Feature/configure flows (#345)

- Keeps processing in different flows separate so that data can go to different stores / collections etc.
- Potentially supports different processing flows
- Tidies the processing API with common base-classes for e.g. LLMs, and automatic configuration of 'clients' to use the right queue names in a flow

trustgraph-base/trustgraph/base/document_embeddings_client.py

@@ -0,0 +1,38 @@
+
+from . request_response_spec import RequestResponse, RequestResponseSpec
+from .. schema import DocumentEmbeddingsRequest, DocumentEmbeddingsResponse
+from .. knowledge import Uri, Literal
+
+class DocumentEmbeddingsClient(RequestResponse):
+    async def query(self, vectors, limit=20, user="trustgraph",
+                    collection="default", timeout=30):
+
+        resp = await self.request(
+            DocumentEmbeddingsRequest(
+                vectors = vectors,
+                limit = limit,
+                user = user,
+                collection = collection
+            ),
+            timeout=timeout
+        )
+
+        print(resp, flush=True)
+
+        if resp.error:
+            raise RuntimeError(resp.error.message)
+
+        return resp.documents
+
+class DocumentEmbeddingsClientSpec(RequestResponseSpec):
+    def __init__(
+            self, request_name, response_name,
+    ):
+        super(DocumentEmbeddingsClientSpec, self).__init__(
+            request_name = request_name,
+            request_schema = DocumentEmbeddingsRequest,
+            response_name = response_name,
+            response_schema = DocumentEmbeddingsResponse,
+            impl = DocumentEmbeddingsClient,
+        )
+