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 @@ setuptools.setup(
         "jsonschema",
         "aiohttp",
         "aiopulsar-py",
+        "pinecone[grpc]",
     ],
     scripts=[
         "scripts/api-gateway",
@@ -74,8 +75,10 @@ setuptools.setup(
         "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()
+

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

@@ -0,0 +1,156 @@
+
+"""
+Graph embeddings query service.  Input is vector, output is list of
+entities.  Pinecone implementation.
+"""
+
+from pinecone import Pinecone, ServerlessSpec
+from pinecone.grpc import PineconeGRPC, GRPCClientConfig
+
+import uuid
+import os
+
+from .... schema import GraphEmbeddingsRequest, GraphEmbeddingsResponse
+from .... schema import Error, Value
+from .... schema import graph_embeddings_request_queue
+from .... schema import graph_embeddings_response_queue
+from .... base import ConsumerProducer
+
+module = ".".join(__name__.split(".")[1:-1])
+
+default_input_queue = graph_embeddings_request_queue
+default_output_queue = graph_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": GraphEmbeddingsRequest,
+                "output_schema": GraphEmbeddingsResponse,
+                "url": self.url,
+            }
+        )
+
+    def create_value(self, ent):
+        if ent.startswith("http://") or ent.startswith("https://"):
+            return Value(value=ent, is_uri=True)
+        else:
+            return Value(value=ent, is_uri=False)
+        
+    def handle(self, msg):
+
+        try:
+
+            v = msg.value()
+
+            # Sender-produced ID
+            id = msg.properties()["id"]
+
+            print(f"Handling input {id}...", flush=True)
+
+            entities = set()
+
+            for vec in v.vectors:
+
+                dim = len(vec)
+
+                index_name = (
+                    "t-" + 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
+                )
+
+                for r in results.matches:
+                    ent = r.metadata["entity"]
+                    entities.add(ent)
+
+            # Convert set to list
+            entities = list(entities)
+
+            ents2 = []
+
+            for ent in entities:
+                ents2.append(self.create_value(ent))
+
+            entities = ents2
+
+            print("Send response...", flush=True)
+            r = GraphEmbeddingsResponse(entities=entities, 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 = GraphEmbeddingsResponse(
+                error=Error(
+                    type = "llm-error",
+                    message = str(e),
+                ),
+                entities=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(
+            '-a', '--api-key',
+            default=default_api_key,
+            help='Pinecone API key. (default from PINECONE_API_KEY)'
+        )
+
+        parser.add_argument(
+            '-u', '--url',
+            help='Pinecone URL.  If unspecified, serverless is used'
+        )
+
+def run():
+
+    Processor.start(module, __doc__)
+

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

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

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

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

trustgraph-flow/trustgraph/storage/doc_embeddings/pinecone/write.py

@@ -0,0 +1,167 @@
+
+"""
+Accepts entity/vector pairs and writes them to a Qdrant store.
+"""
+
+from qdrant_client import QdrantClient
+from qdrant_client.models import PointStruct
+from qdrant_client.models import Distance, VectorParams
+
+import time
+import uuid
+import os
+
+from .... schema import ChunkEmbeddings
+from .... schema import chunk_embeddings_ingest_queue
+from .... log_level import LogLevel
+from .... base import Consumer
+
+module = ".".join(__name__.split(".")[1:-1])
+
+default_input_queue = chunk_embeddings_ingest_queue
+default_subscriber = module
+default_api_key = os.getenv("PINECONE_API_KEY", "not-specified")
+default_cloud = "aws"
+default_region = "us-east-1"
+
+class Processor(Consumer):
+
+    def __init__(self, **params):
+
+        input_queue = params.get("input_queue", default_input_queue)
+        subscriber = params.get("subscriber", default_subscriber)
+
+        self.url = params.get("url", None)
+        self.cloud = params.get("cloud", default_cloud)
+        self.region = params.get("region", default_region)
+        self.api_key = params.get("api_key", default_api_key)
+
+        if self.api_key is None:
+            raise RuntimeError("Pinecone API key must be specified")
+
+        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,
+                "subscriber": subscriber,
+                "input_schema": ChunkEmbeddings,
+                "url": self.url,
+            }
+        )
+
+        self.last_index_name = None
+
+    def handle(self, msg):
+
+        v = msg.value()
+
+        chunk = v.chunk.decode("utf-8")
+
+        if chunk == "": return
+
+        for vec in v.vectors:
+
+            dim = len(vec)
+            collection = (
+                "d-" + v.metadata.user + "-" + str(dim)
+            )
+
+            if index_name != self.last_index_name:
+
+                if not self.pinecone.has_index(index_name):
+
+                    try:
+
+                        self.pinecone.create_index(
+                            name = index_name,
+                            dimension = dim,
+                            metric = "cosine",
+                            spec = ServerlessSpec(
+                                cloud = self.cloud,
+                                region = self.region,
+                            )
+                        )
+
+                        for i in range(0, 1000):
+
+                            if self.pinecone.describe_index(
+                                    index_name
+                            ).status["ready"]:
+                                break
+
+                            time.sleep(1)
+
+                        if not self.pinecone.describe_index(
+                                index_name
+                        ).status["ready"]:
+                            raise RuntimeError(
+                                "Gave up waiting for index creation"
+                            )
+
+                    except Exception as e:
+                        print("Pinecone index creation failed")
+                        raise e
+
+                    print(f"Index {index_name} created", flush=True)
+
+                self.last_index_name = index_name
+
+            index = self.pinecone.Index(index_name)
+
+            records = [
+                {
+                    "id": id,
+                    "values": vec,
+                    "metadata": { "doc": chunk },
+                }
+            ]
+
+            index.upsert(
+                vectors = records,
+                namespace = v.metadata.collection,
+            )
+
+    @staticmethod
+    def add_args(parser):
+
+        Consumer.add_args(
+            parser, default_input_queue, default_subscriber,
+        )
+
+        parser.add_argument(
+            '-a', '--api-key',
+            default=default_api_key,
+            help='Pinecone API key. (default from PINECONE_API_KEY)'
+        )
+
+        parser.add_argument(
+            '-u', '--url',
+            help='Pinecone URL.  If unspecified, serverless is used'
+        )
+
+        parser.add_argument(
+            '--cloud',
+            default=default_cloud,
+            help=f'Pinecone cloud, (default: {default_cloud}'
+        )
+
+        parser.add_argument(
+            '--region',
+            default=default_region,
+            help=f'Pinecone region, (default: {default_region}'
+        )
+
+def run():
+
+    Processor.start(module, __doc__)
+

