efficiently find other pieces of text that are similar.
LangChain provides integrations with over 25 different embedding providers and methods,
from open-source to proprietary API,
allowing you to choose the one best suited for your needs.
LangChain provides a standard interface, allowing you to easily swap between models.Vector storesWith the rise of embeddings, there has emerged a need for databases to support efficient storage and searching of these embeddings.
LangChain provides integrations with over 50 different vectorstores, from open-source local ones to cloud-hosted proprietary ones,
allowing you to choose the one best suited for your needs.
LangChain exposes a standard interface, allowing you to easily swap between vector stores.RetrieversOnce the data is in the database, you still need to retrieve it.
LangChain supports many different retrieval algorithms and is one of the places where we add the most value.
We support basic methods that are easy to get started - namely simple semantic search.
However, we have also added a collection of algorithms on top of this to increase performance.
These include:Parent Document Retriever: This allows you to create multiple embeddings per parent document, allowing you to look up smaller chunks but return larger context.Self Query Retriever: User questions often contain a reference to something that isn't just semantic but rather expresses some logic that can best be represented as a metadata filter. Self-query allows you to parse out the semantic part of a query from other metadata filters present in the query.Ensemble Retriever: Sometimes you may want to retrieve documents from multiple different sources, or using multiple different algorithms. The ensemble retriever allows you to easily do this.And more!PreviousXML parserNextDocument loadersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Many LLM applications require user-specific data that is not part of the model's training set.
Many LLM applications require user-specific data that is not part of the model's training set. ->: efficiently find other pieces of text that are similar.
LangChain provides integrations with over 25 different embedding providers and methods,
from open-source to proprietary API,
allowing you to choose the one best suited for your needs.
LangChain provides a standard interface, allowing you to easily swap between models.Vector storesWith the rise of embeddings, there has emerged a need for databases to support efficient storage and searching of these embeddings.
LangChain provides integrations with over 50 different vectorstores, from open-source local ones to cloud-hosted proprietary ones,
allowing you to choose the one best suited for your needs.
LangChain exposes a standard interface, allowing you to easily swap between vector stores.RetrieversOnce the data is in the database, you still need to retrieve it.
LangChain supports many different retrieval algorithms and is one of the places where we add the most value.
We support basic methods that are easy to get started - namely simple semantic search.
However, we have also added a collection of algorithms on top of this to increase performance.
These include:Parent Document Retriever: This allows you to create multiple embeddings per parent document, allowing you to look up smaller chunks but return larger context.Self Query Retriever: User questions often contain a reference to something that isn't just semantic but rather expresses some logic that can best be represented as a metadata filter. Self-query allows you to parse out the semantic part of a query from other metadata filters present in the query.Ensemble Retriever: Sometimes you may want to retrieve documents from multiple different sources, or using multiple different algorithms. The ensemble retriever allows you to easily do this.And more!PreviousXML parserNextDocument loadersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.