From f4b9f37dbeddb9ca32463776c3e4688914d51466 Mon Sep 17 00:00:00 2001
From: Jared Ross <jaredrossj@gmail.com>
Date: Wed, 18 Oct 2023 15:19:44 -0600
Subject: [PATCH] added endpoint to embed query with text

---
 gamechangerml/api/fastapi/routers/search.py   | 22 ++++++++++++++++++-
 .../src/search/sent_transformer/model.py      |  6 ++++-
 2 files changed, 26 insertions(+), 2 deletions(-)

diff --git a/gamechangerml/api/fastapi/routers/search.py b/gamechangerml/api/fastapi/routers/search.py
index cc84597b..72a62951 100644
--- a/gamechangerml/api/fastapi/routers/search.py
+++ b/gamechangerml/api/fastapi/routers/search.py
@@ -77,8 +77,28 @@ async def text_extract_infer(body: dict, extractType: str, response: Response) -
         raise
     return results
 
+@router.post("/embedSemanticQuery", status_code=200)
+async def semantic_search(
+    body: dict,
+    response: Response,
+) -> dict:
+    """
+    embedSemanticQuery - takes in a query and returns the txtai embeddings as a list of floats to be used by ElasticSearch
+
+    Args:
+        (dict) json format of the search query.\n 
+            query: (str, required) a string of any length to embed and use for semantic search.\n
+            Example: {"query": "Where is the science and technology reinvention laboratory?"}
+        Response: Response class; for status codes(apart of fastapi do not need to pass param)
+    Returns:
+        results: list of floats
+    """
+    query_text = body["query"]
+    embeddings = MODELS.semantic_searcher.embed_query(query_text)
+    return  list(embeddings.astype(float))
+
 
-@ router.post("/semanticSearch", status_code=200)
+@router.post("/semanticSearch", status_code=200)
 async def semantic_search(
     body: dict,
     response: Response,
diff --git a/gamechangerml/src/search/sent_transformer/model.py b/gamechangerml/src/search/sent_transformer/model.py
index 3d89cd2d..6a37a449 100644
--- a/gamechangerml/src/search/sent_transformer/model.py
+++ b/gamechangerml/src/search/sent_transformer/model.py
@@ -470,4 +470,8 @@ def search(
 
             return top_results
         else:
-            return []
\ No newline at end of file
+            return []
+    
+    def embed_query(self, query_text):
+        embeddings = self.embedder.transform(query_text)
+        return embeddings
\ No newline at end of file