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[GH-ISSUE #463] docs: add model selection dropdowns for common providers #3

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opened 2026-02-17 17:18:57 -05:00 by yindo · 14 comments
yindo commented 2026-02-17 17:18:57 -05:00
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Originally created by @ZappaBoy on GitHub (Mar 8, 2024).
Original GitHub issue: https://github.com/langchain-ai/docs/issues/463

Issue with current documentation:

A very large part of the documentation end examples provided are about OpenAI models. I clearly understand the importance of build, integrating, and improving langchain, langgraph, langsmith, and other products on the top of OpenAI models, but there is a great community around the other solutions, especially around solutions that use Ollama and HuggingFace.

I'm referring to a lot of resources provided like the following:

The links above show very useful usages of these products but there is no reference at all about other models other than OpenAI.

The only resources I can personally be able to found are some comments in the issues and something like this:
https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.ollama_functions.OllamaFunctions.html

Also looking for "Ollama" or "Hugging" keywords in the examples provided there are only two references for both, also all referred to the same two playbooks langgraph_crag_mistral.ipynb and langgraph_self_rag_mistral_nomic.ipynb:

Also regarding OllamaFunctions I understand that this is in langchain_experimental package but I would be really happy to see it working as a sort of replacement for ChatOpenAI.

I don't want to sound ungrateful, langchain-ai products are awesome but these improvements about the other communities can be very useful and they could greatly improve both the use and development of solutions through the langchain-ai products.

Idea or request for content:

No response

Originally created by @ZappaBoy on GitHub (Mar 8, 2024). Original GitHub issue: https://github.com/langchain-ai/docs/issues/463 ### Issue with current documentation: A very large part of the documentation end examples provided are about OpenAI models. I clearly understand the importance of build, integrating, and improving langchain, langgraph, langsmith, and other products on the top of OpenAI models, but there is a great community around the other solutions, especially around solutions that use Ollama and HuggingFace. I'm referring to a lot of resources provided like the following: - https://python.langchain.com/docs/langgraph#prebuilt-examples - https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/agent_supervisor.ipynb - https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/hierarchical_agent_teams.ipynb - https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/multi-agent-collaboration.ipynb - https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_agentic_rag.ipynb - etc... The links above show very useful usages of these products but there is no reference at all about other models other than OpenAI. The only resources I can personally be able to found are some comments in the issues and something like this: https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.ollama_functions.OllamaFunctions.html Also looking for "Ollama" or "Hugging" keywords in the `examples` provided there are only two references for both, also all referred to the same two playbooks [langgraph_crag_mistral.ipynb](https://github.com/langchain-ai/langgraph/blob/b5b1c33ee6f290cf6744110709b8b601863bdbde/examples/rag/langgraph_crag_mistral.ipynb#L79) and [langgraph_self_rag_mistral_nomic.ipynb](https://github.com/langchain-ai/langgraph/blob/b5b1c33ee6f290cf6744110709b8b601863bdbde/examples/rag/langgraph_self_rag_mistral_nomic.ipynb#L64): - https://github.com/search?q=repo%3Alangchain-ai%2Flanggraph+path%3A%2F%5Eexamples%5C%2F%2F+Ollama&type=code - https://github.com/search?q=repo%3Alangchain-ai%2Flanggraph+path%3A%2F%5Eexamples%5C%2F%2F+Hugging&type=code Also regarding `OllamaFunctions` I understand that this is in `langchain_experimental` package but I would be really happy to see it working as a sort of replacement for `ChatOpenAI`. I don't want to sound ungrateful, `langchain-ai` products are awesome but these improvements about the other communities can be very useful and they could greatly improve both the use and development of solutions through the `langchain-ai` products. ### Idea or request for content: _No response_
yindo added the langchainlanggraphexternal labels 2026-02-17 17:18:57 -05:00
yindo commented 2026-02-17 17:18:57 -05:00
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@hinthornw commented on GitHub (Mar 12, 2024):

Related: langchain-ai/langgraph#33

@hinthornw commented on GitHub (Mar 12, 2024): Related: langchain-ai/langgraph#33
yindo commented 2026-02-17 17:18:57 -05:00
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@MikeyBeez commented on GitHub (May 17, 2024):

Don't waste your time. ClosedAI funds Langchain. Anyway, the latency that's added into ollama makes it unusable.

