From 1abc7f44a6d73c99a665522f6f5d1fbeb615d88e Mon Sep 17 00:00:00 2001 From: JayGhiya Date: Fri, 25 Oct 2024 22:24:07 +0530 Subject: [PATCH 1/5] feat: moved from native cypher to neomodel and it feels at home --- .gitignore | 1 + .../dspy/dspy_unoplat_fs_annotation_subset.py | 4 - .../database/graph/unoplat_graph_ingestion.py | 76 ++--- .../graph/unoplat_graph_processing.py | 318 +++++++++++------- 4 files changed, 218 insertions(+), 181 deletions(-) diff --git a/.gitignore b/.gitignore index 42bc5d9..16ff804 100755 --- a/.gitignore +++ b/.gitignore @@ -72,3 +72,4 @@ unoplat-code-confluence-query-engine/dist unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/__pycache__ unoplat-code-confluence-query-engine/dspy/__pycache__ experiments +unoplat-code-confluence-commons/dist diff --git a/unoplat-code-confluence/unoplat_code_confluence/data_models/dspy/dspy_unoplat_fs_annotation_subset.py b/unoplat-code-confluence/unoplat_code_confluence/data_models/dspy/dspy_unoplat_fs_annotation_subset.py index 81c0a8e..b72602c 100644 --- a/unoplat-code-confluence/unoplat_code_confluence/data_models/dspy/dspy_unoplat_fs_annotation_subset.py +++ b/unoplat-code-confluence/unoplat_code_confluence/data_models/dspy/dspy_unoplat_fs_annotation_subset.py @@ -1,7 +1,3 @@ - - - - from typing import Optional from pydantic import BaseModel, Field diff --git a/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_ingestion.py b/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_ingestion.py index bfc3b80..a5fd423 100644 --- a/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_ingestion.py +++ b/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_ingestion.py @@ -2,58 +2,38 @@ from typing import Dict, Any from unoplat_code_confluence.configuration.external_config import AppConfig - +from neomodel import config,db class UnoplatGraphIngestion: - def __init__(self, config: AppConfig): - uri = config.neo4j_uri - username = config.neo4j_username - password = config.neo4j_password - self.driver = GraphDatabase.driver(uri, auth=(username, password)) - - def close(self): - self.driver.close() - - def merge_node(self, label: str, properties: Dict[str, Any]) -> None: - with self.driver.session() as session: - # Construct the MERGE clause with explicit properties - merge_clause = f"MERGE (n:{label} {{" - merge_clause += ", ".join([f"{key}: $props.{key}" for key in properties.keys()]) - merge_clause += "})" - - # Construct the SET clause to update the properties - set_clause = "SET " + ", ".join([f"n.{key} = $props.{key}" for key in properties.keys()]) + def __init__(self, app_config: AppConfig): + uri = app_config.neo4j_uri + username = app_config.neo4j_username + password = app_config.neo4j_password + #self.driver = GraphDatabase.driver(uri, auth=(username, password)) + config.DATABASE_URL = f'bolt://{username}:{password}@{uri.split("://")[-1]}' + db.set_connection(config.DATABASE_URL) - # Combine the MERGE and SET clauses - query = f"{merge_clause} {set_clause}" - session.run(query, props=properties) - - def merge_relationship(self, start_node_label: str, start_node_property: str, - end_node_label: str, end_node_property: str, - relationship_type: str) -> None: - query = f""" - MATCH (a:{start_node_label} {{qualified_name: $start_value}}), (b:{end_node_label} {{qualified_name: $end_value}}) - MERGE (a)-[:{relationship_type}]->(b) - """ - with self.driver.session() as session: - session.run(query, start_value=start_node_property, end_value=end_node_property) + def close(self): + db.close_connection() + + def create_schema(self): + db.install_all_labels() def create_vector_index(self, label: str, property: str, dimension: int = None, similarity_function: str = 'cosine') -> None: - with self.driver.session() as session: - query = f"CREATE VECTOR INDEX {label}_{property}_vector_index FOR (n:{label}) ON (n.{property})" - if dimension is not None: - query += f" OPTIONS {{indexConfig: {{`vector.dimensions`: {dimension}, `vector.similarity_function`: '{similarity_function}'}}}}" - try: - session.run(query) - except Exception as e: - if "equivalent index already exists" in str(e): - print(f"Vector index for {label}.{property} already exists. Skipping creation.") - else: - raise # Re-raise the exception if it's not about existing index + query = f"CREATE VECTOR INDEX {label}_{property}_vector_index FOR (n:{label}) ON (n.{property})" + if dimension is not None: + query += f" OPTIONS {{indexConfig: {{`vector.dimensions`: {dimension}, `vector.similarity_function`: '{similarity_function}'}}}}" + try: + # Using neomodel's db object to execute the query + db.cypher_query(query) + except Exception as e: + if "equivalent index already exists" in str(e): + print(f"Vector index for {label}.{property} already exists. Skipping creation.") + else: + raise # Re-raise the exception if it's not about existing index def create_text_index(self, label: str, property: str) -> None: - with self.driver.session() as session: - index_name = f"{label.lower()}_{property.lower()}_text_index" - query = f"CREATE TEXT INDEX {index_name} FOR (n:{label}) ON (n.{property})" - session.run(query) - + index_name = f"{label.lower()}_{property.lower()}_text_index" + query = f"CREATE TEXT INDEX {index_name} FOR (n:{label}) ON (n.