feat: Add Vertex AI embedder

agents-api/agents_api/activities/embed_docs.py

@@ -2,7 +2,7 @@
 from temporalio import activity
 
 from ..clients import cozo
-from ..clients import embed as embedder
+from ..clients import vertexai as embedder
 from ..env import testing
 from ..models.docs.embed_snippets import embed_snippets as embed_snippets_query
 from .types import EmbedDocsPayload

agents-api/agents_api/clients/vertexai.py

@@ -0,0 +1,18 @@
+import litellm
+from litellm import aembedding
+
+from ..env import google_project_id, vertex_location
+
+litellm.vertex_project = google_project_id
+litellm.vertex_location = vertex_location
+
+
+async def embed(
+    inputs: list[str], dimensions: int = 1024, join_inputs: bool = True
+) -> list[list[float]]:
+    input = ["\n\n".join(inputs)] if join_inputs else inputs
+    response = await aembedding(
+        model="vertex_ai/text-embedding-004", input=input, dimensions=dimensions
+    )
+
+    return response.data or []

agents-api/agents_api/env.py

@@ -77,6 +77,9 @@
 temporal_endpoint: Any = env.str("TEMPORAL_ENDPOINT", default="localhost:7233")
 temporal_task_queue: Any = env.str("TEMPORAL_TASK_QUEUE", default="julep-task-queue")
 
+# Google cloud
+google_project_id: str = env.str("GOOGLE_PROJECT_ID")
+vertex_location: str = env.str("VERTEX_LOCATION", default="us-central1")
 
 # Consolidate environment variables
 environment: Dict[str, Any] = dict(
@@ -97,6 +100,7 @@
     temporal_namespace=temporal_namespace,
     embedding_model_id=embedding_model_id,
     testing=testing,
+    google_project_id=google_project_id,
 )
 
 if debug or testing:

agents-api/agents_api/models/chat/gather_messages.py

@@ -9,7 +9,7 @@
 from agents_api.autogen.Chat import ChatInput
 
 from ...autogen.openapi_model import DocReference, History
-from ...clients import embed
+from ...clients import vertexai as embed
 from ...common.protocol.developers import Developer
 from ...common.protocol.sessions import ChatContext
 from ..docs.search_docs_hybrid import search_docs_hybrid
@@ -61,7 +61,7 @@ async def gather_messages(
         return past_messages, []
 
     # Search matching docs
-    [query_embedding, *_] = await embed.embed(
+    query_embedding = await embed.embed(
         inputs=[
             f"{msg.get('name') or msg['role']}: {msg['content']}"
             for msg in new_raw_messages

agents-api/agents_api/routers/docs/embed.py

@@ -1,9 +1,9 @@
 from typing import Annotated
+from uuid import UUID
 
 from fastapi import Depends
-from uuid import UUID
 
-import agents_api.clients.embed as embedder
+import agents_api.clients.vertexai as embedder
 
 from ...autogen.openapi_model import (
     EmbedQueryRequest,

agents-api/tests/test_chat_routes.py

@@ -3,7 +3,8 @@
 from ward import test
 
 from agents_api.autogen.openapi_model import ChatInput, CreateSessionRequest
-from agents_api.clients import embed, litellm
+from agents_api.clients import litellm
+from agents_api.clients import vertexai as embed
 from agents_api.common.protocol.sessions import ChatContext
 from agents_api.models.chat.gather_messages import gather_messages
 from agents_api.models.chat.prepare_chat_context import prepare_chat_context

llm-proxy/litellm-config.yaml

@@ -103,6 +103,12 @@ model_list:
     api_base: os.environ/EMBEDDING_SERVICE_BASE
     tags: ["free"]
 
+- model_name: text-embedding-004
+  litellm_params:
+    model: vertex_ai/text-embedding-004
+    vertex_project: os.environ/GOOGLE_PROJECT_ID
+    vertex_location: os.environ/VERTEX_LOCATION
+
 
 # -*= Free models =*-
 # -------------------