@MikeyBeez commented on GitHub (May 17, 2024): Don't waste your time. ClosedAI funds Langchain. Anyway, the latency that's added into ollama makes it unusable.
yindo commented 2026-02-17 17:18:57 -05:00
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@ZappaBoy commented on GitHub (May 18, 2024):

Don't waste your time. ClosedAI funds Langchain. Anyway, the latency that's added into ollama makes it unusable.

Honestly, I moved from langchain to AutoGen after the third day that my issues on langgraph were ignored.

@ZappaBoy commented on GitHub (May 18, 2024): > Don't waste your time. ClosedAI funds Langchain. Anyway, the latency that's added into ollama makes it unusable. Honestly, I moved from langchain to AutoGen after the third day that my issues on langgraph were ignored.
yindo commented 2026-02-17 17:18:57 -05:00
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@UserName-wang commented on GitHub (Jun 8, 2024):

Don't waste your time. ClosedAI funds Langchain. Anyway, the latency that's added into ollama makes it unusable.

Honestly, I moved from langchain to AutoGen after the third day that my issues on langgraph were ignored.

Hi, How about the latency when use AutoGen?

@UserName-wang commented on GitHub (Jun 8, 2024): > > Don't waste your time. ClosedAI funds Langchain. Anyway, the latency that's added into ollama makes it unusable. > > Honestly, I moved from langchain to AutoGen after the third day that my issues on langgraph were ignored. Hi, How about the latency when use AutoGen?
yindo commented 2026-02-17 17:18:57 -05:00
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@ZappaBoy commented on GitHub (Jun 9, 2024):

Don't waste your time. ClosedAI funds Langchain. Anyway, the latency that's added into ollama makes it unusable.

Honestly, I moved from langchain to AutoGen after the third day that my issues on langgraph were ignored.

Hi, How about the latency when use AutoGen?

I don't think that this is the right discussion to talk about a different technology.

@ZappaBoy commented on GitHub (Jun 9, 2024): > > > Don't waste your time. ClosedAI funds Langchain. Anyway, the latency that's added into ollama makes it unusable. > > > > > > Honestly, I moved from langchain to AutoGen after the third day that my issues on langgraph were ignored. > > Hi, How about the latency when use AutoGen? I don't think that this is the right discussion to talk about a different technology.
yindo commented 2026-02-17 17:18:57 -05:00
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@romilandc commented on GitHub (Jun 10, 2024):

For Ollama use llm = ChatOpenAI(api_key='ollama', model="llama3", base_url='http://localhost:11434/v1', temperature=4)

@romilandc commented on GitHub (Jun 10, 2024): For Ollama use `llm = ChatOpenAI(api_key='ollama', model="llama3", base_url='http://localhost:11434/v1', temperature=4)`
yindo commented 2026-02-17 17:18:57 -05:00
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@MikeyBeez commented on GitHub (Jun 15, 2024):

I can use the llms module but it seems to cause problems sometimes. Besides, why would you need a Langchain's API to connect to a model when Ollama has an API already. Everything I want to do doesn't require langchain, and langchain keeps changing so fast, if it works one day, it doesn't the next. The only thing I use langchain for at the moment is storing embeddings in chromadb, and I may dump both chroma and langchain entirely. I can store embeddings in json and use numpy for similarity searches. I'm coming to the realization that I don't want a large KB of memories anyway.

@MikeyBeez commented on GitHub (Jun 15, 2024): I can use the llms module but it seems to cause problems sometimes. Besides, why would you need a Langchain's API to connect to a model when Ollama has an API already. Everything I want to do doesn't require langchain, and langchain keeps changing so fast, if it works one day, it doesn't the next. The only thing I use langchain for at the moment is storing embeddings in chromadb, and I may dump both chroma and langchain entirely. I can store embeddings in json and use numpy for similarity searches. I'm coming to the realization that I don't want a large KB of memories anyway.
yindo commented 2026-02-17 17:18:57 -05:00
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@JTMarsh556 commented on GitHub (Jul 15, 2024):

I have to say I completely agree with the OP. Nearly everything is funneled into the "Open"AI tube and I definitely feel that most other options are largely alienated. As someone who avoids that company I find myself spending more time on developing workarounds than I do anything else. Is this going to change or is this the intended course?

