Copied in base files for fastembed

trustgraph-flow/scripts/embeddings-fastembed

@@ -0,0 +1,6 @@
+#!/usr/bin/env python3
+
+from trustgraph.embeddings.ollama import run
+
+run()
+

trustgraph-flow/setup.py

@@ -41,6 +41,7 @@ setuptools.setup(
         "cohere",
         "cryptography",
         "falkordb",
+        "fastembed",
         "google-generativeai",
         "ibis",
         "jsonschema",

trustgraph-flow/trustgraph/embeddings/fastembed/__init__.py

@@ -0,0 +1,3 @@
+
+from . processor import *
+

trustgraph-flow/trustgraph/embeddings/fastembed/__main__.py

@@ -0,0 +1,7 @@
+#!/usr/bin/env python3
+
+from . processor import run
+
+if __name__ == '__main__':
+    run()
+

trustgraph-flow/trustgraph/embeddings/fastembed/processor.py

@@ -0,0 +1,97 @@
+
+"""
+Embeddings service, applies an embeddings model selected from HuggingFace.
+Input is text, output is embeddings vector.
+"""
+
+from ... schema import EmbeddingsRequest, EmbeddingsResponse
+from ... schema import embeddings_request_queue, embeddings_response_queue
+from ... log_level import LogLevel
+from ... base import ConsumerProducer
+from ollama import Client
+import os
+
+module = ".".join(__name__.split(".")[1:-1])
+
+default_input_queue = embeddings_request_queue
+default_output_queue = embeddings_response_queue
+default_subscriber = module
+default_model="mxbai-embed-large"
+default_ollama = os.getenv("OLLAMA_HOST", 'http://localhost:11434')
+
+class Processor(ConsumerProducer):
+
+    def __init__(self, **params):
+
+        input_queue = params.get("input_queue", default_input_queue)
+        output_queue = params.get("output_queue", default_output_queue)
+        subscriber = params.get("subscriber", default_subscriber)
+
+        ollama = params.get("ollama", default_ollama)
+        model = params.get("model", default_model)
+
+        super(Processor, self).__init__(
+            **params | {
+                "input_queue": input_queue,
+                "output_queue": output_queue,
+                "subscriber": subscriber,
+                "input_schema": EmbeddingsRequest,
+                "output_schema": EmbeddingsResponse,
+                "ollama": ollama,
+                "model": model,
+            }
+        )
+
+        self.client = Client(host=ollama)
+        self.model = model
+
+    def handle(self, msg):
+
+        v = msg.value()
+
+        # Sender-produced ID
+
+        id = msg.properties()["id"]
+
+        print(f"Handling input {id}...", flush=True)
+
+        text = v.text
+        embeds = self.client.embed(
+            model = self.model,
+            input = text
+        )
+
+        print("Send response...", flush=True)
+        r = EmbeddingsResponse(
+            vectors=embeds.embeddings,
+            error=None,
+        )
+
+        self.producer.send(r, properties={"id": id})
+
+        print("Done.", flush=True)
+
+    @staticmethod
+    def add_args(parser):
+
+        ConsumerProducer.add_args(
+            parser, default_input_queue, default_subscriber,
+            default_output_queue,
+        )
+
+        parser.add_argument(
+            '-m', '--model',
+            default=default_model,
+            help=f'Embeddings model (default: {default_model})'
+        )
+
+        parser.add_argument(
+            '-r', '--ollama',
+            default=default_ollama,
+            help=f'ollama (default: {default_ollama})'
+        )
+
+def run():
+
+    Processor.start(module, __doc__)
+