Merge 6fafedca6a into aa7388f2f7

docker/.env.example

@@ -48,7 +48,12 @@ ETL_SERVICE=DOCLING
 # Local: sentence-transformers/all-MiniLM-L6-v2
 # OpenAI: openai://text-embedding-ada-002 (set OPENAI_API_KEY below)
 # Cohere: cohere://embed-english-light-v3.0 (set COHERE_API_KEY below)
+# Ollama or OpenAI-compatible embedding endpoint:
+#   EMBEDDING_MODEL=litellm://ollama/nomic-embed-text
+#   EMBEDDING_BASE_URL=http://host.docker.internal:11434
 EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
+# EMBEDDING_BASE_URL=
+# OLLAMA_EMBEDDING_BASE_URL=
 
 # ------------------------------------------------------------------------------
 # How You Access SurfSense

surfsense_backend/.env.example

@@ -214,6 +214,10 @@ COMPOSIO_REDIRECT_URI=http://localhost:8000/api/v1/auth/composio/connector/callb
 #     # Get Cohere embeddings
 #     embeddings = AutoEmbeddings.get_embeddings("cohere://embed-english-light-v3.0", api_key="...")
 EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
+# Optional: use a separate endpoint for Chonkie/LiteLLM embedding models, for
+# example EMBEDDING_MODEL=litellm://ollama/nomic-embed-text.
+# EMBEDDING_BASE_URL=http://host.docker.internal:11434
+# OLLAMA_EMBEDDING_BASE_URL=http://host.docker.internal:11434
 
 # Rerankers Config
 RERANKERS_ENABLED=TRUE or FALSE(Default: FALSE)

surfsense_backend/app/config/__init__.py

@@ -9,6 +9,11 @@ from chonkie import AutoEmbeddings, CodeChunker, RecursiveChunker
 from dotenv import load_dotenv
 from rerankers import Reranker
 
+from app.config.embedding_settings import (
+    build_embedding_kwargs,
+    resolve_embedding_base_url,
+)
+
 # Get the base directory of the project
 BASE_DIR = Path(__file__).resolve().parent.parent.parent
 
@@ -876,16 +881,13 @@ class Config:
 
     # Chonkie Configuration | Edit this to your needs
     EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL")
+    EMBEDDING_BASE_URL = resolve_embedding_base_url()
     # Azure OpenAI credentials from environment variables
     AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
     AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
 
-    # Pass Azure credentials to embeddings when using Azure OpenAI
+    # Pass provider-specific settings to embeddings when supported.
-    embedding_kwargs = {}
+    embedding_kwargs = build_embedding_kwargs(embedding_model=EMBEDDING_MODEL)
-    if AZURE_OPENAI_ENDPOINT:
-        embedding_kwargs["azure_endpoint"] = AZURE_OPENAI_ENDPOINT
-    if AZURE_OPENAI_API_KEY:
-        embedding_kwargs["azure_api_key"] = AZURE_OPENAI_API_KEY
 
     embedding_model_instance = AutoEmbeddings.get_embeddings(
         EMBEDDING_MODEL,

