litellm VS fluent-bit

There are a few libs that already abstract this away, for example:

- https://github.com/BerriAI/litellm

- https://jxnl.github.io/instructor/

- langchain

It's not hard for me to imagine a future where there is something like the CNCF for AI models, tools, and infra.

Hey, are you just looking for litellm - https://github.com/BerriAI/litellm

context - i'm the repo maintainer

It should be possible using LiteLLM and a patch or a proxy.

https://github.com/BerriAI/litellm

This article will demonstrate how txtai can integrate with llama.cpp, LiteLLM and custom generation methods. For custom generation, we'll show how to run inference with a Mamba model.

I use this with ollama and works perfectly https://github.com/BerriAI/litellm

Another abstraction layer library is: https://github.com/BerriAI/litellm

For me the killer feature of a library like this would be if it implemented function calling. Even if it was for a very restricted grammar - like the traditional ReAct prompt:

  Solve a question answering task with interleaving Thought, Action, Observation   usteps. Thought can reason about the current situation, and Action can be three types:

Welcome to Open Interpreter. ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── ▌ OpenAI API key not found To use GPT-4 (recommended) please provide an OpenAI API key. To use Code-Llama (free but less capable) press enter. ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── OpenAI API key: [the API Key I inputed] Tip: To save this key for later, run export OPENAI_API_KEY=your_api_key on Mac/Linux or setx OPENAI_API_KEY your_api_key on Windows. ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── ▌ Model set to GPT-4 Open Interpreter will require approval before running code. Use interpreter -y to bypass this. Press CTRL-C to exit. > export OPENAI_API_KEY=your_api_key Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'. Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/3.12/bin/interpreter", line 8, in sys.exit(cli()) ^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/core/core.py", line 22, in cli cli(self) File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/cli/cli.py", line 254, in cli interpreter.chat() File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/core/core.py", line 76, in chat for _ in self._streaming_chat(message=message, display=display): File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/core/core.py", line 97, in _streaming_chat yield from terminal_interface(self, message) File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/terminal_interface/terminal_interface.py", line 62, in terminal_interface for chunk in interpreter.chat(message, display=False, stream=True): File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/core/core.py", line 105, in _streaming_chat yield from self._respond() File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/core/core.py", line 131, in _respond yield from respond(self) File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/core/respond.py", line 61, in respond for chunk in interpreter._llm(messages_for_llm): File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/llm/setup_openai_coding_llm.py", line 94, in coding_llm response = litellm.completion(**params) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/utils.py", line 792, in wrapper raise e File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/utils.py", line 751, in wrapper result = original_function(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/timeout.py", line 53, in wrapper result = future.result(timeout=local_timeout_duration) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/_base.py", line 456, in result return self.__get_result() ^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result raise self._exception File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/timeout.py", line 42, in async_func return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/main.py", line 1183, in completion raise exception_type( ^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/utils.py", line 2959, in exception_type raise e File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/utils.py", line 2355, in exception_type raise original_exception File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/main.py", line 441, in completion raise e File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/main.py", line 423, in completion response = openai.ChatCompletion.create( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/openai/api_resources/chat_completion.py", line 25, in create return super().create(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 155, in create response, _, api_key = requestor.request( ^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/openai/api_requestor.py", line 299, in request resp, got_stream = self._interpret_response(result, stream) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/openai/api_requestor.py", line 710, in _interpret_response self._interpret_response_line( File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/openai/api_requestor.py", line 775, in _interpret_response_line raise self.handle_error_response( openai.error.InvalidRequestError: The model `gpt-4` does not exist or you do not have access to it. Learn more: https://help.openai.com/en/articles/7102672-how-can-i-access-gpt-4.

