Added KG Topics

prometheus/prometheus.yml

@@ -27,6 +27,7 @@ scrape_configs:
         - 'vectorize:8000'
         - 'embeddings:8000'
         - 'kg-extract-definitions:8000'
+        - 'kg-extract-topics:8000'
         - 'kg-extract-relationships:8000'
         - 'store-graph-embeddings:8000'
         - 'store-triples:8000'

scripts/kg-extract-topics

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

setup.py

@@ -75,6 +75,7 @@ setuptools.setup(
         "scripts/graph-to-turtle",
         "scripts/init-pulsar-manager",
         "scripts/kg-extract-definitions",
+        "scripts/kg-extract-topics",
         "scripts/kg-extract-relationships",
         "scripts/load-graph-embeddings",
         "scripts/load-pdf",

trustgraph/clients/prompt_client.py

@@ -44,6 +44,13 @@ class PromptClient(BaseClient):
             kind="extract-definitions", chunk=chunk,
             timeout=timeout
         ).definitions
+    
+    def request_topics(self, chunk, timeout=300):
+
+        return self.call(
+            kind="extract-topics", chunk=chunk,
+            timeout=timeout
+        ).topics
 
     def request_relationships(self, chunk, timeout=300):
 

trustgraph/extract/kg/topics/__init__.py

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

trustgraph/extract/kg/topics/__main__.py

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

trustgraph/extract/kg/topics/extract.py

@@ -0,0 +1,134 @@
+
+"""
+Simple decoder, accepts embeddings+text chunks input, applies entity analysis to
+get entity definitions which are output as graph edges.
+"""
+
+import urllib.parse
+import json
+
+from .... schema import ChunkEmbeddings, Triple, Source, Value
+from .... schema import chunk_embeddings_ingest_queue, triples_store_queue
+from .... schema import prompt_request_queue
+from .... schema import prompt_response_queue
+from .... log_level import LogLevel
+from .... clients.prompt_client import PromptClient
+from .... rdf import TRUSTGRAPH_ENTITIES, DEFINITION
+from .... base import ConsumerProducer
+
+DEFINITION_VALUE = Value(value=DEFINITION, is_uri=True)
+
+module = ".".join(__name__.split(".")[1:-1])
+
+default_input_queue = chunk_embeddings_ingest_queue
+default_output_queue = triples_store_queue
+default_subscriber = module
+
+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)
+        pr_request_queue = params.get(
+            "prompt_request_queue", prompt_request_queue
+        )
+        pr_response_queue = params.get(
+            "prompt_response_queue", prompt_response_queue
+        )
+
+        super(Processor, self).__init__(
+            **params | {
+                "input_queue": input_queue,
+                "output_queue": output_queue,
+                "subscriber": subscriber,
+                "input_schema": ChunkEmbeddings,
+                "output_schema": Triple,
+                "prompt_request_queue": pr_request_queue,
+                "prompt_response_queue": pr_response_queue,
+            }
+        )
+
+        self.prompt = PromptClient(
+            pulsar_host=self.pulsar_host,
+            input_queue=pr_request_queue,
+            output_queue=pr_response_queue,
+            subscriber = module + "-prompt",
+        )
+
+    def to_uri(self, text):
+
+        part = text.replace(" ", "-").lower().encode("utf-8")
+        quoted = urllib.parse.quote(part)
+        uri = TRUSTGRAPH_ENTITIES + quoted
+
+        return uri
+
+    def get_topics(self, chunk):
+
+        return self.prompt.request_topics(chunk)
+
+    def emit_edge(self, s, p, o):
+
+        t = Triple(s=s, p=p, o=o)
+        self.producer.send(t)
+
+    def handle(self, msg):
+
+        v = msg.value()
+        print(f"Indexing {v.source.id}...", flush=True)
+
+        chunk = v.chunk.decode("utf-8")
+
+        try:
+
+            defs = self.get_topics(chunk)
+
+            for defn in defs:
+
+                s = defn.name
+                o = defn.definition
+
+                if s == "": continue
+                if o == "": continue
+
+                if s is None: continue
+                if o is None: continue
+
+                s_uri = self.to_uri(s)
+
+                s_value = Value(value=str(s_uri), is_uri=True)
+                o_value = Value(value=str(o), is_uri=False)
+
+                self.emit_edge(s_value, DEFINITION_VALUE, o_value)
+
+        except Exception as e:
+            print("Exception: ", e, flush=True)
+
+        print("Done.", flush=True)
+
+    @staticmethod
+    def add_args(parser):
+
+        ConsumerProducer.add_args(
+            parser, default_input_queue, default_subscriber,
+            default_output_queue,
+        )
+
+        parser.add_argument(
+            '--prompt-request-queue',
+            default=prompt_request_queue,
+            help=f'Prompt request queue (default: {prompt_request_queue})',
+        )
+
+        parser.add_argument(
+            '--prompt-completion-response-queue',
+            default=prompt_response_queue,
+            help=f'Prompt response queue (default: {prompt_response_queue})',
+        )
+
+def run():
+
+    Processor.start(module, __doc__)
+

trustgraph/model/prompt/generic/prompts.py

@@ -1,50 +1,66 @@
 
 def to_relationships(text):
 
-    prompt = f"""<instructions>
-Study the following text and derive entity relationships.  For each
-relationship, derive the subject, predicate and object of the relationship.
-Output relationships in JSON format as an arary of objects with fields:
-- subject: the subject of the relationship
-- predicate: the predicate
-- object: the object of the relationship
-- object-entity: false if the object is a simple data type: name, value or date.  true if it is an entity.
-</instructions>
+    prompt = f"""You are a helpful assistant that performs information extraction tasks for a provided text.
 
