Feature/pinecone integration (#170)

* Added Pinecone for GE write & query

* Add templates

* Doc embedding support

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

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+#!/usr/bin/env python3
+
+from . hf import run
+
+if __name__ == '__main__':
+    run()
+

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

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+
+"""
+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

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+
+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

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+
+"""
+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__)
+