surfsense_backend/app/config/embedding_settings.py

@@ -0,0 +1,48 @@
+import os
+from collections.abc import Mapping
+
+EMBEDDING_BASE_URL_ENV = "EMBEDDING_BASE_URL"
+OLLAMA_EMBEDDING_BASE_URL_ENV = "OLLAMA_EMBEDDING_BASE_URL"
+
+
+def _clean_env_value(value: str | None) -> str | None:
+    if value is None:
+        return None
+    stripped = value.strip()
+    return stripped or None
+
+
+def resolve_embedding_base_url(environ: Mapping[str, str] | None = None) -> str | None:
+    """Return the configured embedding endpoint, if any."""
+    environ = os.environ if environ is None else environ
+    return _clean_env_value(environ.get(EMBEDDING_BASE_URL_ENV)) or _clean_env_value(
+        environ.get(OLLAMA_EMBEDDING_BASE_URL_ENV)
+    )
+
+
+def _supports_embedding_api_base(embedding_model: str | None) -> bool:
+    return (embedding_model or "").startswith("litellm://")
+
+
+def build_embedding_kwargs(
+    environ: Mapping[str, str] | None = None,
+    *,
+    embedding_model: str | None = None,
+) -> dict[str, str]:
+    """Build keyword arguments for Chonkie's embedding provider."""
+    environ = os.environ if environ is None else environ
+
+    embedding_kwargs: dict[str, str] = {}
+    embedding_base_url = resolve_embedding_base_url(environ)
+    if embedding_base_url and _supports_embedding_api_base(embedding_model):
+        embedding_kwargs["api_base"] = embedding_base_url
+
+    azure_openai_endpoint = _clean_env_value(environ.get("AZURE_OPENAI_ENDPOINT"))
+    azure_openai_api_key = _clean_env_value(environ.get("AZURE_OPENAI_API_KEY"))
+
+    if azure_openai_endpoint:
+        embedding_kwargs["azure_endpoint"] = azure_openai_endpoint
+    if azure_openai_api_key:
+        embedding_kwargs["azure_api_key"] = azure_openai_api_key
+
+    return embedding_kwargs

surfsense_backend/tests/unit/config/test_embedding_settings.py

@@ -0,0 +1,76 @@
+import pytest
+
+from app.config.embedding_settings import (
+    build_embedding_kwargs,
+    resolve_embedding_base_url,
+)
+
+pytestmark = pytest.mark.unit
+
+
+def test_resolve_embedding_base_url_prefers_generic_value() -> None:
+    environ = {
+        "EMBEDDING_BASE_URL": " http://embed-host:11434 ",
+        "OLLAMA_EMBEDDING_BASE_URL": "http://ollama-embed:11434",
+    }
+
+    assert resolve_embedding_base_url(environ) == "http://embed-host:11434"
+
+
+def test_resolve_embedding_base_url_falls_back_to_ollama_specific_value() -> None:
+    environ = {
+        "EMBEDDING_BASE_URL": " ",
+        "OLLAMA_EMBEDDING_BASE_URL": "http://ollama-embed:11434",
+    }
+
+    assert resolve_embedding_base_url(environ) == "http://ollama-embed:11434"
+
+
+def test_build_embedding_kwargs_maps_base_url_to_litellm_api_base() -> None:
+    kwargs = build_embedding_kwargs(
+        {"EMBEDDING_BASE_URL": "http://host.docker.internal:11435"},
+        embedding_model="litellm://ollama/nomic-embed-text",
+    )
+
+    assert kwargs == {"api_base": "http://host.docker.internal:11435"}
+
+
+def test_build_embedding_kwargs_does_not_leak_api_base_to_other_providers() -> None:
+    kwargs = build_embedding_kwargs(
+        {"EMBEDDING_BASE_URL": "http://host.docker.internal:11435"},
+        embedding_model="cohere://embed-english-light-v3.0",
+    )
+
+    assert kwargs == {}
+
+
+def test_build_embedding_kwargs_preserves_azure_settings() -> None:
+    kwargs = build_embedding_kwargs(
+        {
+            "AZURE_OPENAI_ENDPOINT": "https://example.openai.azure.com",
+            "AZURE_OPENAI_API_KEY": "test-key",
+        },
+        embedding_model="azure_openai://text-embedding-3-small",
+    )
+
+    assert kwargs == {
+        "azure_endpoint": "https://example.openai.azure.com",
+        "azure_api_key": "test-key",
+    }
+
+
+def test_build_embedding_kwargs_combines_litellm_and_azure_env_when_set() -> None:
+    kwargs = build_embedding_kwargs(
+        {
+            "EMBEDDING_BASE_URL": "http://host.docker.internal:4000/v1",
+            "AZURE_OPENAI_ENDPOINT": "https://example.openai.azure.com",
+            "AZURE_OPENAI_API_KEY": "test-key",
+        },
+        embedding_model="litellm://openai/text-embedding-3-small",
+    )
+
+    assert kwargs == {
+        "api_base": "http://host.docker.internal:4000/v1",
+        "azure_endpoint": "https://example.openai.azure.com",
+        "azure_api_key": "test-key",
+    }

