Merge branch 'master' into short-circuit-api-rate-limiter

documentation/docusaurus.config.js

@@ -175,6 +175,12 @@ const config = {
         theme: prismThemes.github,
         darkTheme: prismThemes.dracula,
       },
+      algolia: {
+        appId: "NBR0FXJNGW",
+        apiKey: "8841b34192a28b2d06f04dd28d768017",
+        indexName: "khoj",
+        contextualSearch: false,
+      }
     }),
 };
 

pyproject.toml

@@ -62,8 +62,8 @@ dependencies = [
     "pymupdf >= 1.23.5",
     "django == 4.2.7",
     "authlib == 1.2.1",
-    "gpt4all >= 2.0.0; platform_system == 'Linux' and platform_machine == 'x86_64'",
+    "gpt4all >= 2.1.0; platform_system == 'Linux' and platform_machine == 'x86_64'",
-    "gpt4all >= 2.0.0; platform_system == 'Windows' or platform_system == 'Darwin'",
+    "gpt4all >= 2.1.0; platform_system == 'Windows' or platform_system == 'Darwin'",
     "itsdangerous == 2.1.2",
     "httpx == 0.25.0",
     "pgvector == 0.2.4",

src/interface/desktop/main.js

@@ -208,7 +208,10 @@ function pushDataToKhoj (regenerate = false) {
             })
             .catch(error => {
                 console.error(error);
-                if (error.response.status == 429) {
+                if (error.code == 'ECONNREFUSED') {
+                    const win = BrowserWindow.getAllWindows()[0];
+                    if (win) win.webContents.send('update-state', state);
+                } else if (error.response.status == 429) {
                     const win = BrowserWindow.getAllWindows()[0];
                     if (win) win.webContents.send('needsSubscription', true);
                     if (win) win.webContents.send('update-state', state);

src/khoj/configure.py

@@ -147,7 +147,15 @@ def configure_server(
         state.cross_encoder_model = dict()
 
         for model in search_models:
-            state.embeddings_model.update({model.name: EmbeddingsModel(model.bi_encoder)})
+            state.embeddings_model.update(
+                {
+                    model.name: EmbeddingsModel(
+                        model.bi_encoder,
+                        model.embeddings_inference_endpoint,
+                        model.embeddings_inference_endpoint_api_key,
+                    )
+                }
+            )
             state.cross_encoder_model.update({model.name: CrossEncoderModel(model.cross_encoder)})
 
         state.SearchType = configure_search_types()

src/khoj/database/migrations/0025_searchmodelconfig_embeddings_inference_endpoint_and_more.py

@@ -0,0 +1,22 @@
+# Generated by Django 4.2.7 on 2024-01-15 18:12
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+    dependencies = [
+        ("database", "0024_alter_entry_embeddings"),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name="searchmodelconfig",
+            name="embeddings_inference_endpoint",
+            field=models.CharField(blank=True, default=None, max_length=200, null=True),
+        ),
+        migrations.AddField(
+            model_name="searchmodelconfig",
+            name="embeddings_inference_endpoint_api_key",
+            field=models.CharField(blank=True, default=None, max_length=200, null=True),
+        ),
+    ]

src/khoj/database/models/__init__.py

@@ -110,6 +110,8 @@ class SearchModelConfig(BaseModel):
     model_type = models.CharField(max_length=200, choices=ModelType.choices, default=ModelType.TEXT)
     bi_encoder = models.CharField(max_length=200, default="thenlper/gte-small")
     cross_encoder = models.CharField(max_length=200, default="cross-encoder/ms-marco-MiniLM-L-6-v2")
+    embeddings_inference_endpoint = models.CharField(max_length=200, default=None, null=True, blank=True)
+    embeddings_inference_endpoint_api_key = models.CharField(max_length=200, default=None, null=True, blank=True)
 
 
 class TextToImageModelConfig(BaseModel):

src/khoj/processor/conversation/offline/chat_model.py

@@ -123,9 +123,9 @@ def filter_questions(questions: List[str]):
 
 
 def converse_offline(
-    references,
-    online_results,
     user_query,
+    references=[],
+    online_results=[],
     conversation_log={},
     model: str = "mistral-7b-instruct-v0.1.Q4_0.gguf",
     loaded_model: Union[Any, None] = None,

src/khoj/processor/conversation/offline/utils.py

@@ -21,9 +21,11 @@ def download_model(model_name: str):
         # Try load chat model to GPU if:
         # 1. Loading chat model to GPU isn't disabled via CLI and
         # 2. Machine has GPU
-        # 3. GPU has enough free memory to load the chat model
+        # 3. GPU has enough free memory to load the chat model with max context length of 4096
         device = (
-            "gpu" if state.chat_on_gpu and gpt4all.pyllmodel.LLModel().list_gpu(chat_model_config["path"]) else "cpu"
+            "gpu"
+            if state.chat_on_gpu and gpt4all.pyllmodel.LLModel().list_gpu(chat_model_config["path"], 4096)
+            else "cpu"
         )
     except ValueError:
         device = "cpu"
@@ -35,7 +37,7 @@ def download_model(model_name: str):
             raise e
 