{property})" + # Using neomodel's db object to execute the query + db.cypher_query(query) diff --git a/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_processing.py b/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_processing.py index 1471a2d..19f3a03 100644 --- a/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_processing.py +++ b/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_processing.py @@ -5,141 +5,201 @@ from unoplat_code_confluence.embedding.unoplat_embedding_gen import UnoplatEmbeddingGenerator from unoplat_code_confluence.data_models.dspy.dspy_unoplat_codebase_summary import DspyUnoplatCodebaseSummary from unoplat_code_confluence.configuration.external_config import AppConfig +from unoplat_code_confluence_commons.graph_models.confluence_codebase import ConfluenceCodebase +from unoplat_code_confluence_commons.graph_models.confluence_package import ConfluencePackage +from unoplat_code_confluence_commons.graph_models.confluence_class import ConfluenceClass +from unoplat_code_confluence_commons.graph_models.confluence_method import ConfluenceMethod +from neomodel import config, db +import logging class UnoplatGraphProcessing: - def __init__(self, config: AppConfig): - self.graph_ingestion = UnoplatGraphIngestion(config) - self.embedding_generator = UnoplatEmbeddingGenerator(config) + def __init__(self, app_config: AppConfig): + self.unoplat_graph_ingestion = UnoplatGraphIngestion(app_config) + self.embedding_generator = UnoplatEmbeddingGenerator(app_config) + # Configure neomodel connection + neo4j_url = f'bolt://{app_config.neo4j_username}:{app_config.neo4j_password}@{app_config.neo4j_uri.split("://")[-1]}' + config.DATABASE_URL = neo4j_url + db.set_connection(neo4j_url) + self.unoplat_graph_ingestion.create_schema() def process_codebase_summary(self, codebase_summary: DspyUnoplatCodebaseSummary): - # Generate embeddings for codebase objective and implementation summary - objective_embedding = self.embedding_generator.generate_embeddings_for_single_text(codebase_summary.codebase_objective) - implementation_embedding = self.embedding_generator.generate_embeddings_for_single_text(codebase_summary.codebase_summary) - - # Create a node for the codebase summary - self.graph_ingestion.merge_node( - label="Codebase", - properties={ - "qualified_name": codebase_summary.codebase_name, - "objective": codebase_summary.codebase_objective, - "objective_embedding": objective_embedding, - "implementation_summary": codebase_summary.codebase_summary, - "implementation_embedding": implementation_embedding + try: + # Generate embeddings + objective_embedding = self.embedding_generator.generate_embeddings_for_single_text( + codebase_summary.codebase_objective) + implementation_embedding = self.embedding_generator.generate_embeddings_for_single_text( + codebase_summary.codebase_summary) + + # Create or update codebase node + + codebase_node_dict = [ + { + "qualified_name": codebase_summary.codebase_name, + "codebase_objective": codebase_summary.codebase_objective, + "codebase_objective_embedding": objective_embedding, + "codebase_summary": codebase_summary.codebase_summary, + "codebase_implementation_embedding": implementation_embedding } - ) - - # Initialize stack for DFS - stack = [("Codebase", codebase_summary.codebase_name, package_name, package_summary) - for package_name, package_summary in codebase_summary.codebase_package.items()] - - while stack: - parent_label, parent_name, package_name, package_summary = stack.pop() - self._process_package(parent_label, parent_name, package_name, package_summary, stack) - - # Create index on all nodes - self._create_vector_index_on_all_nodes() - - # Close the connection to the graph database - self.graph_ingestion.close() - - def _process_package(self, parent_label: str, parent_name: str, package_name: str, package_summary: DspyUnoplatPackageSummary, stack): - # Generate embeddings for package objective and implementation summary - package_objective_embedding = self.embedding_generator.generate_embeddings_for_single_text(package_summary.package_objective) - package_implementation_embedding = self.embedding_generator.generate_embeddings_for_single_text(package_summary.package_summary) - - # Create a node for the package - self.graph_ingestion.merge_node( - label="Package", - properties={ - "qualified_name": f"{parent_name}.{package_name}", - "objective": package_summary.package_objective, - "objective_embedding": package_objective_embedding, - "implementation_summary": package_summary.package_summary, - "implementation_embedding": package_implementation_embedding - } - ) - - # Create a relationship between parent and package - self.graph_ingestion.merge_relationship( - start_node_label=parent_label, - start_node_property=parent_name, - end_node_label="Package", - end_node_property=f"{parent_name}.{package_name}", - relationship_type="CONTAINS" - ) - - # Process each class in the package - for class_summary in package_summary.class_summary: - self._process_class(f"{parent_name}.{package_name}", class_summary.node_name, class_summary) - - # Add subpackages to the stack for DFS - for subpackage_name, subpackage_summary in package_summary.sub_package_summaries.items(): - stack.append(("Package", f"{parent_name}.{package_name}", subpackage_name, subpackage_summary)) - - def _process_class(self, package_name: str, class_name: str, class_summary: DspyUnoplatNodeSummary): - # Generate embeddings for class objective and implementation summary - class_objective_embedding = self.embedding_generator.generate_embeddings_for_single_text(class_summary.node_objective) - class_implementation_embedding = self.embedding_generator.generate_embeddings_for_single_text(class_summary.node_summary) - - # Create a node for the class - self.graph_ingestion.merge_node( - label="Class", - properties={ - "qualified_name": f"{package_name}.{class_name}", - "objective": class_summary.node_objective, - "objective_embedding": class_objective_embedding, - "implementation_summary": class_summary.node_summary, - "implementation_embedding": class_implementation_embedding - } - ) - - # Create a relationship between package and class - self.graph_ingestion.merge_relationship( - start_node_label="Package", - start_node_property=package_name, - end_node_label="Class", - end_node_property=f"{package_name}.{class_name}", - relationship_type="CONTAINS" - ) - - # Process each method in the class - for method_summary in class_summary.functions_summary: - self._process_method(f"{package_name}.