surfsense_web/content/docs/docker-installation/docker-compose.mdx

@@ -39,6 +39,7 @@ All configuration lives in a single `docker/.env` file (or `surfsense/.env` if y
 | `AUTH_TYPE` | Authentication method: `LOCAL` (email/password) or `GOOGLE` (OAuth) | `LOCAL` |
 | `ETL_SERVICE` | Document parsing: `DOCLING` (local), `UNSTRUCTURED`, or `LLAMACLOUD` | `DOCLING` |
 | `EMBEDDING_MODEL` | Embedding model for vector search | `sentence-transformers/all-MiniLM-L6-v2` |
+| `EMBEDDING_BASE_URL` | Optional separate endpoint for Chonkie/LiteLLM embedding models. Use with values like `EMBEDDING_MODEL=litellm://ollama/nomic-embed-text`. `OLLAMA_EMBEDDING_BASE_URL` is also accepted as an Ollama-specific fallback. | *(empty)* |
 | `TTS_SERVICE` | Text-to-speech provider for podcasts | `local/kokoro` |
 | `STT_SERVICE` | Speech-to-text provider for audio files | `local/base` |
 | `REGISTRATION_ENABLED` | Allow new user registrations | `TRUE` |

surfsense_web/content/docs/local-models/ollama.mdx

@@ -50,6 +50,23 @@ http://<host>:11434
 
 Replace `<host>` with the LAN IP or domain for that machine.
 
+## Embeddings on a Separate Ollama Server
+
+Search-space model connections configure chat and completion models. The global
+embedding model is configured in the backend environment.
+
+Use Chonkie's LiteLLM embedding provider when embeddings run on Ollama:
+
+```dotenv
+EMBEDDING_MODEL=litellm://ollama/nomic-embed-text
+EMBEDDING_BASE_URL=http://host.docker.internal:11434
+```
+
+If chat and embeddings run on different Ollama instances, keep the chat model
+connection pointed at the chat server and set `EMBEDDING_BASE_URL` to the
+embedding server. `OLLAMA_EMBEDDING_BASE_URL` is also supported as an
+Ollama-specific fallback when `EMBEDDING_BASE_URL` is not set.
+
 ## Add the Connection
 
 1. Open Search Space Settings.

surfsense_web/content/docs/manual-installation.mdx

@@ -85,6 +85,7 @@ Edit the `.env` file and set the following variables:
 | GOOGLE_OAUTH_CLIENT_ID     | (Optional) Client ID from Google Cloud Console (required if AUTH_TYPE=GOOGLE)                                                                                                                        |
 | GOOGLE_OAUTH_CLIENT_SECRET | (Optional) Client secret from Google Cloud Console (required if AUTH_TYPE=GOOGLE)                                                                                                                    |
 | EMBEDDING_MODEL            | Name of the embedding model (e.g., `sentence-transformers/all-MiniLM-L6-v2`, `openai://text-embedding-ada-002`)                                                                                                                 |
+| EMBEDDING_BASE_URL         | (Optional) Separate endpoint for Chonkie/LiteLLM embedding models, such as `http://localhost:11434` with `EMBEDDING_MODEL=litellm://ollama/nomic-embed-text`. `OLLAMA_EMBEDDING_BASE_URL` is also supported as an Ollama-specific fallback. |
 | RERANKERS_ENABLED          | (Optional) Enable or disable document reranking for improved search results (e.g., `TRUE` or `FALSE`, default: `FALSE`)                                                                  |
 | RERANKERS_MODEL_NAME       | Name of the reranker model (e.g., `ms-marco-MiniLM-L-12-v2`) (required if RERANKERS_ENABLED=TRUE)                                                                                        |
 | RERANKERS_MODEL_TYPE       | Type of reranker model (e.g., `flashrank`) (required if RERANKERS_ENABLED=TRUE)                                                                                                          |