-<text>
+Read the provided text. You will model the text as an information network for a RDF knowledge graph.
+
+Information network rules:
+- An information network has subjects connected by predicates to objects.
+- A subject can have many predicates and objects.
+- A subject can be connected by a predicate to another subject.
+- Objects shall be either nouns or adjectives.
+
+Here is the provided text: 
 {text}
-</text>
 
-<requirements>
-You will respond only with raw JSON format data. Do not provide
-explanations. Do not use special characters in the abstract text. The
-abstract must be written as plain text.  Do not add markdown formatting
-or headers or prefixes.
-</requirements>"""
+Instructions:
+- Obey the information network rules.
+- Ignore document formatting. 
+- Do not provide explanations or any additional text. 
+- Do not use special characters.
+- The key "object-entity" is true if it is a Named-Entity.
+- Respond only with a well-formed JSON using the following example:
+
+JSON example: [{{"subject": string, "predicate": string, "object": string, "object-entity": boolean}}]
+"""
+    
+    return prompt
+
+def to_topics(text):
+
+    prompt = f"""You are a helpful assistant that performs information extraction tasks for a provided text.\nRead the provided text. You will identify topics and their definitions.
+
+Here is the provided text: 
+{text}
+
+Instructions:
+- Ignore document formatting. 
+- Do not provide explanations or any additional text. 
+- Do not use special characters.
+- Identify only topics that are unique to the provided text.
+- Respond only with a well-formed JSON using the following example:
+
+JSON example: [{{"topic": string, "definition": string}}]
+"""
     
     return prompt
     
 def to_definitions(text):
 
-    prompt = f"""<instructions>
-Study the following text and derive definitions for any discovered entities.
-Do not provide definitions for entities whose definitions are incomplete
-or unknown.
-Output relationships in JSON format as an arary of objects with fields:
-- entity: the name of the entity
-- definition: English text which defines the entity
-</instructions>
+    prompt = f"""You are a helpful assistant that performs information extraction tasks for a provided text.\nRead the provided text. You will identify named-entities and their definitions.
 
-<text>
+Here is the provided text: 
 {text}
-</text>
 
-<requirements>
-You will respond only with raw JSON format data. Do not provide
-explanations. Do not use special characters in the abstract text. The
-abstract will be written as plain text.  Do not add markdown formatting
-or headers or prefixes.  Do not include null or unknown definitions.
-</requirements>"""
+Instructions:
+- Ignore document formatting. 
+- Do not provide explanations or any additional text. 
+- Do not use special characters.
+- Identity only entities that are named-entities.
+- Respond only with a well-formed JSON using the following example:
+
+JSON example: [{{"entity": string, "definition": string}}]"""
     
     return prompt
 

trustgraph/model/prompt/generic/service.py

@@ -13,7 +13,7 @@ from .... schema import prompt_request_queue, prompt_response_queue
 from .... base import ConsumerProducer
 from .... clients.llm_client import LlmClient
 
-from . prompts import to_definitions, to_relationships
+from . prompts import to_definitions, to_relationships, to_topics
 from . prompts import to_kg_query, to_document_query, to_rows
 
 module = ".".join(__name__.split(".")[1:-1])
@@ -80,6 +80,11 @@ class Processor(ConsumerProducer):
             self.handle_extract_definitions(id, v)
             return
 
+        elif kind == "extract-topics":
+
+            self.handle_extract_topics(id, v)
+            return
+
         elif kind == "extract-relationships":
 
             self.handle_extract_relationships(id, v)
@@ -164,6 +169,65 @@ class Processor(ConsumerProducer):
 
             self.producer.send(r, properties={"id": id})
 
+    def handle_extract_topics(self, id, v):
+
+        try:
+
+            prompt = to_topics(v.chunk)
+
+            ans = self.llm.request(prompt)
+
+            # Silently ignore JSON parse error
+            try:
+                defs = self.parse_json(ans)
+            except:
+                print("JSON parse error, ignored", flush=True)
+                defs = []
+
+            output = []
+
+            for defn in defs:
+
+                try:
+                    e = defn["topic"]
+                    d = defn["definition"]
+
+                    if e == "": continue
+                    if e is None: continue
+                    if d == "": continue
+                    if d is None: continue
+
+                    output.append(
+                        Definition(
+                            name=e, definition=d
+                        )
+                    )
+
+                except:
+                    print("definition fields missing, ignored", flush=True)
+
+            print("Send response...", flush=True)
+            r = PromptResponse(topics=output, 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 = PromptResponse(
+                error=Error(
+                    type = "llm-error",
+                    message = str(e),
+                ),
+                response=None,
+            )
+
+            self.producer.send(r, properties={"id": id})
+
     def handle_extract_relationships(self, id, v):
 
         try:

trustgraph/schema/prompt.py

@@ -12,6 +12,10 @@ class Definition(Record):
     name = String()
     definition = String()
 
+class Topic(Record):
+    name = String()
+    definition = String()
+
 class Relationship(Record):
     s = String()
     p = String()
@@ -46,6 +50,7 @@ class PromptResponse(Record):
     error = Error()
     answer = String()
     definitions = Array(Definition())
+    topics = Array(Topic())
     relationships = Array(Relationship())
     rows = Array(Map(String()))