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For the purpose of fine tuning embedding, my textnode has both metadata and text. So, when

For the purpose of fine tuning embedding, my textnode has both metadata and text. So, when preparing training and validation dataset for fine tuning metadata+text will pair with questions to form a datapoint or will it only be text?

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kkapa.ai

Please stand by @balanp as I search the knowledge base. I normally take about 20 seconds or so. :wave:

kkapa.ai

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Based on the information provided in the extracts, when preparing the training and validation dataset for fine-tuning embeddings, each pair of (generated question, text chunk used as context) becomes a datapoint in the fine-tuning dataset. The text chunk used as context is derived from the text nodes. However, the extracts do not provide specific information on whether the metadata associated with the text nodes is also used in forming the datapoints.

Here is the relevant code snippet for generating the dataset:

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from llama_index.finetuning import (
    generate_qa_embedding_pairs,
    EmbeddingQAFinetuneDataset,
)

train_dataset = generate_qa_embedding_pairs(train_nodes)
val_dataset = generate_qa_embedding_pairs(val_nodes)

train_dataset.save_json("train_dataset.json")
val_dataset.save_json("val_dataset.json")

For more detailed information, you may want to refer to the LlamaIndex documentation () or the source code on GitHub ().
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bbalanp

@kapa.ai In general in llama index when indexes are made from data from nodes, do only data present in text field of textnode is converted into vector embedding or data in metadata key is also converted to vector embeddings?

kkapa.ai

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kkapa.ai

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In LlamaIndex, when indexes are made from data from nodes, both the text and the metadata present in the TextNode are considered. The text is converted into vector embeddings, and the metadata fields are stored as such in the underlying vector database. Here is the relevant code snippet:

Plain Text

from llama_index import VectorStoreIndex, StorageContext
from llama_index.vector_stores import ChromaVectorStore
from llama_index.schema import TextNode

This code is used to insert some sample nodes containing text chunks into the vector database. Each TextNode not only contains the text, but also metadata e.g. category and country. These metadata fields will get converted/stored as such in the underlying vector db.

For more detailed information, you may want to refer to the LlamaIndex documentation () or the source code on GitHub ().
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bbalanp

@kapa.ai okay understood. Now I have a csv file having questions in one column called questions and relevant context in another column called context.How can I convert this into a json file suitable for fine tuning embedding in llama index?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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Based on the information provided in the extracts, the LlamaIndex library expects the training and validation datasets to be in a specific JSON format. Here's a Python code snippet that can help you convert your CSV file into the required JSON format:

Plain Text

import pandas as pd
import json

# Load your CSV file
df = pd.read_csv('your_file.csv')

# Convert the DataFrame to a list of dictionaries
data = df.to_dict('records')

# Prepare the data for LlamaIndex
for record in data:
    record['query'] = record.pop('questions')
    record['response'] = record.pop('context')

# Save the data to a JSON file
with open('output.json', 'w') as f:
    json.dump(data, f)

This script reads your CSV file, converts it into a list of dictionaries, and then renames the 'questions' field to 'query' and the 'context' field to 'response'. It then saves this data to a JSON file.

Please replace 'your_file.csv' with the path to your CSV file, and 'output.json' with the desired output path for the JSON file.

Remember to install the necessary Python libraries (pandas and json) if you haven't already done so:

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pip install pandas

bbalanp

@kapa.ai if I want to fine tune an embedding model from hugging face, is there a way to do this?

kkapa.ai

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kkapa.ai

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Yes, you can fine-tune an embedding model from Hugging Face using LlamaIndex. Here's a general outline of the process:

Prepare the Data: Use the generate_qa_embedding_pairs function to prepare your data.

Fine-tune the Model: Use the SentenceTransformersFinetuneEngine to fine-tune your model. You can specify the model from Hugging Face using the model_id parameter.

Evaluate the Model: Evaluate your model on a validation knowledge corpus.

Here's a code snippet that demonstrates how to fine-tune a Hugging Face model:

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from llama_index.finetuning import SentenceTransformersFinetuneEngine

finetune_engine = SentenceTransformersFinetuneEngine(
    train_dataset,
    model_id="BAAI/bge-small-en",  # replace with your Hugging Face model
    model_output_path="test_model",
    val_dataset=val_dataset,
)

finetune_engine.finetune()

embed_model = finetune_engine.get_finetuned_model()

In this code, replace "BAAI/bge-small-en" with the ID of your Hugging Face model. The train_dataset and val_dataset variables should be your training and validation datasets, respectively.