{class_name}",method_summary.function_name, method_summary) - - def _process_method(self, class_name: str, method_name: str, method_summary: DspyUnoplatFunctionSummary): - # Generate embeddings for method objective and implementation summary - method_objective_embedding = self.embedding_generator.generate_embeddings_for_single_text(method_summary.function_summary.objective) - method_implementation_embedding = self.embedding_generator.generate_embeddings_for_single_text(method_summary.function_summary.implementation_summary) - - # Create a node for the method - self.graph_ingestion.merge_node( - label="Method", - properties={ - "qualified_name": f"{class_name}.{method_name}", - "objective": method_summary.function_summary.objective, - "objective_embedding": method_objective_embedding, - "implementation_summary": method_summary.function_summary.implementation_summary, - "implementation_embedding": method_implementation_embedding - } - ) - - # Create a relationship between class and method - self.graph_ingestion.merge_relationship( - start_node_label="Class", - start_node_property=class_name, - end_node_label="Method", - end_node_property=f"{class_name}.{method_name}", - relationship_type="CONTAINS" - ) + ] + + ConfluenceCodebase.create_or_update(*codebase_node_dict) + #TODO: handle none scenarios + codebase_node: ConfluenceCodebase = ConfluenceCodebase.nodes.get_or_none(qualified_name=codebase_summary.codebase_name) + + # Process packages using DFS + stack = [(codebase_node, package_name, package_summary) + for package_name, package_summary in codebase_summary.codebase_package.items()] + + while stack: + parent_node, package_name, package_summary = stack.pop() + processed_package = self._process_package(parent_node, package_name, package_summary) + + # Add subpackages to stack + for subpackage_name, subpackage_summary in package_summary.sub_package_summaries.items(): + stack.append((processed_package, subpackage_name, subpackage_summary)) + + + + except Exception as e: + logging.error(f"Error processing codebase: {str(e)}") + raise + finally: + db.close_connection() + + def _process_package(self, parent_node, package_name: str, package_summary: DspyUnoplatPackageSummary): + try: + # Generate embeddings + package_objective_embedding = self.embedding_generator.generate_embeddings_for_single_text( + package_summary.package_objective) + package_implementation_embedding = self.embedding_generator.generate_embeddings_for_single_text( + package_summary.package_summary) + + # Create package node + package_node_dict = [{ + "qualified_name": f"{parent_node.qualified_name}.{package_name}", + "package_objective": package_summary.package_objective, + "package_objective_embedding": package_objective_embedding, + "package_implementation_summary": package_summary.package_summary, + "package_implementation_summary_embedding": package_implementation_embedding + }] + + ConfluencePackage.create_or_update(*package_node_dict) + package_node: ConfluencePackage = ConfluencePackage.nodes.get_or_none(qualified_name=f"{parent_node.qualified_name}.{package_name}") + + # Connect to parent + if isinstance(parent_node, ConfluenceCodebase): + + if not parent_node.packages.is_connected(package_node): + parent_node_relation_result = parent_node.packages.connect(package_node) + logging.debug(f"parent_node_relation_result: {parent_node_relation_result}") + + if not package_node.confluence_codebase.is_connected(parent_node): + package_node_relation_result = package_node.confluence_codebase.connect(parent_node) + logging.debug(f"package_node_relation_result: {package_node_relation_result}") + + elif isinstance(parent_node, ConfluencePackage): + + if not parent_node.sub_packages.is_connected(package_node): + package_sub_package_relation_result = parent_node.sub_packages.connect(package_node) + logging.debug(f"package_sub_package_relation_result: {package_sub_package_relation_result}") + + if not package_node.sub_packages.is_connected(parent_node): + sub_package_package_relation_result = package_node.sub_packages.connect(parent_node) + logging.debug(f"sub_package_package_relation_result: {sub_package_package_relation_result}") + # Process classes + for class_summary in package_summary.class_summary: + self._process_class(package_node, class_summary) + + return package_node + + except Exception as e: + logging.error(f"Error processing package {package_name}: {str(e)}") + raise + + def _process_class(self, package_node: ConfluencePackage, class_summary: DspyUnoplatNodeSummary): + try: + # Generate embeddings + class_objective_embedding = self.embedding_generator.generate_embeddings_for_single_text( + class_summary.node_objective) + class_implementation_embedding = self.embedding_generator.generate_embeddings_for_single_text( + class_summary.node_summary) + + # Create class node + class_node_dict = [{ + "qualified_name": f"{package_node.qualified_name}.{class_summary.node_name}", + "class_name": class_summary.node_name, + "class_objective": class_summary.node_objective, + "class_objective_embedding": class_objective_embedding, + "class_implementation_summary": class_summary.node_summary, + "class_implementation_embedding": class_implementation_embedding + }] + + ConfluenceClass.create_or_update(*class_node_dict) + class_node: ConfluenceClass = ConfluenceClass.nodes.get_or_none(qualified_name=f"{package_node.qualified_name}.