.langchain.com/docs/integrations/tools/human_tools |
c19072af2b23-2 | You can customize prompt_func and input_func according to your need (as shown below).agent_chain.run("What's my friend Eric's surname?")# Answer with 'Zhu' > Entering new AgentExecutor chain... I don't know Eric's surname, so I should ask a human for guidance. Action: Human Action Input: "What is Eric's surname?" What is Eric's surname? Zhu Observation: Zhu Thought:I now know Eric's surname is Zhu. Final Answer: Eric's surname is Zhu. > Finished chain. "Eric's surname is Zhu."Configuring the Input Function​By default, the HumanInputRun tool uses the python input function to get input from the user.
You can customize the input_func to be anything you'd like. | https://python.langchain.com/docs/integrations/tools/human_tools |
c19072af2b23-3 | For instance, if you want to accept multi-line input, you could do the following:def get_input() -> str: print("Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.") contents = [] while True: try: line = input() except EOFError: break if line == "q": break contents.append(line) return "\n".join(contents)# You can modify the tool when loadingtools = load_tools(["human", "ddg-search"], llm=math_llm, input_func=get_input)# Or you can directly instantiate the toolfrom langchain.tools import HumanInputRuntool = HumanInputRun(input_func=get_input)agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent_chain.run("I need help attributing a quote") > Entering new AgentExecutor chain... I should ask a human for guidance Action: Human Action Input: "Can you help me attribute a quote?" Can you help me attribute a quote? Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end. vini vidi vici q Observation: vini vidi vici | https://python.langchain.com/docs/integrations/tools/human_tools |
c19072af2b23-4 | Observation: vini vidi vici Thought:I need to provide more context about the quote Action: Human Action Input: "The quote is 'Veni, vidi, vici'" The quote is 'Veni, vidi, vici' Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end. oh who said it q Observation: oh who said it Thought:I can use DuckDuckGo Search to find out who said the quote Action: DuckDuckGo Search Action Input: "Who said 'Veni, vidi, vici'?" Observation: Updated on September 06, 2019. "Veni, vidi, vici" is a famous phrase said to have been spoken by the Roman Emperor Julius Caesar (100-44 BCE) in a bit of stylish bragging that impressed many of the writers of his day and beyond. The phrase means roughly "I came, I saw, I conquered" and it could be pronounced approximately Vehnee, Veedee ... Veni, vidi, vici (Classical Latin: [we�ni� wi�di� wi�ki�], Ecclesiastical Latin: [ˈveni ˈvidi ˈvitʃi]; "I came; I saw; I conquered") is a Latin phrase used to refer to a swift, conclusive victory.The phrase is popularly attributed to Julius Caesar who, according to Appian, used the phrase in a letter to the Roman Senate around 47 BC after he had achieved a quick victory | https://python.langchain.com/docs/integrations/tools/human_tools |
c19072af2b23-5 | the phrase in a letter to the Roman Senate around 47 BC after he had achieved a quick victory in his short ... veni, vidi, vici Latin quotation from Julius Caesar ve· ni, vi· di, vi· ci ˌw�-nē ˌwē-dē ˈwē-kē ˌv�-nē ˌvē-dē ˈvē-chē : I came, I saw, I conquered Articles Related to veni, vidi, vici 'In Vino Veritas' and Other Latin... Dictionary Entries Near veni, vidi, vici Venite veni, vidi, vici Venizélos See More Nearby Entries Cite this Entry Style The simplest explanation for why veni, vidi, vici is a popular saying is that it comes from Julius Caesar, one of history's most famous figures, and has a simple, strong meaning: I'm powerful and fast. But it's not just the meaning that makes the phrase so powerful. Caesar was a gifted writer, and the phrase makes use of Latin grammar to ... One of the best known and most frequently quoted Latin expression, veni, vidi, vici may be found hundreds of times throughout the centuries used as an expression of triumph. The words are said to have been used by Caesar as he was enjoying a triumph. Thought:I now know the final answer Final Answer: Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered". > Finished chain. 'Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I | https://python.langchain.com/docs/integrations/tools/human_tools |
c19072af2b23-6 | "Veni, vidi, vici" which means "I came, I saw, I conquered".'Previoushuggingface_toolsNextIFTTT WebHooksConfiguring the Input FunctionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/human_tools |
b37d57db085d-0 | DataForSeo API Wrapper | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/dataforseo |
b37d57db085d-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsDataForSeo API WrapperOn this pageDataForSeo API WrapperThis notebook demonstrates how to use the DataForSeo API wrapper to obtain search engine results. The DataForSeo API allows users to retrieve SERP from most popular search engines like Google, Bing, Yahoo. It also allows to get SERPs from different search engine types like Maps, News, Events, etc.from langchain.utilities import DataForSeoAPIWrapperSetting up the API wrapper with your credentials​You can obtain your API credentials by registering on the DataForSeo website.import osos.environ["DATAFORSEO_LOGIN"] = "your_api_access_username"os.environ["DATAFORSEO_PASSWORD"] = "your_api_access_password"wrapper = DataForSeoAPIWrapper()The run method will return the first result snippet from one of the following elements: answer_box, knowledge_graph, featured_snippet, shopping, organic.wrapper.run("Weather in Los Angeles")The Difference Between run and results​run and results are two methods provided by the | https://python.langchain.com/docs/integrations/tools/dataforseo |
b37d57db085d-2 | Difference Between run and results​run and results are two methods provided by the DataForSeoAPIWrapper class.The run method executes the search and returns the first result snippet from the answer box, knowledge graph, featured snippet, shopping, or organic results. These elements are sorted by priority from highest to lowest.The results method returns a JSON response configured according to the parameters set in the wrapper. This allows for more flexibility in terms of what data you want to return from the API.Getting Results as JSON​You can customize the result types and fields you want to return in the JSON response. You can also set a maximum count for the number of top results to return.json_wrapper = DataForSeoAPIWrapper( json_result_types=["organic", "knowledge_graph", "answer_box"], json_result_fields=["type", "title", "description", "text"], top_count=3,)json_wrapper.results("Bill Gates")Customizing Location and Language​You can specify the location and language of your search results by passing additional parameters to the API wrapper.customized_wrapper = DataForSeoAPIWrapper( top_count=10, json_result_types=["organic", "local_pack"], json_result_fields=["title", "description", "type"], params={"location_name": "Germany", "language_code": "en"},)customized_wrapper.results("coffee near me")Customizing the Search Engine​You can also specify the search engine you want to use.customized_wrapper = DataForSeoAPIWrapper( top_count=10, json_result_types=["organic", "local_pack"], json_result_fields=["title", "description", "type"], params={"location_name": "Germany", "language_code": "en", "se_name": | https://python.langchain.com/docs/integrations/tools/dataforseo |
b37d57db085d-3 | params={"location_name": "Germany", "language_code": "en", "se_name": "bing"},)customized_wrapper.results("coffee near me")Customizing the Search Type​The API wrapper also allows you to specify the type of search you want to perform. For example, you can perform a maps search.maps_search = DataForSeoAPIWrapper( top_count=10, json_result_fields=["title", "value", "address", "rating", "type"], params={ "location_coordinate": "52.512,13.36,12z", "language_code": "en", "se_type": "maps", },)maps_search.results("coffee near me")Integration with Langchain Agents​You can use the Tool class from the langchain.agents module to integrate the DataForSeoAPIWrapper with a langchain agent. The Tool class encapsulates a function that the agent can call.from langchain.agents import Toolsearch = DataForSeoAPIWrapper( top_count=3, json_result_types=["organic"], json_result_fields=["title", "description", "type"],)tool = Tool( name="google-search-answer", description="My new answer tool", func=search.run,)json_tool = Tool( name="google-search-json", description="My new json tool", func=search.results,)PreviousChatGPT PluginsNextDuckDuckGo SearchSetting up the API wrapper with your credentialsThe Difference Between run and resultsGetting Results as JSONCustomizing Location and LanguageCustomizing the Search EngineCustomizing the Search TypeIntegration with Langchain AgentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © | https://python.langchain.com/docs/integrations/tools/dataforseo |