For more detailed information, you can refer to the LlamaIndex documentation () or the source code on GitHub ().
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bbalanp

@kapa.ai so if I use "model_id=intfloat/e5-mistral-7b-instruct" will this work. Also if there is max_length parameter inside the tokenizer how can I pass those

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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Yes, you can specify the Hugging Face model ID as "intfloat/e5-mistral-7b-instruct" in the model_id parameter of the SentenceTransformersFinetuneEngine. However, please ensure that the model is compatible with the Sentence Transformers library, as not all Hugging Face models are.

Regarding the max_length parameter for the tokenizer, it's not explicitly mentioned in the provided extracts how to pass this parameter during the fine-tuning process in LlamaIndex. However, in general with Hugging Face models, you can specify this parameter when you initialize the tokenizer. Here's an example:

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from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("intfloat/e5-mistral-7b-instruct", max_length=512)

In this code, replace "intfloat/e5-mistral-7b-instruct" with the ID of your Hugging Face model, and 512 with your desired maximum length.

For more detailed information, you may want to refer to the LlamaIndex documentation () or the source code on GitHub ().
----------------------
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----------------------

bbalanp

@kapa.ai If use EmbeddingAdapterFinetuneEngine is there a way to pass the max_length parameter to tokenizer or can I pass my initialized tokenizer to EmbeddingAdapterFinetuneEngine

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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Based on the provided extracts, the EmbeddingAdapterFinetuneEngine in LlamaIndex does not explicitly provide a way to pass the max_length parameter to the tokenizer or to pass an initialized tokenizer.

Here's the code snippet for initializing the EmbeddingAdapterFinetuneEngine:

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from llama_index.finetuning import EmbeddingAdapterFinetuneEngine
from llama_index.embeddings import resolve_embed_model
import torch

base_embed_model = resolve_embed_model("local:BAAI/bge-small-en")

finetune_engine = EmbeddingAdapterFinetuneEngine(
    train_dataset,
    base_embed_model,
    model_output_path="model_output_test",
    epochs=4,
    verbose=True,
)

In this code, the resolve_embed_model function is used to load the base embedding model, but it's not clear from the extracts whether this function accepts a tokenizer or max_length parameter.

For more detailed information, you may want to refer to the LlamaIndex documentation () or the source code on GitHub ().
----------------------
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----------------------

bbalanp

@kapa.ai can I pass hugging face model id "intfloat/e5-mistral-7b-instruct" to resolve model

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
Yes, you can pass the Hugging Face model ID "intfloat/e5-mistral-7b-instruct" to the resolve_embed_model function in LlamaIndex. Here's how you can do it:

Plain Text

from llama_index.embeddings import resolve_embed_model

base_embed_model = resolve_embed_model("intfloat/e5-mistral-7b-instruct")

This will load the specified Hugging Face model. Please ensure that the model is compatible with the LlamaIndex library.

For more detailed information, you may want to refer to the LlamaIndex documentation () or the source code on GitHub ().
----------------------
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----------------------

bbalanp

@kapa.ai say I fine tuned my embedding model using pytorch and hugging face.And now want to use my fine tuned model in SubQuestionQueryEngine, how can do this?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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To use your fine-tuned model in the SubQuestionQueryEngine of LlamaIndex, you would first need to load your fine-tuned model as an embedding model. Then, you can pass this embedding model to the ServiceContext which is then used to create the SubQuestionQueryEngine. Here's a general outline of the process:

Load the Fine-tuned Model: Use the HuggingFaceEmbedding or resolve_embed_model function to load your fine-tuned model.

Create a Service Context: Use the ServiceContext.from_defaults function to create a service context, passing in your fine-tuned model as the embed_model parameter.

Create the Query Engine: Use the VectorStoreIndex.from_documents function to create a vector store index, and then use the as_query_engine method to create the query engine.