     # Now load the downloaded chat model onto appropriate device
-    chat_model = gpt4all.GPT4All(model_name=model_name, device=device, allow_download=False)
+    chat_model = gpt4all.GPT4All(model_name=model_name, n_ctx=4096, device=device, allow_download=False)
     logger.debug(f"Loaded chat model to {device.upper()}.")
 
     return chat_model

src/khoj/processor/embeddings.py

@@ -1,23 +1,69 @@
+import logging
 from typing import List
 
+import requests
+import tqdm
 from sentence_transformers import CrossEncoder, SentenceTransformer
 from torch import nn
 
 from khoj.utils.helpers import get_device
 from khoj.utils.rawconfig import SearchResponse
 
+logger = logging.getLogger(__name__)
+
 
 class EmbeddingsModel:
-    def __init__(self, model_name: str = "thenlper/gte-small"):
+    def __init__(
+        self,
+        model_name: str = "thenlper/gte-small",
+        embeddings_inference_endpoint: str = None,
+        embeddings_inference_endpoint_api_key: str = None,
+    ):
         self.encode_kwargs = {"normalize_embeddings": True}
         self.model_kwargs = {"device": get_device()}
         self.model_name = model_name
+        self.inference_endpoint = embeddings_inference_endpoint
+        self.api_key = embeddings_inference_endpoint_api_key
         self.embeddings_model = SentenceTransformer(self.model_name, **self.model_kwargs)
 
     def embed_query(self, query):
+        if self.api_key is not None and self.inference_endpoint is not None:
+            target_url = f"{self.inference_endpoint}"
+            payload = {"inputs": [query]}
+            headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
+            response = requests.post(target_url, json=payload, headers=headers)
+            return response.json()["embeddings"][0]
         return self.embeddings_model.encode([query], show_progress_bar=False, **self.encode_kwargs)[0]
 
     def embed_documents(self, docs):
+        if self.api_key is not None and self.inference_endpoint is not None:
+            target_url = f"{self.inference_endpoint}"
+            if "huggingface" not in target_url:
+                logger.warning(
+                    f"Using custom inference endpoint {target_url} is not yet supported. Please us a HuggingFace inference endpoint."
+                )
+                return self.embeddings_model.encode(docs, show_progress_bar=True, **self.encode_kwargs).tolist()
+            # break up the docs payload in chunks of 1000 to avoid hitting rate limits
+            headers = {
+                "Authorization": f"Bearer {self.api_key}",
+                "Content-Type": "application/json",
+            }
+            with tqdm.tqdm(total=len(docs)) as pbar:
+                for i in range(0, len(docs), 1000):
+                    payload = {"inputs": docs[i : i + 1000]}
+                    response = requests.post(target_url, json=payload, headers=headers)
+                    try:
+                        response.raise_for_status()
+                    except requests.exceptions.HTTPError as e:
+                        print(f"Error: {e}")
+                        print(f"Response: {response.json()}")
+                        raise e
+                    if i == 0:
+                        embeddings = response.json()["embeddings"]
+                    else:
+                        embeddings += response.json()["embeddings"]
+                    pbar.update(1000)
+            return embeddings
         return self.embeddings_model.encode(docs, show_progress_bar=True, **self.encode_kwargs).tolist()
 
 

src/khoj/routers/api.py

@@ -6,6 +6,7 @@ import os
 import time
 import uuid
 from typing import Any, Dict, List, Optional, Union
+from urllib.parse import unquote
 
 from asgiref.sync import sync_to_async
 from fastapi import APIRouter, Depends, File, HTTPException, Request, UploadFile
@@ -362,6 +363,7 @@ async def chat(
     rate_limiter_per_day=Depends(ApiUserRateLimiter(requests=10, subscribed_requests=600, window=60 * 60 * 24)),
 ) -> Response:
     user: KhojUser = request.user.object
+    q = unquote(q)
 
     await is_ready_to_chat(user)
     conversation_command = get_conversation_command(query=q, any_references=True)

tests/test_gpt4all_chat_director.py

@@ -1,3 +1,4 @@
+import os
 import urllib.parse
 from urllib.parse import quote
 
@@ -53,6 +54,7 @@ def test_chat_with_no_chat_history_or_retrieved_content_gpt4all(client_offline_c
 
 
 # ----------------------------------------------------------------------------------------------------
+@pytest.mark.skipif(os.getenv("SERPER_DEV_API_KEY") is None, reason="requires SERPER_DEV_API_KEY")
 @pytest.mark.chatquality
 @pytest.mark.django_db(transaction=True)
 def test_chat_with_online_content(chat_client):