> For the complete documentation index, see [llms.txt](https://docs.thub.tech/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.thub.tech/langchain/retrivers.md).
# 🔍Retrivers
#### **1)AWS Bedrock Knowledge Base Retriever**
Purpose: Retrieves relevant documents or chunks from a Knowledge Base created and managed in Amazon Bedrock.
**How It Works:**
* Uses the Bedrock service's native vector store and retrieval tools.
* Typically paired with a Bedrock-supported embedding model (like Titan or Cohere).
**Use Case:** Enterprise RAG applications hosted entirely on AWS infrastructure for scalability and security.
#### 2**)Custom Retriever**
Purpose: Allows you to define your own logic for retrieving documents from a vector database or custom source.
**How It Works:**
* You can implement custom search logic (e.g., specific filters, hybrid retrieval, advanced ranking).
* Often used when built-in retrievers (like Pinecone or Qdrant) don't meet specific needs.
**Use Case:** When you need fine-grained control over how data is retrieved—e.g., combining metadata filtering, hybrid ranking, or integrating proprietary databases.
#### 3)Embeddings Filter Retriever
A document compressor that uses embeddings to drop documents unrelated to the query.
Purpose: Allows filtering of retrieved documents based on embedding similarity threshold from a vector store.
**How it Works:**
• Uses embeddings to compare query similarity with stored documents.\
• Applies a similarity threshold to filter out less relevant results.\
• Returns only documents that meet the defined similarity score.
**Use Cases:**
• Improving search accuracy by removing low-relevance results.\
• Fine-tuning retrieval quality in RAG systems.
#### 4)HyDE Retriever
Use HyDE retriever to retrieve from a vector store.
Purpose: Enhances retrieval by generating a hypothetical document using an LLM before performing the search.
**How it Works:**
• Takes the user query and generates a hypothetical answer using a language model.\
• Converts this generated text into embeddings.\
• Uses these embeddings to retrieve more relevant documents from the vector store.
**Use Cases:**
• Improving retrieval for vague or short queries.\
• Boosting performance in semantic search and RAG pipelines.
#### 5)LLM Filter Retriever
Iterate over the initially returned documents and extract, from each, only the content that is relevant to the query.
Purpose: Filters retrieved documents using a language model to ensure only relevant results are returned.
**How it Works:**
• Retrieves documents from a vector store.\
• Uses an LLM to evaluate and filter the results.\
• Keeps only documents that are contextually relevant to the query.
**Use Cases:**
• Removing irrelevant or noisy results.\
• Improving answer quality in AI applications.
#### 6) Multi Query Retriever
Purpose: Improves retrieval by generating multiple variations of a query to fetch more comprehensive results.
**How it Works:**
• Uses an LLM to generate multiple query variations from a single input.\
• Executes each query against the vector store.\
• Combines results to provide a broader and more accurate context.
**Use Cases:**
• Handling ambiguous or complex queries.\
• Increasing recall in retrieval-based systems.
#### 7)Prompt Retriever
Store prompt template with name & description to be later queried by MultiPromptChain.
Purpose: Retrieves predefined prompts to guide the model in generating structured and domain-specific responses.
**How it Works:**
• Uses stored prompt templates based on a given prompt name.\
• Applies system message and description to guide the model.\
• Provides structured instructions for consistent output generation.
**Use Cases:**
• Domain-specific assistants (e.g., physics, medical, legal).\
• Reusing predefined prompts across workflows.
#### 8)Reciprocal Rank Fusion Retriever
Reciprocal Rank Fusion to re-rank search results by multiple query generation.
Purpose: Combines results from multiple retrieval methods to improve overall ranking and relevance.
**How it Works:**
• Retrieves results using a vector store retriever.\
• Uses multiple ranking strategies.\
• Applies Reciprocal Rank Fusion (RRF) to merge and re-rank results.\
• Returns the most relevant combined results.
**Use Cases:**
• Improving retrieval accuracy.\
• Combining multiple retrieval strategies.
#### **9)Similarity Score Threshold Retriever**
Return results based on the minimum similarity percentage.
Purpose: Filters retrieved documents based on a minimum similarity score to ensure only relevant results are returned.
**How it Works:**
• Performs similarity search on a vector store.\
• Calculates similarity scores for retrieved documents.\
• Filters out results below the defined threshold.\
• Returns only high-relevance documents.
**Use Cases:**
• Removing low-quality or irrelevant results.\
• Fine-tuning retrieval precision in RAG systems.
#### 10)Vector Store Retriever
Store vector store as retriever to be later queried by Multi Retrieval QA Chain.
Purpose: Retrieves relevant documents directly from a vector store based on similarity search.
**How it Works:**
• Takes a query and converts it into embeddings.\
• Searches the vector store for similar embeddings.\
• Retrieves the most relevant documents.\
• Uses retriever name and description for identification.
**Use Cases:**
• Basic semantic search.\
• Retrieving documents for RAG pipelines.
#### 11)Voyage AI Rerank Retriever
Voyage AI Rerank indexes the documents from most to least semantically relevant to the query.
Purpose: Improves retrieval quality by re-ranking documents using Voyage AI models.
**How it Works:**
• Retrieves initial results from a vector store.\
• Sends results along with the query to Voyage AI rerank model.\
• Reorders documents based on relevance.\
• Returns the most relevant ranked results.
**Use Cases:**
• Improving search result accuracy.\
• Enhancing ranking in retrieval pipelines.