b37d57db085d-4 | with Langchain AgentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/dataforseo |
22ef249b4db0-0 | PubMed Tool | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/pubmed |
22ef249b4db0-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsPubMed ToolPubMed ToolThis notebook goes over how to use PubMed as a toolPubMed® comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.from langchain.tools import PubmedQueryRuntool = PubmedQueryRun()tool.run("chatgpt") 'Published: <Year>2023</Year><Month>May</Month><Day>31</Day>\nTitle: Dermatology in the wake of an AI revolution: who gets a say?\nSummary: \n\nPublished: <Year>2023</Year><Month>May</Month><Day>30</Day>\nTitle: What is ChatGPT and what do we do with it? Implications of the age of AI for nursing and midwifery practice and education: An editorial.\nSummary: \n\nPublished: | https://python.langchain.com/docs/integrations/tools/pubmed |
22ef249b4db0-2 | and midwifery practice and education: An editorial.\nSummary: \n\nPublished: <Year>2023</Year><Month>Jun</Month><Day>02</Day>\nTitle: The Impact of ChatGPT on the Nursing Profession: Revolutionizing Patient Care and Education.\nSummary: The nursing field has undergone notable changes over time and is projected to undergo further modifications in the future, owing to the advent of sophisticated technologies and growing healthcare needs. The advent of ChatGPT, an AI-powered language model, is expected to exert a significant influence on the nursing profession, specifically in the domains of patient care and instruction. The present article delves into the ramifications of ChatGPT within the nursing domain and accentuates its capacity and constraints to transform the discipline.'PreviousOpenWeatherMap APINextRequestsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/pubmed |
f4e7592abaf2-0 | SearxNG Search API | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsSearxNG Search APIOn this pageSearxNG Search APIThis notebook goes over how to use a self hosted SearxNG search API to search the web.You can check this link for more informations about Searx API parameters.import pprintfrom langchain.utilities import SearxSearchWrappersearch = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")For some engines, if a direct answer is available the warpper will print the answer instead of the full list of search results. You can use the results method of the wrapper if you want to obtain all the results.search.run("What is the capital of France") 'Paris is the capital of France, the largest country of Europe with 550 000 km2 (65 millions inhabitants). Paris has 2.234 million inhabitants end 2011. She is the core of Ile de France region (12 million people).'Custom Parameters​SearxNG supports 135 search engines. You can also customize | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-2 | Parameters​SearxNG supports 135 search engines. You can also customize the Searx wrapper with arbitrary named parameters that will be passed to the Searx search API . In the below example we will making a more interesting use of custom search parameters from searx search api.In this example we will be using the engines parameters to query wikipediasearch = SearxSearchWrapper( searx_host="http://127.0.0.1:8888", k=5) # k is for max number of itemssearch.run("large language model ", engines=["wiki"]) 'Large language models (LLMs) represent a major advancement in AI, with the promise of transforming domains through learned knowledge. LLM sizes have been increasing 10X every year for the last few years, and as these models grow in complexity and size, so do their capabilities.\n\nGPT-3 can translate language, write essays, generate computer code, and more — all with limited to no supervision. In July 2020, OpenAI unveiled GPT-3, a language model that was easily the largest known at the time. Put simply, GPT-3 is trained to predict the next word in a sentence, much like how a text message autocomplete feature works.\n\nA large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Large language models are among the most successful applications of transformer models.\n\nAll of today’s well-known language models—e.g., GPT-3 from OpenAI, PaLM or LaMDA from Google, Galactica or OPT from Meta, Megatron-Turing from Nvidia/Microsoft, Jurassic-1 from AI21 Labs—are...\n\nLarge language models (LLMs) such | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-3 | from AI21 Labs—are...\n\nLarge language models (LLMs) such as GPT-3are increasingly being used to generate text. These tools should be used with care, since they can generate content that is biased, non-verifiable, constitutes original research, or violates copyrights.'Passing other Searx parameters for searx like languagesearch = SearxSearchWrapper(searx_host="http://127.0.0.1:8888", k=1)search.run("deep learning", language="es", engines=["wiki"]) 'Aprendizaje profundo (en inglés, deep learning) es un conjunto de algoritmos de aprendizaje automático (en inglés, machine learning) que intenta modelar abstracciones de alto nivel en datos usando arquitecturas computacionales que admiten transformaciones no lineales múltiples e iterativas de datos expresados en forma matricial o tensorial. 1'Obtaining results with metadata​In this example we will be looking for scientific paper using the categories parameter and limiting the results to a time_range (not all engines support the time range option).We also would like to obtain the results in a structured way including metadata. For this we will be using the results method of the wrapper.search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")results = search.results( "Large Language Model prompt", num_results=5, categories="science", time_range="year",)pprint.pp(results) [{'snippet': '… on natural language instructions, large language models (… the ' 'prompt used to steer the model, | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-4 | 'prompt used to steer the model, and most effective prompts … to ' 'prompt engineering, we propose Automatic Prompt …', 'title': 'Large language models are human-level prompt engineers', 'link': 'https://arxiv.org/abs/2211.01910', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… Large language models (LLMs) have introduced new possibilities ' 'for prototyping with AI [18]. Pre-trained on a large amount of ' 'text data, models … language instructions called prompts. …', 'title': 'Promptchainer: Chaining large language model prompts through ' 'visual programming', 'link': 'https://dl.acm.org/doi/abs/10.1145/3491101.3519729', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… can introspect the large prompt model. We derive the view ' 'ϕ0(X) and the model h0 from T01. However, instead of fully ' 'fine-tuning T0 during co-training, we focus on soft prompt | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-5 | 'fine-tuning T0 during co-training, we focus on soft prompt ' 'tuning, …', 'title': 'Co-training improves prompt-based learning for large language ' 'models', 'link': 'https://proceedings.mlr.press/v162/lang22a.html', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… With the success of large language models (LLMs) of code and ' 'their use as … prompt design process become important. In this ' 'work, we propose a framework called Repo-Level Prompt …', 'title': 'Repository-level prompt generation for large language models of ' 'code', 'link': 'https://arxiv.org/abs/2206.12839', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… Figure 2 | The benefits of different components of a prompt ' 'for the largest language model (Gopher), as estimated from ' 'hierarchical logistic regression. Each point estimates the ' 'unique | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-6 | the ' 'unique …', 'title': 'Can language models learn from explanations in context?', 'link': 'https://arxiv.org/abs/2204.02329', 'engines': ['google scholar'], 'category': 'science'}]Get papers from arxivresults = search.results( "Large Language Model prompt", num_results=5, engines=["arxiv"])pprint.pp(results) [{'snippet': 'Thanks to the advanced improvement of large pre-trained language ' 'models, prompt-based fine-tuning is shown to be effective on a ' 'variety of downstream tasks. Though many prompting methods have ' 'been investigated, it remains unknown which type of prompts are ' 'the most effective among three types of prompts (i.e., ' 'human-designed prompts, schema prompts and null prompts). In ' 'this work, we empirically compare the three types of prompts ' 'under both few-shot and fully-supervised settings. Our ' 'experimental results show that schema prompts are the most ' 'effective in general. Besides, | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-7 | 'effective in general. Besides, the performance gaps tend to ' 'diminish when the scale of training data grows large.', 'title': 'Do Prompts Solve NLP Tasks Using Natural Language?', 'link': 'http://arxiv.org/abs/2203.00902v1', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Cross-prompt automated essay scoring (AES) requires the system ' 'to use non target-prompt essays to award scores to a ' 'target-prompt essay. Since obtaining a large quantity of ' 'pre-graded essays to a particular prompt is often difficult and ' 'unrealistic, the task of cross-prompt AES is vital for the ' 'development of real-world AES systems, yet it remains an ' 'under-explored area of research. Models designed for ' 'prompt-specific AES rely heavily on prompt-specific knowledge ' 'and perform poorly in the cross-prompt setting, whereas current ' 'approaches | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-8 | ' 'approaches to cross-prompt AES either require a certain quantity ' 'of labelled target-prompt essays or require a large quantity of ' 'unlabelled target-prompt essays to perform transfer learning in ' 'a multi-step manner. To address these issues, we introduce ' 'Prompt Agnostic Essay Scorer (PAES) for cross-prompt AES. Our ' 'method requires no access to labelled or unlabelled ' 'target-prompt data during training and is a single-stage ' 'approach. PAES is easy to apply in practice and achieves ' 'state-of-the-art performance on the Automated Student Assessment ' 'Prize (ASAP) dataset.', 'title': 'Prompt Agnostic Essay Scorer: A Domain Generalization Approach to ' 'Cross-prompt Automated Essay Scoring', 'link': 'http://arxiv.org/abs/2008.01441v1', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-9 | 'category': 'science'}, {'snippet': 'Research on prompting has shown excellent performance with ' 'little or even no supervised training across many tasks. ' 'However, prompting for machine translation is still ' 'under-explored in the literature. We fill this gap by offering a ' 'systematic study on prompting strategies for translation, ' 'examining various factors for prompt template and demonstration ' 'example selection. We further explore the use of monolingual ' 'data and the feasibility of cross-lingual, cross-domain, and ' 'sentence-to-document transfer learning in prompting. Extensive ' 'experiments with GLM-130B (Zeng et al., 2022) as the testbed ' 'show that 1) the number and the quality of prompt examples ' 'matter, where using suboptimal examples degenerates translation; ' '2) several features of prompt examples, such as semantic ' | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-10 | several features of prompt examples, such as semantic ' 'similarity, show significant Spearman correlation with their ' 'prompting performance; yet, none of the correlations are strong ' 'enough; 3) using pseudo parallel prompt examples constructed ' 'from monolingual data via zero-shot prompting could improve ' 'translation; and 4) improved performance is achievable by ' 'transferring knowledge from prompt examples selected in other ' 'settings. We finally provide an analysis on the model outputs ' 'and discuss several problems that prompting still suffers from.', 'title': 'Prompting Large Language Model for Machine Translation: A Case ' 'Study', 'link': 'http://arxiv.org/abs/2301.07069v2', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Large language models can perform new tasks in a zero-shot ' 'fashion, given natural language prompts that specify the desired ' 'behavior. Such prompts are typically hand | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-11 | 'behavior. Such prompts are typically hand engineered, but can ' 'also be learned with gradient-based methods from labeled data. ' 'However, it is underexplored what factors make the prompts ' 'effective, especially when the prompts are natural language. In ' 'this paper, we investigate common attributes shared by effective ' 'prompts. We first propose a human readable prompt tuning method ' '(F LUENT P ROMPT) based on Langevin dynamics that incorporates a ' 'fluency constraint to find a diverse distribution of effective ' 'and fluent prompts. Our analysis reveals that effective prompts ' 'are topically related to the task domain and calibrate the prior ' 'probability of label words. Based on these findings, we also ' 'propose a method for generating prompts using only unlabeled ' 'data, outperforming strong baselines by an average of 7.0% ' | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-12 | ' 'accuracy across three tasks.', 'title': "Toward Human Readable Prompt Tuning: Kubrick's The Shining is a " 'good movie, and a good prompt too?', 'link': 'http://arxiv.org/abs/2212.10539v1', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Prevailing methods for mapping large generative language models ' "to supervised tasks may fail to sufficiently probe models' novel " 'capabilities. Using GPT-3 as a case study, we show that 0-shot ' 'prompts can significantly outperform few-shot prompts. We ' 'suggest that the function of few-shot examples in these cases is ' 'better described as locating an already learned task rather than ' 'meta-learning. This analysis motivates rethinking the role of ' 'prompts in controlling and evaluating powerful language models. ' 'In this work, we discuss methods of prompt programming, ' | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-13 | prompt programming, ' 'emphasizing the usefulness of considering prompts through the ' 'lens of natural language. We explore techniques for exploiting ' 'the capacity of narratives and cultural anchors to encode ' 'nuanced intentions and techniques for encouraging deconstruction ' 'of a problem into components before producing a verdict. ' 'Informed by this more encompassing theory of prompt programming, ' 'we also introduce the idea of a metaprompt that seeds the model ' 'to generate its own natural language prompts for a range of ' 'tasks. Finally, we discuss how these more general methods of ' 'interacting with language models can be incorporated into ' 'existing and future benchmarks and practical applications.', 'title': 'Prompt Programming for Large Language Models: Beyond the Few-Shot ' 'Paradigm', 'link': 'http://arxiv.org/abs/2102.07350v1', 'engines': ['arxiv'], | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-14 | 'engines': ['arxiv'], 'category': 'science'}]In this example we query for large language models under the it category. We then filter the results that come from github.results = search.results("large language model", num_results=20, categories="it")pprint.pp(list(filter(lambda r: r["engines"][0] == "github", results))) [{'snippet': 'Guide to using pre-trained large language models of source code', 'title': 'Code-LMs', 'link': 'https://github.com/VHellendoorn/Code-LMs', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Dramatron uses large language models to generate coherent ' 'scripts and screenplays.', 'title': 'dramatron', 'link': 'https://github.com/deepmind/dramatron', 'engines': ['github'], 'category': 'it'}]We could also directly query for results from github and other source forges.results = search.results( "large language model", num_results=20, engines=["github", "gitlab"])pprint.pp(results) [{'snippet': "Implementation of 'A Watermark for Large Language Models' paper " 'by Kirchenbauer & Geiping et. al.', 'title': 'Peutlefaire / LMWatermark', 'link': 'https://gitlab.com/BrianPulfer/LMWatermark', | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-15 | 'link': 'https://gitlab.com/BrianPulfer/LMWatermark', 'engines': ['gitlab'], 'category': 'it'}, {'snippet': 'Guide to using pre-trained large language models of source code', 'title': 'Code-LMs', 'link': 'https://github.com/VHellendoorn/Code-LMs', 'engines': ['github'], 'category': 'it'}, {'snippet': '', 'title': 'Simen Burud / Large-scale Language Models for Conversational ' 'Speech Recognition', 'link': 'https://gitlab.com/BrianPulfer', 'engines': ['gitlab'], 'category': 'it'}, {'snippet': 'Dramatron uses large language models to generate coherent ' 'scripts and screenplays.', 'title': 'dramatron', 'link': 'https://github.com/deepmind/dramatron', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Code for loralib, an implementation of "LoRA: Low-Rank ' 'Adaptation of Large Language Models"', 'title': 'LoRA', 'link': 'https://github.com/microsoft/LoRA', 'engines': | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-16 | 'https://github.com/microsoft/LoRA', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Code for the paper "Evaluating Large Language Models Trained on ' 'Code"', 'title': 'human-eval', 'link': 'https://github.com/openai/human-eval', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A trend starts from "Chain of Thought Prompting Elicits ' 'Reasoning in Large Language Models".', 'title': 'Chain-of-ThoughtsPapers', 'link': 'https://github.com/Timothyxxx/Chain-of-ThoughtsPapers', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Mistral: A strong, northwesterly wind: Framework for transparent ' 'and accessible large-scale language model training, built with ' 'Hugging Face 🤗 Transformers.', 'title': 'mistral', 'link': 'https://github.com/stanford-crfm/mistral', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A prize for finding tasks that cause large language | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-17 | 'it'}, {'snippet': 'A prize for finding tasks that cause large language models to ' 'show inverse scaling', 'title': 'prize', 'link': 'https://github.com/inverse-scaling/prize', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Optimus: the first large-scale pre-trained VAE language model', 'title': 'Optimus', 'link': 'https://github.com/ChunyuanLI/Optimus', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Seminar on Large Language Models (COMP790-101 at UNC Chapel ' 'Hill, Fall 2022)', 'title': 'llm-seminar', 'link': 'https://github.com/craffel/llm-seminar', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A central, open resource for data and tools related to ' 'chain-of-thought reasoning in large language models. Developed @ ' 'Samwald research group: https://samwald.info/', 'title': 'ThoughtSource', 'link': 'https://github.com/OpenBioLink/ThoughtSource', | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-18 | 'link': 'https://github.com/OpenBioLink/ThoughtSource', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A comprehensive list of papers using large language/multi-modal ' 'models for Robotics/RL, including papers, codes, and related ' 'websites', 'title': 'Awesome-LLM-Robotics', 'link': 'https://github.com/GT-RIPL/Awesome-LLM-Robotics', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Tools for curating biomedical training data for large-scale ' 'language modeling', 'title': 'biomedical', 'link': 'https://github.com/bigscience-workshop/biomedical', 'engines': ['github'], 'category': 'it'}, {'snippet': 'ChatGPT @ Home: Large Language Model (LLM) chatbot application, ' 'written by ChatGPT', 'title': 'ChatGPT-at-Home', 'link': 'https://github.com/Sentdex/ChatGPT-at-Home', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Design and Deploy Large | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-19 | 'category': 'it'}, {'snippet': 'Design and Deploy Large Language Model Apps', 'title': 'dust', 'link': 'https://github.com/dust-tt/dust', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Polyglot: Large Language Models of Well-balanced Competence in ' 'Multi-languages', 'title': 'polyglot', 'link': 'https://github.com/EleutherAI/polyglot', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Code release for "Learning Video Representations from Large ' 'Language Models"', 'title': 'LaViLa', 'link': 'https://github.com/facebookresearch/LaViLa', 'engines': ['github'], 'category': 'it'}, {'snippet': 'SmoothQuant: Accurate and Efficient Post-Training Quantization ' 'for Large Language Models', 'title': 'smoothquant', 'link': 'https://github.com/mit-han-lab/smoothquant', 'engines': ['github'], 'category': 'it'}, {'snippet': 'This repository contains the code, data, and models of the paper ' | https://python.langchain.com/docs/integrations/tools/searx_search |
f4e7592abaf2-20 | {'snippet': 'This repository contains the code, data, and models of the paper ' 'titled "XL-Sum: Large-Scale Multilingual Abstractive ' 'Summarization for 44 Languages" published in Findings of the ' 'Association for Computational Linguistics: ACL-IJCNLP 2021.', 'title': 'xl-sum', 'link': 'https://github.com/csebuetnlp/xl-sum', 'engines': ['github'], 'category': 'it'}]PreviousSearch ToolsNextSerpAPICustom ParametersObtaining results with metadataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/searx_search |
ecc342c86039-0 | awslambda | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/awslambda |
ecc342c86039-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsawslambdaOn this pageawslambdaAWS Lambda API​This notebook goes over how to use the AWS Lambda Tool component.AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS), designed to allow developers to build and run applications and services without the need for provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.By including a awslambda in the list of tools provided to an Agent, you can grant your Agent the ability to invoke code running in your AWS Cloud for whatever purposes you need.When an Agent uses the awslambda tool, it will provide an argument of type string which will in turn be passed into the Lambda function via the event parameter.First, you need to install boto3 python package.pip install boto3 > /dev/nullIn order for an agent to use the tool, you must provide it with the name and description that match the functionality of you lambda function's logic. You | https://python.langchain.com/docs/integrations/tools/awslambda |
ecc342c86039-2 | must provide it with the name and description that match the functionality of you lambda function's logic. You must also provide the name of your function. Note that because this tool is effectively just a wrapper around the boto3 library, you will need to run aws configure in order to make use of the tool. For more detail, see herefrom langchain import OpenAIfrom langchain.agents import load_tools, AgentTypellm = OpenAI(temperature=0)tools = load_tools( ["awslambda"], awslambda_tool_name="email-sender", awslambda_tool_description="sends an email with the specified content to [email protected]", function_name="testFunction1",)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("Send an email to [email protected] saying hello world.")PreviousArXiv API ToolNextShell ToolAWS Lambda APICommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/awslambda |
790702555cc9-0 | IFTTT WebHooks | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/ifttt |
790702555cc9-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsIFTTT WebHooksOn this pageIFTTT WebHooksThis notebook shows how to use IFTTT Webhooks.From https://github.com/SidU/teams-langchain-js/wiki/Connecting-IFTTT-Services.Creating a webhook​Go to https://ifttt.com/createConfiguring the "If This"​Click on the "If This" button in the IFTTT interface.Search for "Webhooks" in the search bar.Choose the first option for "Receive a web request with a JSON payload."Choose an Event Name that is specific to the service you plan to connect to.
This will make it easier for you to manage the webhook URL.
For example, if you're connecting to Spotify, you could use "Spotify" as your | https://python.langchain.com/docs/integrations/tools/ifttt |
790702555cc9-2 | For example, if you're connecting to Spotify, you could use "Spotify" as your
Event Name.Click the "Create Trigger" button to save your settings and create your webhook.Configuring the "Then That"​Tap on the "Then That" button in the IFTTT interface.Search for the service you want to connect, such as Spotify.Choose an action from the service, such as "Add track to a playlist".Configure the action by specifying the necessary details, such as the playlist name,
e.g., "Songs from AI".Reference the JSON Payload received by the Webhook in your action. For the Spotify
scenario, choose "{{JsonPayload}}" as your search query.Tap the "Create Action" button to save your action settings.Once you have finished configuring your action, click the "Finish" button to
complete the setup.Congratulations! You have successfully connected the Webhook to the desired
service, and you're ready to start receiving data and triggering actions �Finishing up​To get your webhook URL go to https://ifttt.com/maker_webhooks/settingsCopy the IFTTT key value from there. The URL is of the form | https://python.langchain.com/docs/integrations/tools/ifttt |
790702555cc9-3 | https://maker.ifttt.com/use/YOUR_IFTTT_KEY. Grab the YOUR_IFTTT_KEY value.from langchain.tools.ifttt import IFTTTWebhookimport oskey = os.environ["IFTTTKey"]url = f"https://maker.ifttt.com/trigger/spotify/json/with/key/{key}"tool = IFTTTWebhook( name="Spotify", description="Add a song to spotify playlist", url=url)tool.run("taylor swift") "Congratulations! You've fired the spotify JSON event"PreviousHuman as a toolNextLemon AI NLP Workflow AutomationCreating a webhookConfiguring the "If This"Configuring the "Then That"Finishing upCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/ifttt |
4469d76a869d-0 | Metaphor Search | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/metaphor_search |
4469d76a869d-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsMetaphor SearchMetaphor SearchMetaphor is a search engine fully designed to be used by LLMs. You can search and then get the contents for any page.This notebook goes over how to use Metaphor search.First, you need to set up the proper API keys and environment variables. Get 1000 free searches/month here.Then enter your API key as an environment variable.import osos.environ["METAPHOR_API_KEY"] = ""from langchain.utilities import MetaphorSearchAPIWrappersearch = MetaphorSearchAPIWrapper()Call the APIresults takes in a Metaphor-optimized search query and a number of results (up to 500). It returns a list of results with title, url, author, and creation date.search.results("The best blog post about AI safety is definitely this: ", 10)Adding filtersWe can also add filters to our search. include_domains: Optional[List[str]] - List of domains to include in the search. If specified, results will only come from these domains. Only one of include_domains and | https://python.langchain.com/docs/integrations/tools/metaphor_search |
4469d76a869d-2 | the search. If specified, results will only come from these domains. Only one of include_domains and exclude_domains should be specified.exclude_domains: Optional[List[str]] - List of domains to exclude in the search. If specified, results will only come from these domains. Only one of include_domains and exclude_domains should be specified.start_crawl_date: Optional[str] - "Crawl date" refers to the date that Metaphor discovered a link, which is more granular and can be more useful than published date. If start_crawl_date is specified, results will only include links that were crawled after start_crawl_date. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ)end_crawl_date: Optional[str] - "Crawl date" refers to the date that Metaphor discovered a link, which is more granular and can be more useful than published date. If endCrawlDate is specified, results will only include links that were crawled before end_crawl_date. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ)start_published_date: Optional[str] - If specified, only links with a published date after start_published_date will be returned. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ). Note that for some links, we have no published date, and these links will be excluded from the results if start_published_date is specified.end_published_date: Optional[str] - If specified, only links with a published date before end_published_date will be returned. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ). Note that for some links, we have no published date, and these links will be excluded from the results if end_published_date is specified.See full docs here.search.results( "The best blog post about AI safety is definitely this: ", | https://python.langchain.com/docs/integrations/tools/metaphor_search |
4469d76a869d-3 | here.search.results( "The best blog post about AI safety is definitely this: ", 10, include_domains=["lesswrong.com"], start_published_date="2019-01-01",)Use Metaphor as a toolMetaphor can be used as a tool that gets URLs that other tools such as browsing tools.from langchain.agents.agent_toolkits import PlayWrightBrowserToolkitfrom langchain.tools.playwright.utils import ( create_async_playwright_browser, # A synchronous browser is available, though it isn't compatible with jupyter.)async_browser = create_async_playwright_browser()toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)tools = toolkit.get_tools()tools_by_name = {tool.name: tool for tool in tools}print(tools_by_name.keys())navigate_tool = tools_by_name["navigate_browser"]extract_text = tools_by_name["extract_text"]from langchain.agents import initialize_agent, AgentTypefrom langchain.chat_models import ChatOpenAIfrom langchain.tools import MetaphorSearchResultsllm = ChatOpenAI(model_name="gpt-4", temperature=0.7)metaphor_tool = MetaphorSearchResults(api_wrapper=search)agent_chain = initialize_agent( [metaphor_tool, extract_text, navigate_tool], llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)agent_chain.run( "find me an interesting tweet about AI safety using Metaphor, then tell me the first sentence in the post. Do not finish until able to retrieve the first sentence.")PreviousLemon AI NLP Workflow AutomationNextOpenWeatherMap APICommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/metaphor_search |
44ec99aff3b5-0 | Grouped by provider | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerGrouped by provider📄� WandB TracingThere are two recommended ways to trace your LangChains:📄� AI21 LabsThis page covers how to use the AI21 ecosystem within LangChain.📄� AimAim makes it super easy to visualize and debug LangChain executions. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents.📄� AirbyteAirbyte is a data integration platform for ELT pipelines from APIs,📄� AirtableAirtable is a cloud collaboration service.📄� Aleph AlphaAleph Alpha was founded in 2019 with the mission to research and build the foundational technology for an era of strong AI. The team of international scientists, engineers, and innovators researches, develops, and deploys transformative AI like large language and multimodal models and runs the fastest European commercial AI cluster.📄� Alibaba Cloud OpensearchAlibaba Cloud Opensearch OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-3 | OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises.📄� Amazon API GatewayAmazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. APIs act as the "front door" for applications to access data, business logic, or functionality from your backend services. Using API Gateway, you can create RESTful APIs and WebSocket APIs that enable real-time two-way communication applications. API Gateway supports containerized and serverless workloads, as well as web applications.📄� AnalyticDBThis page covers how to use the AnalyticDB ecosystem within LangChain.📄� AnnoyAnnoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.📄� AnyscaleThis page covers how to use the Anyscale ecosystem within LangChain.📄� ApifyThis page covers how to use Apify within LangChain.📄� ArangoDBArangoDB is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud – anywhere.📄� ArgillaArgilla - Open-source data platform for LLMs📄� ArthurArthur is a model monitoring and observability | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-4 | ArthurArthur is a model monitoring and observability platform.📄� ArxivarXiv is an open-access archive for 2 million scholarly articles in the fields of physics,📄� AtlasDBThis page covers how to use Nomic's Atlas ecosystem within LangChain.📄� AwaDBAwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.📄� AWS S3 DirectoryAmazon Simple Storage Service (Amazon S3) is an object storage service.📄� AZLyricsAZLyrics is a large, legal, every day growing collection of lyrics.