Closed rohanprasadsap closed 2 days ago
You can use lm-studio
or any other provider that uses openai api format.
embedder=dict(
provider="openai",
config=dict(
model="CompendiumLabs/bge-large-en-v1.5-gguf",
api_key="lm-studio",
api_base="http://localhost:1234/v1"
),
),
Not in current version v0.30.11 with ollama, see other post.
this one: https://github.com/xorbitsai/inference
您可以使用
lm-studio
或任何其他使用 openai api 格式的提供商。embedder=dict( provider="openai", config=dict( model="CompendiumLabs/bge-large-en-v1.5-gguf", api_key="lm-studio", api_base="http://localhost:1234/v1" ), ),
oh,that's not very good all in open-AI
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I am trying to use the the crewai_tools, the RAG tools in particular. Lets take the DirectorySearchTool() in this case.
How do I use a custom embedder provider? The only options that embed chain have are from the following list. 'openai', 'gpt4all', 'huggingface', 'vertexai', 'azure_openai', 'google', 'mistralai', 'nvidia' Embedding models for which I need API keys.
I know that CrewAI uses embedchain module for RAG operations and the default provider is set to openai. And to use openai embedding model one needs to set OPENAI_API_KEY as environment variable.
But the situation is that the LLMs that I am using is from the from my Company's/Organization's langchain.openai import ChatOpenAI module (A custom module basically) and I don't need to set API keys and stuff for using the LLMs. Something like this:
which is of type:.proxy.langchain.openai.ChatOpenAI
Question: How do I use a custom embedder provider? Is there a provider called "custom" or anything that i can use in my case?