Here's a code snippet that demonstrates this process:

Plain Text

from llama_index import set_global_tokenizer, ServiceContext
from llama_index.embeddings import HuggingFaceEmbedding
from transformers import AutoTokenizer

# Set the global tokenizer
set_global_tokenizer(
    AutoTokenizer.from_pretrained("YourFineTunedModel").encode
)

# Load your fine-tuned model
embed_model = HuggingFaceEmbedding(model_name="YourFineTunedModel")

# Create a service context
service_context = ServiceContext.from_defaults(
    llm=llm,
    embed_model=embed_model,
)

# Create the query engine
query_engine = index.as_query_engine(service_context=service_context)

In this code, replace "YourFineTunedModel" with the ID or path of your fine-tuned model.

kkapa.ai

bbalanp

@kapa.ai For finetuning embedding model using llamaindex I have a json file of a list of dictionary of from {"Query":"","Context":""}. How to transform it so that EmbeddingAdapterFinetuneEngine will be able to use it

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
To transform your JSON file into a format that EmbeddingAdapterFinetuneEngine can use, you need to load the JSON file and convert it into an EmbeddingQAFinetuneDataset. Here's a Python code snippet that can help you do this:

Plain Text

import json
from llama_index.finetuning import EmbeddingQAFinetuneDataset

# Load the JSON file
with open('your_file.json', 'r') as f:
    data = json.load(f)

# Convert the data into an EmbeddingQAFinetuneDataset
dataset = EmbeddingQAFinetuneDataset.from_list(data)

In this script, replace 'your_file.json' with the path to your JSON file. The from_list method of EmbeddingQAFinetuneDataset takes a list of dictionaries, where each dictionary has a 'query' key and a 'response' key.

For more detailed information, you may want to refer to the LlamaIndex documentation () or the source code on GitHub ().
----------------------
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----------------------

bbalanp

@kapa.ai also if I have a gpu. How can I finetune it on it using EmbeddingAdapterFinetuneEngine

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
The EmbeddingAdapterFinetuneEngine in LlamaIndex should automatically use a GPU if one is available. PyTorch, which is used by LlamaIndex, defaults to using a GPU when available.

However, the provided extracts do not explicitly mention how to specify the use of a GPU with EmbeddingAdapterFinetuneEngine. In general, you can ensure PyTorch uses a GPU by setting the device to 'cuda' if a GPU is available:

Plain Text

import torch

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

Then, you would typically move your model and data to the GPU. However, it's not clear from the provided extracts how to do this with EmbeddingAdapterFinetuneEngine.

For more detailed information, you may want to refer to the LlamaIndex documentation () or the source code on GitHub ().
----------------------
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----------------------

bbalanp

@kapa.ai after running the following line

Convert the data into an EmbeddingQAFinetuneDataset

dataset = EmbeddingQAFinetuneDataset.from_list(data)
I have error AttributeError: type object 'EmbeddingQAFinetuneDataset' has no attribute 'from_list'

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
I apologize for the confusion. Based on the provided extracts, it seems there might have been a misunderstanding. The EmbeddingQAFinetuneDataset class in LlamaIndex does not have a from_list method. Instead, it has a from_json method that you can use to load your dataset from a JSON file. Here's how you can do it:

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from llama_index.finetuning import EmbeddingQAFinetuneDataset

# Load the dataset from a JSON file
dataset = EmbeddingQAFinetuneDataset.from_json('your_file.json')

In this script, replace 'your_file.json' with the path to your JSON file.

For more detailed information, you may want to refer to the LlamaIndex documentation () or the source code on GitHub ().
----------------------
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----------------------

bbalanp

@kapa.ai running the above code gives the following error
TypeError: llama_index.finetuning.embeddings.common.EmbeddingQAFinetuneDataset() argument after ** must be a mapping, not list

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
I apologize for the confusion. The EmbeddingQAFinetuneDataset class in LlamaIndex does not have a from_json method according to the provided extracts.

The EmbeddingQAFinetuneDataset is initialized with a list of dictionaries, where each dictionary represents a data sample with 'query' and 'response' keys. However, the exact format of these dictionaries is not specified in the provided extracts.

Here's a general way to load a JSON file into a list of dictionaries in Python:

Plain Text

import json

# Load the JSON file
with open('your_file.json', 'r') as f:
    data = json.load(f)

In this script, replace 'your_file.json' with the path to your JSON file. This will give you a list of dictionaries stored in the data variable.

To use this data with EmbeddingQAFinetuneDataset, you would typically pass this list to the class constructor. However, the provided extracts do not specify how to do this.

For more detailed information, you may want to refer to the LlamaIndex documentation () or the source code on GitHub ().
----------------------
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----------------------

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