Embeddings

BBP

hey all,

I ran the custom embeddings code here as is: https://gpt-index.readthedocs.io/en/stable/examples/embeddings/custom_embeddings.html

and I got this:

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Traceback (most recent call last):
  File "/home/bi-ai/ai/bottoms-up-embeddings/main.py", line 40, in <module>
    embed_model=InstructorEmbeddings(embed_batch_size=2), chunk_size=512
TypeError: Can't instantiate abstract class InstructorEmbeddings with abstract methods _aget_query_embedding, class_name

then I put in stub implementations:

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def class_name(self) -> str:
        return "InstructorEmbeddings"
    
async def _aget_query_embedding(self, query: str) -> List[float]:
    return self._get_query_embedding(query)

and got this:

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Traceback (most recent call last):
  File "/home/bi-ai/ai/bottoms-up-embeddings/main.py", line 46, in <module>
    embed_model=InstructorEmbeddings(embed_batch_size=2), chunk_size=512
  File "/home/bi-ai/ai/bottoms-up-embeddings/main.py", line 19, in __init__
    self._model = INSTRUCTOR(instructor_model_name)
  File "pydantic/main.py", line 357, in pydantic.main.BaseModel.__setattr__
ValueError: "InstructorEmbeddings" object has no field "_model"

25 comments

LLogan M

Easieast fix is you need to set self._model after running super().init(...)

LLogan M

Sorry the example is slightly out of date, need to update that

LLogan M

You could also check out the source code from some existing embeddings for an example

https://github.com/jerryjliu/llama_index/blob/main/llama_index/embeddings/langchain.py

BBP

in this repo, is self._model being set?

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super().__init__(
            embed_batch_size=embed_batch_size,
            callback_manager=callback_manager,
            model_name=model_name,
        )

    @classmethod
    def class_name(cls) -> str:
        """Get class name."""
        return "LangchainEmbedding"

BBP

I don't see it anywhere

BBP

bruh sorry for pleb question, I'm literally learning Python on the fly lolll

BBP

ah I could put this:

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self._model = INSTRUCTOR(instructor_model_name)

after super().init() I getcha

BBP

lemme try that real quick

BBP

didn't work 😦

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class InstructorEmbeddings(BaseEmbedding):
    def __init__(
        self,
        instructor_model_name: str = "hkunlp/instructor-large",
        instruction: str = "Represent a document for semantic search:",
        **kwargs: Any,
    ) -> None:
        self._instruction = instruction
        super().__init__(**kwargs)
        self._model = INSTRUCTOR(instructor_model_name)

    def _get_query_embedding(self, query: str) -> List[float]:
        embeddings = self._model.encode([[self._instruction, query]])
        return embeddings[0]

BBP

oh wait forgot to implement the stubs again, one sec

BBP

ok RIP new error:

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  File "/home/bi-ai/ai/bottoms-up-embeddings/main.py", line 18, in __init__
    self._instruction = instruction
  File "pydantic/main.py", line 357, in pydantic.main.BaseModel.__setattr__
ValueError: "InstructorEmbeddings" object has no field "_instruction"

BBP

lemme just nuke that field then...

BBP

led me back to this:

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  File "pydantic/main.py", line 357, in pydantic.main.BaseModel.__setattr__
ValueError: "InstructorEmbeddings" object has no field "_model"

BBP

with this code:

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class InstructorEmbeddings(BaseEmbedding):
    def __init__(
        self,
        instructor_model_name: str = "hkunlp/instructor-large",
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        self._model = INSTRUCTOR(instructor_model_name)

    def _get_query_embedding(self, query: str) -> List[float]:
        embeddings = self._model.encode([[self._instruction, query]])
        return embeddings[0]

BBP

I'll go check out that repo you gave and try to extend the BaseEmbedding class that way 🫡

BBP

lemme know if you get around to updating that example please 🙏

BBP

I'm catching up on part 4 of

BBP

of 'bottoms up tutorial' to be able to get through part 5

BBP

appreciate all your 🔥 content and help GRC! 🔥 🙏 ❤️ 🫡

LLogan M

Updated the notebook here: https://github.com/jerryjliu/llama_index/blob/13fdf5363872e297cb5b0f863daed0e89047bfa8/docs/examples/embeddings/custom_embeddings.ipynb

BBP

my 👑 provides 🙏

BBP

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@classmethod
    def from_defaults(
        cls,
        llm_predictor: Optional[BaseLLMPredictor] = None,
        llm: Optional[LLMType] = "default",
        prompt_helper: Optional[PromptHelper] = None,
        embed_model: Optional[EmbedType] = "default",

Ser I'm looking at this method for creating the service_context, particularly the string "default"

Does this mean that if I don't pass in my own embed model, the default will be from openAI?

Trying to determine if everytime I run this line:

index = VectorStoreIndex.from_documents(documents, service_context=service_context)

The index is actually just a bunch of vectors with numbers regardless if I pass in an embed model?

LLogan M

Yes the default is openai

There's two main functions to pick the emebdding model and the llm, resolve_embed_model() and resolve_llm()

LLogan M

And yeah, a vector index is essentially a map of vectors/numbers to their source text

BBP

thank you! ❤️

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