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langchain
Discontinued ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain] (by hwchase17)
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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sidekick
Discontinued Universal APIs for unstructured data. Sync documents from SaaS tools to a SQL or vector database, where they can be easily queried by AI applications [Moved to: https://github.com/psychic-api/psychic] (by ai-sidekick)
4. Insert the content in the context window of a prompt, and use a LLM like GPT to respond to the query based on information from the content.
A few years ago it was ridiculous to even consider building your own internal search product. Now, with libraries like LangChain (https://github.com/hwchase17/langchain), LlamaIndex (https://github.com/jerryjliu/llama_index), and open sources tools like Sidekick (https://github.com/ai-sidekick/sidekick), it’s possible to build a product that works just as well as most workplace search vendors in a matter of days, with the added benefit of being free + fully customizable. (Disclaimer: I am one of the cofounders at Sidekick)
It’s also not hard to find developers who would love to get their hands dirty with emerging technologies like vector databases, LLMs, and GPT agents. The economic incentive structure for internal search products (and maybe all SaaS product) has been flipped on its head. Why spend $100k+ year on a vendor that comes with 1-3 month implementation when you can spend half that time building something in-house that plugs in to where your team already works like Teams or Slack?
Examples:
4. Insert the content in the context window of a prompt, and use a LLM like GPT to respond to the query based on information from the content.
A few years ago it was ridiculous to even consider building your own internal search product. Now, with libraries like LangChain (https://github.com/hwchase17/langchain), LlamaIndex (https://github.com/jerryjliu/llama_index), and open sources tools like Sidekick (https://github.com/ai-sidekick/sidekick), it’s possible to build a product that works just as well as most workplace search vendors in a matter of days, with the added benefit of being free + fully customizable. (Disclaimer: I am one of the cofounders at Sidekick)
It’s also not hard to find developers who would love to get their hands dirty with emerging technologies like vector databases, LLMs, and GPT agents. The economic incentive structure for internal search products (and maybe all SaaS product) has been flipped on its head. Why spend $100k+ year on a vendor that comes with 1-3 month implementation when you can spend half that time building something in-house that plugs in to where your team already works like Teams or Slack?
Examples:
4. Insert the content in the context window of a prompt, and use a LLM like GPT to respond to the query based on information from the content.
A few years ago it was ridiculous to even consider building your own internal search product. Now, with libraries like LangChain (https://github.com/hwchase17/langchain), LlamaIndex (https://github.com/jerryjliu/llama_index), and open sources tools like Sidekick (https://github.com/ai-sidekick/sidekick), it’s possible to build a product that works just as well as most workplace search vendors in a matter of days, with the added benefit of being free + fully customizable. (Disclaimer: I am one of the cofounders at Sidekick)
It’s also not hard to find developers who would love to get their hands dirty with emerging technologies like vector databases, LLMs, and GPT agents. The economic incentive structure for internal search products (and maybe all SaaS product) has been flipped on its head. Why spend $100k+ year on a vendor that comes with 1-3 month implementation when you can spend half that time building something in-house that plugs in to where your team already works like Teams or Slack?
Examples: