opea-project
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+.. _DocIndexRetriever_Guide:
+
+DocIndexRetriever
+####################
+
+.. note:: This guide is in its early development and is a work-in-progress with
+   placeholder content.
+
+Overview
+********
+
+DocIndexRetriever is the most widely adopted use case for leveraging the different 
+methodologies to match user query against a set of free-text records. DocIndexRetriever 
+is essential to RAG system, which bridges the knowledge gap by dynamically fetching
+relevant information from external sources, ensuring that responses generated remain 
+factual and current. The core of this architecture are vector databases, which are 
+instrumental in enabling efficient and semantic retrieval of information. These 
+databases store data as vectors, allowing RAG to swiftly access the most pertinent 
+documents or data points based on semantic similarity.
+
+
+Purpose
+*******
+
+* **Enable document retrieval with LLMs**: DocIndexRetriever is designed to 
+  facilitate the retrieval of documents or information from a large corpus of 
+  text data using Large Language Models (LLMs). 
+
+Key Implementation Details
+**************************
+
+User Interface:
+  The interface that interactivates with users, gets inputs from users and 
+  serves responses to users.
+DocIndexRetriever GateWay:
+  The agent that maintains the connections between user-end and service-end, 
+  forwards requests and responses to appropriate nodes.
+DocIndexRetriever MegaService:
+  The central component that converts user query to vector representation,
+  retrieves relevant documents from the vector database and reranks relevant 
+  documents to select the most related documents.
+Data Preparation MicroService:
+  The component that prepares the data for the vector database.
+
+How It Works
+************
+
+The DocIndexRetriever example is implemented using the component-level microservices
+defined in [GenAIComps](https://github.com/opea-project/GenAIComps). The flow chart 
+below shows the information flow between different microservices for this example.
+
+
+.. mermaid::
+
+    ---
+    config:
+    flowchart:
+        nodeSpacing: 400
+        rankSpacing: 100
+        curve: linear
+    themeVariables:
+        fontSize: 50px
+    ---
+    flowchart LR
+        %% Colors %%
+        classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
+        classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
+        classDef orchid fill:#C26DBC,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
+        classDef invisible fill:transparent,stroke:transparent;
+        style DocIndexRetriever-MegaService stroke:#000000
+
+        %% Subgraphs %%
+        subgraph DocIndexRetriever-MegaService["DocIndexRetriever MegaService "]
+            direction LR
+            EM([Embedding MicroService]):::blue
+            RET([Retrieval MicroService]):::blue
+            RER([Rerank MicroService]):::blue
+        end
+        subgraph UserInput[" User Input "]
+            direction LR
+            a([User Input Query]):::orchid
+            Ingest([Ingest data]):::orchid
+        end
+
+        DP([Data Preparation MicroService]):::blue
+        TEI_RER{{Reranking service<br>}}
+        TEI_EM{{Embedding service <br>}}
+        VDB{{Vector DB<br><br>}}
+        R_RET{{Retriever service <br>}}
+        GW([DocIndexRetriever GateWay<br>]):::orange
+
+        %% Data Preparation flow
+        %% Ingest data flow
+        direction LR
+        Ingest[Ingest data] --> DP
+        DP <-.-> TEI_EM
+
+        %% Questions interaction
+        direction LR
+        a[User Input Query] --> GW
+        GW <==> DocIndexRetriever-MegaService
+        EM ==> RET
+        RET ==> RER
+
+        %% Embedding service flow
+        direction LR
+        EM <-.-> TEI_EM
+        RET <-.-> R_RET
+        RER <-.-> TEI_RER
+
+        direction TB
+        %% Vector DB interaction
+        R_RET <-.-> VDB
+        DP <-.-> VDB
+
+
+This diagram illustrates the flow of information in the DocIndexRetriever system. 
+Firstly, the user provides docments to the system, which are ingested by the
+Data Preparation MicroService. The Data Preparation MicroService prepares the data
+for the vector database. The User Input Query is then sent to the DocIndexRetriever
+Gateway, which forwards the query to the DocIndexRetriever MegaService. The
+DocIndexRetriever MegaService uses the Embedding MicroService to convert the query
+to a vector representation. The Retrieval MicroService retrieves relevant documents
+from the vector database, and the Rerank MicroService reranks the relevant documents
+to select the most related documents. The reranked documents are then sent back to
+the DocIndexRetriever Gateway, which forwards the documents to the user.
+
+
+The architecture follows a series of steps to process user queries and generate 
+responses:
+
+1. **Embedding**: The Embedding MicroService converts the user query into a vector 
+   representation.
+#. **Retriever**: The Retrieval MicroService retrieves relevant documents from the 
+   vector database based on the vector representation of the user query.
+#. **Reranker**: The Rerank MicroService reranks the relevant documents to select 
+   the most related documents.
+#. **Vector Database**: The Vector Database stores data as vectors, allowing the 
+   system to swiftly access the most pertinent documents or data points based on 
+   semantic similarity.
+#. **Data Preparation**: The Data Preparation MicroService prepares the data for the 
+   vector database.
+
+Deployment
+**********
+
+Here are some deployment options depending on your hardware and environment.
+
+Single Node
++++++++++++++++
+.. toctree::
+   :maxdepth: 1
+
+   Xeon Scalable Processor <deploy/xeon>
+   Gaudi <deploy/gaudi>