[LangChain] Indexes

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Introduction

LangChain Indexes are a way to store and search text data. They are based on the idea of vector spaces, which are mathematical objects that represent the similarity between different pieces of text.

Benefits of using LangChain Indexes

There are several benefits to using LangChain Indexes:

  • Faster search: Indexes can make it much faster to search for text data.
  • More accurate results: Indexes can help to improve the accuracy of search results.
  • More flexible queries: Indexes can support more flexible queries, such as queries that match on partial words or phrases.

How to use LangChain Indexes

To use LangChain Indexes, you need to first create an index. Then, you can use the index to search for text data.

Here is an example of how to create a LangChain Index:

import langchain

index = langchain.create_index("my_index")

document = {
  "title": "My First Document",
  "content": "This is my first document."
}

index.add_document(document)

Once you have created an index, you can use it to search for text data. For example, you could use the index above to search for documents that contain the word “document”.

Here is an example of how to search for text data using a LangChain Index:

import langchain

index = langchain.create_index("my_index")

results = index.search("document")

for result in results:
  print(result["title"])

This code will search the index for documents that contain the word “document”. The results of the search will be a list of documents, each of which will have a title.

Code samples

Here are some code samples that demonstrate how to use LangChain Indexes:

  • Creating an index: This code sample creates a LangChain Index.
import langchain

index = langchain.create_index("my_index")
  • Adding a document to an index: This code sample adds a document to a LangChain Index.
import langchain

index = langchain.create_index("my_index")

document = {
  "title": "My First Document",
  "content": "This is my first document."
}

index.add_document(document)
  • Searching for text data: This code sample searches for text data using a LangChain Index.
import langchain

index = langchain.create_index("my_index")

results = index.search("document")

for result in results:
  print(result["title"])

Conclusion

LangChain Indexes are a powerful way to store and search text data. They can be used to make search faster, more accurate, and more flexible.

https://github.com/hwchase17/langchain

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