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

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

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

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

trustgraph-flow/trustgraph/storage/graph_embeddings/pinecone/write.py

@@ -0,0 +1,167 @@
+
+"""
+Accepts entity/vector pairs and writes them to a Pinecone store.
+"""
+
+from pinecone import Pinecone, ServerlessSpec
+from pinecone.grpc import PineconeGRPC, GRPCClientConfig
+
+import time
+import uuid
+import os
+
+from .... schema import GraphEmbeddings
+from .... schema import graph_embeddings_store_queue
+from .... log_level import LogLevel
+from .... base import Consumer
+
+module = ".".join(__name__.split(".")[1:-1])
+
+default_input_queue = graph_embeddings_store_queue
+default_subscriber = module
+default_api_key = os.getenv("PINECONE_API_KEY", "not-specified")
+default_cloud = "aws"
+default_region = "us-east-1"
+
+class Processor(Consumer):
+
+    def __init__(self, **params):
+
+        input_queue = params.get("input_queue", default_input_queue)
+        subscriber = params.get("subscriber", default_subscriber)
+
+        self.url = params.get("url", None)
+        self.cloud = params.get("cloud", default_cloud)
+        self.region = params.get("region", default_region)
+        self.api_key = params.get("api_key", default_api_key)
+
+        if self.api_key is None:
+            raise RuntimeError("Pinecone API key must be specified")
+
+        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,
+                "subscriber": subscriber,
+                "input_schema": GraphEmbeddings,
+                "url": self.url,
+            }
+        )
+
+        self.last_index_name = None
+
+    def handle(self, msg):
+
+        v = msg.value()
+
+        id = str(uuid.uuid4())
+
+        if v.entity.value == "" or v.entity.value is None: return
+
+        for vec in v.vectors:
+
+            dim = len(vec)
+
+            index_name = (
+                "t-" + v.metadata.user + "-" + str(dim)
+            )
+
+            if index_name != self.last_index_name:
+
+                if not self.pinecone.has_index(index_name):
+
+                    try:
+
+                        self.pinecone.create_index(
+                            name = index_name,
+                            dimension = dim,
+                            metric = "cosine",
+                            spec = ServerlessSpec(
+                                cloud = self.cloud,
+                                region = self.region,
+                            )
+                        )
+
+                        for i in range(0, 1000):
+
+                            if self.pinecone.describe_index(
+                                    index_name
+                            ).status["ready"]:
+                                break
+
+                            time.sleep(1)
+
+                        if not self.pinecone.describe_index(
+                                index_name
+                        ).status["ready"]:
+                            raise RuntimeError(
+                                "Gave up waiting for index creation"
+                            )
+
+                    except Exception as e:
+                        print("Pinecone index creation failed")
+                        raise e
+
+                    print(f"Index {index_name} created", flush=True)
+
+                self.last_index_name = index_name
+
+            index = self.pinecone.Index(index_name)
+
+            records = [
+                {
+                    "id": id,
+                    "values": vec,
+                    "metadata": { "entity": v.entity.value },
+                }
+            ]
+
+            index.upsert(
+                vectors = records,
+                namespace = v.metadata.collection,
+            )
+
+    @staticmethod
+    def add_args(parser):
+
+        Consumer.add_args(
+            parser, default_input_queue, default_subscriber,
+        )
+
+        parser.add_argument(
+            '-a', '--api-key',
+            default=default_api_key,
+            help='Pinecone API key. (default from PINECONE_API_KEY)'
+        )
+
+        parser.add_argument(
+            '-u', '--url',
+            help='Pinecone URL.  If unspecified, serverless is used'
+        )
+
+        parser.add_argument(
+            '--cloud',
+            default=default_cloud,
+            help=f'Pinecone cloud, (default: {default_cloud}'
+        )
+
+        parser.add_argument(
+            '--region',
+            default=default_region,
+            help=f'Pinecone region, (default: {default_region}'
+        )
+
+def run():
+
+    Processor.start(module, __doc__)
+