@JTMarsh556 commented on GitHub (Jul 15, 2024): I have to say I completely agree with the OP. Nearly everything is funneled into the "Open"AI tube and I definitely feel that most other options are largely alienated. As someone who avoids that company I find myself spending more time on developing workarounds than I do anything else. Is this going to change or is this the intended course?
yindo commented 2026-02-17 17:18:57 -05:00
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@nnnnwinder commented on GitHub (Sep 4, 2024):

Can a custom LLM with tool-carrying capabilities, such as a locally deployed LLM (e.g., Qwen), now be used as a LangGraph node? Are there any examples of its use?

@nnnnwinder commented on GitHub (Sep 4, 2024): Can a custom LLM with tool-carrying capabilities, such as a locally deployed LLM (e.g., Qwen), now be used as a LangGraph node? Are there any examples of its use?
yindo commented 2026-02-17 17:18:57 -05:00
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@raj-acrivon commented on GitHub (Sep 6, 2024):

ChatOllama seems lack for functional calling.
If I use ChatGroq than my code worked correctly.

Are there any good example of SQL Agent or any other code based agent using ChatOllama and not using ChatGroq, ChatAnthropic, or ChatOpenAI.

Please share here if there is any example repo

@raj-acrivon commented on GitHub (Sep 6, 2024): ChatOllama seems lack for functional calling. If I use ChatGroq than my code worked correctly. Are there any good example of SQL Agent or any other code based agent using ChatOllama and not using ChatGroq, ChatAnthropic, or ChatOpenAI. Please share here if there is any example repo
yindo commented 2026-02-17 17:18:58 -05:00
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@gbaian10 commented on GitHub (Sep 6, 2024):

ChatOllama seems lack for functional calling. If I use ChatGroq than my code worked correctly.

Are there any good example of SQL Agent or any other code based agent using ChatOllama and not using ChatGroq, ChatAnthropic, or ChatOpenAI.

Please share here if there is any example repo

@raj-acrivon
Did you use the old version of ChatOllama from langchain-community?
If so, please switch to the langchain-ollama package and ensure that the model you are using supports tool_call.
You can find examples here

@gbaian10 commented on GitHub (Sep 6, 2024): > ChatOllama seems lack for functional calling. If I use ChatGroq than my code worked correctly. > > Are there any good example of SQL Agent or any other code based agent using ChatOllama and not using ChatGroq, ChatAnthropic, or ChatOpenAI. > > Please share here if there is any example repo @raj-acrivon Did you use the old version of ChatOllama from `langchain-community`? If so, please switch to the `langchain-ollama` package and ensure that the model you are using supports tool_call. You can find examples [here](https://python.langchain.com/v0.2/docs/integrations/chat/ollama/)
yindo commented 2026-02-17 17:18:58 -05:00
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@ramankhurana commented on GitHub (Oct 20, 2024):

Did someone find a way to use Ollama and use Langgraph routing techniques, say Supervisor to build something? it seems to have some issues when using with Ollama (with LLama3.1) but with OpenAI API it works fine.

@ramankhurana commented on GitHub (Oct 20, 2024): Did someone find a way to use Ollama and use Langgraph routing techniques, say Supervisor to build something? it seems to have some issues when using with Ollama (with LLama3.1) but with OpenAI API it works fine.
yindo commented 2026-02-17 17:18:58 -05:00
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@JTMarsh556 commented on GitHub (Oct 24, 2024):

Personally, I found you can just a combination of state and JSON output parser to make everything work with any model. I went that route when support was more "Open"AI focused and I keep trying to get in alignment with the built in tool calling but this works well and I haven't come across a point where I benefit from changing it.

@JTMarsh556 commented on GitHub (Oct 24, 2024): Personally, I found you can just a combination of state and JSON output parser to make everything work with any model. I went that route when support was more "Open"AI focused and I keep trying to get in alignment with the built in tool calling but this works well and I haven't come across a point where I benefit from changing it.
yindo commented 2026-02-17 17:18:58 -05:00
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@mdrxy commented on GitHub (Sep 10, 2025):

This is on the roadmap

@mdrxy commented on GitHub (Sep 10, 2025): This is on the roadmap
yindo changed title from docs: add model selection dropdowns for common providers to [GH-ISSUE #463] docs: add model selection dropdowns for common providers 2026-06-05 17:24:30 -04:00
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