apply embeddings to pandas dataframe

[andysingal]

System Info

jupyter notebook, RTX 3090

Who can help?

@agola11 @hwchase17 @ey

Information

• [ ] The official example notebooks/scripts
• [ ] My own modified scripts

Related Components

• [ ] LLMs/Chat Models
• [ ] Embedding Models
• [ ] Prompts / Prompt Templates / Prompt Selectors
• [ ] Output Parsers
• [ ] Document Loaders
• [ ] Vector Stores / Retrievers
• [ ] Memory
• [ ] Agents / Agent Executors
• [ ] Tools / Toolkits
• [ ] Chains
• [ ] Callbacks/Tracing
• [ ] Async

Reproduction

from langchain.embeddings import SentenceTransformerEmbeddings 

embedding=lambda x: x['combined_info'].apply(lambda text: embeddings.embed_documents(text))

does not work Any workarounds on it?

Expected behavior

outputs embeddings

[dosubot[bot]]

🤖

Hello,

From the code snippet you've provided, it seems like you're trying to apply the embed_documents method of the SentenceTransformerEmbeddings class to each text in the 'combined_info' column of your dataframe. However, it seems like you haven't instantiated the SentenceTransformerEmbeddings class.

Here's how you can do it:

from langchain.embeddings import SentenceTransformerEmbeddings 

embeddings = SentenceTransformerEmbeddings()
embedding = lambda x: x['combined_info'].apply(lambda text: embeddings.embed_documents(text))

This should work if 'combined_info' is a column in your dataframe that contains the text you want to embed. If you're still encountering issues, could you please provide more information about the structure of your dataframe and the exact error message you're receiving? This will help me understand the problem better and provide a more accurate solution.

Also, please ensure that you have the necessary dependencies installed and that your environment meets the requirements for using the SentenceTransformerEmbeddings class.

Let me know if this helps or if you need further assistance.

Sources

• libs/langchain/langchain/embeddings/embaas.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[andysingal]

🤖

Hello,

From the code snippet you've provided, it seems like you're trying to apply the embed_documents method of the SentenceTransformerEmbeddings class to each text in the 'combined_info' column of your dataframe. However, it seems like you haven't instantiated the SentenceTransformerEmbeddings class.

Here's how you can do it:

from langchain.embeddings import SentenceTransformerEmbeddings 

embeddings = SentenceTransformerEmbeddings()
embedding = lambda x: x['combined_info'].apply(lambda text: embeddings.embed_documents(text))

This should work if 'combined_info' is a column in your dataframe that contains the text you want to embed. If you're still encountering issues, could you please provide more information about the structure of your dataframe and the exact error message you're receiving? This will help me understand the problem better and provide a more accurate solution.

Also, please ensure that you have the necessary dependencies installed and that your environment meets the requirements for using the SentenceTransformerEmbeddings class.

Let me know if this helps or if you need further assistance.

Sources

• libs/langchain/langchain/embeddings/embaas.py

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

Thanks @dosu-beta Dependencies:

!pip install -Uqqq langchain openai tiktoken pandas matplotlib seaborn sklearn emoji unstructured chromadb transformers InstructorEmbedding sentence_transformers

from langchain.embeddings import SentenceTransformerEmbeddings 

# embedding model parameters
embedding_model = "text-embedding-ada-002"
embedding_encoding = "cl100k_base"  # this the encoding for text-embedding-ada-002
max_tokens = 8000  # the maximum for text-embedding-ada-002 is 8191
encoding = tiktoken.get_encoding(embedding_encoding)

# SENTENCE TRANSFORMER EMBEDDINGS:

embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")

