Feature/pinecone integration (#170)

* Added Pinecone for GE write & query

* Add templates

* Doc embedding support

templates/components.jsonnet

@@ -25,6 +25,7 @@
    "trustgraph-base": import "components/trustgraph.jsonnet",
    "vector-store-milvus": import "components/milvus.jsonnet",
    "vector-store-qdrant": import "components/qdrant.jsonnet",
+   "vector-store-pinecone": import "components/pinecone.jsonnet",
    "vertexai": import "components/vertexai.jsonnet",
    "null": {},
 
@@ -34,6 +35,7 @@
    "cassandra": import "components/cassandra.jsonnet",
    "neo4j": import "components/neo4j.jsonnet",
    "qdrant": import "components/qdrant.jsonnet",
+   "pinecone": import "components/pinecone.jsonnet",
    "milvus": import "components/milvus.jsonnet",
    "trustgraph": import "components/trustgraph.jsonnet",
 

templates/components/pinecone.jsonnet

@@ -0,0 +1,153 @@
+local base = import "base/base.jsonnet";
+local images = import "values/images.jsonnet";
+local url = import "values/url.jsonnet";
+local cassandra_hosts = "cassandra";
+
+{
+
+    "pinecone-cloud":: "aws",
+    "pinecone-region":: "us-east-1",
+
+    "store-graph-embeddings" +: {
+
+        create:: function(engine)
+
+            local envSecrets = engine.envSecrets("pinecone-api-key")
+                .with_env_var("PINECONE_API_KEY", "pinecone-api-key");
+
+            local container =
+                engine.container("store-graph-embeddings")
+                    .with_image(images.trustgraph)
+                    .with_command([
+                        "ge-write-pinecone",
+                        "-p",
+                        url.pulsar,
+                    ])
+                    .with_env_var_secrets(envSecrets)
+                    .with_limits("0.5", "128M")
+                    .with_reservations("0.1", "128M");
+
+            local containerSet = engine.containers(
+                "store-graph-embeddings", [ container ]
+            );
+
+            local service =
+                engine.internalService(containerSet)
+                .with_port(8080, 8080, "metrics");
+
+            engine.resources([
+                envSecrets,
+                containerSet,
+                service,
+            ])
+
+    },
+
+    "query-graph-embeddings" +: {
+
+        create:: function(engine)
+
+            local envSecrets = engine.envSecrets("pinecone-api-key")
+                .with_env_var("PINECONE_API_KEY", "pinecone-api-key");
+
+            local container =
+                engine.container("query-graph-embeddings")
+                    .with_image(images.trustgraph)
+                    .with_command([
+                        "ge-query-pinecone",
+                        "-p",
+                        url.pulsar,
+                    ])
+                    .with_env_var_secrets(envSecrets)
+                    .with_limits("0.5", "128M")
+                    .with_reservations("0.1", "128M");
+
+            local containerSet = engine.containers(
+                "query-graph-embeddings", [ container ]
+            );
+
+            local service =
+                engine.internalService(containerSet)
+                .with_port(8080, 8080, "metrics");
+
+            engine.resources([
+                envSecrets,
+                containerSet,
+                service,
+            ])
+
+    },
+
+    "store-doc-embeddings" +: {
+
+        create:: function(engine)
+
+            local envSecrets = engine.envSecrets("pinecone-api-key")
+                .with_env_var("PINECONE_API_KEY", "pinecone-api-key");
+
+            local container =
+                engine.container("store-doc-embeddings")
+                    .with_image(images.trustgraph)
+                    .with_command([
+                        "de-write-pinecone",
+                        "-p",
+                        url.pulsar,
+                    ])
+                    .with_env_var_secrets(envSecrets)
+                    .with_limits("0.5", "128M")
+                    .with_reservations("0.1", "128M");
+
+            local containerSet = engine.containers(
+                "store-doc-embeddings", [ container ]
+            );
+
+            local service =
+                engine.internalService(containerSet)
+                .with_port(8080, 8080, "metrics");
+
+            engine.resources([
+                envSecrets,
+                containerSet,
+                service,
+            ])
+
+    },
+
+    "query-doc-embeddings" +: {
+
+        create:: function(engine)
+
+            local envSecrets = engine.envSecrets("pinecone-api-key")
+                .with_env_var("PINECONE_API_KEY", "pinecone-api-key");
+
+            local container =
+                engine.container("query-doc-embeddings")
+                    .with_image(images.trustgraph)
+                    .with_command([
+                        "de-query-pinecone",
+                        "-p",
+                        url.pulsar,
+                    ])
+                    .with_env_var_secrets(envSecrets)
+                    .with_limits("0.5", "128M")
+                    .with_reservations("0.1", "128M");
+
+            local containerSet = engine.containers(
+                "query-doc-embeddings", [ container ]
+            );
+
+            local service =
+                engine.internalService(containerSet)
+                .with_port(8080, 8080, "metrics");
+
+            engine.resources([
+                envSecrets,
+                containerSet,
+                service,
+            ])
+
+
+    }
+
+}
+

