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Building an AI Assistant: Retrieval of Information

Imagine having an AI assistant that can not only answer your questions but also delve into your company’s document collection to find the most relevant information. This powerful capability is within reach with LangChain, a user-friendly framework for building LLM applications. 

This article explores how LangChain facilitates information retrieval from your vector store, the foundation of your AI assistant. 

What is a Vector Store? 

Think of a vector store as a specialized database that efficiently stores and retrieves information from your documents. LangChain utilizes Chroma, a vector store that leverages “embeddings.” These are numerical representations of text generated by powerful AI models. By converting text into a numerical format, Chroma can quickly find similar information within your document collection. 

How Does Retrieval Work? 

LangChain offers various retrieval techniques to find the best answer: 

  1. Similarity Search: This is the most basic approach, where the system identifies documents in the vector store with embeddings most similar to the user’s query. 
  2. Maximum Marginal Relevance (MMR): This method goes beyond just similarity. It retrieves a diverse set of documents, ensuring you get a well-rounded answer that incorporates different perspectives. 

Retrieval Method 


Similarity Search 

Finds most similar documents 

Maximum Marginal Relevance (MMR) 

Finds a diverse set of relevant documents 


Finding Specific Information 

LangChain empowers you to “reach” to relevant information within documents. For instance, you can ask: 

  • “What did they say about regression in the third lecture?” (assuming your documents have metadata like lecture number) 

LangChain can filter results based on this metadata, ensuring highly relevant answers. 

Advanced Retrieval Techniques 

LangChain offers additional features to enhance information retrieval: 

  • Contextual Compression: This technique uses an LLM to summarize retrieved documents, providing a concise overview of the information. This is particularly helpful for lengthy documents. 
  • Combining Techniques: LangChain allows you to combine retrieval methods like MMR and contextual compression for even more refined results. 

Beyond Vector Stores 

LangChain isn’t limited to vector stores. It can retrieve information from various sources, including PDFs or directly from text: 

  • PDF Retrieval: LangChain can process PDFs, extract text, and then use retrieval techniques to find relevant information within the document. 
  • TF-IDF Retrieval: This method retrieves documents based on the frequency of terms within the documents, offering another approach to finding relevant information. 


LangChain simplifies the process of building an LLM-powered AI assistant that can effectively retrieve information from your company’s documents. With its diverse retrieval techniques and ability to work with various data sources, LangChain empowers you to unlock the knowledge within your organization.

In the last five years, we at CoReCo Technologies, have worked with 60+ various size businesses from across the globe, from various industries and have been part of 110+ such success stories. We applied the latest technologies for adding value to our customers’ businesses through our commitment to excellence.

For more details about such case studies, visit us at and if you would like to convert this virtual conversation into a real collaboration, please write to [email protected] 


Atul Patil
Atul Patil

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