{class_summary.node_name}") + + # Connect to package + if not package_node.classes.is_connected(class_node): + package_node_class_relation_result = package_node.classes.connect(class_node) + logging.debug(f"package_node_class_relation_result: {package_node_class_relation_result}") + + if not class_node.package.is_connected(package_node): + class_node_package_relation_result = class_node.package.connect(package_node) + logging.debug(f"class_node_package_relation_result: {class_node_package_relation_result}") + + # Process methods + for method_summary in class_summary.functions_summary: + self._process_method(class_node, method_summary) + + except Exception as e: + logging.error(f"Error processing class {class_summary.node_name}: {str(e)}") + raise + + def _process_method(self, class_node: ConfluenceClass, method_summary: DspyUnoplatFunctionSummary): + try: + # Generate embeddings + method_objective_embedding = self.embedding_generator.generate_embeddings_for_single_text( + method_summary.function_summary.objective) + method_implementation_embedding = self.embedding_generator.generate_embeddings_for_single_text( + method_summary.function_summary.implementation_summary) + + # Create method node + method_node_dict = [{ + "qualified_name": f"{class_node.qualified_name}.{method_summary.function_name}", + "function_name": method_summary.function_name, + "function_objective": method_summary.function_summary.objective, + "function_objective_embedding": method_objective_embedding, + "function_implementation_summary": method_summary.function_summary.implementation_summary, + "function_summary_embedding": method_implementation_embedding + }] + + ConfluenceMethod.create_or_update(*method_node_dict) + method_node: ConfluenceMethod = ConfluenceMethod.nodes.get_or_none(qualified_name=f"{class_node.qualified_name}.{method_summary.function_name}") + + # Connect to class + if not class_node.methods.is_connected(method_node): + class_node_method_relation_result = class_node.methods.connect(method_node) + logging.debug(f"class_node_method_relation_result: {class_node_method_relation_result}") + + if not method_node.confluence_class.is_connected(class_node): + method_node_class_relation_result = method_node.confluence_class.connect(class_node) + logging.debug(f"method_node_class_relation_result: {method_node_class_relation_result}") + + except Exception as e: + logging.error(f"Error processing method {method_summary.function_name}: {str(e)}") + raise def _create_vector_index_on_all_nodes(self): - # Create vector indexes for all node types - node_types = ["Codebase","Package","Class","Method"] - embedding_types = ["objective_embedding", "implementation_embedding"] - - for node_type in node_types: - for embedding_type in embedding_types: - self._create_vector_index(node_type, embedding_type) + # Create vector indexes for all node types + node_embedding_properties = { + "ConfluenceCodebase": ["codebase_objective_embedding", "codebase_implementation_embedding"], + "ConfluencePackage": ["package_objective_embedding", "package_implementation_summary_embedding"], + "ConfluenceClass": ["class_objective_embedding", "class_implementation_summary_embedding"], + "ConfluenceMethod": ["function_objective_embedding", "function_summary_embedding"] + } + + for node_type, embedding_properties in node_embedding_properties.items(): + for embedding_property in embedding_properties: + self._create_vector_index(node_type, embedding_property) def _create_vector_index(self, node_label: str, embedding_property: str): - self.graph_ingestion.create_vector_index(node_label, embedding_property) + self.unoplat_graph_ingestion.create_vector_index(node_label, embedding_property) From 218d8633875ddfc27063c7f4117e76a1236068cf Mon Sep 17 00:00:00 2001 From: JayGhiya Date: Tue, 29 Oct 2024 19:06:40 +0530 Subject: [PATCH 2/5] feat: enabled neomodel in ingestion. Enabled dimensions of vector index based on embedding model selected from hugging face --- .../graph/neo4j_helper.py | 12 +++---- .../processing/query_engine_process.py | 15 ++++---- unoplat-code-confluence/pyproject.toml | 1 + .../database/graph/unoplat_graph_ingestion.py | 7 ++-- .../graph/unoplat_graph_processing.py | 36 +++++++++---------- .../downloader/downloader.py | 21 +++++------ .../dspy_codebase_summary.py | 3 +- .../embedding/unoplat_embedding_gen.py | 4 +++ 8 files changed, 52 insertions(+), 47 deletions(-) diff --git a/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/graph/neo4j_helper.py b/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/graph/neo4j_helper.py index 0cab8ef..91331c6 100644 --- a/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/graph/neo4j_helper.py +++ b/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/graph/neo4j_helper.py @@ -41,7 +41,7 @@ def search_similar_nodes(self, vector_index, query_embedding, top_k=5): def get_existing_codebases(self): query = """ - MATCH (cb:Codebase) + MATCH (cb:ConfluenceCodebase) RETURN cb.qualified_name AS codebase_name """ result = self.run_query(query) @@ -52,7 +52,7 @@ def get_existing_codebases(self): def get_package_details(self,package_name): query = """ - MATCH (p:Package {qualified_name: $package_name}) + MATCH (p:ConfluencePackage {qualified_name: $package_name}) RETURN p.qualified_name AS package_name, p.objective AS package_objective, @@ -62,7 +62,7 @@ def get_package_details(self,package_name): def get_class_details(self,class_name): query = """ - MATCH (c:Class {qualified_name: $class_name}) + MATCH (c:ConfluenceClass {qualified_name: $class_name}) RETURN c.qualified_name AS class_name, c.objective AS class_objective, @@ -72,7 +72,7 @@ def get_class_details(self,class_name): def get_codebase_details(self,codebase_name): query = """ - MATCH (cb:Codebase {qualified_name: $codebase_name}) + MATCH (cb:ConfluenceCodebase {qualified_name: $codebase_name}) RETURN cb.qualified_name AS codebase_name, cb.objective AS codebase_objective, @@ -82,8 +82,8 @@ def get_codebase_details(self,codebase_name): def get_function_hierarchy_and_details(self, function_name): query = """ - MATCH (f:Method {qualified_name: $function_name}) - OPTIONAL MATCH (f)<-[:CONTAINS]-(c:Class)<-[:CONTAINS]-(p:Package)<-[:CONTAINS]-(cb:Codebase) + MATCH (f:ConfluenceMethod {qualified_name: $function_name}) + OPTIONAL MATCH (f)<-[:CONTAINS]-(c:ConfluenceClass)<-[:CONTAINS]-(p:ConfluencePackage)<-[:CONTAINS]-(cb:ConfluenceCodebase) RETURN cb.qualified_name AS codebase_name, cb.objective AS codebase_objective, diff --git a/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/processing/query_engine_process.py b/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/processing/query_engine_process.py index c6353c9..435b226 100644 --- a/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/processing/query_engine_process.py +++ b/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/processing/query_engine_process.py @@ -48,8 +48,9 @@ async def process_query(self, user_query: str) -> str: if int(ConfluenceUserIntent.FUNCTIONAL_IMPLEMENTATION.value) in user_intent_list: # Search similar functions - results = self.graph_helper.search_similar_nodes(vector_index="Method_implementation_embedding_vector_index", query_embedding=user_query_embedding, top_k=5) + results = self.graph_helper.search_similar_nodes(vector_index="function_implementation_summary_embedding_vector_index", query_embedding=user_query_embedding, top_k=5) context = {result["name"]: result["summary"] for result in results} + log.debug(f"context for function: {context}") if len(context) > 1: rerank_results = self.rerank_module(user_query=user_query, possible_answers=context).answer.relevant_answers @@ -69,9 +70,9 @@ async def process_query(self, user_query: str) -> str: elif int(ConfluenceUserIntent.CODE_SUMMARIZATION.value) in user_intent_list: - results = self.graph_helper.search_similar_nodes(vector_index="Codebase_implementation_embedding_vector_index", query_embedding=user_query_embedding,top_k=5) + results = self.graph_helper.search_similar_nodes(vector_index="codebase_implementation_summary_embedding_vector_index", query_embedding=user_query_embedding,top_k=5) context = {result["name"]: result["summary"] for result in results} - + log.debug(f"context for codebase: {context}") if len(context) > 1: rerank_results = self.rerank_module(user_query=user_query, possible_answers=context).answer.relevant_answers filtered_rerank_results = {k: v for k, v in rerank_results.items() if v > 7} @@ -90,9 +91,9 @@ async def process_query(self, user_query: str) -> str: final_response = final_response + self.user_query_response_module(user_query=user_query, code_metadata=context).answer elif int(ConfluenceUserIntent.PACKAGE_OVERVIEW.value) in user_intent_list: - results = self.graph_helper.search_similar_nodes(vector_index="Package_implementation_embedding_vector_index", query_embedding=user_query_embedding,top_k=5) + results = self.graph_helper.search_similar_nodes(vector_index="package_implementation_summary_embedding_vector_index", query_embedding=user_query_embedding,top_k=5) context = {result["name"]: result["summary"] for result in results} - + log.debug(f"context for package: {context}") if len(context) > 1: rerank_results = self.rerank_module(user_query=user_query, possible_answers=context).answer.relevant_answers filtered_rerank_results = {k: v for k, v in rerank_results.items() if v > 7} @@ -111,9 +112,9 @@ async def process_query(self, user_query: str) -> str: final_response = final_response + self.user_query_response_module(user_query=user_query, code_metadata=context).answer elif int(ConfluenceUserIntent.CLASS_DETAILS.value) in user_intent_list: - results = self.graph_helper.search_similar_nodes(vector_index="Class_implementation_embedding_vector_index", query_embedding=user_query_embedding, top_k=5) + results = self.graph_helper.search_similar_nodes(vector_index="class_implementation_summary_embedding_vector_index", query_embedding=user_query_embedding, top_k=5) context = {result["name"]: result["summary"] for result in results} - + log.debug(f"context for class: {context}") if len(context) > 1: rerank_results = self.rerank_module(user_query=user_query, possible_answers=context).answer.relevant_answers filtered_rerank_results = {k: v for k, v in rerank_results.items() if v > 7} diff --git a/unoplat-code-confluence/pyproject.toml b/unoplat-code-confluence/pyproject.toml index 1e62704..c1f4f4d 100644 --- a/unoplat-code-confluence/pyproject.toml +++ b/unoplat-code-confluence/pyproject.toml @@ -24,6 +24,7 @@ einops = "^0.8.0" rich = "^13.9.2" neo4j = "5.19.0" neomodel = "^5.3.3" +unoplat-code-confluence-commons = {git = "https://github.com/unoplat/unoplat-code-confluence.git", rev = "main", subdirectory = "unoplat-code-confluence-commons"} [tool.poetry.scripts] unoplat-code-confluence = "unoplat_code_confluence.__main__:main" diff --git a/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_ingestion.py b/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_ingestion.py index a5fd423..ed12edd 100644 --- a/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_ingestion.py +++ b/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_ingestion.py @@ -1,8 +1,8 @@ from neo4j import GraphDatabase from typing import Dict, Any - from unoplat_code_confluence.configuration.external_config import AppConfig from neomodel import config,db + class UnoplatGraphIngestion: def __init__(self, app_config: AppConfig): uri = app_config.neo4j_uri @@ -12,7 +12,6 @@ def __init__(self, app_config: AppConfig): config.DATABASE_URL = f'bolt://{username}:{password}@{uri.split("://")[-1]}' db.set_connection(config.DATABASE_URL) - def close(self): db.close_connection() @@ -20,7 +19,7 @@ def create_schema(self): db.install_all_labels() def create_vector_index(self, label: str, property: str, dimension: int = None, similarity_function: str = 'cosine') -> None: - query = f"CREATE VECTOR INDEX {label}_{property}_vector_index FOR (n:{label}) ON (n.{property})" + query = f"CREATE VECTOR INDEX {property}_vector_index FOR (n:{label}) ON (n.{property})" if dimension is not None: query += f" OPTIONS {{indexConfig: {{`vector.dimensions`: {dimension}, `vector.similarity_function`: '{similarity_function}'}}}}" try: @@ -33,7 +32,7 @@ def create_vector_index(self, label: str, property: str, dimension: int = None, raise # Re-raise the exception if it's not about existing index def create_text_index(self, label: str, property: str) -> None: - index_name = f"{label.lower()}_{property.lower()}_text_index" + index_name = f"{property.lower()}_text_index" query = f"CREATE TEXT INDEX {index_name} FOR (n:{label}) ON (n.{property})" # Using neomodel's db object to execute the query db.cypher_query(query) diff --git a/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_processing.py b/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_processing.py index 19f3a03..4f7d5de 100644 --- a/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_processing.py +++ b/unoplat-code-confluence/unoplat_code_confluence/database/graph/unoplat_graph_processing.py @@ -35,10 +35,10 @@ def process_codebase_summary(self, codebase_summary: DspyUnoplatCodebaseSummary) codebase_node_dict = [ { "qualified_name": codebase_summary.codebase_name, - "codebase_objective": codebase_summary.codebase_objective, + "objective": codebase_summary.codebase_objective, "codebase_objective_embedding": objective_embedding, - "codebase_summary": codebase_summary.codebase_summary, - "codebase_implementation_embedding": implementation_embedding + "implementation_summary": codebase_summary.codebase_summary, + "codebase_implementation_summary_embedding": implementation_embedding } ] @@ -58,7 +58,7 @@ def process_codebase_summary(self, codebase_summary: DspyUnoplatCodebaseSummary) for subpackage_name, subpackage_summary in package_summary.sub_package_summaries.items(): stack.append((processed_package, subpackage_name, subpackage_summary)) - + self._create_vector_index_on_all_nodes(self.embedding_generator.get_dimensions()) except Exception as e: logging.error(f"Error processing codebase: {str(e)}") @@ -77,9 +77,9 @@ def _process_package(self, parent_node, package_name: str, package_summary: Dspy # Create package node package_node_dict = [{ "qualified_name": f"{parent_node.qualified_name}.{package_name}", - "package_objective": package_summary.package_objective, + "objective": package_summary.package_objective, "package_objective_embedding": package_objective_embedding, - "package_implementation_summary": package_summary.package_summary, + "implementation_summary": package_summary.package_summary, "package_implementation_summary_embedding": package_implementation_embedding }] @@ -128,10 +128,10 @@ def _process_class(self, package_node: ConfluencePackage, class_summary: DspyUno class_node_dict = [{ "qualified_name": f"{package_node.qualified_name}.{class_summary.node_name}", "class_name": class_summary.node_name, - "class_objective": class_summary.node_objective, + "objective": class_summary.node_objective, "class_objective_embedding": class_objective_embedding, - "class_implementation_summary": class_summary.node_summary, - "class_implementation_embedding": class_implementation_embedding + "implementation_summary": class_summary.node_summary, + "class_implementation_summary_embedding": class_implementation_embedding }] ConfluenceClass.create_or_update(*class_node_dict) @@ -166,10 +166,10 @@ def _process_method(self, class_node: ConfluenceClass, method_summary: DspyUnopl method_node_dict = [{ "qualified_name": f"{class_node.qualified_name}.{method_summary.function_name}", "function_name": method_summary.function_name, - "function_objective": method_summary.function_summary.objective, + "objective": method_summary.function_summary.objective, "function_objective_embedding": method_objective_embedding, - "function_implementation_summary": method_summary.function_summary.implementation_summary, - "function_summary_embedding": method_implementation_embedding + "implementation_summary": method_summary.function_summary.implementation_summary, + "function_implementation_summary_embedding": method_implementation_embedding }] ConfluenceMethod.create_or_update(*method_node_dict) @@ -188,18 +188,18 @@ def _process_method(self, class_node: ConfluenceClass, method_summary: DspyUnopl logging.error(f"Error processing method {method_summary.function_name}: {str(e)}") raise - def _create_vector_index_on_all_nodes(self): + def _create_vector_index_on_all_nodes(self, dimensions: int): # Create vector indexes for all node types node_embedding_properties = { - "ConfluenceCodebase": ["codebase_objective_embedding", "codebase_implementation_embedding"], + "ConfluenceCodebase": ["codebase_objective_embedding", "codebase_implementation_summary_embedding"], "ConfluencePackage": ["package_objective_embedding", "package_implementation_summary_embedding"], "ConfluenceClass": ["class_objective_embedding", "class_implementation_summary_embedding"], - "ConfluenceMethod": ["function_objective_embedding", "function_summary_embedding"] + "ConfluenceMethod": ["function_objective_embedding", "function_implementation_summary_embedding"] } for node_type, embedding_properties in node_embedding_properties.items(): for embedding_property in embedding_properties: - self._create_vector_index(node_type, embedding_property) + self._create_vector_index(node_label=node_type, embedding_property=embedding_property, dimensions=dimensions) - def _create_vector_index(self, node_label: str, embedding_property: str): - self.unoplat_graph_ingestion.create_vector_index(node_label, embedding_property) + def _create_vector_index(self, node_label: str, embedding_property: str, dimensions: int): + self.unoplat_graph_ingestion.create_vector_index(label=node_label, dimension=dimensions,property=embedding_property) diff --git a/unoplat-code-confluence/unoplat_code_confluence/downloader/downloader.py b/unoplat-code-confluence/unoplat_code_confluence/downloader/downloader.py index 119fdc6..e3b406c 100644 --- a/unoplat-code-confluence/unoplat_code_confluence/downloader/downloader.py +++ b/unoplat-code-confluence/unoplat_code_confluence/downloader/downloader.py @@ -8,13 +8,13 @@ class Downloader: @staticmethod - def get_latest_release_info(repo_name, github_token=None): - logger.info(f"Fetching latest release info for repository: {repo_name}") + def get_specific_release_info(repo_name, release_tag, github_token=None): + logger.info(f"Fetching release info for repository: {repo_name} with tag: {release_tag}") g = Github(github_token) if github_token else Github() # Optionally use a token for higher rate limits: Github("your_github_access_token") repo = g.get_repo(repo_name) - latest_release = repo.get_latest_release() - logger.info(f"Latest release tag: {latest_release.tag_name}") - return latest_release.tag_name, latest_release.get_assets() + specific_release = repo.get_release(release_tag) + logger.info(f"Specific release tag: {specific_release.tag_name}") + return specific_release.tag_name, specific_release.get_assets() @staticmethod def download_file(url, download_dir, filename): @@ -39,7 +39,8 @@ def download_file(url, download_dir, filename): @staticmethod def download_latest_jar(repo_name, download_dir, github_token=None): logger.info(f"Downloading latest JAR for repository: {repo_name}") - tag_name, assets = Downloader.get_latest_release_info(repo_name, github_token) + #todo: make this dynamic but there are breaking upstream changes as of now so hardcoding for now. + tag_name, assets = Downloader.get_specific_release_info(repo_name, 'v2.1.5',github_token) jar_pattern = re.compile(r"scanner_cli-(.*)-all\.jar") # Regex to match the jar file jar_asset = next((asset for asset in assets if jar_pattern.match(asset.name)), None) if not jar_asset: @@ -51,10 +52,10 @@ def download_latest_jar(repo_name, download_dir, github_token=None): existing_jars = [f for f in os.listdir(download_dir) if jar_pattern.match(f)] if existing_jars: - highest_version_local = max(existing_jars, key=lambda x: version.parse(jar_pattern.match(x).group(1))) - if version.parse(jar_pattern.match(highest_version_local).group(1)) >= version.parse(jar_pattern.match(highest_version_asset_name).group(1)): - logger.info(f"Using existing JAR: {highest_version_local}") - return os.path.join(download_dir, highest_version_local) + matching_jar = next((jar for jar in existing_jars if tag_name[1:] in jar), None) + if matching_jar: + logger.info(f"Using existing JAR for version {tag_name}: {matching_jar}") + return os.path.join(download_dir, matching_jar) # If no local JAR is higher version, download the latest logger.info(f"JAR found: {highest_version_asset_name}, starting download...") diff --git a/unoplat-code-confluence/unoplat_code_confluence/dspy_codebase_summary.py b/unoplat-code-confluence/unoplat_code_confluence/dspy_codebase_summary.py index 081bda3..c303277 100644 --- a/unoplat-code-confluence/unoplat_code_confluence/dspy_codebase_summary.py +++ b/unoplat-code-confluence/unoplat_code_confluence/dspy_codebase_summary.py @@ -3,7 +3,6 @@ from unoplat_code_confluence.data_models.dspy.dspy_unoplat_package_summary import DspyUnoplatPackageSummary - #TODO: optimise using gpt4 judge and miprov2 class CodeConfluenceCodebaseSignature(dspy.Signature): """This signature takes in existing summary of a codebase and package objective of a package one at a time and refines codebase_existing_summary with factual and grounded insights based on package_objective and returns final_codebase_summary as enhanced final summary of codebase.""" @@ -34,7 +33,7 @@ def forward(self, package_objective_dict: Dict[str, DspyUnoplatPackageSummary]): codebase_objective_hint="Capture all important highlights from summary and then generate the codebase objective in structured manner for the codebase by being concise but cautious to not miss any important details." codebase_objective_signature: CodeConfluenceCodebaseObjectiveSignature = self.generate_codebase_objective(final_codebase_summary=codebase_summary,hint=codebase_objective_hint) - return dspy.Prediction(answer=codebase_objective_signature.codebase_objective,summary=signature_package_summary.final_codebase_summary) + return dspy.Prediction(answer=codebase_objective_signature.codebase_objective,summary=codebase_summary) diff --git a/unoplat-code-confluence/unoplat_code_confluence/embedding/unoplat_embedding_gen.py b/unoplat-code-confluence/unoplat_code_confluence/embedding/unoplat_embedding_gen.py index 8b0b5f7..d971152 100644 --- a/unoplat-code-confluence/unoplat_code_confluence/embedding/unoplat_embedding_gen.py +++ b/unoplat-code-confluence/unoplat_code_confluence/embedding/unoplat_embedding_gen.py @@ -5,6 +5,7 @@ class UnoplatEmbeddingGenerator: def __init__(self, config: AppConfig): self.model = SentenceTransformer(config.sentence_transformer_model, trust_remote_code=True) + self.dimensions = self.model.get_sentence_embedding_dimension() def generate_embeddings(self, texts: List[str]) -> List[List[float]]: task = 'retrieval.query' @@ -13,4 +14,7 @@ def generate_embeddings(self, texts: List[str]) -> List[List[float]]: def generate_embeddings_for_single_text(self, text: str) -> List[float]: task = 'retrieval.query' return self.model.encode(text, task=task).tolist() + + def get_dimensions(self) -> int: + return self.dimensions From 7e1fa10ae345450c975f72c51fc67b3ac6cb8933 Mon Sep 17 00:00:00 2001 From: JayGhiya Date: Tue, 29 Oct 2024 19:08:43 +0530 Subject: [PATCH 3/5] refactor: removed poetry lock files --- .../poetry.lock | 3795 ------------- unoplat-code-confluence/poetry.lock | 4777 ----------------- 2 files changed, 8572 deletions(-) delete mode 100644 unoplat-code-confluence-query-engine/poetry.lock delete mode 100644 unoplat-code-confluence/poetry.lock diff --git a/unoplat-code-confluence-query-engine/poetry.lock b/unoplat-code-confluence-query-engine/poetry.lock deleted file mode 100644 index 9e1e1ec..0000000 --- a/unoplat-code-confluence-query-engine/poetry.lock +++ /dev/null @@ -1,3795 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - 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4648a89ab37543afaf989afa6350e1e537743984 Mon Sep 17 00:00:00 2001 From: JayGhiya Date: Tue, 29 Oct 2024 19:24:35 +0530 Subject: [PATCH 4/5] refactor: fix script to handle both relationships contains and belongs to --- scripts/export_data_graph.