Convert Entity Relationship Extraction in DSPy to using CoT (gusye123…

…4#44)

* Converted TypedPredictor to CoT and removed pydantic models using experimental DSPy in notebook

* Fix entity extraction unittests after removing pydantic models and changing to CoT

* Add working random search fine tuning with better metrics

* Still cannot get MIPROv2 to work

* Working MIPROv2 with TypedChainOfThought

* Updated metrics to compute all relationships at once, updated prompt instructions that works for qwen2-7b

* Add updated notebooks with fine tuning using MIPROv2 and qwen2-7b as task model

* Add compiled model for generate dataset with updated unittests

---------

Co-authored-by: terence-gpt <numberchiffre@users.noreply.github.com>

examples/benchmarks/dspy_entity.py

@@ -114,19 +114,20 @@ async def run_benchmark(text: str):
     system_prompt_dspy = f"{system_prompt} Time: {time.time()}."
     lm = dspy.OpenAI(
         model="deepseek-chat", 
-        model_type="chat", 
+        model_type="chat",
+        api_provider="openai",
         api_key=os.environ["DEEPSEEK_API_KEY"], 
         base_url=os.environ["DEEPSEEK_BASE_URL"], 
-        system_prompt=system_prompt_dspy, 
+        system_prompt=system_prompt, 
         temperature=1.0,
-        top_p=1,
-        max_tokens=4096
+        max_tokens=8192
     )
-    dspy.settings.configure(lm=lm)
+    dspy.settings.configure(lm=lm, experimental=True)
     graph_storage_with_dspy, time_with_dspy = await benchmark_entity_extraction(text, system_prompt_dspy, use_dspy=True)
     print(f"Execution time with DSPy-AI: {time_with_dspy:.2f} seconds")
     print_extraction_results(graph_storage_with_dspy)
 
+    import pdb; pdb.set_trace()
     print("Running benchmark without DSPy-AI:")
     system_prompt_no_dspy = f"{system_prompt} Time: {time.time()}."
     graph_storage_without_dspy, time_without_dspy = await benchmark_entity_extraction(text, system_prompt_no_dspy, use_dspy=False)

examples/using_dspy_entity_extraction.py

@@ -138,14 +138,14 @@ def query():
     """
     lm = dspy.OpenAI(
         model="deepseek-chat", 
-        model_type="chat", 
+        model_type="chat",
+        api_provider="openai",
         api_key=os.environ["DEEPSEEK_API_KEY"], 
         base_url=os.environ["DEEPSEEK_BASE_URL"], 
         system_prompt=system_prompt, 
         temperature=1.0,
-        top_p=1,
-        max_tokens=4096
+        max_tokens=8192
     )
-    dspy.settings.configure(lm=lm)
+    dspy.settings.configure(lm=lm, experimental=True)
     insert()
     query()

nano_graphrag/entity_extraction/extract.py

@@ -1,6 +1,7 @@
 from typing import Union
 import pickle
 import asyncio
+from openai import BadRequestError
 from collections import defaultdict
 import dspy
 from nano_graphrag._storage import BaseGraphStorage
@@ -11,40 +12,67 @@
 )
 from nano_graphrag.prompt import PROMPTS
 from nano_graphrag._utils import logger, compute_mdhash_id
-from nano_graphrag.entity_extraction.module import EntityRelationshipExtractor
+from nano_graphrag.entity_extraction.module import TypedEntityRelationshipExtractor
 from nano_graphrag._op import _merge_edges_then_upsert, _merge_nodes_then_upsert
 
 
 async def generate_dataset(
     chunks: dict[str, TextChunkSchema],
     filepath: str,
-    save_dataset: bool = True
+    save_dataset: bool = True,
+    global_config: dict = {}
 ) -> list[dspy.Example]:
-    entity_extractor = EntityRelationshipExtractor()
+    entity_extractor = TypedEntityRelationshipExtractor()
+
+    if global_config.get("use_compiled_dspy_entity_relationship", False):
+        entity_extractor.load(global_config["entity_relationship_module_path"])
+
     ordered_chunks = list(chunks.items())
+    already_processed = 0
+    already_entities = 0
+    already_relations = 0
 
     async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]) -> dspy.Example:
+        nonlocal already_processed, already_entities, already_relations
         chunk_dp = chunk_key_dp[1]
         content = chunk_dp["content"]
-        prediction = await asyncio.to_thread(
-            entity_extractor, input_text=content
-        )
+        try:
+            prediction = await asyncio.to_thread(
+                entity_extractor, input_text=content
+            )
+            entities, relationships = prediction.entities, prediction.relationships
+        except BadRequestError as e:
+            logger.error(f"Error in TypedEntityRelationshipExtractor: {e}")
+            entities, relationships = [], []
         example = dspy.Example(
             input_text=content, 
-            entities=prediction.entities, 
-            relationships=prediction.relationships
+            entities=entities, 
+            relationships=relationships
         ).with_inputs("input_text")
+        already_entities += len(entities)
+        already_relations += len(relationships)
+        already_processed += 1
+        now_ticks = PROMPTS["process_tickers"][
+            already_processed % len(PROMPTS["process_tickers"])
+        ]
+        print(
+            f"{now_ticks} Processed {already_processed} chunks, {already_entities} entities(duplicated), {already_relations} relations(duplicated)\r",
+            end="",
+            flush=True,
+        )
         return example
 
     examples = await asyncio.gather(
         *[_process_single_content(c) for c in ordered_chunks]
     )
+    filtered_examples = [example for example in examples if len(example.entities) > 0 and len(example.relationships) > 0]
+    num_filtered_examples = len(examples) - len(filtered_examples)
     if save_dataset:
         with open(filepath, 'wb') as f:
-            pickle.dump(examples, f)
-            logger.info(f"Saved {len(examples)} examples with keys: {examples[0].keys()}")
+            pickle.dump(filtered_examples, f)
+            logger.info(f"Saved {len(filtered_examples)} examples with keys: {filtered_examples[0].keys()}, filtered {num_filtered_examples} examples")
 
