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Retrievers | 🦜️🔗 LangChain
A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store. A retriever does not need to be able to store documents, only to return (or retrieve) them. Vector stores can be used as the backbone of a retriever, but there are other types of retrievers as well.
Retrievers accept a string query as input and return a list of Document's as output.
Name Index Type Uses an LLM When to Use Description Vectorstore Vectorstore No If you are just getting started and looking for something quick and easy. This is the simplest method and the one that is easiest to get started with. It creates embeddings for each piece of text. ParentDocument Vectorstore + Document Store No If your pages have lots of smaller pieces of distinct information that are best indexed by themselves, but best retrieved all together. This indexes multiple chunks for each document. Then you find the chunks that are most similar in embedding space, but you retrieve the whole parent document and return that (rather than individual chunks). Multi Vector Vectorstore + Document Store Sometimes during indexing If you are able to extract information from documents that you think is more relevant to index than the text itself. This creates multiple vectors for each document. Each vector could be created in a myriad of ways - examples include summaries of the text and hypothetical questions. Self Query Vectorstore Yes If users are asking questions that are better answered by fetching documents based on metadata rather than similarity with the text. This uses an LLM to transform user input into two things: (1) a string to look up semantically, (2) a metadata filer to go along with it. This is useful because oftentimes questions are about the METADATA of documents (not the content itself). Contextual Compression Any Sometimes If you are finding that your retrieved documents contain too much irrelevant information and are distracting the LLM. This puts a post-processing step on top of another retriever and extracts only the most relevant information from retrieved documents. This can be done with embeddings or an LLM. Time-Weighted Vectorstore Vectorstore No If you have timestamps associated with your documents, and you want to retrieve the most recent ones This fetches documents based on a combination of semantic similarity (as in normal vector retrieval) and recency (looking at timestamps of indexed documents) Multi-Query Retriever Any Yes If users are asking questions that are complex and require multiple pieces of distinct information to respond This uses an LLM to generate multiple queries from the original one. This is useful when the original query needs pieces of information about multiple topics to be properly answered. By generating multiple queries, we can then fetch documents for each of them. Ensemble Any No If you have multiple retrieval methods and want to try combining them. This fetches documents from multiple retrievers and then combines them. Long-Context Reorder Any No If you are working with a long-context model and noticing that it's not paying attention to information in the middle of retrieved documents. This fetches documents from an underlying retriever, and then reorders them so that the most similar are near the beginning and end. This is useful because it's been shown that for longer context models they sometimes don't pay attention to information in the middle of the context window.
LangChain also integrates with many third-party retrieval services. For a full list of these, check out this list of all integrations.
Since retrievers are Runnable's, we can easily compose them with other Runnable objects:
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI()
def format_docs(docs):
return "\n\n".join([d.page_content for d in docs])
chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| model
| StrOutputParser()
)
chain.invoke("What did the president say about technology?")
RetroSearch is an open source project built by @garambo
| Open a GitHub Issue
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