From e1076a3fbf8a92fba05d5dd899d5dc93657fcb61 Mon Sep 17 00:00:00 2001 From: YoanSallami Date: Fri, 22 Nov 2024 12:08:00 +0100 Subject: [PATCH] Clean up falkorDB memory systems and fix test workflow --- .github/workflows/python-package.yaml | 2 +- .../Agents/Graph interpreter Agent.md | 2 +- .../falkordb/falkordb_document_memory.py | 27 +- .../falkordb/falkordb_fact_memory.py | 131 +- .../integration/falkordb/falkordb_memory.py | 2 +- .../falkordb/falkordb_program_memory.py | 18 +- .../falkordb/falkordb_trace_memory.py | 15 +- notebooks/using_falkordb.ipynb | 13 + poetry.lock | 3936 +++++++++-------- ...st_falkordb_document_memory_integration.py | 15 + .../test_falkordb_document_retriever.py | 60 + 11 files changed, 2259 insertions(+), 1962 deletions(-) create mode 100644 tests/modules/retrievers/integration/falkordb/test_falkordb_document_retriever.py diff --git a/.github/workflows/python-package.yaml b/.github/workflows/python-package.yaml index bd17aa6..1ee5802 100755 --- a/.github/workflows/python-package.yaml +++ b/.github/workflows/python-package.yaml @@ -27,7 +27,7 @@ jobs: - name: Display Python version run: python -c "import sys; print(sys.version)" - name: Start FalkorDB - run: docker run -d -p 6379:6379 falkordb/falkordb:edge & + run: docker run -d -p 6379:6379 falkordb/falkordb:v4.2.2 & - name: Install redis tools run: sudo apt-get install -y redis-tools - name: Verify that FalkorDB is up diff --git a/docs/Modules API/Agents/Graph interpreter Agent.md b/docs/Modules API/Agents/Graph interpreter Agent.md index adb3d1b..18886f7 100644 --- a/docs/Modules API/Agents/Graph interpreter Agent.md +++ b/docs/Modules API/Agents/Graph interpreter Agent.md @@ -1,6 +1,6 @@ # Graph Interpreter Agent -The `GraphInterpreterAgent` is the agent system that execute the Cypher software stored in memory, it can branch over the graph programs by asking itself question when encountering decision steps, and use tools when encountering an Action step and jump to other programs when encountering Program steps. +The `GraphInterpreterAgent` is the agent system that execute the Cypher software stored in memory, it can branch over the graph programs by asking itself questions when encountering Decision steps, and use tools when encountering an Action step and jump to other programs when encountering Program steps. ## Usage diff --git a/hybridagi/memory/integration/falkordb/falkordb_document_memory.py b/hybridagi/memory/integration/falkordb/falkordb_document_memory.py index c96237b..87ccbaa 100644 --- a/hybridagi/memory/integration/falkordb/falkordb_document_memory.py +++ b/hybridagi/memory/integration/falkordb/falkordb_document_memory.py @@ -161,30 +161,33 @@ def get(self, id_or_ids: Union[UUID, str, List[Union[UUID, str]]]) -> DocumentLi doc_id = str(doc_id) if self.exist(doc_id): query_result = self._graph.query( - "MATCH (d:Document {id: $id}) RETURN d", - params={"id": doc_id} + " ".join( + [ + "MATCH (d:Document {id: $id})", + "RETURN d.text as text,", + "d.parent_id as parent_id,", + "d.metadata as metadata,", + "d.vector as vector", + ] + ), + params={"id": doc_id}, ) - text = query_result.result_set[0][0].properties["text"] - metadata = query_result.result_set[0][0].properties["metadata"] - if "parent_id" in query_result.result_set[0][0].properties: - parent_id = query_result.result_set[0][0].properties["parent_id"] - else: - parent_id = None + text = query_result.result_set[0][0] + parent_id = query_result.result_set[0][1] + metadata = query_result.result_set[0][2] if parent_id: try: parent_id = UUID(parent_id) except Exception: pass - else: - parent_id = None try: doc_id = UUID(doc_id) except Exception: pass doc = Document(id=doc_id, parent_id=parent_id, text=text) doc.metadata = json.loads(metadata) - if "vector" in query_result.result_set[0][0].properties: - doc.vector = query_result.result_set[0][0].properties["vector"] + if query_result.result_set[0][3]: + doc.vector = query_result.result_set[0][3] result.docs.append(doc) return result diff --git a/hybridagi/memory/integration/falkordb/falkordb_fact_memory.py b/hybridagi/memory/integration/falkordb/falkordb_fact_memory.py index faad8dd..97b42ff 100644 --- a/hybridagi/memory/integration/falkordb/falkordb_fact_memory.py +++ b/hybridagi/memory/integration/falkordb/falkordb_fact_memory.py @@ -54,7 +54,7 @@ def exist(self, entity_or_fact_id: Union[UUID, str]) -> bool: return self.exist_fact(entity_or_fact_id) def exist_fact(self, index: Union[UUID, str]) -> bool: - query = "MATCH ()-[r:FACT {id: $index}]->() RETURN r" + query = "MATCH ()-[r:FACT {_id_: $index}]->() RETURN r._id_ as id" result = self._graph.query(query, params={"index": str(index)}) return len(result.result_set) > 0 @@ -174,30 +174,32 @@ def get_entities(self, id_or_ids: Union[UUID, str, List[Union[UUID, str]]]) -> E result = EntityList() ids = [str(i) for i in id_or_ids] for entity_id in entities_ids: - query_result = self._graph.query( - " ".join([ - "MATCH (e:Entity {id: $id})", - "RETURN e"]), - params={"id": entity_id} - ) - if len(query_result.result_set) > 0: - entity_data = query_result.result_set[0][0] + if self.exist(entity_id): + query_result = self._graph.query( + " ".join( + [ + "MATCH (e:Entity {id: $id})", + "RETURN", + "e.name as name,", + "e.label as label,", + "e.description as description,", + "e.vector as vector", + ] + ), + params={"id": entity_id} + ) try: - entity_id = UUID(entity_data.properties["id"]) + entity_id = UUID(entity_id) except Exception: - entity_id = entity_data.properties["id"] - if "description" in entity_data.properties: - description = entity_data.properties["description"] - else: - description = None - if "vector" in entity_data.properties: - vector = entity_data.properties["vector"] - else: - vector = None + pass + name = query_result.result_set[0][0] + label = query_result.result_set[0][1] + description = query_result.result_set[0][2] + vector = query_result.result_set[0][3] entity = Entity( id=entity_id, - name=entity_data.properties["name"], - label=entity_data.properties["label"], + name=name, + label=label, description=description, vector=vector, ) @@ -221,70 +223,41 @@ def get_facts(self, id_or_ids: Union[UUID, str, List[Union[UUID, str]]]) -> Fact result = FactList() for fact_id in facts_ids: fact_id = str(fact_id) - query_result = self._graph.query( - " ".join([ - "MATCH (s:Entity)-[r:FACT {_id_:$id}]->(o:Entity)", - "RETURN r, s, o"]), - params={"id": fact_id}, - ) - if len(query_result.result_set) > 0: - fact_data, subject_data, object_data = query_result.result_set[0] - try: - subject_id = UUID(subject_data.properties["id"]) - except Exception: - subject_id = subject_data.properties["id"] - if "description" in subject_data.properties: - description = subject_data.properties["description"] - else: - description = None - if "vector" in subject_data.properties: - vector = subject_data.properties["vector"] - else: - vector = None - subj = Entity( - id=subject_id, - name=subject_data.properties["name"], - label=subject_data.properties["label"], - description=description, - vector=vector, - metadata=json.loads(subject_data.properties["metadata"]), + if self.exist_fact(fact_id): + query_result = self._graph.query( + " ".join( + [ + "MATCH (s:Entity)-[r:FACT {_id_:$id}]->(o:Entity)", + "RETURN", + "r.relationship as relationship_name,", + "r.metadata as metadata,", + "r.vector as relation_vector,", + "s.id as subject_id,", + "o.id as object_id", + ] + ), + params={"id": fact_id}, ) - rel = Relationship(name=fact_data.properties["relationship"]) + relationship_name = query_result.result_set[0][0] + relationship_metadata = query_result.result_set[0][1] + relationship_vector = query_result.result_set[0][2] + subject_id = query_result.result_set[0][3] + object_id = query_result.result_set[0][4] + entities = self.get_entities([subject_id, object_id]) + subject_entity = entities.entities[0] + object_entity = entities.entities[1] + rel = Relationship(name=relationship_name) try: - object_id = UUID(object_data.properties["id"]) + fact_id = UUID(fact_id) except Exception: - object_id = object_data.properties["id"] - if "description" in object_data.properties: - description = object_data.properties["description"] - else: - description = None - if "vector" in object_data.properties: - vector = object_data.properties["vector"] - else: - vector = None - obj = Entity( - id=object_id, - name=object_data.properties["name"], - label=object_data.properties["label"], - description=description, - vector=vector, - metadata=json.loads(object_data.properties["metadata"]), - ) - try: - fact_id = UUID(fact_data.properties["_id_"]) - except