trustgraph-flow/scripts/ge-query-pinecone

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

trustgraph-flow/scripts/ge-write-pinecone

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

trustgraph-flow/setup.py

@@ -60,6 +60,7 @@
         "jsonschema",
         "aiohttp",
         "aiopulsar-py",
+        "pinecone[grpc]",
     ],
     scripts=[
         "scripts/api-gateway",
@@ -74,8 +75,10 @@
         "scripts/embeddings-ollama",
         "scripts/embeddings-vectorize",
         "scripts/ge-query-milvus",
+        "scripts/ge-query-pinecone",
         "scripts/ge-query-qdrant",
         "scripts/ge-write-milvus",
+        "scripts/ge-write-pinecone",
         "scripts/ge-write-qdrant",
         "scripts/graph-rag",
         "scripts/kg-extract-definitions",

trustgraph-flow/trustgraph/query/doc_embeddings/pinecone/__init__.py

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

trustgraph-flow/trustgraph/query/doc_embeddings/pinecone/__main__.py

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

trustgraph-flow/trustgraph/query/doc_embeddings/pinecone/service.py

@@ -0,0 +1,142 @@
+
+"""
+Document embeddings query service.  Input is vector, output is an array
+of chunks.  Pinecone implementation.
+"""
+
+from pinecone import Pinecone, ServerlessSpec
+from pinecone.grpc import PineconeGRPC, GRPCClientConfig
+
+import uuid
+import os
+
+from .... schema import DocumentEmbeddingsRequest, DocumentEmbeddingsResponse
+from .... schema import Error, Value
+from .... schema import document_embeddings_request_queue
+from .... schema import document_embeddings_response_queue
+from .... base import ConsumerProducer
+
+module = ".".join(__name__.split(".")[1:-1])
+
+default_input_queue = document_embeddings_request_queue
+default_output_queue = document_embeddings_response_queue
+default_subscriber = module
+default_api_key = os.getenv("PINECONE_API_KEY", "not-specified")
+
+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)
+
+        self.url = params.get("url", None)
+        self.api_key = params.get("api_key", default_api_key)
+
+        if self.url:
+
+            self.pinecone = PineconeGRPC(
+                api_key = self.api_key,
+                host = self.url
+            )
+
+        else:
+
+            self.pinecone = Pinecone(api_key = self.api_key)
+
+        super(Processor, self).__init__(
+            **params | {
+                "input_queue": input_queue,
+                "output_queue": output_queue,
+                "subscriber": subscriber,
+                "input_schema": DocumentEmbeddingsRequest,
+                "output_schema": DocumentEmbeddingsResponse,
+                "url": self.url,
+            }
+        )
+
+    def handle(self, msg):
+
+        try:
+
+            v = msg.value()
+
+            # Sender-produced ID
+            id = msg.properties()["id"]
+
+            print(f"Handling input {id}...", flush=True)
+
+            chunks = []
+
+            for vec in v.vectors:
+
+                dim = len(vec)
+
+                index_name = (
+                    "d-" + v.user + "-" + str(dim)
+                )
+
+                index = self.pinecone.Index(index_name)
+
+                results = index.query(
+                    namespace=v.collection,
+                    vector=vec,
+                    top_k=v.limit,
+                    include_values=False,
+                    include_metadata=True
+                )
+
+                search_result = self.client.query_points(
+                    collection_name=collection,
+                    query=vec,
+                    limit=v.limit,
+                    with_payload=True,
+                ).points
+
+                for r in results.matches:
+                    doc = r.metadata["doc"]
+                    chunks.add(doc)
+
+            print("Send response...", flush=True)
+            r = DocumentEmbeddingsResponse(documents=chunks, error=None)
+            self.producer.send(r, properties={"id": id})
+
+            print("Done.", flush=True)
+
+        except Exception as e:
+
+            print(f"Exception: {e}")
+
+            print("Send error response...", flush=True)
+
+            r = DocumentEmbeddingsResponse(
+                error=Error(
+                    type = "llm-error",
+                    message = str(e),
+                ),
+                documents=None,
+            )
+
+            self.producer.send(r, properties={"id": id})
+
+            self.consumer.acknowledge(msg)
+
+    @staticmethod
+    def add_args(parser):
+
+        ConsumerProducer.add_args(
+            parser, default_input_queue, default_subscriber,
+            default_output_queue,
+        )
+
+        parser.add_argument(
+            '-t', '--store-uri',
+            default=default_store_uri,
+            help=f'Milvus store URI (default: {default_store_uri})'
+        )
+
+def run():
+
+    Processor.start(module, __doc__)
+

trustgraph-flow/trustgraph/query/graph_embeddings/pinecone/__init__.py

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

trustgraph-flow/trustgraph/query/graph_embeddings/pinecone/__main__.py

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