📄� Azure Blob StorageAzure Blob Storage is Microsoft's object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn't adhere to a particular data model or definition, such as text or binary data.📄� Azure Cognitive SearchAzure Cognitive Search (formerly known as Azure Search) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.📄� Azure OpenAIMicrosoft Azure, often referred to as Azure is a cloud computing platform run by Microsoft, which offers access, management, and development of applications and services through global data centers. It provides a range of capabilities, including software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). Microsoft Azure supports many programming languages, tools, and frameworks, including Microsoft-specific and third-party software and systems.📄� BananaThis page covers how to use the Banana ecosystem within | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-5 | BananaThis page covers how to use the Banana ecosystem within LangChain.📄� BasetenLearn how to use LangChain with models deployed on Baseten.📄� BeamThis page covers how to use Beam within LangChain.📄� BedrockAmazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.📄� BiliBiliBilibili is one of the most beloved long-form video sites in China.📄� BlackboardBlackboard Learn (previously the Blackboard Learning Management System)📄� Brave SearchBrave Search is a search engine developed by Brave Software.📄� CassandraApache Cassandra® is a free and open-source, distributed, wide-column📄� CerebriumAIThis page covers how to use the CerebriumAI ecosystem within LangChain.📄� ChaindeskChaindesk is an open source document retrieval platform that helps to connect your personal data with Large Language Models.📄� ChromaChroma is a database for building AI applications with embeddings.📄� ClarifaiClarifai is one of first deep learning platforms having been founded in 2013. Clarifai provides an AI platform with the full AI lifecycle for data exploration, data labeling, model training, evaluation and inference around images, video, text and audio data. In the LangChain ecosystem, as far as we're aware, Clarifai is the only provider that supports LLMs, embeddings and a | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-6 | we're aware, Clarifai is the only provider that supports LLMs, embeddings and a vector store in one production scale platform, making it an excellent choice to operationalize your LangChain implementations.📄� ClearMLClearML is a ML/DL development and production suite, it contains 5 main modules:📄� CnosDBCnosDB is an open source distributed time series database with high performance, high compression rate and high ease of use.📄� CohereCohere is a Canadian startup that provides natural language processing models📄� College ConfidentialCollege Confidential gives information on 3,800+ colleges and universities.📄� CometIn this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with Comet.📄� ConfluenceConfluence is a wiki collaboration platform that saves and organizes all of the project-related material. Confluence is a knowledge base that primarily handles content management activities.📄� C TransformersThis page covers how to use the C Transformers library within LangChain.📄� DatabricksThis notebook covers how to connect to the Databricks runtimes and Databricks SQL using the SQLDatabase wrapper of LangChain.📄� Datadog Tracingddtrace is a Datadog application performance monitoring (APM) library which provides an integration to monitor your LangChain application.📄� Datadog LogsDatadog is a monitoring and analytics platform for cloud-scale applications.📄� DataForSEOThis page provides instructions on how to use the DataForSEO search APIs within | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-7 | DataForSEOThis page provides instructions on how to use the DataForSEO search APIs within LangChain.📄� DeepInfraThis page covers how to use the DeepInfra ecosystem within LangChain.📄� Deep LakeThis page covers how to use the Deep Lake ecosystem within LangChain.📄� DiffbotDiffbot is a service to read web pages. Unlike traditional web scraping tools,📄� DiscordDiscord is a VoIP and instant messaging social platform. Users have the ability to communicate📄� DocugamiDocugami converts business documents into a Document XML Knowledge Graph, generating forests📄� DuckDBDuckDB is an in-process SQL OLAP database management system.📄� ElasticsearchElasticsearch is a distributed, RESTful search and analytics engine.📄� EverNoteEverNote is intended for archiving and creating notes in which photos, audio and saved web content can be embedded. Notes are stored in virtual "notebooks" and can be tagged, annotated, edited, searched, and exported.📄� Facebook ChatMessenger) is an American proprietary instant messaging app and📄� FigmaFigma is a collaborative web application for interface design.📄� FlyteFlyte is an open-source orchestrator that facilitates building production-grade data and ML pipelines.📄� ForefrontAIThis page covers how to use the ForefrontAI ecosystem within LangChain.📄� GitGit is a distributed version control system that tracks changes in any set of computer files, usually used for coordinating work among programmers collaboratively developing source code during | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-8 | in any set of computer files, usually used for coordinating work among programmers collaboratively developing source code during software development.📄� GitBookGitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs.📄� GoldenGolden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve relevant structured data about relevant entities.📄� Google BigQueryGoogle BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.📄� Google Cloud StorageGoogle Cloud Storage is a managed service for storing unstructured data.📄� Google DriveGoogle Drive is a file storage and synchronization service developed by Google.📄� Google SearchThis page covers how to use the Google Search API within LangChain.📄� Google SerperThis page covers how to use the Serper Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search.📄� GooseAIThis page covers how to use the GooseAI ecosystem within LangChain.📄� GPT4AllThis page covers how to use the GPT4All wrapper within LangChain. The tutorial is divided into two parts: installation and setup, followed by usage with an example.📄� GraphsignalThis page covers how to use Graphsignal to trace and monitor LangChain. Graphsignal enables full visibility into your application. It provides latency breakdowns by | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-9 | and monitor LangChain. Graphsignal enables full visibility into your application. It provides latency breakdowns by chains and tools, exceptions with full context, data monitoring, compute/GPU utilization, OpenAI cost analytics, and more.📄� GrobidThis page covers how to use the Grobid to parse articles for LangChain.📄� GutenbergProject Gutenberg is an online library of free eBooks.📄� Hacker NewsHacker News (sometimes abbreviated as HN) is a social news📄� Hazy ResearchThis page covers how to use the Hazy Research ecosystem within LangChain.📄� HeliconeThis page covers how to use the Helicone ecosystem within LangChain.📄� HologresHologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time.📄� Hugging FaceThis page covers how to use the Hugging Face ecosystem (including the Hugging Face Hub) within LangChain.📄� iFixitiFixit is the largest, open repair community on the web. The site contains nearly 100k📄� IMSDbIMSDb is the Internet Movie Script Database.📄� InfinoInfino is an open-source observability platform that stores both metrics and application logs together.📄� JinaThis page covers how to use the Jina ecosystem within LangChain.📄� LanceDBThis page covers how to use LanceDB within LangChain.📄� LangChain Decorators | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-10 | use LanceDB within LangChain.📄ï¸� LangChain Decorators ✨lanchchain decorators is a layer on the top of LangChain that provides syntactic sugar ğŸ� for writing custom langchain prompts and chains📄ï¸� Llama.cppThis page covers how to use llama.cpp within LangChain.📄ï¸� MarqoThis page covers how to use the Marqo ecosystem within LangChain.📄ï¸� MediaWikiDumpMediaWiki XML Dumps contain the content of a wiki📄ï¸� MetalThis page covers how to use Metal within LangChain.📄ï¸� Microsoft OneDriveMicrosoft OneDrive (formerly SkyDrive) is a file-hosting service operated by Microsoft.📄ï¸� Microsoft PowerPointMicrosoft PowerPoint is a presentation program by Microsoft.📄ï¸� Microsoft WordMicrosoft Word is a word processor developed by Microsoft.📄ï¸� MilvusThis page covers how to use the Milvus ecosystem within LangChain.📄ï¸� MLflow AI GatewayThe MLflow AI Gateway service is a powerful tool designed to streamline the usage and management of various large language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests. See the MLflow AI Gateway documentation for more details.