Add support per user for configuring the preferred search model from the config page

- Honor this setting across the relevant places where embeddings are used
- Convert the VectorField object to have None for dimensions in order to make the search model easily configurable

pyproject.toml

@@ -66,7 +66,7 @@ dependencies = [
     "gpt4all >= 2.0.0; platform_system == 'Windows' or platform_system == 'Darwin'",
     "itsdangerous == 2.1.2",
     "httpx == 0.25.0",
-    "pgvector == 0.2.3",
+    "pgvector == 0.2.4",
     "psycopg2-binary == 2.9.9",
     "google-auth == 2.23.3",
     "python-multipart == 0.0.6",

src/khoj/database/adapters/__init__.py

@@ -269,6 +269,14 @@ def get_or_create_search_models():
     return search_models
 
 
+async def aset_user_search_model(user: KhojUser, search_model_config_id: int):
+    config = await SearchModelConfig.objects.filter(id=search_model_config_id).afirst()
+    if not config:
+        return None
+    new_config, _ = await UserSearchModelConfig.objects.aupdate_or_create(user=user, defaults={"setting": config})
+    return new_config
+
+
 class ConversationAdapters:
     @staticmethod
     def get_conversation_by_user(user: KhojUser):

src/khoj/database/migrations/0024_alter_entry_embeddings.py

@@ -0,0 +1,18 @@
+# Generated by Django 4.2.7 on 2023-12-20 07:27
+
+from django.db import migrations
+import pgvector.django
+
+
+class Migration(migrations.Migration):
+    dependencies = [
+        ("database", "0023_usersearchmodelconfig"),
+    ]
+
+    operations = [
+        migrations.AlterField(
+            model_name="entry",
+            name="embeddings",
+            field=pgvector.django.VectorField(),
+        ),
+    ]

src/khoj/database/models/__init__.py

@@ -185,7 +185,7 @@ class Entry(BaseModel):
         GITHUB = "github"
 
     user = models.ForeignKey(KhojUser, on_delete=models.CASCADE, default=None, null=True, blank=True)
-    embeddings = VectorField(dimensions=384)
+    embeddings = VectorField(dimensions=None)
     raw = models.TextField()
     compiled = models.TextField()
     heading = models.CharField(max_length=1000, default=None, null=True, blank=True)

src/khoj/interface/web/assets/icons/matrix_blob.svg

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="utf-8"?><!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
+<svg width="800px" height="800px" viewBox="0 0 64 64" xmlns="http://www.w3.org/2000/svg" stroke-width="3" stroke="#000000" fill="none"><circle cx="34.52" cy="11.43" r="5.82"/><circle cx="53.63" cy="31.6" r="5.82"/><circle cx="34.52" cy="50.57" r="5.82"/><circle cx="15.16" cy="42.03" r="5.82"/><circle cx="15.16" cy="19.27" r="5.82"/><circle cx="34.51" cy="29.27" r="4.7"/><line x1="20.17" y1="16.3" x2="28.9" y2="12.93"/><line x1="38.6" y1="15.59" x2="49.48" y2="27.52"/><line x1="50.07" y1="36.2" x2="38.67" y2="46.49"/><line x1="18.36" y1="24.13" x2="30.91" y2="46.01"/><line x1="20.31" y1="44.74" x2="28.7" y2="48.63"/><line x1="17.34" y1="36.63" x2="31.37" y2="16.32"/><line x1="20.52" y1="21.55" x2="30.34" y2="27.1"/><line x1="39.22" y1="29.8" x2="47.81" y2="30.45"/><line x1="34.51" y1="33.98" x2="34.52" y2="44.74"/></svg>

src/khoj/interface/web/base_config.html

@@ -296,6 +296,7 @@
             height: 32px;
         }
 
+        select#search-models,
         select#chat-models {
             margin-bottom: 0;
             padding: 8px;

src/khoj/interface/web/config.html

@@ -146,6 +146,26 @@
                     </button>
                 </div>
             </div>
+            <div class="card">
+                <div class="card-title-row">
+                    <img class="card-icon" src="/static/assets/icons/matrix_blob.svg" alt="Chat">
+                    <h3 class="card-title">
+                        Text Model
+                    </h3>
+                </div>
+                <div class="card-description-row">
+                    <select id="search-models">
+                        {% for option in search_model_options %}
+                            <option value="{{ option.id }}" {% if option.id == selected_search_model_config %}selected{% endif %}>{{ option.name }}</option>
+                        {% endfor %}
+                    </select>
+                </div>
+                <div class="card-action-row">
+                    <button id="save-search-model" class="card-button happy" onclick="updateSearchModel()">
+                        Save
+                    </button>
+                </div>
+            </div>
         </div>
     </div>
     <div id="clients" class="section">
@@ -266,6 +286,30 @@
         })
     };
 