anime = pd.read_csv('/content/data/anime_with_synopsis.csv').dropna()
anime = anime.assign(
    combined_info = anime.apply(lambda row: f"Title: {row['Name']}. Overview: {row['sypnopsis']} Genres: {row['Genres']}", axis=1),
    n_tokens=lambda x: x['combined_info'].apply(lambda text: len(encoding.encode(text)))
).reindex(np.random.permutation(anime.index))
anime = anime[anime.n_tokens <= max_tokens]
anime.head()
Dataset: https://www.kaggle.com/datasets/hernan4444/anime-recommendation-database-2020?resource=download&select=anime_with_synopsis.csv
```Getting no results 

[dosubot[bot]]

🤖

Hello,

Based on the context provided, it seems like you're trying to apply the embed_documents method of the SentenceTransformerEmbeddings class to each text in the 'combined_info' column of your dataframe. Here's how you can do it:

First, you need to instantiate the SentenceTransformerEmbeddings class. Here's how you can do it:

from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings

embedding = SentenceTransformerEmbeddings()

Then, you can apply the embed_documents method to your dataframe. This method expects a list of documents (strings) as input and returns their embeddings. Here's an example:

import pandas as pd

# Assuming df is your DataFrame and 'combined_info' is the column containing text data
documents = df['combined_info'].tolist()

# Get embeddings
embeddings = embedding.embed_documents(documents)

As for the necessary dependencies and environment requirements, the context does not provide explicit information. However, given that LangChain is a Python framework, you would need a Python environment to run it. Also, since it uses SentenceTransformer for embeddings, you would need to have SentenceTransformer and its dependencies installed. These typically include PyTorch, Transformers, and Scikit-learn among others. Please refer to the official documentation of LangChain and SentenceTransformer for the exact requirements.

Please note that the above code snippets are based on the provided context and may need to be adjusted based on your specific use case and data.

I hope this helps! If you have any other questions, feel free to ask.

Sources

• libs/langchain/tests/integration_tests/embeddings/test_sentence_transformer.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[andysingal]

🤖

Hello,

Based on the context provided, it seems like you're trying to apply the embed_documents method of the SentenceTransformerEmbeddings class to each text in the 'combined_info' column of your dataframe. Here's how you can do it:

First, you need to instantiate the SentenceTransformerEmbeddings class. Here's how you can do it:

from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings

embedding = SentenceTransformerEmbeddings()

Then, you can apply the embed_documents method to your dataframe. This method expects a list of documents (strings) as input and returns their embeddings. Here's an example:

import pandas as pd

# Assuming df is your DataFrame and 'combined_info' is the column containing text data
documents = df['combined_info'].tolist()

# Get embeddings
embeddings = embedding.embed_documents(documents)

As for the necessary dependencies and environment requirements, the context does not provide explicit information. However, given that LangChain is a Python framework, you would need a Python environment to run it. Also, since it uses SentenceTransformer for embeddings, you would need to have SentenceTransformer and its dependencies installed. These typically include PyTorch, Transformers, and Scikit-learn among others. Please refer to the official documentation of LangChain and SentenceTransformer for the exact requirements.

Please note that the above code snippets are based on the provided context and may need to be adjusted based on your specific use case and data.

I hope this helps! If you have any other questions, feel free to ask.

Sources

• libs/langchain/tests/integration_tests/embeddings/test_sentence_transformer.py

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

That is good, but if i need other attributes like genre and userid.. this strategy does not help

[dosubot[bot]]

🤖

Hello,

Yes, your approach to applying the embed_documents method of the SentenceTransformerEmbeddings class from the LangChain Python framework to a dataframe column is correct.

As for the dependencies, you're correct that LangChain uses SentenceTransformer for embeddings, which includes PyTorch, Transformers, and Scikit-learn among others. However, you also need to ensure that the sentence_transformers package is installed, as it's a necessary dependency for using the SentenceTransformerEmbeddings class. This package is not included in the mentioned dependencies, so please make sure it's installed.

Regarding the environment requirements for running this on a Jupyter notebook with an RTX 3090, there are no specific requirements mentioned in the LangChain documentation. However, please ensure that your CUDA toolkit version is compatible with the PyTorch version you're using, as PyTorch utilizes CUDA for GPU-accelerated tensor computations.

Here's how you can apply the embed_documents method to a dataframe column:

df['embedded_text'] = df['combined_info'].apply(lambda x: embeddings.embed_documents([x]))

This assumes that df is your DataFrame and combined_info is the column containing the text you want to embed. Please note that the embed_documents method expects a list of documents, so even if you're only embedding one document at a time, you need to pass it as a list.

The embed_documents method in the SentenceTransformerEmbeddings class within the LangChain framework is used to convert a list of documents (strings) into their corresponding vector representations. These vectors are generated using the SentenceTransformer model, which is a transformer-based model specifically designed for generating sentence embeddings.

I hope this helps! If you have any other questions, feel free to ask.

Best, Dosu

Sources

• docs/extras/integrations/text_embedding/sentence_transformers.ipynb
• libs/langchain/tests/integration_tests/embeddings/test_sentence_transformer.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[andysingal]

df['embedded_text'] = df['combined_info'].apply(lambda x: embeddings.embed_documents([x]))

i tried the same but for some reason not working.. taking too long.... i also installed: sentence-transformers

[dosubot[bot]]

Hi, @andysingal,

I'm helping the LangChain team manage their backlog and am marking this issue as stale. It looks like you're seeking help with applying embeddings to a pandas dataframe using the langchain library, and you've received guidance on using the SentenceTransformerEmbeddings class from me. However, it seems that the issue remains unresolved, and you've mentioned encountering issues even after installing the sentence-transformers package.

Could you please confirm if this issue is still relevant to the latest version of the LangChain repository? If it is, kindly let the LangChain team know by commenting on the issue. Otherwise, feel free to close the issue yourself, or it will be automatically closed in 7 days. Thank you!