A few of our team members at Airbyte (and Joe, who killed it!) recently played with building our own internal support chat bot, using Airbyte, Langchain, Pinecone and OpenAI, that would answer any questions we ask when developing a new connector on Airbyte.
As we prototyped it, we realized that it could be applied for many other use cases and sources of data, so... we created a tutorial that other community members can leverage [http://airbyte.com/tutorials/chat-with-your-data-using-opena...] and the Github repo to run it [https://github.com/airbytehq/tutorial-connector-dev-bot]
The tutorial shows: \n- How to extract unstructured data from a variety of sources using Airbyte Open Source\n- How to load data into a vector database (here Pinecone), preparing the data for LLM usage along the way\n- How to integrate a vector database into ChatGPT to ask questions about your proprietary data
I hope some of it is useful, and would love your feedback!"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["langchain"],"value":"Show HN: Chat with your data using LangChain, Pinecone, and Airbyte"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["langchain"],"value":"https://airbyte.com/tutorials/chat-with-your-data-using-openai-pinecone-airbyte-and-langchain"}},"_tags":["story","author_mtricot","story_37050532","show_hn"],"author":"mtricot","children":[37050684,37051571,37051606,37051797,37053474,37054105,37054678,37054784,37054807,37055380,37056141,37056906,37057358,37059448,37062150,37203256],"created_at":"2023-08-08T15:32:13Z","created_at_i":1691508733,"num_comments":59,"objectID":"37050532","points":220,"story_id":37050532,"story_text":"Hi HN,
A few of our team members at Airbyte (and Joe, who killed it!) recently played with building our own internal support chat bot, using Airbyte, Langchain, Pinecone and OpenAI, that would answer any questions we ask when developing a new connector on Airbyte.
As we prototyped it, we realized that it could be applied for many other use cases and sources of data, so... we created a tutorial that other community members can leverage [http://airbyte.com/tutorials/chat-with-your-data-using-opena...] and the Github repo to run it [https://github.com/airbytehq/tutorial-connector-dev-bot]
The tutorial shows: \n- How to extract unstructured data from a variety of sources using Airbyte Open Source\n- How to load data into a vector database (here Pinecone), preparing the data for LLM usage along the way\n- How to integrate a vector database into ChatGPT to ask questions about your proprietary data
I hope some of it is useful, and would love your feedback!","title":"Show HN: Chat with your data using LangChain, Pinecone, and Airbyte","updated_at":"2025-07-26T10:18:23Z","url":"https://airbyte.com/tutorials/chat-with-your-data-using-openai-pinecone-airbyte-and-langchain"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"gk1"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["langchain"],"value":"Retrieval in LangChain"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["langchain"],"value":"https://blog.langchain.dev/retrieval/"}},"_tags":["story","author_gk1","story_35301943"],"author":"gk1","children":[35326875,35326998,35327123,35327204,35327506,35327896,35327955,35328902,35329004,35329564,35329894,35333887,35333966,35335002],"created_at":"2023-03-25T12:21:39Z","created_at_i":1679746899,"num_comments":69,"objectID":"35301943","points":212,"story_id":35301943,"title":"Retrieval in 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Over the last several weekends, I've been building LLMFlows as an alternative to langchain.
There's been a lot of discussion on the shortcomings of langchain in the past few weeks, but when I first tried it in March, I thought there are 3 main problems:\n1. Too many abstractions\n2. Hidden prompts and opinionated logic in chains which makes it hard to customize\n3. Hard to debug
This inspired me to try and build a framework that solves these 3 issues, and therefore I started building LLFlows with the "philosophy" of being "simple, explicit, and transparent."
A few weekends later, I think I finally managed to reach a state where I feel it's ready to be shared.
I would love to hear your feedback! Thank you!"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["langchain"],"value":"Show HN: LLMFlows \u2013 LangChain alternative for explicit and transparent apps"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/stoyan-stoyanov/llmflows"}},"_tags":["story","author_spstoyanov","story_36920423","show_hn"],"author":"spstoyanov","children":[36922529,36922947,36923721,36923754,36923835,36924391,36925320],"created_at":"2023-07-29T14:03:50Z","created_at_i":1690639430,"num_comments":22,"objectID":"36920423","points":49,"story_id":36920423,"story_text":"Hi HN! Over the last several weekends, I've been building LLMFlows as an alternative to langchain.
There's been a lot of discussion on the shortcomings of langchain in the past few weeks, but when I first tried it in March, I thought there are 3 main problems:\n1. Too many abstractions\n2. Hidden prompts and opinionated logic in chains which makes it hard to customize\n3. Hard to debug
This inspired me to try and build a framework that solves these 3 issues, and therefore I started building LLFlows with the "philosophy" of being "simple, explicit, and transparent."
A few weekends later, I think I finally managed to reach a state where I feel it's ready to be shared.
I would love to hear your feedback! Thank you!","title":"Show HN: LLMFlows \u2013 LangChain alternative for explicit and transparent apps","updated_at":"2025-08-15T22:19:49Z","url":"https://github.com/stoyan-stoyanov/llmflows"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"bediashpreet"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["langchain"],"value":"Agno: Agent framework 10,000x faster than LangChain"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://docs.agno.com/introduction"}},"_tags":["story","author_bediashpreet","story_43274435"],"author":"bediashpreet","children":[43276093,43276332,43276720,43276938,43276972,43277493,43277760,43279171,43281512,43286279,43297535],"created_at":"2025-03-05T23:54:16Z","created_at_i":1741218856,"num_comments":23,"objectID":"43274435","points":47,"story_id":43274435,"title":"Agno: Agent framework 10,000x faster than LangChain","updated_at":"2025-05-30T22:30:45Z","url":"https://docs.agno.com/introduction"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"brakmic"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["langchain"],"value":"Structured Output with LangChain and Llamafile"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["langchain"],"value":"https://blog.brakmic.com/structured-output-with-langchain-and-llamafile/"}},"_tags":["story","author_brakmic","story_44348415"],"author":"brakmic","children":[44387163,44387378,44387522,44388365,44388959],"created_at":"2025-06-22T17:01:10Z","created_at_i":1750611670,"num_comments":20,"objectID":"44348415","points":46,"story_id":44348415,"title":"Structured Output with LangChain and Llamafile","updated_at":"2025-07-04T13:20:28Z","url":"https://blog.brakmic.com/structured-output-with-langchain-and-llamafile/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"valianter"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["langchain"],"value":"I've been working with Langchain to develop a POC and when I try to understand the bugs in my code I feel like I am going down this huge rabbit hole of code. Every time I click into a piece of code there are 10 more places that I have to look that MIGHT be the source of the bug.
I get the idea that things should be abstracted away and that's the point of a library but this feels like a little much."},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["langchain"],"value":"LangChain Is a Black Box"}},"_tags":["story","author_valianter","story_41192069","ask_hn"],"author":"valianter","children":[41192352,41192559,41194155,41194238,41194287,41194553,41194811,41194877,41195478,41196341,41196959,41197619,41197790,41198817,41201326,41201526,41201951,41260150],"created_at":"2024-08-08T14:42:46Z","created_at_i":1723128166,"num_comments":28,"objectID":"41192069","points":42,"story_id":41192069,"story_text":"I've been working with Langchain to develop a POC and when I try to understand the bugs in my code I feel like I am going down this huge rabbit hole of code. Every time I click into a piece of code there are 10 more places that I have to look that MIGHT be the source of the bug.
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Berri lets you deploy directly from your coding environment, eliminating the need to download, transfer your code to a wrapper, deploy and debug. Taking an LLM app from your notebook and sharing it with people is made easy.
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