Overview of Langchain's integration with feature stores?

[ericksonc]

I have a question about Langchain's features and support around feature stores.

By means of comparison, in the case of vector databases, reading through the documentation made it clear that Langchain offers a wealth of modules and techniques designed around making it easier to connect vector stores to LLMs, offering ways to make it easier to approach all aspects of it, from making it easy to parse different types of source material, chunking the text, generating the embeddings, and then work with vector query results and apply those to the prompt.

In contrast, feature stores are the focus of the very first documentation article under "Modules - Model I/​O - Prompts - Prompt templates", and its placement there would seem to imply that there's a similar level of significance to feature stores as there are for vector databases. However, I find scant mention of feature stores as I've progressed through the documentation, in contrast to the many examples of vector database integration.

Is this a case where there's actually a lot of ways that langchain supports feature store integration, but they just aren't thoroughly documented to the degree vector database approaches are? If so, what's an overview of them, or a good place to get started? Or is this more a case of where the documentation is indicating that "using a feature store is a good idea" but there's not a lot of code and functionality that's explicitly designed to interact with them?

[dosubot[bot]]

🤖

Hello,

Thank you for your detailed question about LangChain's support for feature stores. You're correct that the documentation might not be as extensive for feature stores as it is for vector databases. However, LangChain does provide a significant amount of support for feature stores.

LangChain provides a module named vectorstores for integrating with feature stores. This module includes various classes for different types of vector stores, which can be used to store and retrieve embedded data. Some of the classes provided include AlibabaCloudOpenSearch, AnalyticDB, Annoy, AtlasDB, AwaDB, AzureSearch, Bagel, Cassandra, Chroma, Clarifai, Clickhouse, DashVector, DeepLake, Dingo, DocArrayHnswSearch, DocArrayInMemorySearch, ElasticKnnSearch, ElasticVectorSearch, ElasticsearchStore, Epsilla, FAISS, Hologres, LanceDB, Marqo, MatchingEngine, Meilisearch, Milvus, MongoDBAtlasVectorSearch, MyScale, OpenSearchVectorSearch, PGEmbedding, PGVector, Pinecone, Qdrant, Redis, Rockset, SKLearnVectorStore, ScaNN, SingleStoreDB, StarRocks, SupabaseVectorStore, Tair, Tigris, Typesense, USearch, Vectara, Weaviate, ZepVectorStore, Zilliz, and many more. Each of these classes can be used to integrate with the corresponding feature store, allowing you to store and retrieve embedded data. You can find more details in the vectorstores module.

As for the parsing and chunking of text for feature stores, LangChain uses the Tokenizer class to encode and decode text into chunks. It also provides subclasses of RecursiveCharacterTextSplitter, namely MarkdownTextSplitter and LatexTextSplitter, for splitting the text along Markdown-formatted headings and Latex-formatted layout elements respectively. You can find more details in the text_splitter module.

I hope this gives you a better understanding of how LangChain supports feature store integration. If you have any more questions or need further clarification, feel free to ask.

Sources

• libs/langchain/langchain/vectorstores/init.py
• libs/langchain/langchain/text_splitter.py

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[ericksonc]

@dosu-bot So it sounds like the short answer would be "no, there isn't much functionality related to feature stores, however, some of the more general features that can be useful with vector databases (e.g. parsing) may also be useful in working with feature stores as well" - does that sound about right?

[dosubot[bot]]

🤖

Hello,

Thank you for your detailed question. You're correct that LangChain provides a module named vectorstores for integrating with vector databases. This module includes various classes for different types of vector stores, which can be used to store and retrieve embedded data.

As for feature stores, while there isn't a specific module dedicated to feature stores, some classes in LangChain can be used to interact with them. Specifically, the DocArrayHnswSearch and DocArrayInMemorySearch classes from the langchain.vectorstores.docarray module can be used for this purpose. These classes likely provide functionality for searching within a document array, which could be used as a feature store.

Here is the supporting evidence from the context:

from langchain.vectorstores.docarray.hnsw import DocArrayHnswSearch
from langchain.vectorstores.docarray.in_memory import DocArrayInMemorySearch

__all__ = [
    "DocArrayHnswSearch",
    "DocArrayInMemorySearch",
]

(Source: libs/langchain/langchain/vectorstores/docarray/init.py)

As for the vectorstores module, it provides a specific implementation of the VectorStore class for an in-memory vector store based on the scikit-learn library's NearestNeighbors implementation. It includes methods for creating a connection to the vector store, initializing the vector store with necessary components, adding texts to the vector store, and performing similarity search.

The SKLearnVectorStore class also includes methods for maximal marginal relevance (MMR) search, such as max_marginal_relevance_search_by_vector and max_marginal_relevance_search. These methods return documents selected using the MMR, which optimizes for similarity to the query and diversity among selected documents. The degree of diversity among the results can be controlled by the lambda_mult parameter, with 0 corresponding to maximum diversity and 1 to minimum diversity.

The SKLearnVectorStore class also provides a class method from_texts for creating an instance of SKLearnVectorStore from a list of texts, an Embeddings instance, and optional metadata and IDs.

In conclusion, while there isn't a specific module for feature stores, there are classes and functions in LangChain that can be used to interact with feature stores. The parsing and other general features that are useful with vector databases may also be useful when working with feature stores.

I hope this answers your question. If you have any further queries, feel free to ask.

Best regards.

Sources

• Using agents with custom tools completely changes the input if question is asked in different language
• libs/langchain/langchain/vectorstores/docarray/init.py
• libs/langchain/langchain/vectorstores/init.py
• libs/langchain/langchain/vectorstores/base.py
• libs/langchain/langchain/vectorstores/singlestoredb.py
• libs/langchain/langchain/vectorstores/sklearn.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.