for configuring retries and fallbacks for any part of your LCEL chain. This is a great way to make your chains more reliable at scale. We‚Äôre currently working on adding streaming support for retries/fallbacks, so you can get the added reliability without any latency cost.accessing intermediate results: for more complex chains it‚Äôs often very useful to access the results of intermediate steps even before the final output is produced. This can be used let end-users know something is happening, or even just to debug your chain. We‚Äôve added support for streaming intermediate results, and it‚Äôs available on every LangServe server.input and output schemas: input and output schemas give every LCEL chain Pydantic and JSONSchema schemas inferred from the structure of your chain. This can be used for validation of inputs and outputs, and is an integral part of LangServe.tracing with LangSmith: all chains built with LCEL have first-class tracing support, which can be used to debug your chains, or to understand what‚Äôs happening in production. To enable this all you have to do is add your LangSmith API key as an environment variable.PreviousLangChain Expression Language (LCEL)NextModulesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
The LangChain Expression Language was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest ‚Äúprompt + LLM‚Äù chain to the most complex chains (we‚Äôve seen folks successfully running in production LCEL chains with 100s of steps). To highlight a few of the reasons you might want to use LCEL:
The LangChain Expression Language was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest ‚Äúprompt + LLM‚Äù chain to the most complex chains (we‚Äôve seen folks successfully running in production LCEL chains with 100s of steps). To highlight a few of the reasons you might want to use LCEL: ->: for configuring retries and fallbacks for any part of your LCEL chain. This is a great way to make your chains more reliable at scale. We‚Äôre currently working on adding streaming support for retries/fallbacks, so you can get the added reliability without any latency cost.accessing intermediate results: for more complex chains it‚Äôs often very useful to access the results of intermediate steps even before the final output is produced. This can be used let end-users know something is happening, or even just to debug your chain. We‚Äôve added support for streaming intermediate results, and it‚Äôs available on every LangServe server.input and output schemas: input and output schemas give every LCEL chain Pydantic and JSONSchema schemas inferred from the structure of your chain. This can be used for validation of inputs and outputs, and is an integral part of LangServe.tracing with LangSmith: all chains built with LCEL have first-class tracing support, which can be used to debug your chains, or to understand what‚Äôs happening in production. To enable this all you have to do is add your LangSmith API key as an environment variable.PreviousLangChain Expression Language (LCEL)NextModulesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.