Exception: - fact_id = fact_data.properties["_id_"] - if "vector" in fact_data.properties: - vector = fact_data.properties["vector"] - else: - vector = None + pass fact = Fact( id=fact_id, - subj=subj, + subj=subject_entity, rel=rel, - obj=obj, - vector=vector, - metadata=json.loads(fact_data.properties["metadata"]), + obj=object_entity, + vector=relationship_vector, + metadata=json.loads(relationship_metadata), ) result.facts.append(fact) return result \ No newline at end of file diff --git a/hybridagi/memory/integration/falkordb/falkordb_memory.py b/hybridagi/memory/integration/falkordb/falkordb_memory.py index ac39469..b62936f 100644 --- a/hybridagi/memory/integration/falkordb/falkordb_memory.py +++ b/hybridagi/memory/integration/falkordb/falkordb_memory.py @@ -56,7 +56,7 @@ def __init__( self.clear() def exist(self, index: Union[UUID, str], label:str) -> bool: - query = "MATCH (n:"+label+" {id: $index}) RETURN n" + query = "MATCH (n:"+label+" {id: $index}) RETURN n.id as id" result = self._graph.query(query, params={"index": str(index)}) return len(result.result_set) > 0 diff --git a/hybridagi/memory/integration/falkordb/falkordb_program_memory.py b/hybridagi/memory/integration/falkordb/falkordb_program_memory.py index 62585b2..0d2d649 100644 --- a/hybridagi/memory/integration/falkordb/falkordb_program_memory.py +++ b/hybridagi/memory/integration/falkordb/falkordb_program_memory.py @@ -134,16 +134,23 @@ def get(self, id_or_ids: Union[UUID, str, List[Union[UUID, str]]]) -> GraphProgr prog_id = str(prog_id) if self.exist(prog_id): query_result = self._graph.query( - "MATCH (p:Program {id: $id}) RETURN p", + " ".join( + [ + "MATCH (p:Program {id: $id})", + "RETURN", + "p.program as program,", + "p.metadata as metadata,", + "p.vector as vector", + ] + ), params={"id": prog_id} ) - cypher_program = query_result.result_set[0][0].properties["program"] - metadata = query_result.result_set[0][0].properties["metadata"] + cypher_program = query_result.result_set[0][0] + metadata = query_result.result_set[0][1] prog = GraphProgram(name=prog_id) prog.from_cypher(cypher_program) prog.metadata = json.loads(metadata) - if "vector" in query_result.result_set[0][0].properties: - prog.vector = query_result.result_set[0][0].properties["vector"] + prog.vector = query_result.result_set[0][2] result.progs.append(prog) return result @@ -179,7 +186,6 @@ def depends_on(self, source_id: Union[UUID, str], target_id: Union[UUID, str]) - "MATCH (n:Program {id:$source})-[r:DEPENDS_ON*]->(m:Program {id:$target}) RETURN r", params = params, ) - print(result.result_set) if len(result.result_set) > 0: return True return False diff --git a/hybridagi/memory/integration/falkordb/falkordb_trace_memory.py b/hybridagi/memory/integration/falkordb/falkordb_trace_memory.py index 4ddd53c..efcd7ae 100644 --- a/hybridagi/memory/integration/falkordb/falkordb_trace_memory.py +++ b/hybridagi/memory/integration/falkordb/falkordb_trace_memory.py @@ -128,8 +128,19 @@ def get(self, id_or_ids: Union[UUID, str, List[Union[UUID, str]]]) -> AgentStepL """ ids = [str(id_or_ids)] if isinstance(id_or_ids, (UUID, str)) else [str(id) for id in id_or_ids] result = self._graph.query( - "MATCH (s:AgentStep) WHERE s.id IN $ids " - "RETURN s.id, s.step_type, s.parent_id, s.vector, s.name, s.description", + " ".join( + [ + "MATCH (s:AgentStep)", + "WHERE s.id IN $ids", + "RETURN", + "s.id as id,", + "s.step_type as step_type,", + "s.parent_id as parent_id,", + "s.vector as vector,", + "s.name as name,", + "s.description as description", + ] + ), params={"ids": ids} ) steps = AgentStepList() diff --git a/notebooks/using_falkordb.ipynb b/notebooks/using_falkordb.ipynb index b7094ec..764c802 100644 --- a/notebooks/using_falkordb.ipynb +++ b/notebooks/using_falkordb.ipynb @@ -1,5 +1,18 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First launch falkorDB using the following command:\n", + "\n", + "```\n", + "docker run -p 6379:6379 -p 3000:3000 -it --rm -v ./data:/data falkordb/falkordb:v4.2.2\n", + "```\n", + "\n", + "The open you browser to `http://localhost:3000`" + ] + }, { "cell_type": "code", "execution_count": 1, diff --git a/poetry.lock b/poetry.lock index 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import Document, DocumentList +import numpy as np def test_falkordb_program_memory_empty(): document_memory = FalkorDBDocumentMemory(index_name="test_doc_memory_constructor") @@ -22,6 +23,20 @@ def test_local_document_memory_get_one_doc(): assert result_doc.parent_id == doc.parent_id assert result_doc.metadata == doc.metadata +def test_local_document_memory_get_one_embedded_doc(): + embeddings = FakeEmbeddings(dim=256) + document_memory = FalkorDBDocumentMemory(index_name="test_get_one_doc") + doc = Document(text="This is a test text") + doc.vector = embeddings.embed_text("This is a test text") + document_memory.update(doc) + result_doc = document_memory.get(doc.id).docs[0] + assert len(document_memory.get(doc.id).docs) == 1 + assert result_doc.id == doc.id + assert result_doc.text == doc.text + assert result_doc.parent_id == doc.parent_id + assert result_doc.metadata == doc.metadata + assert len(result_doc.vector) > 0 + def test_local_document_memory_override_doc(): document_memory = FalkorDBDocumentMemory(index_name="test_override_doc") doc = Document(text="This is a test text") diff --git a/tests/modules/retrievers/integration/falkordb/test_falkordb_document_retriever.py b/tests/modules/retrievers/integration/falkordb/test_falkordb_document_retriever.py new file mode 100644 index 0000000..0790c23 --- /dev/null +++ b/tests/modules/retrievers/integration/falkordb/test_falkordb_document_retriever.py @@ -0,0 +1,60 @@ +import dspy +import hybridagi.core.graph_program as gp +from hybridagi.core.pipeline import Pipeline +from hybridagi.core.datatypes import Query, Document, DocumentList +from hybridagi.memory.integration.falkordb import FalkorDBDocumentMemory +from dspy.utils.dummies import DummyLM +from hybridagi.embeddings import SentenceTransformerEmbeddings +from hybridagi.modules.deduplicators import EntityDeduplicator +from hybridagi.modules.splitters import DocumentSentenceSplitter +from hybridagi.modules.embedders import DocumentEmbedder + +from hybridagi.modules.retrievers.integration.falkordb import FalkorDBDocumentRetriever + +def test_document_retriever(): + input_data = [ + "In a small town nestled between rolling hills, there lived a young girl named Lily. She was known for her vivid imagination and her dream to become a writer. One day, she found an old typewriter in her grandmother's attic. With each clack of the keys, Lily's stories came to life, filling the pages with adventures and dreams. Her first book, a tale of a brave little mouse, became a local sensation, inspiring others to chase their dreams.", + "The Great Wall of China is one of the most impressive architectural feats in history. Stretching over 13,000 miles, it was built over several centuries to protect the Chinese states and empires from raids and invasions. The wall includes watchtowers, troop barracks, garrison stations, and signaling capabilities through the means of smoke or fire.", + "Photosynthesis is the process by which green plants, algae, and some bacteria convert light energy, typically from the sun, into chemical energy stored in glucose. This process occurs in the chloroplasts of plant cells and involves the absorption of carbon dioxide and water, releasing oxygen as a byproduct. Photosynthesis is crucial for life on Earth as it provides the primary source of energy for nearly all organisms.", + ] + input_docs = DocumentList() + for data in input_data: + input_docs.docs.append(Document(text=data)) + + pipeline = Pipeline() + + embeddings = SentenceTransformerEmbeddings( + model_name_or_path = "all-MiniLM-L6-v2", + dim = 384, # The dimention of the embeddings vector (also called dense vector) + normalize_embeddings=True, + ) + + pipeline = Pipeline() + + pipeline.add("chunk_documents", DocumentSentenceSplitter( + method = "word", + chunk_size = 100, + chunk_overlap = 0, + )) + + pipeline.add("embed_chunks", DocumentEmbedder(embeddings=embeddings)) + + output_docs = pipeline(input_docs) + + document_memory = FalkorDBDocumentMemory( + index_name="test_document_retriever", + wipe_on_start=True, + ) + + document_memory.update(output_docs) + + document_retriever = FalkorDBDocumentRetriever( + document_memory = document_memory, + embeddings = embeddings, + max_distance = 1.0, + distance = "cosine", + k = 5, + ) + + result = document_retriever(Query(query="What is photosynthesis")) + assert len(result.docs) > 0 \ No newline at end of file