Make Search Models More Configurable. Upgrade Default Cross-Encoder (#722)

- Upgrade default cross-encoder to mixedbread ai's mxbai-rerank-xsmall
- Support more embedding models by making query, docs encoding configurable

documentation/docs/miscellaneous/advanced.md

@@ -7,9 +7,11 @@ sidebar_position: 3
 ## Search across Different Languages (Self-Hosting)
 To search for notes in multiple, different languages, you can use a [multi-lingual model](https://www.sbert.net/docs/pretrained_models.html#multi-lingual-models).<br />
 For example, the [paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) supports [50+ languages](https://www.sbert.net/docs/pretrained_models.html#:~:text=we%20used%20the%20following%2050%2B%20languages), has good search quality and speed. To use it:
-1. Manually update the search config in server's admin settings page. Go to [the search config](http://localhost:42110/server/admin/database/searchmodelconfig/). Either create a new one, if none exists, or update the existing one. Set the bi_encoder to `sentence-transformers/multi-qa-MiniLM-L6-cos-v1` and the cross_encoder to `cross-encoder/ms-marco-MiniLM-L-6-v2`.
+1. Manually update the search config in server's admin settings page. Go to [the search config](http://localhost:42110/server/admin/database/searchmodelconfig/). Either create a new one, if none exists, or update the existing one. Set the bi_encoder to `sentence-transformers/multi-qa-MiniLM-L6-cos-v1` and the cross_encoder to `mixedbread-ai/mxbai-rerank-xsmall-v1`.
 2. Regenerate your content index from all the relevant clients. This step is very important, as you'll need to re-encode all your content with the new model.
 
+Note: If you use a search model that expects a prefix (e.g [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)) to the query (or docs) string before encoding. Update the `bi_encoder_query_encode_config` field with `{prompt: <prefix-prompt>}`. Eg. `{prompt: "Represent this query for searching documents"}`. You can pass a valid JSON object that the SentenceTransformer `encode` function accepts
+
 ## Query Filters
 
 Use structured query syntax to filter entries from your knowledge based used by search results or chat responses.

src/khoj/configure.py

@@ -216,6 +216,9 @@ def configure_server(
                         model.bi_encoder,
                         model.embeddings_inference_endpoint,
                         model.embeddings_inference_endpoint_api_key,
+                        query_encode_kwargs=model.bi_encoder_query_encode_config,
+                        docs_encode_kwargs=model.bi_encoder_docs_encode_config,
+                        model_kwargs=model.bi_encoder_model_config,
                     )
                 }
             )

src/khoj/database/migrations/0037_searchmodelconfig_bi_encoder_docs_encode_config_and_more.py

@@ -0,0 +1,32 @@
+# Generated by Django 4.2.10 on 2024-04-24 04:19
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+    dependencies = [
+        ("database", "0036_delete_offlinechatprocessorconversationconfig"),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name="searchmodelconfig",
+            name="bi_encoder_docs_encode_config",
+            field=models.JSONField(default=dict),
+        ),
+        migrations.AddField(
+            model_name="searchmodelconfig",
+            name="bi_encoder_model_config",
+            field=models.JSONField(default=dict),
+        ),
+        migrations.AddField(
+            model_name="searchmodelconfig",
+            name="bi_encoder_query_encode_config",
+            field=models.JSONField(default=dict),
+        ),
+        migrations.AlterField(
+            model_name="searchmodelconfig",
+            name="cross_encoder",
+            field=models.CharField(default="mixedbread-ai/mxbai-rerank-xsmall-v1", max_length=200),
+        ),
+    ]

src/khoj/database/models/__init__.py

@@ -179,13 +179,27 @@ class SearchModelConfig(BaseModel):
     class ModelType(models.TextChoices):
         TEXT = "text"
 
+    # This is the model name exposed to users on their settings page
     name = models.CharField(max_length=200, default="default")
+    # Type of content the model can generate embeddings for
     model_type = models.CharField(max_length=200, choices=ModelType.choices, default=ModelType.TEXT)
+    # Bi-encoder model of sentence-transformer type to load from HuggingFace
     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")
+    # Config passed to the sentence-transformer model constructor. E.g device="cuda:0", trust_remote_server=True etc.
+    bi_encoder_model_config = models.JSONField(default=dict)
+    # Query encode configs like prompt, precision, normalize_embeddings, etc. for sentence-transformer models
+    bi_encoder_query_encode_config = models.JSONField(default=dict)
+    # Docs encode configs like prompt, precision, normalize_embeddings, etc. for sentence-transformer models
+    bi_encoder_docs_encode_config = models.JSONField(default=dict)
+    # Cross-encoder model of sentence-transformer type to load from HuggingFace
+    cross_encoder = models.CharField(max_length=200, default="mixedbread-ai/mxbai-rerank-xsmall-v1")
+    # Inference server API endpoint to use for embeddings inference. Bi-encoder model should be hosted on this server
     embeddings_inference_endpoint = models.CharField(max_length=200, default=None, null=True, blank=True)
+    # Inference server API Key to use for embeddings inference. Bi-encoder model should be hosted on this server
     embeddings_inference_endpoint_api_key = models.CharField(max_length=200, default=None, null=True, blank=True)
+    # Inference server API endpoint to use for embeddings inference. Cross-encoder model should be hosted on this server
     cross_encoder_inference_endpoint = models.CharField(max_length=200, default=None, null=True, blank=True)
+    # Inference server API Key to use for embeddings inference. Cross-encoder model should be hosted on this server
     cross_encoder_inference_endpoint_api_key = models.CharField(max_length=200, default=None, null=True, blank=True)
 