-    return examples
+    return filtered_examples
 
 
 async def extract_entities_dspy(
@@ -53,7 +81,7 @@ async def extract_entities_dspy(
     entity_vdb: BaseVectorStorage,
     global_config: dict,
 ) -> Union[BaseGraphStorage, None]:
-    entity_extractor = EntityRelationshipExtractor()
+    entity_extractor = TypedEntityRelationshipExtractor()
 
     if global_config.get("use_compiled_dspy_entity_relationship", False):
         entity_extractor.load(global_config["entity_relationship_module_path"])
@@ -68,23 +96,26 @@ async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]):
         chunk_key = chunk_key_dp[0]
         chunk_dp = chunk_key_dp[1]
         content = chunk_dp["content"]
-        prediction = await asyncio.to_thread(
-            entity_extractor, input_text=content
-        )
-
+        try:
+            prediction = await asyncio.to_thread(
+                entity_extractor, input_text=content
+            )
+            entities, relationships = prediction.entities, prediction.relationships
+        except BadRequestError as e:
+            logger.error(f"Error in TypedEntityRelationshipExtractor: {e}")
+            entities, relationships = [], []
+
         maybe_nodes = defaultdict(list)
         maybe_edges = defaultdict(list)
 
-        for entity in prediction.entities.context:
-            entity_dict = entity.dict()
-            entity_dict["source_id"] = chunk_key
-            maybe_nodes[entity_dict['entity_name']].append(entity_dict)
+        for entity in entities:
+            entity["source_id"] = chunk_key
+            maybe_nodes[entity['entity_name']].append(entity)
             already_entities += 1
 
-        for relationship in prediction.relationships.context:
-            relationship_dict = relationship.dict()
-            relationship_dict["source_id"] = chunk_key
-            maybe_edges[(relationship_dict['src_id'], relationship_dict['tgt_id'])].append(relationship_dict)
+        for relationship in relationships:
+            relationship["source_id"] = chunk_key
+            maybe_edges[(relationship['src_id'], relationship['tgt_id'])].append(relationship)
             already_relations += 1
 
         already_processed += 1

nano_graphrag/entity_extraction/metric.py

@@ -1,35 +1,42 @@
 import dspy
-import numpy as np
-
-
-class AssessRelationship(dspy.Signature):
-    """Assess the similarity of two relationships."""
-    gold_relationship = dspy.InputField()
-    predicted_relationship = dspy.InputField()
-    similarity_score = dspy.OutputField(desc="Similarity score between 0 and 1")
-
-
-def relationship_similarity_metric(gold: dspy.Example, pred: dspy.Prediction, trace=None) -> float:
-    similarity_scores = []
-
-    for gold_rel, pred_rel in zip(gold.relationships.context, pred.relationships.context):
-        assessment = dspy.Predict(AssessRelationship)(
-            gold_relationship=gold_rel,
-            predicted_relationship=pred_rel
-        )
-
-        try:
-            score = float(assessment.similarity_score)
-            similarity_scores.append(score)
-        except ValueError:
-            similarity_scores.append(0.0)
-
-    return np.mean(similarity_scores) if similarity_scores else 0.0
+from nano_graphrag.entity_extraction.module import Relationship
+
+
+class AssessRelationships(dspy.Signature):
+    """
+    Assess the similarity between gold and predicted relationships:
+    1. Match relationships based on src_id and tgt_id pairs, allowing for slight variations in entity names.
+    2. For matched pairs, compare:
+       a) Description similarity (semantic meaning)
+       b) Weight similarity
+       c) Order similarity
+    3. Consider unmatched relationships as penalties.
+    4. Aggregate scores, accounting for precision and recall.
+    5. Return a final similarity score between 0 (no similarity) and 1 (perfect match).
+
+    Key considerations:
+    - Prioritize matching based on entity pairs over exact string matches.
+    - Use semantic similarity for descriptions rather than exact matches.
+    - Weight the importance of different aspects (e.g., entity matching, description, weight, order).
+    - Balance the impact of matched and unmatched relationships in the final score.
+    """
+
+    gold_relationships: list[Relationship] = dspy.InputField(desc="The gold-standard relationships to compare against.")
+    predicted_relationships: list[Relationship] = dspy.InputField(desc="The predicted relationships to compare against the gold-standard relationships.")
+    similarity_score: float = dspy.OutputField(desc="Similarity score between 0 and 1, with 1 being the highest similarity.")
+
+
+def relationships_similarity_metric(gold: dspy.Example, pred: dspy.Prediction, trace=None) -> float:
+    model = dspy.TypedChainOfThought(AssessRelationships)
+    gold_relationships = [Relationship(**item) for item in gold['relationships']]
+    predicted_relationships = [Relationship(**item) for item in pred['relationships']]
+    similarity_score = float(model(gold_relationships=gold_relationships, predicted_relationships=predicted_relationships).similarity_score)
+    return similarity_score
 
 
 def entity_recall_metric(gold: dspy.Example, pred: dspy.Prediction, trace=None) -> float:
-    true_set = set(item.entity_name for item in gold.entities.context)
-    pred_set = set(item.entity_name for item in pred.entities.context)
+    true_set = set(item['entity_name'] for item in gold['entities'])
+    pred_set = set(item['entity_name'] for item in pred['entities'])
     true_positives = len(pred_set.intersection(true_set))
     false_negatives = len(true_set - pred_set)
     recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0