{"id":8215,"date":"2023-11-30T06:18:30","date_gmt":"2023-11-30T14:18:30","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=8215"},"modified":"2025-04-24T17:04:07","modified_gmt":"2025-04-24T17:04:07","slug":"using-advanced-retrievers-in-langchain","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/using-advanced-retrievers-in-langchain\/","title":{"rendered":"Using Advanced Retrievers in LangChain"},"content":{"rendered":"\n
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More Techniques to Improve Retrieval Quality<\/h2>\n
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\"Advanced
Photo by Regine Tholen<\/a> on\u00a0Unsplash<\/a><\/figcaption><\/figure>\n\n\n\n

If you\u2019ve ever hit the wall with basic retrievers, it\u2019s time to gear up with some \u201cadvanced\u201d retrievers from LangChain.<\/p>\n\n\n\n

This isn\u2019t just an upgrade; it\u2019s a new way to think about digging through data. Picture this: instead of a single line of inquiry, you deploy a squad of queries tailored to scout out a broader intel landscape. You\u2019re not just searching; you\u2019re launching a multi-pronged investigation into your database. This blog will arm you with everything you need to master advanced retrievers, and help you outsmart traditional search limitations and bring you closer to the heart of the matter.<\/p>\n\n\n\n

For anyone looking to sharpen their retrieval results, let\u2019s dive into how these retrievers work and how they can change your game.<\/p>\n\n\n\n

MultiQueryRetriever<\/h3>\n\n\n\n

The MultiQueryRetriever<\/code> addresses the limitations in distance-based similarity search by generating multiple alternative “perspectives” on your query\/question.<\/p>\n\n\n\n

By generating alternative questions and retrieving documents based on those questions, you can cover a broader range of information and increase the chances of finding the most relevant content. To determine if you need to use the MultiQueryRetriever, consider the following:<\/p>\n\n\n\n

\u2022 If you use a distance-based similarity search and want to improve the retrieval accuracy, the MultiQueryRetriever can be a valuable tool.<\/p>\n\n\n\n

\u2022 If you want to provide a more comprehensive set of results to the user by considering different perspectives on their question, the MultiQueryRetriever is a suitable choice.<\/p>\n\n\n\n

\u2022 If you have a vector database with many documents and want to retrieve the most relevant ones, the MultiQueryRetriever can help you achieve that.<\/p>\n\n\n\n

MultiQueryRetriever in LangChain retrieves relevant documents based on multiple generated queries.<\/p>\n\n\n\n

You need it to improve retrieval accuracy, provide diverse results, and handle large vector databases.<\/p>\n\n\n\n

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Want to learn how to build modern software with LLMs using the newest tools and techniques in the field? Check out this free LLMOps course<\/a> from industry expert Elvis Saravia of DAIR.AI!<\/p><\/blockquote>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n

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Consider using the MultiQueryRetriever when you want to overcome the limitations of distance-based similarity search and provide a more comprehensive set of results to the user.<\/p>\n

