langchain

Commit Graph

Author	SHA1	Message	Date
Wenbin Fang	a7e09d46c5	Add podcast api tool to use NLP to search all podcasts or episodes. (#1833 ) Use the following code to test: ```python import os from langchain.llms import OpenAI from langchain.chains.api import podcast_docs from langchain.chains import APIChain # Get api key here: https://openai.com/pricing os.environ["OPENAI_API_KEY"] = "sk-xxxxx" # Get api key here: https://www.listennotes.com/api/pricing/ listen_api_key = 'xxx' llm = OpenAI(temperature=0) headers = {"X-ListenAPI-Key": listen_api_key} chain = APIChain.from_llm_and_api_docs(llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True) chain.run("Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results") ``` Known issues: the api response data might be too big, and we'll get such error: `openai.error.InvalidRequestError: This model's maximum context length is 4097 tokens, however you requested 6733 tokens (6477 in your prompt; 256 for the completion). Please reduce your prompt; or completion length.`	1 year ago
Zach Schillaci	624c72c266	Add wikipedia tool doc (#1579 )	1 year ago
blob42	3ecdea8be4	SearxNG meta search api helper (#854 ) This is a work in progress PR to track my progres. ## TODO: - [x] Get results using the specifed searx host - [x] Prioritize returning an `answer` or results otherwise - [ ] expose the field `infobox` when available - [ ] expose `score` of result to help agent's decision - [ ] expose the `suggestions` field to agents so they could try new queries if no results are found with the orignial query ? - [ ] Dynamic tool description for agents ? - Searx offers many engines and a search syntax that agents can take advantage of. It would be nice to generate a dynamic Tool description so that it can be used many times as a tool but for different purposes. - [x] Limit number of results - [ ] Implement paging - [x] Miror the usage of the Google Search tool - [x] easy selection of search engines - [x] Documentation - [ ] update HowTo guide notebook on Search Tools - [ ] Handle async - [ ] Tests ### Add examples / documentation on possible uses with - [ ] getting factual answers with `!wiki` option and `infoboxes` - [ ] getting `suggestions` - [ ] getting `corrections` --------- Co-authored-by: blob42 <spike@w530> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
rogerserper	e46cd3b7db	Google Search API integration with serper.dev (wrapper, tests, docs, … (#909 ) Adds Google Search integration with [Serper](https://serper.dev) a low-cost alternative to SerpAPI (10x cheaper + generous free tier). Includes documentation, tests and examples. Hopefully I am not missing anything. Developers can sign up for a free account at [serper.dev](https://serper.dev) and obtain an api key. ## Usage ```python from langchain.utilities import GoogleSerperAPIWrapper from langchain.llms.openai import OpenAI from langchain.agents import initialize_agent, Tool import os os.environ["SERPER_API_KEY"] = "" os.environ['OPENAI_API_KEY'] = "" llm = OpenAI(temperature=0) search = GoogleSerperAPIWrapper() tools = [ Tool( name="Intermediate Answer", func=search.run ) ] self_ask_with_search = initialize_agent(tools, llm, agent="self-ask-with-search", verbose=True) self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?") ``` ### Output ``` Entering new AgentExecutor chain... Yes. Follow up: Who is the reigning men's U.S. Open champion? Intermediate answer: Current champions Carlos Alcaraz, 2022 men's singles champion. Follow up: Where is Carlos Alcaraz from? Intermediate answer: El Palmar, Spain So the final answer is: El Palmar, Spain > Finished chain. 'El Palmar, Spain' ```	1 year ago
Harrison Chase	3d41af0aba	Harrison/load tools kwargs (#681 ) Co-authored-by: Bruno Bornsztein <bruno.bornsztein@gmail.com>	1 year ago
Harrison Chase	ffc7e04d44	Harrison/wolfram alpha (#579 ) Co-authored-by: Nicolas <nicolascamara29@gmail.com>	1 year ago
Harrison Chase	985496f4be	Docs refactor (#480 ) Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>	1 year ago

7 Commits (0561162dd295b989a54bb1ecf966b0e3d7823864)