diff --git a/servers/external-rag/.gitignore b/servers/external-rag/.gitignore
new file mode 100644
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--- /dev/null
+++ b/servers/external-rag/.gitignore
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+memory.json
\ No newline at end of file
diff --git a/servers/external-rag/main.py b/servers/external-rag/main.py
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+++ b/servers/external-rag/main.py
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+import os
+from fastapi import FastAPI, HTTPException, Query
+from pydantic import BaseModel, Field
+from typing import List
+
+# --- RAG Libraries ---
+from langchain_community.vectorstores import FAISS
+from sentence_transformers import SentenceTransformer
+from langchain.embeddings import HuggingFaceEmbeddings
+
+app = FastAPI(
+    title="RAG Retriever API",
+    version="1.0.0",
+    description="Retrieval-Only API: Queries to vectorstore using LangChain, FAISS, and sentence-transformers.",
+)
+
+
+class RetrievalQueryInput(BaseModel):
+    queries: List[str] = Field(
+        ..., description="List of queries to retrieve from the vectorstore"
+    )
+    k: int = Field(3, description="Number of results per query", example=3)
+
+
+class RetrievedDoc(BaseModel):
+    query: str
+    results: List[str]
+
+
+class RetrievalResponse(BaseModel):
+    responses: List[RetrievedDoc]
+
+
+# --------- Initialize Retriever (on app startup) --------
+VECTORSTORE_PATH = "faiss_index"  # Path to your FAISS vector store
+EMBEDDING_MODEL_NAME = "all-MiniLM-L6-v2"  # Widely used, fast
+
+
+def get_retriever():
+    embedder = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME)
+    vectorstore = FAISS.load_local(VECTORSTORE_PATH, embeddings=embedder)
+    retriever = vectorstore.as_retriever()
+    return retriever
+
+
+retriever = get_retriever()
+# --------------------------------------------------------
+
+
+@app.post(
+    "/retrieve",
+    response_model=RetrievalResponse,
+    summary="Retrieve top-k docs for each query",
+)
+def retrieve_docs(input: RetrievalQueryInput):
+    """
+    Given a list of user queries, returns top-k retrieved documents per query.
+    """
+    try:
+        out = []
+        for q in input.queries:
+            docs = retriever.get_relevant_documents(q, k=input.k)
+            results = [doc.page_content for doc in docs]
+            out.append(RetrievedDoc(query=q, results=results))
+        return RetrievalResponse(responses=out)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
diff --git a/servers/external-rag/requirements.txt b/servers/external-rag/requirements.txt
new file mode 100644
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+++ b/servers/external-rag/requirements.txt
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+fastapi
+uvicorn[standard]
+pydantic
+python-multipart
+
+langchain
+langchain_community
+sentence_transformers
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