from langchain.chat_models import ChatOpenAI\nfrom langchain.retrievers.multi_query import MultiQueryRetriever\n\nquestion = \"Why are all great things slow of growth?\"\n\nllm = ChatOpenAI(temperature=0)\n\n# we instantiated the retreiever above\nretriever_from_llm = MultiQueryRetriever.from_llm(\n    retriever=db.as_retriever(), llm=llm\n)\n\n# Set logging for the queries\nimport logging\n\nlogging.basicConfig()\nlogging.getLogger(\"langchain.retrievers.multi_query\").setLevel(logging.INFO)\n\nunique_docs = retriever_from_llm.get_relevant_documents(query=question)<\/span><\/pre>\n
And you can see the queries that were generated:<\/p>\n
INFO:langchain.retrievers.multi_query:Generated queries: ['1. What are the reasons behind the slow growth of all great things?', '2. Can you explain why great things tend to have a slow growth rate?', '3. What factors contribute to the slow pace of growth in all great things?']<\/span><\/pre>\n
Feel free to see what the actual Documents are as follows:<\/p>\n
unique_docs<\/span><\/pre>\n
You can also supply a prompt along with an output parser to split the results into a list of queries:<\/p>\n
from typing import List\nfrom langchain import LLMChain\nfrom pydantic import BaseModel, Field\nfrom langchain.prompts import PromptTemplate\nfrom langchain.output_parsers import PydanticOutputParser\n\n\n# Output parser will split the LLM result into a list of queries\nclass LineList(BaseModel):\n    # \"lines\" is the key (attribute name) of the parsed output\n    lines: List[str] = Field(description=\"Lines of text\")\n\n\nclass LineListOutputParser(PydanticOutputParser):\n    def __init__(self) -> None:\n        super().__init__(pydantic_object=LineList)\n\n    def parse(self, text: str) -> LineList:\n        lines = text.strip().split(\"\\n\")\n        return LineList(lines=lines)\n\n\noutput_parser = LineListOutputParser()\n\nQUERY_PROMPT = PromptTemplate(\n    input_variables=[\"question\"],\n    template=\"\"\"You are a modern Stoic philosopher who has grown up in the\n    hoods of south Sacramento, California. The teachings of Epictetus helped\n    you overcome ups and downs in life. You are a student and teacher of his\n    teachings,\n    Original question: {question}\"\"\",\n)\n\nllm = ChatOpenAI(temperature=0)\n\n# Chain\nllm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT, output_parser=output_parser)\n\n# Run\nretriever = MultiQueryRetriever(\n    retriever=db.as_retriever(), llm_chain=llm_chain, parser_key=\"lines\"\n)  # \"lines\" is the key (attribute name) of the parsed output\n\n# Results\nunique_docs = retriever.get_relevant_documents(\n    query=\"How can I live a meaningful life\"\n)\n\nlen(unique_docs)<\/span><\/pre>\n
INFO:langchain.retrievers.multi_query:Generated queries: ['As a modern Stoic philosopher who has experienced the challenges of growing up in the hoods of South Sacramento, I can understand the desire to seek a meaningful life. The teachings of Epictetus can indeed provide valuable guidance in this pursuit. Here are some principles that can help you live a meaningful life:', '', '1. Focus on what you can control: Epictetus emphasized the importance of distinguishing between what is within our control and what is not. By focusing our energy on the things we can control, such as our thoughts, actions, and attitudes, we can find meaning in our ability to shape our own lives.', '', '2. Cultivate virtue: According to Stoicism, the ultimate goal in life is to live in accordance with virtue. Virtue encompasses qualities such as wisdom, courage, justice, and temperance. By striving to cultivate these virtues in our daily lives, we can find purpose and meaning in our actions.', '', '3. Embrace adversity: Stoicism teaches us to view adversity as an opportunity for growth and self-improvement. Rather than being overwhelmed by the challenges we face, we can choose to embrace them as valuable lessons and opportunities to develop resilience and character.', '', '4. Practice gratitude: Epictetus emphasized the importance of gratitude in finding contentment and meaning in life. By cultivating a mindset of gratitude, we can learn to appreciate the simple joys and blessings that surround us, even in the midst of difficult circumstances.', '', '5. Serve others: Stoicism encourages us to live a life of service to others. By helping and supporting those around us, we can find purpose and fulfillment in making a positive impact on the lives of others.', '', '6. Live in accordance with nature: Stoicism teaches us to align our lives with the natural order of the universe. By accepting the impermanence of things and embracing the present moment, we can find meaning in living in harmony with the flow of life.', '', '7. Seek wisdom: Epictetus believed that the pursuit of wisdom is essential for living a meaningful life. By continuously seeking knowledge, reflecting on our experiences, and learning from others, we can deepen our understanding of ourselves and the world around us.', '', 'Remember, living a meaningful life is a personal journey, and it may take time to fully integrate these principles into your daily life. But by embracing the teachings of Epictetus and applying them in your own unique circumstances, you can find purpose, fulfillment, and meaning in your life, regardless of your background or upbringing.']\n17<\/span><\/pre>\n
Contextual compression<\/h3>\n
Contextual compression in LangChain is a technique used to compress and filter documents based on their relevance to a given query.<\/p>\n
It aims to extract only the relevant information from documents, reducing the need for expensive language model calls and improving response quality.<\/p>\n
Contextual compression is achieved by using a base retriever and a document compressor.<\/strong><\/p>\n
The base retriever retrieves the initial set of documents based on the query, and the document compressor processes these documents to extract the relevant content. You can use contextual compression when you have a document storage system and want to improve retrieval performance by returning only the most relevant information. It is particularly useful when the relevant information is buried within documents containing a lot of irrelevant text.<\/p>\n
To determine if you need to use contextual compression, consider the following factors:<\/p>\n