📄ï¸� MLflowThis notebook goes over how to track your LangChain experiments into your MLflow Server📄ï¸� ModalThis page covers how to use the Modal ecosystem to run LangChain custom | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-11 | ModalThis page covers how to use the Modal ecosystem to run LangChain custom LLMs.📄� ModelScopeThis page covers how to use the modelscope ecosystem within LangChain.📄� Modern TreasuryModern Treasury simplifies complex payment operations. It is a unified platform to power products and processes that move money.📄� MomentoMomento Cache is the world's first truly serverless caching service. It provides instant elasticity, scale-to-zero📄� MotherduckMotherduck is a managed DuckDB-in-the-cloud service.📄� MyScaleThis page covers how to use MyScale vector database within LangChain.📄� NLPCloudThis page covers how to use the NLPCloud ecosystem within LangChain.📄� Notion DBNotion is a collaboration platform with modified Markdown support that integrates kanban📄� ObsidianObsidian is a powerful and extensible knowledge base📄� OpenAIOpenAI is American artificial intelligence (AI) research laboratory📄� OpenLLMThis page demonstrates how to use OpenLLM📄� OpenSearchThis page covers how to use the OpenSearch ecosystem within LangChain.📄� OpenWeatherMapOpenWeatherMap provides all essential weather data for a specific location:📄� PetalsThis page covers how to use the Petals ecosystem within LangChain.📄� PGVectorThis page covers how to use the Postgres PGVector ecosystem within LangChain📄� PineconeThis page covers how to use the Pinecone ecosystem within | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-12 | PineconeThis page covers how to use the Pinecone ecosystem within LangChain.📄� PipelineAIThis page covers how to use the PipelineAI ecosystem within LangChain.🗃� Portkey1 items📄� PredibaseLearn how to use LangChain with models on Predibase.📄� Prediction GuardThis page covers how to use the Prediction Guard ecosystem within LangChain.📄� PromptLayerThis page covers how to use PromptLayer within LangChain.📄� PsychicPsychic is a platform for integrating with SaaS tools like Notion, Zendesk,📄� QdrantThis page covers how to use the Qdrant ecosystem within LangChain.📄� Ray ServeRay Serve is a scalable model serving library for building online inference APIs. Serve is particularly well suited for system composition, enabling you to build a complex inference service consisting of multiple chains and business logic all in Python code.📄� RebuffRebuff is a self-hardening prompt injection detector.📄� RedditReddit is an American social news aggregation, content rating, and discussion website.📄� RedisThis page covers how to use the Redis ecosystem within LangChain.📄� ReplicateThis page covers how to run models on Replicate within LangChain.📄� RoamROAM is a note-taking tool for networked thought, designed to create a personal knowledge base.📄� RocksetRockset is a real-time analytics database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-13 | database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged Index™ on structured and semi-structured data with an efficient store for vector embeddings. Its support for running SQL on schemaless data makes it a perfect choice for running vector search with metadata filters.📄� RunhouseThis page covers how to use the Runhouse ecosystem within LangChain.📄� RWKV-4This page covers how to use the RWKV-4 wrapper within LangChain.📄� SageMaker EndpointAmazon SageMaker is a system that can build, train, and deploy machine learning (ML) models with fully managed infrastructure, tools, and workflows.📄� SearxNG Search APIThis page covers how to use the SearxNG search API within LangChain.📄� SerpAPIThis page covers how to use the SerpAPI search APIs within LangChain.📄� Shale ProtocolShale Protocol provides production-ready inference APIs for open LLMs. It's a Plug & Play API as it's hosted on a highly scalable GPU cloud infrastructure.📄� SingleStoreDBSingleStoreDB is a high-performance distributed SQL database that supports deployment both in the cloud and on-premises. It provides vector storage, and vector functions including dotproduct and euclideandistance, thereby supporting AI applications that require text similarity matching.📄� scikit-learnscikit-learn is an open source collection of machine learning algorithms,📄� SlackSlack is an instant messaging program.📄� spaCyspaCy is an open-source software library for advanced natural language processing, written in the | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-14 | spaCyspaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython.📄� SpreedlySpreedly is a service that allows you to securely store credit cards and use them to transact against any number of payment gateways and third party APIs. It does this by simultaneously providing a card tokenization/vault service as well as a gateway and receiver integration service. Payment methods tokenized by Spreedly are stored at Spreedly, allowing you to independently store a card and then pass that card to different end points based on your business requirements.📄� StarRocksStarRocks is a High-Performance Analytical Database.📄� StochasticAIThis page covers how to use the StochasticAI ecosystem within LangChain.📄� StripeStripe is an Irish-American financial services and software as a service (SaaS) company. It offers payment-processing software and application programming interfaces for e-commerce websites and mobile applications.📄� TairThis page covers how to use the Tair ecosystem within LangChain.📄� TelegramTelegram Messenger is a globally accessible freemium, cross-platform, encrypted, cloud-based and centralized instant messaging service. The application also provides optional end-to-end encrypted chats and video calling, VoIP, file sharing and several other features.📄� TigrisTigris is an open source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications.📄� 2Markdown2markdown service transforms website content into structured markdown files.📄� TrelloTrello is a web-based project management and collaboration tool that allows individuals and teams to organize and track | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-15 | is a web-based project management and collaboration tool that allows individuals and teams to organize and track their tasks and projects. It provides a visual interface known as a "board" where users can create lists and cards to represent their tasks and activities.📄� TruLensThis page covers how to use TruLens to evaluate and track LLM apps built on langchain.📄� TwitterTwitter is an online social media and social networking service.📄� TypesenseTypesense is an open source, in-memory search engine, that you can either📄� UnstructuredThe unstructured package from🗃� Vectara2 items📄� VespaVespa is a fully featured search engine and vector database.📄� Weights & BiasesThis notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to see.📄� WeatherOpenWeatherMap is an open source weather service provider.📄� WeaviateThis page covers how to use the Weaviate ecosystem within LangChain.📄� WhatsAppWhatsApp (also called WhatsApp Messenger) is a freeware, cross-platform, centralized instant messaging (IM) and voice-over-IP (VoIP) service. It allows users to send text and voice messages, make voice and video calls, and share images, documents, user locations, and other content.📄� WhyLabsWhyLabs is an observability platform designed to monitor data pipelines and ML applications for data quality regressions, data | https://python.langchain.com/docs/integrations/providers/ |
44ec99aff3b5-16 | is an observability platform designed to monitor data pipelines and ML applications for data quality regressions, data drift, and model performance degradation. Built on top of an open-source package called whylogs, the platform enables Data Scientists and Engineers to:📄� WikipediaWikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.📄� Wolfram AlphaWolframAlpha is an answer engine developed by Wolfram Research.📄� WriterThis page covers how to use the Writer ecosystem within LangChain.📄� Yeager.aiThis page covers how to use Yeager.ai to generate LangChain tools and agents.📄� YouTubeYouTube is an online video sharing and social media platform by Google.📄� ZepZep - A long-term memory store for LLM applications.📄� ZillizZilliz Cloud is a fully managed service on cloud for LF AI Milvus®,PreviousZillizNextWandB TracingCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/ |
598e48e6d79c-0 | Shale Protocol | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/shaleprotocol |
598e48e6d79c-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/shaleprotocol |