Fluentbit

$ docker run --rm fluent-bit-dummy WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested Fluent Bit v1.9.10 * Copyright (C) 2015-2022 The Fluent Bit Authors * Fluent Bit is a CNCF sub-project under the umbrella of Fluentd * https://fluentbit.io [2023/12/24 16:06:59] [ info] [fluent bit] version=1.9.10, commit=557c8336e7, pid=1 [2023/12/24 16:06:59] [ info] [storage] version=1.4.0, type=memory-only, sync=normal, checksum=disabled, max_chunks_up=128 [2023/12/24 16:06:59] [ info] [cmetrics] version=0.3.7 [2023/12/24 16:06:59] [ info] [output:stdout:stdout.0] worker #0 started [2023/12/24 16:06:59] [ info] [sp] stream processor started [0] dummy.0: [1703434019.553880465, {"message"=>"custom dummy"}] [0] dummy.0: [1703434020.555768799, {"message"=>"custom dummy"}] [0] dummy.0: [1703434021.550525174, {"message"=>"custom dummy"}] [0] dummy.0: [1703434022.551563050, {"message"=>"custom dummy"}] [0] dummy.0: [1703434023.551944509, {"message"=>"custom dummy"}] [0] dummy.0: [1703434024.550027843, {"message"=>"custom dummy"}] [0] dummy.0: [1703434025.550901801, {"message"=>"custom dummy"}] [0] dummy.0: [1703434026.549279385, {"message"=>"custom dummy"}] ^C[2023/12/24 16:07:08] [engine] caught signal (SIGINT) [0] dummy.0: [1703434027.549678344, {"message"=>"custom dummy"}] [2023/12/24 16:07:08] [ warn] [engine] service will shutdown in max 5 seconds [2023/12/24 16:07:08] [ info] [engine] service has stopped (0 pending tasks) [2023/12/24 16:07:08] [ info] [output:stdout:stdout.0] thread worker #0 stopping... [2023/12/24 16:07:08] [ info] [output:stdout:stdout.0] thread worker #0 stopped

> Libc is a lot more tricky about signals, since not all libc functions can be safely called from handlers.

And this is a huge thing. People do all kinds of operations in signal handlers completely oblivious to the pitfalls. Pitfalls which often do not manifest, making it a great "it works for me" territory.

I once raised a ticket on fluentbit[1] about it but they have abused signal handlers so thoroughly that I do not think they can mitigate the issue without a major rewriting of the signal and crash handling.

[1] https://github.com/fluent/fluent-bit/issues/4836

Fluentbit is Fluentd's lightweight alternative to itself.

https://fluentbit.io

From here, we can explore other developments and tutorials on Kubernetes, such as o11y or observability (PLG, ELK, ELF, TICK, Jaeger, Pyroscope), service mesh (Linkerd, Istio, NSM, Consul Connect, Cillium), and progressive delivery (ArgoCD, FluxCD, Spinnaker).

From this (https://github.com/fluent/fluent-bit/issues/723), I can see there is no grok support for fluent-bit.

apiVersion: v1 kind: ConfigMap metadata: name: fluent-bit-config namespace: newrelic labels: k8s-app: newrelic-logging data: # Configuration files: server, input, filters and output # ====================================================== fluent-bit.conf: | [SERVICE] Flush 1 Log_Level ${LOG_LEVEL} Daemon off Parsers_File parsers.conf HTTP_Server On HTTP_Listen 0.0.0.0 HTTP_Port 2020 @INCLUDE input-kubernetes.conf @INCLUDE output-newrelic.conf @INCLUDE filter-kubernetes.conf input-kubernetes.conf: | [INPUT] Name tail Tag kube.* Path ${PATH} Parser ${LOG_PARSER} DB /var/log/flb_kube.db Mem_Buf_Limit 7MB Skip_Long_Lines On Refresh_Interval 10 filter-kubernetes.conf: | [FILTER] Name multiline Match * multiline.parser multiline-regex [FILTER] Name record_modifier Match * Record cluster_name ${CLUSTER_NAME} [FILTER] Name kubernetes Match kube.* Kube_URL https://kubernetes.default.svc.cluster.local:443 Merge_Log Off output-newrelic.conf: | [OUTPUT] Name newrelic Match * licenseKey ${LICENSE_KEY} endpoint ${ENDPOINT} parsers.conf: | # Relevant parsers retrieved from: https://github.com/fluent/fluent-bit/blob/master/conf/parsers.conf [PARSER] Name docker Format json Time_Key time Time_Format %Y-%m-%dT%H:%M:%S.%L Time_Keep On [PARSER] Name cri Format regex Regex ^(?[^ ]+) (?stdout|stderr) (?[^ ]*) (?.*)$ Time_Key time Time_Format %Y-%m-%dT%H:%M:%S.%L%z [MULTILINE_PARSER] name multiline-regex key_content message type regex flush_timeout 1000 # # Regex rules for multiline parsing # --------------------------------- # # configuration hints: # # - first state always has the name: start_state # - every field in the rule must be inside double quotes # # rules | state name | regex pattern | next state # ------|---------------|--------------------------------|----------- rule "start_state" "/(Dec \d+ \d+\:\d+\:\d+)(.*)/" "cont" rule "cont" "/^\s+at.*/" "cont"

There are also log forwarding tools like promtail and fluentbit that can be used to both ship logs to something like Loki and produce metrics.

FluentBit, is a logging processor that can help you to push all of your application logs to a central location like an ElasticSearch or OpenSearch cluster.