use new rag api names/endpts for the showcases (#8376)

GitOrigin-RevId: 7441cf0cd5beee7674a7177370e63bb2940140c0

Author: berkecanrizai

docs/2.developers/6.ai-pipelines/30.REST-API.md

@@ -21,9 +21,9 @@ The REST API provides the following endpoints:
 |----------------------------|----------|---------------------------|---------------------------------------------------------------------------------|
 | [Retrieve Closest Documents](#retrieve-closest-documents) | POST     | `/v1/query`               | Retrieve the closest documents from the vector store based on a query.        |
 | [Retrieve Vector Store Statistics](#retrieve-vector-store-statistics) | GET      | `/v1/statistics`          | Retrieve statistics about the vector store.                       |
-| [Answer with RAG](#answer-with-rag)            | POST     | `/v1/pw_ai_answer`        | Generate an answer to a query using a RAG pipeline.                            |
-| [Summarize Texts](#summarize-texts)            | POST     | `/v1/pw_ai_summary`       | Summarize a list of texts.                         |
-| [List Indexed Documents](#list-indexed-documents)     | POST     | `/v1/pw_list_documents`   | List documents indexed in the vector store. |
+| [Answer with RAG](#answer-with-rag)            | POST     | `/v2/answer`        | Generate an answer to a query using a RAG pipeline.                            |
+| [Summarize Texts](#summarize-texts)            | POST     | `/v2/summarize`       | Summarize a list of texts.                         |
+| [List Indexed Documents](#list-indexed-documents)     | POST     | `/v2/list_documents`   | List documents indexed in the vector store. |
 
 
 ## Retrieve Closest Documents
@@ -75,7 +75,7 @@ Retrieve statistical information about the vector store, such as file counts and
 
 ## Answer with RAG
 
-**Endpoint**: `POST /v1/pw_ai_answer`
+**Endpoint**: `POST /v2/answer`
 
 Provide a response to a query using a RAG pipeline.
 
@@ -104,7 +104,7 @@ Provide a response to a query using a RAG pipeline.
 
 ## Summarize Texts
 
-**Endpoint**: `POST /v1/pw_ai_summary`
+**Endpoint**: `POST /v2/summarize`
 
 Summarize a list of texts.
 
@@ -131,7 +131,7 @@ Summarize a list of texts.
 
 ## List Indexed Documents
 
-**Endpoint**: `POST /v1/pw_list_documents`
+**Endpoint**: `POST /v2/list_documents`
 
 List documents currently indexed in the vector store, with optional filtering or metadata selection.
 

examples/notebooks/showcases/multimodal-rag-using-Gemini.ipynb

@@ -604,7 +604,7 @@
       "source": [
         "# Example usage\n",
         "\n",
-        "response = client.pw_ai_answer(\"What is the Total Stockholders' equity as of December 31, 2022?\")\n",
+        "response = client.answer(\"What is the Total Stockholders' equity as of December 31, 2022?\")\n",
         "print(response)\n"
       ]
     },
@@ -651,4 +651,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

examples/notebooks/tutorials/rag-evaluations.ipynb

@@ -672,7 +672,7 @@
         "from pathway.xpacks.llm.question_answering import RAGClient\n",
         "\n",
         "pathway_client = RAGClient(pathway_host, pathway_port)\n",
-        "pathway_client.pw_list_documents()"
+        "pathway_client.list_documents()"
       ]
     },
     {
@@ -715,7 +715,7 @@
         "        if verbose:\n",
         "            print(f\"Predicting question: {single_sample.user_input}\")\n",
         "\n",
-        "        pw_response: dict = pathway_client.pw_ai_answer(\n",
+        "        pw_response: dict = pathway_client.answer(\n",
         "            prompt=single_sample.user_input, return_context_docs=True\n",
         "        )\n",
         "        resp: str = pw_response[\"response\"]\n",
@@ -1047,7 +1047,7 @@
       "outputs": [],
       "source": [
         "pathway_client = RAGClient(pathway_host, pathway_port)\n",
-        "pathway_client.pw_list_documents()"
+        "pathway_client.list_documents()"
       ]
     },
     {
@@ -1212,7 +1212,7 @@
       "outputs": [],
       "source": [
         "pathway_client = RAGClient(pathway_host, pathway_port)\n",
-        "pathway_client.pw_list_documents()"
+        "pathway_client.list_documents()"
       ]
     },
     {
@@ -1749,4 +1749,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

integration_tests/rag_evals/connector.py

@@ -137,7 +137,7 @@ def _get_request_headers(self):
 
 
 class RagConnector:
-    """Rag connector for evals. Returns context docs in `pw_ai_answer_question`."""
+    """Rag connector for evals. Returns context docs in `answer_question`."""
 
     def __init__(self, base_url: str):
         self.base_url = base_url
@@ -146,7 +146,7 @@ def __init__(self, base_url: str):
             url=base_url,
         )
 
-    def pw_ai_answer_question(
+    def answer_question(
         self,
         prompt,
         filter=None,
@@ -171,7 +171,7 @@ def pw_ai_answer_question(
 
         return response
 
-    def pw_list_documents(self, filter=None, keys=["path"]):
+    def list_documents(self, filter=None, keys=["path"]):
         api_url = f"{self.base_url}/v2/list_documents"
         payload = {}
 

integration_tests/rag_evals/evaluator.py

@@ -210,7 +210,7 @@ def predicted_dataset_as_dict_list(self) -> list[dict]:
 
     def _predict_single(self, question: str, file: str) -> dict:
         filter = create_file_filter(file)
-        answer = self.connector.pw_ai_answer_question(
+        answer = self.connector.answer_question(
             question,
             filter,
         )
@@ -219,7 +219,7 @@ def _predict_single(self, question: str, file: str) -> dict:
     async def _apredict_single(self, question: str, file: str) -> dict:
         filter = create_file_filter(file)
         answer = await asyncio.to_thread(  # TODO: convert to await with async client
-            self.connector.pw_ai_answer_question,
+            self.connector.answer_question,
             question,
             filter,
         )

integration_tests/rag_evals/test_eval.py

@@ -85,7 +85,7 @@ def wait_for_start(retries: int = 10, interval: int | float = 45.0) -> bool:
             )
             logging.info(f"Indexed documents: {docs}")
             try:
-                docs = conn.pw_list_documents()
+                docs = conn.list_documents()
                 if docs and len(docs) >= EXPECTED_DOCS_COUNT:
                     logging.info(
                         f"Fetched docs: ({len(docs)}) List: {docs}, \
@@ -115,7 +115,7 @@ def checker() -> bool:
             logging.error("Server was not started properly.")
             return False
 
-        docs = conn.pw_list_documents()
+        docs = conn.list_documents()
 
         logging.info(f"Indexed test documents: {docs}")