Try respond even if document search via inference endpoint fails

The huggingface endpoint can be flaky. Khoj shouldn't refuse to
respond to user if document search fails.
It should transparently mention that document lookup failed.
But try respond as best as it can without the document references

This changes provides graceful failover when inference endpoint
requests fail either when encoding query or reranking retrieved docs

src/khoj/processor/embeddings.py

@@ -114,6 +114,7 @@ class CrossEncoderModel:
             payload = {"inputs": {"query": query, "passages": [hit.additional[key] for hit in hits]}}
             headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
             response = requests.post(target_url, json=payload, headers=headers)
+            response.raise_for_status()
             return response.json()["scores"]
 
         cross_inp = [[query, hit.additional[key]] for hit in hits]

src/khoj/routers/api_chat.py

@@ -3,7 +3,6 @@ import base64
 import json
 import logging
 import time
-import warnings
 from datetime import datetime
 from functools import partial
 from typing import Dict, Optional
@@ -839,25 +838,33 @@ async def chat(
         # Gather Context
         ## Extract Document References
         compiled_references, inferred_queries, defiltered_query = [], [], None
-        async for result in extract_references_and_questions(
-            request,
-            meta_log,
-            q,
-            (n or 7),
-            d,
-            conversation_id,
-            conversation_commands,
-            location,
-            partial(send_event, ChatEvent.STATUS),
-            uploaded_image_url=uploaded_image_url,
-            agent=agent,
-        ):
-            if isinstance(result, dict) and ChatEvent.STATUS in result:
-                yield result[ChatEvent.STATUS]
-            else:
-                compiled_references.extend(result[0])
-                inferred_queries.extend(result[1])
-                defiltered_query = result[2]
+        try:
+            async for result in extract_references_and_questions(
+                request,
+                meta_log,
+                q,
+                (n or 7),
+                d,
+                conversation_id,
+                conversation_commands,
+                location,
+                partial(send_event, ChatEvent.STATUS),
+                uploaded_image_url=uploaded_image_url,
+                agent=agent,
+            ):
+                if isinstance(result, dict) and ChatEvent.STATUS in result:
+                    yield result[ChatEvent.STATUS]
+                else:
+                    compiled_references.extend(result[0])
+                    inferred_queries.extend(result[1])
+                    defiltered_query = result[2]
+        except Exception as e:
+            error_message = f"Error searching knowledge base: {e}. Attempting to respond without document references."
+            logger.warning(error_message)
+            async for result in send_event(
+                ChatEvent.STATUS, "Document search failed. I'll try respond without document references"
+            ):
+                yield result
 
         if not is_none_or_empty(compiled_references):
             headings = "\n- " + "\n- ".join(set([c.get("compiled", c).split("\n")[0] for c in compiled_references]))

src/khoj/search_type/text_search.py

@@ -3,6 +3,7 @@ import math
 from pathlib import Path
 from typing import List, Optional, Tuple, Type, Union
 
+import requests
 import torch
 from asgiref.sync import sync_to_async
 from sentence_transformers import util
@@ -231,8 +232,12 @@ def setup(
 
 def cross_encoder_score(query: str, hits: List[SearchResponse], search_model_name: str) -> List[SearchResponse]:
     """Score all retrieved entries using the cross-encoder"""
-    with timer("Cross-Encoder Predict Time", logger, state.device):
-        cross_scores = state.cross_encoder_model[search_model_name].predict(query, hits)
+    try:
+        with timer("Cross-Encoder Predict Time", logger, state.device):
+            cross_scores = state.cross_encoder_model[search_model_name].predict(query, hits)
+    except requests.exceptions.HTTPError as e:
+        logger.error(f"Failed to rerank documents using the inference endpoint. Error: {e}.", exc_info=True)
+        cross_scores = [0.0] * len(hits)
 
     # Convert cross-encoder scores to distances and pass in hits for reranking
     for idx in range(len(cross_scores)):