598e48e6d79c-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerShale ProtocolOn this pageShale ProtocolShale Protocol provides production-ready inference APIs for open LLMs. It's a Plug & Play API as it's hosted on a highly scalable GPU cloud infrastructure. Our free tier supports up to 1K daily requests per key as we want to eliminate the barrier for anyone to start building genAI apps with LLMs. With Shale Protocol, developers/researchers can create apps and explore the capabilities of open LLMs at no cost.This page covers how Shale-Serve API can be incorporated with LangChain.As of June 2023, the API supports Vicuna-13B by default. We are going to support more LLMs such as Falcon-40B in future releases. How to​1. Find the link to our Discord on https://shaleprotocol.com. Generate an API key through the "Shale Bot" on our Discord. No credit card is required and no free trials. It's a forever free tier with 1K limit per day per API key.​2. Use https://shale.live/v1 as OpenAI API drop-in replacement​For examplefrom langchain.llms import OpenAIfrom langchain import PromptTemplate, LLMChainimport osos.environ['OPENAI_API_BASE'] = "https://shale.live/v1"os.environ['OPENAI_API_KEY'] = "ENTER YOUR API KEY"llm = OpenAI()template = """Question: | https://python.langchain.com/docs/integrations/providers/shaleprotocol |
598e48e6d79c-3 | = "ENTER YOUR API KEY"llm = OpenAI()template = """Question: {question}# Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])llm_chain = LLMChain(prompt=prompt, llm=llm)question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"llm_chain.run(question)PreviousSerpAPINextSingleStoreDBHow to1. Find the link to our Discord on https://shaleprotocol.com. Generate an API key through the "Shale Bot" on our Discord. No credit card is required and no free trials. It's a forever free tier with 1K limit per day per API key.2. Use https://shale.live/v1 as OpenAI API drop-in replacementCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/shaleprotocol |
92c936e7167f-0 | Jina | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/jina |
92c936e7167f-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/jina |
92c936e7167f-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerJinaOn this pageJinaThis page covers how to use the Jina ecosystem within LangChain. | https://python.langchain.com/docs/integrations/providers/jina |
92c936e7167f-3 | It is broken into two parts: installation and setup, and then references to specific Jina wrappers.Installation and Setup​Install the Python SDK with pip install jinaGet a Jina AI Cloud auth token from here and set it as an environment variable (JINA_AUTH_TOKEN)Wrappers​Embeddings​There exists a Jina Embeddings wrapper, which you can access with from langchain.embeddings import JinaEmbeddingsFor a more detailed walkthrough of this, see this notebookDeployment​Langchain-serve, powered by Jina, helps take LangChain apps to production with easy to use REST/WebSocket APIs and Slack bots. Usage​Install the package from PyPI. pip install langchain-serveWrap your LangChain app with the @serving decorator. # app.pyfrom lcserve import serving@servingdef ask(input: str) -> str: from langchain import LLMChain, OpenAI from langchain.agents import AgentExecutor, ZeroShotAgent tools = [...] # list of tools prompt = ZeroShotAgent.create_prompt( tools, input_variables=["input", "agent_scratchpad"], ) llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt) agent = ZeroShotAgent( llm_chain=llm_chain, allowed_tools=[tool.name for tool in tools] ) agent_executor = AgentExecutor.from_agent_and_tools( agent=agent, tools=tools, verbose=True, ) return agent_executor.run(input)Deploy on Jina AI Cloud with lc-serve | https://python.langchain.com/docs/integrations/providers/jina |
92c936e7167f-4 | ) return agent_executor.run(input)Deploy on Jina AI Cloud with lc-serve deploy jcloud app. Once deployed, we can send a POST request to the API endpoint to get a response.curl -X 'POST' 'https://<your-app>.wolf.jina.ai/ask' \ -d '{ "input": "Your Quesion here?", "envs": { "OPENAI_API_KEY": "sk-***" }}'You can also self-host the app on your infrastructure with Docker-compose or Kubernetes. See here for more details.Langchain-serve also allows to deploy the apps with WebSocket APIs and Slack Bots both on Jina AI Cloud or self-hosted infrastructure.PreviousInfinoNextLanceDBInstallation and SetupWrappersEmbeddingsDeploymentUsageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/jina |
2c1a96f7a192-0 | SerpAPI | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/serpapi |
2c1a96f7a192-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/serpapi |
2c1a96f7a192-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerSerpAPIOn this pageSerpAPIThis page covers how to use the SerpAPI search APIs within LangChain. | https://python.langchain.com/docs/integrations/providers/serpapi |
2c1a96f7a192-3 | It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper.Installation and Setup​Install requirements with pip install google-search-resultsGet a SerpAPI api key and either set it as an environment variable (SERPAPI_API_KEY)Wrappers​Utility​There exists a SerpAPI utility which wraps this API. To import this utility:from langchain.utilities import SerpAPIWrapperFor a more detailed walkthrough of this wrapper, see this notebook.Tool​You can also easily load this wrapper as a Tool (to use with an Agent).
You can do this with:from langchain.agents import load_toolstools = load_tools(["serpapi"])For more information on this, see this pagePreviousSearxNG Search APINextShale ProtocolInstallation and SetupWrappersUtilityToolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/serpapi |
b3feff302ceb-0 | ClearML | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
b3feff302ceb-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
b3feff302ceb-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerClearMLOn this pageClearMLClearML is a ML/DL development and production suite, it contains 5 main modules:Experiment Manager - Automagical experiment tracking, environments and resultsMLOps - Orchestration, Automation & Pipelines solution for ML/DL jobs (K8s / Cloud / bare-metal)Data-Management - Fully differentiable data management & version control solution on top of object-storage (S3 / GS / Azure / NAS)Model-Serving - cloud-ready Scalable model serving solution! | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
b3feff302ceb-3 | Deploy new model endpoints in under 5 minutes
Includes optimized GPU serving support backed by Nvidia-Triton | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
b3feff302ceb-4 | with out-of-the-box Model MonitoringFire Reports - Create and share rich MarkDown documents supporting embeddable online contentIn order to properly keep track of your langchain experiments and their results, you can enable the ClearML integration. We use the ClearML Experiment Manager that neatly tracks and organizes all your experiment runs.Installation and Setup​pip install clearmlpip install pandaspip install textstatpip install spacypython -m spacy download en_core_web_smGetting API Credentials​We'll be using quite some APIs in this notebook, here is a list and where to get them:ClearML: https://app.clear.ml/settings/workspace-configurationOpenAI: https://platform.openai.com/account/api-keysSerpAPI (google search): https://serpapi.com/dashboardimport osos.environ["CLEARML_API_ACCESS_KEY"] = ""os.environ["CLEARML_API_SECRET_KEY"] = ""os.environ["OPENAI_API_KEY"] = ""os.environ["SERPAPI_API_KEY"] = ""Callbacks​from langchain.callbacks import ClearMLCallbackHandlerfrom datetime import datetimefrom langchain.callbacks import StdOutCallbackHandlerfrom langchain.llms import OpenAI# Setup and use the ClearML Callbackclearml_callback = ClearMLCallbackHandler( task_type="inference", project_name="langchain_callback_demo", task_name="llm", tags=["test"], # Change the following parameters based on the amount of detail you want tracked visualize=True, complexity_metrics=True, stream_logs=True,)callbacks = [StdOutCallbackHandler(), clearml_callback]# Get the OpenAI model ready to gollm = OpenAI(temperature=0, callbacks=callbacks) The clearml callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
b3feff302ceb-5 | in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/allegroai/clearml/issues with the tag `langchain`.Scenario 1: Just an LLM​First, let's just run a single LLM a few times and capture the resulting prompt-answer conversation in ClearML# SCENARIO 1 - LLMllm_result = llm.generate(["Tell me a joke", "Tell me a poem"] * 3)# After every generation run, use flush to make sure all the metrics# prompts and other output are properly saved separatelyclearml_callback.flush_tracker(langchain_asset=llm, name="simple_sequential") {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'} {'action': 'on_llm_start', 'name': | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
b3feff302ceb-6 | me a poem'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
b3feff302ceb-7 | 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
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