 

src/khoj/processor/embeddings.py

@@ -13,7 +13,7 @@ from tenacity import (
 )
 from torch import nn
 
-from khoj.utils.helpers import get_device
+from khoj.utils.helpers import get_device, merge_dicts
 from khoj.utils.rawconfig import SearchResponse
 
 logger = logging.getLogger(__name__)
@@ -25,9 +25,15 @@ class EmbeddingsModel:
         model_name: str = "thenlper/gte-small",
         embeddings_inference_endpoint: str = None,
         embeddings_inference_endpoint_api_key: str = None,
+        query_encode_kwargs: dict = {},
+        docs_encode_kwargs: dict = {},
+        model_kwargs: dict = {},
     ):
-        self.encode_kwargs = {"normalize_embeddings": True}
-        self.model_kwargs = {"device": get_device()}
+        default_query_encode_kwargs = {"show_progress_bar": False, "normalize_embeddings": True}
+        default_docs_encode_kwargs = {"show_progress_bar": True, "normalize_embeddings": True}
+        self.query_encode_kwargs = merge_dicts(query_encode_kwargs, default_query_encode_kwargs)
+        self.docs_encode_kwargs = merge_dicts(docs_encode_kwargs, default_docs_encode_kwargs)
+        self.model_kwargs = merge_dicts(model_kwargs, {"device": get_device()})
         self.model_name = model_name
         self.inference_endpoint = embeddings_inference_endpoint
         self.api_key = embeddings_inference_endpoint_api_key
@@ -39,7 +45,7 @@ class EmbeddingsModel:
     def embed_query(self, query):
         if self.inference_server_enabled():
             return self.embed_with_api([query])[0]
-        return self.embeddings_model.encode([query], show_progress_bar=False, **self.encode_kwargs)[0]
+        return self.embeddings_model.encode([query], **self.query_encode_kwargs)[0]
 
     @retry(
         retry=retry_if_exception_type(requests.exceptions.HTTPError),
@@ -70,7 +76,7 @@ class EmbeddingsModel:
                 logger.warning(
                     f"Unsupported inference endpoint: {self.inference_endpoint}. Only HuggingFace supported. Generating embeddings on device instead."
                 )
-                return self.embeddings_model.encode(docs, show_progress_bar=True, **self.encode_kwargs).tolist()
+                return self.embeddings_model.encode(docs, **self.docs_encode_kwargs).tolist()
             # break up the docs payload in chunks of 1000 to avoid hitting rate limits
             embeddings = []
             with tqdm.tqdm(total=len(docs)) as pbar:
@@ -80,13 +86,13 @@ class EmbeddingsModel:
                     embeddings += generated_embeddings
                     pbar.update(1000)
             return embeddings
-        return self.embeddings_model.encode(docs, show_progress_bar=True, **self.encode_kwargs).tolist()
+        return self.embeddings_model.encode(docs, **self.docs_encode_kwargs).tolist()
 
 
 class CrossEncoderModel:
     def __init__(
         self,
-        model_name: str = "cross-encoder/ms-marco-MiniLM-L-6-v2",
+        model_name: str = "mixedbread-ai/mxbai-rerank-xsmall-v1",
         cross_encoder_inference_endpoint: str = None,
         cross_encoder_inference_endpoint_api_key: str = None,
     ):

tests/helpers.py

@@ -75,7 +75,7 @@ class SearchModelFactory(factory.django.DjangoModelFactory):
     name = "default"
     model_type = "text"
     bi_encoder = "thenlper/gte-small"
-    cross_encoder = "cross-encoder/ms-marco-MiniLM-L-6-v2"
+    cross_encoder = "mixedbread-ai/mxbai-rerank-xsmall-v1"
 
 
 class SubscriptionFactory(factory.django.DjangoModelFactory):