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/export_data_graph.py b/scripts/export_data_graph.py index c905915..c1bd9a7 100644 --- a/scripts/export_data_graph.py +++ b/scripts/export_data_graph.py @@ -19,7 +19,7 @@ def export_graph_to_json(): # Query for all relationships and their properties relationships_query = """ - MATCH (start)-[r:CONTAINS]->(end) + MATCH (start)-[r]->(end) RETURN id(r) as id, type(r) as type, properties(r) as properties, elementId(start) as startNode, elementId(end) as endNode """ From 5e69eb1fe5cf7eaeb2c800b3c9a9a2a67c8d0332 Mon Sep 17 00:00:00 2001 From: JayGhiya Date: Tue, 29 Oct 2024 19:51:56 +0530 Subject: [PATCH 5/5] fix: fix models according to new schema --- .../embedding/unoplat_embedding_generator.py | 10 ++++++++-- .../graph/neo4j_helper.py | 6 +++--- .../processing/query_engine_process.py | 20 +++++++++++-------- 3 files changed, 23 insertions(+), 13 deletions(-) diff --git a/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/embedding/unoplat_embedding_generator.py b/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/embedding/unoplat_embedding_generator.py index 1b006a2..a1b1125 100644 --- a/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/embedding/unoplat_embedding_generator.py +++ b/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/embedding/unoplat_embedding_generator.py @@ -1,14 +1,20 @@ from sentence_transformers import SentenceTransformer from typing import List +#TODO: this code is duplicated across ingestion and query engine. We will not refactor + # as we move the embedding part to infrastructure such as vespa/marqo class UnoplatEmbeddingGenerator: def __init__(self, model_name: str): self.model = SentenceTransformer(model_name, trust_remote_code=True) - + self.dimensions = self.model.get_sentence_embedding_dimension() + def generate_embeddings(self, texts: List[str]) -> List[List[float]]: task = 'retrieval.query' return self.model.encode(texts, task=task).tolist() def generate_embeddings_for_single_text(self, text: str) -> List[float]: task = 'retrieval.query' - return self.model.encode(text, task=task, convert_to_tensor=False) \ No newline at end of file + return self.model.encode(text, task=task, convert_to_tensor=False) + + def get_dimensions(self) -> int: + return self.dimensions diff --git a/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/graph/neo4j_helper.py b/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/graph/neo4j_helper.py index 91331c6..df6d6a9 100644 --- a/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/graph/neo4j_helper.py +++ b/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/graph/neo4j_helper.py @@ -143,19 +143,19 @@ def _create_relationships(self, tx: Transaction, relationships): def create_vector_index(self, label: str, property: str, dimension: int = None, similarity_function: str = 'cosine') -> None: with self.driver.session() as session: - query = f"CREATE VECTOR INDEX {label}_{property}_vector_index FOR (n:{label}) ON (n.{property})" + query = f"CREATE VECTOR INDEX {property}_vector_index FOR (n:{label}) ON (n.{property})" if dimension is not None: query += f" OPTIONS {{indexConfig: {{`vector.dimensions`: {dimension}, `vector.similarity_function`: '{similarity_function}'}}}}" try: session.run(query) except Exception as e: if "equivalent index already exists" in str(e): - print(f"Vector index for {label}.{property} already exists. Skipping creation.") + print(f"Vector index for {property} already exists. Skipping creation.") else: raise # Re-raise the exception if it's not about existing index def create_text_index(self, label: str, property: str) -> None: with self.driver.session() as session: - index_name = f"{label.lower()}_{property.lower()}_text_index" + index_name = f"{property.lower()}_text_index" query = f"CREATE TEXT INDEX {index_name} FOR (n:{label}) ON (n.{property})" session.run(query) \ No newline at end of file diff --git a/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/processing/query_engine_process.py b/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/processing/query_engine_process.py index 435b226..645e7f5 100644 --- a/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/processing/query_engine_process.py +++ b/unoplat-code-confluence-query-engine/unoplat_code_confluence_query_engine/processing/query_engine_process.py @@ -148,15 +148,19 @@ async def load_codebase_graph(self, file_path: str) -> None: async def _create_vector_index_on_all_nodes(self): # Create vector indexes for all node types - node_types = ["Codebase","Package","Class","Method"] - embedding_types = ["objective_embedding", "implementation_embedding"] - - for node_type in node_types: - for embedding_type in embedding_types: - await self._create_vector_index(node_type, embedding_type) + node_embedding_properties = { + "ConfluenceCodebase": ["codebase_objective_embedding", "codebase_implementation_summary_embedding"], + "ConfluencePackage": ["package_objective_embedding", "package_implementation_summary_embedding"], + "ConfluenceClass": ["class_objective_embedding", "class_implementation_summary_embedding"], + "ConfluenceMethod": ["function_objective_embedding", "function_implementation_summary_embedding"] + } + + for node_type, embedding_properties in node_embedding_properties.items(): + for embedding_property in embedding_properties: + await self._create_vector_index(node_label=node_type, embedding_property=embedding_property, dimensions=self.embedding_generator.get_dimensions()) - async def _create_vector_index(self, node_label: str, embedding_property: str): - self.graph_helper.create_vector_index(node_label, embedding_property) + async def _create_vector_index(self, node_label: str, embedding_property: str, dimensions: int): + self.graph_helper.create_vector_index(node_label, embedding_property, dimensions) async def load_existing_codebases(self): return self.graph_helper.get_existing_codebases()