+    function updateSearchModel() {
+        const searchModel = document.getElementById("search-models").value;
+        const saveSearchModelButton = document.getElementById("save-search-model");
+        saveSearchModelButton.disabled = true;
+        saveSearchModelButton.innerHTML = "Saving...";
+
+        fetch('/api/config/data/search/model?id=' + searchModel, {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            }
+        })
+        .then(response => response.json())
+        .then(data => {
+            if (data.status == "ok") {
+                saveSearchModelButton.innerHTML = "Save";
+                saveSearchModelButton.disabled = false;
+            } else {
+                saveSearchModelButton.innerHTML = "Error";
+                saveSearchModelButton.disabled = false;
+            }
+        })
+    };
+
     function clearContentType(content_source) {
         fetch('/api/config/data/content-source/' + content_source, {
             method: 'DELETE',

src/khoj/routers/api.py

@@ -332,6 +332,31 @@ async def update_chat_model(
     return {"status": "ok"}
 
 
+@api.post("/config/data/search/model", status_code=200)
+@requires(["authenticated"])
+async def update_chat_model(
+    request: Request,
+    id: str,
+    client: Optional[str] = None,
+):
+    user = request.user.object
+
+    new_config = await adapters.aset_user_search_model(user, int(id))
+
+    update_telemetry_state(
+        request=request,
+        telemetry_type="api",
+        api="set_search_model",
+        client=client,
+        metadata={"search_model": new_config.setting.name},
+    )
+
+    if new_config is None:
+        return {"status": "error", "message": "Model not found"}
+
+    return {"status": "ok"}
+
+
 # Create Routes
 @api.get("/config/data/default")
 def get_default_config_data():
@@ -410,14 +435,10 @@ async def search(
         defiltered_query = filter.defilter(defiltered_query)
 
     encoded_asymmetric_query = None
-    if t == SearchType.All or t != SearchType.Image:
+    if t != SearchType.Image:
-        text_search_models: List[TextSearchModel] = [
+        with timer("Encoding query took", logger=logger):
-            model for model in state.search_models.__dict__.values() if isinstance(model, TextSearchModel)
+            search_model = await sync_to_async(get_user_search_model_or_default)(user)
-        ]
+            encoded_asymmetric_query = state.embeddings_model[search_model.name].embed_query(defiltered_query)
-        if text_search_models:
-            with timer("Encoding query took", logger=logger):
-                search_model = await sync_to_async(get_user_search_model_or_default)(user)
-                encoded_asymmetric_query = state.embeddings_model[search_model.name].embed_query(defiltered_query)
 
     with concurrent.futures.ThreadPoolExecutor() as executor:
         if t in [
@@ -473,9 +494,9 @@ async def search(
                     results += text_search.collate_results(hits, dedupe=dedupe)
 
                 # Sort results across all content types and take top results
-                results = text_search.rerank_and_sort_results(results, query=defiltered_query, rank_results=r)[
+                results = text_search.rerank_and_sort_results(
-                    :results_count
+                    results, query=defiltered_query, rank_results=r, search_model_name=search_model.name
-                ]
+                )[:results_count]
 
     # Cache results
     if user:

src/khoj/routers/web_client.py

@@ -155,6 +155,11 @@ def config_page(request: Request):
     for conversation_option in conversation_options:
         all_conversation_options.append({"chat_model": conversation_option.chat_model, "id": conversation_option.id})
 
+    search_model_options = adapters.get_or_create_search_models().all()
+    all_search_model_options = list()
+    for search_model_option in search_model_options:
+        all_search_model_options.append({"name": search_model_option.name, "id": search_model_option.id})
+
     return templates.TemplateResponse(
         "config.html",
         context={
@@ -163,6 +168,7 @@ def config_page(request: Request):
             "anonymous_mode": state.anonymous_mode,
             "username": user.username,
             "conversation_options": all_conversation_options,
+            "search_model_options": all_search_model_options,
             "selected_conversation_config": selected_conversation_config.id if selected_conversation_config else None,
             "user_photo": user_picture,
             "billing_enabled": state.billing_enabled,

src/khoj/search_type/text_search.py

@@ -180,13 +180,13 @@ def deduplicated_search_responses(hits: List[SearchResponse]):
             )
 
 
-def rerank_and_sort_results(hits, query, rank_results):
+def rerank_and_sort_results(hits, query, rank_results, search_model_name):
     # If we have more than one result and reranking is enabled
     rank_results = rank_results and len(list(hits)) > 1
 
     # Score all retrieved entries using the cross-encoder
     if rank_results:
-        hits = cross_encoder_score(query, hits)
+        hits = cross_encoder_score(query, hits, search_model_name)
 
     # Sort results by cross-encoder score followed by bi-encoder score
     hits = sort_results(rank_results=rank_results, hits=hits)
@@ -219,10 +219,10 @@ def setup(
         )
 
 
-def cross_encoder_score(query: str, hits: List[SearchResponse]) -> List[SearchResponse]:
+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.predict(query, hits)
+        cross_scores = state.cross_encoder_model[search_model_name].predict(query, hits)
 
     # Convert cross-encoder scores to distances and pass in hits for reranking
     for idx in range(len(cross_scores)):