Harden embedding calls

backend/danswer/natural_language_processing/search_nlp_models.py

@@ -2,6 +2,7 @@ import time
 
 import requests
 from httpx import HTTPError
+from retry import retry
 
 from danswer.configs.model_configs import BATCH_SIZE_ENCODE_CHUNKS
 from danswer.configs.model_configs import (
@@ -72,6 +73,86 @@ class EmbeddingModel:
         model_server_url = build_model_server_url(server_host, server_port)
         self.embed_server_endpoint = f"{model_server_url}/encoder/bi-encoder-embed"
 
+    def _make_model_server_request(self, embed_request: EmbedRequest) -> EmbedResponse:
+        def _make_request() -> EmbedResponse:
+            response = requests.post(
+                self.embed_server_endpoint, json=embed_request.dict()
+            )
+            try:
+                response.raise_for_status()
+            except requests.HTTPError as e:
+                try:
+                    error_detail = response.json().get("detail", str(e))
+                except Exception:
+                    error_detail = response.text
+                raise HTTPError(f"HTTP error occurred: {error_detail}") from e
+            except requests.RequestException as e:
+                raise HTTPError(f"Request failed: {str(e)}") from e
+
+            return EmbedResponse(**response.json())
+
+        # only perform retries for the non-realtime embedding of passages (e.g. for indexing)
+        if embed_request.text_type == EmbedTextType.PASSAGE:
+            return retry(tries=3, delay=5)(_make_request)()
+        else:
+            return _make_request()
+
+    def _encode_api_model(
+        self, texts: list[str], text_type: EmbedTextType, batch_size: int
+    ) -> list[Embedding]:
+        if not self.provider_type:
+            raise ValueError("Provider type is not set for API embedding")
+
+        embeddings: list[Embedding] = []
+
+        text_batches = batch_list(texts, batch_size)
+        for idx, text_batch in enumerate(text_batches, start=1):
+            logger.debug(f"Encoding batch {idx} of {len(text_batches)}")
+            embed_request = EmbedRequest(
+                model_name=self.model_name,
+                texts=text_batch,
+                max_context_length=self.max_seq_length,
+                normalize_embeddings=self.normalize,
+                api_key=self.api_key,
+                provider_type=self.provider_type,
+                text_type=text_type,
+                manual_query_prefix=self.query_prefix,
+                manual_passage_prefix=self.passage_prefix,
+            )
+            response = self._make_model_server_request(embed_request)
+            embeddings.extend(response.embeddings)
+
+        return embeddings
+
+    def _encode_local_model(
+        self,
+        texts: list[str],
+        text_type: EmbedTextType,
+        batch_size: int,
+    ) -> list[Embedding]:
+        text_batches = batch_list(texts, batch_size)
+        embeddings: list[Embedding] = []
+        logger.debug(
+            f"Encoding {len(texts)} texts in {len(text_batches)} batches for local model"
+        )
+        for idx, text_batch in enumerate(text_batches, start=1):
+            logger.debug(f"Encoding batch {idx} of {len(text_batches)}")
+            embed_request = EmbedRequest(
+                model_name=self.model_name,
+                texts=text_batch,
+                max_context_length=self.max_seq_length,
+                normalize_embeddings=self.normalize,
+                api_key=self.api_key,
+                provider_type=self.provider_type,
+                text_type=text_type,
+                manual_query_prefix=self.query_prefix,
+                manual_passage_prefix=self.passage_prefix,
+            )
+
+            response = self._make_model_server_request(embed_request)
+            embeddings.extend(response.embeddings)
+        return embeddings
+
     def encode(
         self,
         texts: list[str],
@@ -99,65 +180,14 @@ class EmbeddingModel:
             ]
 
         if self.provider_type:
-            text_batches = batch_list(texts, api_embedding_batch_size)
-            embed_request = EmbedRequest(
-                model_name=self.model_name,
-                texts=texts,
-                max_context_length=self.max_seq_length,
-                normalize_embeddings=self.normalize,
-                api_key=self.api_key,
-                provider_type=self.provider_type,
-                text_type=text_type,
-                manual_query_prefix=self.query_prefix,
-                manual_passage_prefix=self.passage_prefix,
+            return self._encode_api_model(
+                texts=texts, text_type=text_type, batch_size=api_embedding_batch_size
             )
-            response = requests.post(
-                self.embed_server_endpoint, json=embed_request.dict()
-            )
-            try:
-                response.raise_for_status()
-            except requests.HTTPError as e:
-                error_detail = response.json().get("detail", str(e))
-                raise HTTPError(f"HTTP error occurred: {error_detail}") from e
-            except requests.RequestException as e:
-                raise HTTPError(f"Request failed: {str(e)}") from e
 
-            return EmbedResponse(**response.json()).embeddings
-
-        # Batching for local embedding
-        text_batches = batch_list(texts, local_embedding_batch_size)
-        embeddings: list[Embedding] = []
-        logger.debug(
-            f"Encoding {len(texts)} texts in {len(text_batches)} batches for local model"
+        # if no provider, use local model
+        return self._encode_local_model(
+            texts=texts, text_type=text_type, batch_size=local_embedding_batch_size
         )
-        for idx, text_batch in enumerate(text_batches, start=1):
-            logger.debug(f"Encoding batch {idx} of {len(text_batches)}")
-            embed_request = EmbedRequest(
-                model_name=self.model_name,
-                texts=text_batch,
-                max_context_length=self.max_seq_length,
-                normalize_embeddings=self.normalize,
-                api_key=self.api_key,
-                provider_type=self.provider_type,
-                text_type=text_type,
-                manual_query_prefix=self.query_prefix,
-                manual_passage_prefix=self.passage_prefix,
-            )
-
-            response = requests.post(
-                self.embed_server_endpoint, json=embed_request.dict()
-            )
-            try:
-                response.raise_for_status()
-            except requests.HTTPError as e:
-                error_detail = response.json().get("detail", str(e))
-                raise HTTPError(f"HTTP error occurred: {error_detail}") from e
-            except requests.RequestException as e:
-                raise HTTPError(f"Request failed: {str(e)}") from e
-            # Normalize embeddings is only configured via model_configs.py, be sure to use right
-            # value for the set loss
-            embeddings.extend(EmbedResponse(**response.json()).embeddings)
-        return embeddings
 
 
 class CrossEncoderEnsembleModel: