Merge remote-tracking branch 'origin/main' into feature/cog-537-implement-retrieval-algorithm-from-research-paper

cognee/infrastructure/databases/vector/get_vector_engine.py

@@ -1,6 +1,8 @@
 from .config import get_vectordb_config
 from .embeddings import get_embedding_engine
 from .create_vector_engine import create_vector_engine
+from functools import lru_cache
 
+@lru_cache
 def get_vector_engine():
     return create_vector_engine(get_vectordb_config().to_dict(), get_embedding_engine())

cognee/infrastructure/engine/models/DataPoint.py

@@ -8,6 +8,7 @@ class MetaData(TypedDict):
     index_fields: list[str]
 
 class DataPoint(BaseModel):
+    __tablename__ = "data_point"
     id: UUID = Field(default_factory = uuid4)
     updated_at: Optional[datetime] = datetime.now(timezone.utc)
     _metadata: Optional[MetaData] = {

cognee/infrastructure/llm/prompts/patch_gen_instructions.txt

@@ -0,0 +1,3 @@
+I need you to solve this issue by looking at the provided knowledge graph and 
+generating a single patch file that I can apply directly to this repository using git apply. 
+Please respond with a single patch file in the following format.

cognee/modules/chunking/models/DocumentChunk.py

@@ -3,6 +3,7 @@ from cognee.infrastructure.engine import DataPoint
 from cognee.modules.data.processing.document_types import Document
 
 class DocumentChunk(DataPoint):
+    __tablename__ = "document_chunk"
     text: str
     word_count: int
     chunk_index: int

cognee/modules/engine/models/Entity.py

@@ -3,6 +3,7 @@ from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
 from .EntityType import EntityType
 
 class Entity(DataPoint):
+    __tablename__ = "entity"
     name: str
     is_a: EntityType
     description: str

cognee/modules/engine/models/EntityType.py

@@ -2,6 +2,7 @@ from cognee.infrastructure.engine import DataPoint
 from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
 
 class EntityType(DataPoint):
+    __tablename__ = "entity_type"
     name: str
     type: str
     description: str

cognee/modules/pipelines/Pipeline.py

@@ -1,18 +0,0 @@
-from uuid import UUID, uuid4
-from typing import Optional
-from pydantic import BaseModel
-from .models.Task import Task
-
-class PipelineConfig(BaseModel):
-    batch_count: int = 10
-    description: Optional[str] = None
-
-class Pipeline():
-    id: UUID = uuid4()
-    name: str
-    description: str
-    tasks: list[Task] = []
-
-    def __init__(self, name: str, pipeline_config: PipelineConfig):
-        self.name = name
-        self.description = pipeline_config.description

cognee/tasks/chunks/query_chunks.py

@@ -10,7 +10,7 @@ async def query_chunks(query: str) -> list[dict]:
     """
     vector_engine = get_vector_engine()
 
-    found_chunks = await vector_engine.search("DocumentChunk_text", query, limit = 5)
+    found_chunks = await vector_engine.search("document_chunk_text", query, limit = 5)
 
     chunks = [result.payload for result in found_chunks]
 

cognee/tasks/code/get_local_dependencies_checker.py

@@ -0,0 +1,20 @@
+import argparse
+import asyncio
+from cognee.tasks.repo_processor.get_local_dependencies import get_local_script_dependencies
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Get local script dependencies.")
+
+    # Suggested path: .../cognee/examples/python/simple_example.py
+    parser.add_argument("script_path", type=str, help="Absolute path to the Python script file")
+
+    # Suggested path: .../cognee
+    parser.add_argument("repo_path", type=str, help="Absolute path to the repository root")
+
+    args = parser.parse_args()
+
+    dependencies = asyncio.run(get_local_script_dependencies(args.script_path, args.repo_path))
+
+    print("Dependencies:")
+    for dependency in dependencies:
+        print(dependency)

cognee/tasks/graph/query_graph_connections.py

@@ -27,8 +27,8 @@ async def query_graph_connections(query: str, exploration_levels = 1) -> list[(s
     else:
         vector_engine = get_vector_engine()
         results = await asyncio.gather(
-            vector_engine.search("Entity_name", query_text = query, limit = 5),
-            vector_engine.search("EntityType_name", query_text = query, limit = 5),
+            vector_engine.search("entity_name", query_text = query, limit = 5),
+            vector_engine.search("entity_type_name", query_text = query, limit = 5),
         )
         results = [*results[0], *results[1]]
         relevant_results = [result for result in results if result.score < 0.5][:5]

cognee/tasks/repo_processor/__init__.py

@@ -0,0 +1,3 @@
+import logging
+
+logger = logging.getLogger("task:repo_processor")

cognee/tasks/repo_processor/get_local_dependencies.py

@@ -0,0 +1,126 @@
+import argparse
+import asyncio
+import sys
+from contextlib import contextmanager
+from pathlib import Path
+from typing import List, Dict, Optional
+
+import aiofiles
+import jedi
+import parso
+from parso.tree import BaseNode
+
+from cognee.tasks.repo_processor import logger
+
+
+@contextmanager
+def add_sys_path(path):
+    original_sys_path = sys.path.copy()
+    sys.path.insert(0, path)
+    try:
+        yield
+    finally:
+        sys.path = original_sys_path
+
+
+def _get_code_entities(node: parso.tree.NodeOrLeaf) -> List[Dict[str, any]]:
+    """
+    Recursively extract code entities using parso.
+    """
+    code_entity_list = []
+
+    if not hasattr(node, 'children'):
+        return code_entity_list
+
+    name_nodes = (child for child in node.children if child.type == 'name')
+    for name_node in name_nodes:
+        code_entity = {
+            'name': name_node.value,
+            'line': name_node.start_pos[0],
+            'column': name_node.start_pos[1]
+        }
+        code_entity_list.append(code_entity)
+
+    # Recursively process child nodes
+    for child in node.children:
+        code_entity_list.extend(_get_code_entities(child))
+
+    return code_entity_list
+
+
+def _update_code_entity(script: jedi.Script, code_entity: Dict[str, any]) -> None:
+    """
+    Update a code_entity with (full_name, module_name, module_path) using Jedi
+    """
+    try:
+        results = script.goto(code_entity["line"], code_entity["column"], follow_imports=True)
+        if results:
+            result = results[0]
+            code_entity["full_name"] = getattr(result, "full_name", None)
+            code_entity["module_name"] = getattr(result, "module_name", None)
+            code_entity["module_path"] = getattr(result, "module_path", None)
+    except Exception as e:
+        # logging.warning(f"Failed to analyze code entity {code_entity['name']}: {e}")
+        logger.error(f"Failed to analyze code entity {code_entity['name']}: {e}")
+
+
+async def _extract_dependencies(script_path: str) -> List[str]:
+    try:
+        async with aiofiles.open(script_path, "r") as file:
+            source_code = await file.read()
+    except IOError as e:
+        logger.error(f"Error opening {script_path}: {e}")
+        return []
+
+    jedi.set_debug_function(lambda color, str_out: None)
+    script = jedi.Script(code=source_code, path=script_path)
+
+    tree = parso.parse(source_code)
+    code_entities = _get_code_entities(tree)
+
+    for code_entity in code_entities:
+        _update_code_entity(script, code_entity)
+
+    module_paths = {
+        entity.get("module_path")
+        for entity in code_entities
+        if entity.get("module_path") is not None
+    }
+
+    str_paths = []
+    for module_path in module_paths:
+        try:
+            str_paths.append(str(module_path))
+        except Exception as e:
+            logger.error(f"Error converting path to string: {e}")
+
+    return sorted(str_paths)
+
+
+async def get_local_script_dependencies(script_path: str, repo_path: Optional[str] = None) -> List[str]:
+    """
+    Extract and return a list of unique module paths that the script depends on.
+    """
+    try:
+        script_path = Path(script_path).resolve(strict=True)
+    except (FileNotFoundError, PermissionError) as e:
+        logger.error(f"Error resolving script path: {e}")
+        return []
+
+    if not repo_path:
+        return await _extract_dependencies(script_path)
+
+    try:
+        repo_path = Path(repo_path).resolve(strict=True)
+    except (FileNotFoundError, PermissionError) as e:
+        logger.warning(f"Error resolving repo path: {e}. Proceeding without repo_path.")
+        return await _extract_dependencies(script_path)
+
+    if not script_path.is_relative_to(repo_path):
+        logger.warning(f"Script {script_path} not in repo {repo_path}. Proceeding without repo_path.")
+        return await _extract_dependencies(script_path)
+
+    with add_sys_path(str(repo_path)):
+        dependencies = await _extract_dependencies(script_path)
+
+    return [path for path in dependencies if path.startswith(str(repo_path))]

cognee/tasks/storage/index_data_points.py

@@ -16,10 +16,10 @@ async def index_data_points(data_points: list[DataPoint]):
         data_point_type = type(data_point)
 
         for field_name in data_point._metadata["index_fields"]:
-            index_name = f"{data_point_type.__name__}.{field_name}"
+            index_name = f"{data_point_type.__tablename__}.{field_name}"
 
             if index_name not in created_indexes:
-                await vector_engine.create_vector_index(data_point_type.__name__, field_name)
+                await vector_engine.create_vector_index(data_point_type.__tablename__, field_name)
                 created_indexes[index_name] = True
 
             if index_name not in index_points:

cognee/tasks/summarization/models.py

@@ -3,6 +3,7 @@ from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
 from cognee.modules.data.processing.document_types import Document
 
 class TextSummary(DataPoint):
+    __tablename__ = "text_summary"
     text: str
     made_from: DocumentChunk
 

cognee/tasks/summarization/query_summaries.py

@@ -10,7 +10,7 @@ async def query_summaries(query: str) -> list:
     """
     vector_engine = get_vector_engine()
 
-    summaries_results = await vector_engine.search("TextSummary_text", query, limit = 5)
+    summaries_results = await vector_engine.search("text_summary_text", query, limit = 5)
 
     summaries = [summary.payload for summary in summaries_results]
 

cognee/tests/test_library.py

@@ -32,7 +32,7 @@ async def  main():
 
     from cognee.infrastructure.databases.vector import get_vector_engine
     vector_engine = get_vector_engine()
-    random_node = (await vector_engine.search("Entity_name", "AI"))[0]
+    random_node = (await vector_engine.search("entity_name", "AI"))[0]
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(SearchType.INSIGHTS, query_text = random_node_name)

cognee/tests/test_neo4j.py

@@ -36,7 +36,7 @@ async def main():
 
     from cognee.infrastructure.databases.vector import get_vector_engine
     vector_engine = get_vector_engine()
-    random_node = (await vector_engine.search("Entity_name", "Quantum computer"))[0]
+    random_node = (await vector_engine.search("entity_name", "Quantum computer"))[0]
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(SearchType.INSIGHTS, query_text = random_node_name)

cognee/tests/test_pgvector.py

@@ -65,7 +65,7 @@ async def main():
     from cognee.infrastructure.databases.vector import get_vector_engine
 
     vector_engine = get_vector_engine()
-    random_node = (await vector_engine.search("Entity_name", "Quantum computer"))[0]
+    random_node = (await vector_engine.search("entity_name", "Quantum computer"))[0]
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(SearchType.INSIGHTS, query_text = random_node_name)

cognee/tests/test_qdrant.py

@@ -37,7 +37,7 @@ async def main():
 
     from cognee.infrastructure.databases.vector import get_vector_engine
     vector_engine = get_vector_engine()
-    random_node = (await vector_engine.search("Entity_name", "Quantum computer"))[0]
+    random_node = (await vector_engine.search("entity_name", "Quantum computer"))[0]
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(SearchType.INSIGHTS, query_text = random_node_name)

cognee/tests/test_weaviate.py

@@ -35,7 +35,7 @@ async def main():
 
     from cognee.infrastructure.databases.vector import get_vector_engine
     vector_engine = get_vector_engine()
-    random_node = (await vector_engine.search("Entity_name", "Quantum computer"))[0]
+    random_node = (await vector_engine.search("entity_name", "Quantum computer"))[0]
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(SearchType.INSIGHTS, query_text = random_node_name)

docker-compose.yml

@@ -19,7 +19,7 @@ services:
     deploy:
       resources:
         limits:
-          cpus: '4.0'
+          cpus: '2.0'
           memory: 8GB
 
   frontend:

evals/eval_swe_bench.py

@@ -0,0 +1,118 @@
+import argparse
+import json
+import subprocess
+from pathlib import Path
+
+from datasets import Dataset
+from swebench.harness.utils import load_swebench_dataset
+from swebench.inference.make_datasets.create_instance import PATCH_EXAMPLE
+
+import cognee
+from cognee.api.v1.cognify.code_graph_pipeline import code_graph_pipeline
+from cognee.api.v1.search import SearchType
+from cognee.infrastructure.databases.graph import get_graph_engine
+from cognee.infrastructure.llm.get_llm_client import get_llm_client
+from cognee.infrastructure.llm.prompts import read_query_prompt
+from evals.eval_utils import download_instances
+
+
+async def generate_patch_with_cognee(instance, search_type=SearchType.CHUNKS):
+
+    await cognee.prune.prune_data()
+    await cognee.prune.prune_system(metadata=True)
+
+    dataset_name = "SWE_test_data"
+    code_text = instance["text"]
+    await cognee.add([code_text], dataset_name)
+    await code_graph_pipeline([dataset_name])
+    graph_engine = await get_graph_engine()
+    with open(graph_engine.filename, "r") as f:
+        graph_str = f.read()
+
+    problem_statement = instance['problem_statement']
+    instructions = read_query_prompt("patch_gen_instructions.txt")
+
+    prompt = "\n".join([
+        instructions,
+        "<patch>",
+        PATCH_EXAMPLE,
+        "</patch>",
+        "This is the knowledge graph:",
+        graph_str
+    ])
+
+    llm_client = get_llm_client()
+    answer_prediction = await llm_client.acreate_structured_output(
+        text_input=problem_statement,
+        system_prompt=prompt,
+        response_model=str,
+    )
+    return answer_prediction
+
+
+async def generate_patch_without_cognee(instance):
+    problem_statement = instance['problem_statement']
+    prompt = instance["text"]
+
+    llm_client = get_llm_client()
+    answer_prediction = await llm_client.acreate_structured_output(
+        text_input=problem_statement,
+        system_prompt=prompt,
+        response_model=str,
+    )
+    return answer_prediction
+
+
+async def get_preds(dataset, with_cognee=True):
+    if with_cognee:
+        model_name = "with_cognee"
+        pred_func = generate_patch_with_cognee
+    else:
+        model_name = "without_cognee"
+        pred_func = generate_patch_without_cognee
+
+    preds = [{"instance_id": instance["instance_id"],
+              "model_patch": await pred_func(instance),
+              "model_name_or_path": model_name} for instance in dataset]
+
+    return preds
+
+
+async def main():
+    parser = argparse.ArgumentParser(
+        description="Run LLM predictions on SWE-bench dataset")
+    parser.add_argument('--cognee_off', action='store_true')
+    args = parser.parse_args()
+
+    if args.cognee_off:
+        dataset_name = 'princeton-nlp/SWE-bench_Lite_bm25_13K'
+        dataset = load_swebench_dataset(dataset_name, split='test')
+        predictions_path = "preds_nocognee.json"
+        if not Path(predictions_path).exists():
+            preds = await get_preds(dataset, with_cognee=False)
+            with open(predictions_path, "w") as file:
+                json.dump(preds, file)
+    else:
+        dataset_name = 'princeton-nlp/SWE-bench_Lite'
+        swe_dataset = load_swebench_dataset(
+            dataset_name, split='test')[:1]
+        filepath = Path("SWE-bench_testsample")
+        if filepath.exists():
+            dataset = Dataset.load_from_disk(filepath)
+        else:
+            dataset = download_instances(swe_dataset, filepath)
+        predictions_path = "preds.json"
+        preds = await get_preds(dataset, with_cognee=not args.cognee_off)
+        with open(predictions_path, "w") as file:
+            json.dump(preds, file)
+
+    subprocess.run(["python", "-m", "swebench.harness.run_evaluation",
+                    "--dataset_name", dataset_name,
+                    "--split", "test",
+                    "--predictions_path",  predictions_path,
+                    "--max_workers", "1",
+                    "--run_id", "test_run"])
+
+if __name__ == "__main__":
+    import asyncio
+    asyncio.run(main(), debug=True)

evals/eval_utils.py

@@ -0,0 +1,103 @@
+import os
+from copy import deepcopy
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from datasets import Dataset
+from swebench.inference.make_datasets.create_instance import make_code_text
+from swebench.inference.make_datasets.utils import (AutoContextManager,
+                                                    ingest_directory_contents)
+from tqdm.auto import tqdm
+
+
+def ingest_files(filenames):
+    files_dict = dict()
+    for filename in filenames:
+        with open(filename) as f:
+            content = f.read()
+        files_dict[filename] = content
+    return files_dict
+
+
+def ingest_repos(input_instances):
+    orig_dir = os.getcwd()
+    with TemporaryDirectory(
+        dir="/scratch" if os.path.exists("/scratch") else "/tmp"
+    ) as root_dir:
+        for instance in tqdm(
+            input_instances.values(),
+            total=len(input_instances),
+            desc="Downloading repos on specific commits",
+        ):
+            try:
+                with AutoContextManager(
+                    instance, root_dir
+                ) as cm:
+                    readmes = cm.get_readme_files()
+                    instance["readmes"] = ingest_files(readmes)
+                    instance["file_contents"] = ingest_directory_contents(
+                        cm.repo_path
+                    )
+            finally:
+                # if AutoContextManager fails to exit properly future exits will return the wrong directory
+                os.chdir(orig_dir)
+
+    return input_instances
+
+
+def extract_fields(instance):
+    readmes_text = make_code_text(instance["readmes"])
+    code_text = make_code_text(
+        instance["file_contents"], add_line_numbers=False)
+
+    text_inputs = "\n".join([readmes_text, code_text])
+    text_inputs = text_inputs.strip() + "\n\n"
+    # text_inputs = code_text
+    patch = "\n".join(["<patch>", instance["patch"], "</patch>"])
+    return {**instance, "text": text_inputs, "patch": patch}
+
+
+def create_dataset(input_instances):
+    columns = [
+        "instance_id",
+        "text",
+        "repo",
+        "base_commit",
+        "problem_statement",
+        "hints_text",
+        "created_at",
+        "patch",
+        "test_patch",
+        "version",
+        "FAIL_TO_PASS",
+        "PASS_TO_PASS",
+        "environment_setup_commit",
+    ]
+
+    data_table = {key: list() for key in columns}
+    for instance in input_instances.values():
+        datum = extract_fields(instance)
+        for key in columns:
+            data_table[key].append(datum[key] if key in datum else "")
+    dataset = Dataset.from_dict(data_table)
+
+    return dataset
+
+
+def download_instances(
+    input_data,
+    path=Path("SWE-bench_testsample"),
+    verbose=False,
+):
+    """Downloads code from github.
+
+    Args:
+    - input_data: dictionary with unprocessed input instances.
+    - verbose: set ContextManager verbose to True
+    """
+    input_instances = {x["instance_id"]: x for x in input_data}
+    input_instances_copy = deepcopy(input_instances)
+    input_instances_with_text = ingest_repos(input_instances_copy)
+    dataset = create_dataset(input_instances_with_text)
+    dataset.save_to_disk(path)
+    return dataset

notebooks/cognee_demo.ipynb

@@ -758,7 +758,7 @@
     "from cognee.infrastructure.databases.vector import get_vector_engine\n",
     "\n",
     "vector_engine = get_vector_engine()\n",
-    "results = await search(vector_engine, \"Entity_name\", \"sarah.nguyen@example.com\")\n",
+    "results = await search(vector_engine, \"entity_name\", \"sarah.nguyen@example.com\")\n",
     "for result in results:\n",
     "    print(result)"
    ]
@@ -788,7 +788,7 @@
    "source": [
     "from cognee.api.v1.search import SearchType\n",
     "\n",
-    "node = (await vector_engine.search(\"Entity_name\", \"sarah.nguyen@example.com\"))[0]\n",
+    "node = (await vector_engine.search(\"entity_name\", \"sarah.nguyen@example.com\"))[0]\n",
     "node_name = node.payload[\"text\"]\n",
     "\n",
     "search_results = await cognee.search(SearchType.SUMMARIES, query_text = node_name)\n",
@@ -881,7 +881,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

notebooks/cognee_llama_index.ipynb

@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +174,7 @@
     "\n",
     "# Query cognee for summaries\n",
     "search_results = await cognee.search(\n",
-    "    SearchType.SUMMARIES, query=\"What are the main news discussed in the document?\"\n",
+    "    SearchType.SUMMARIES, query_text=\"What are the main news discussed in the document?\"\n",
     ")\n",
     "# Display search results\n",
     "print(\"\\n Summary of main news discussed:\\n\")\n",

poetry.lock

@@ -404,60 +404,69 @@ files = [
 
 [[package]]
 name = "asyncpg"
-version = "0.29.0"
+version = "0.30.0"
 description = "An asyncio PostgreSQL driver"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
-async-timeout = {version = ">=4.0.3", markers = "python_version < \"3.12.0\""}
+async-timeout = {version = ">=4.0.3", markers = "python_version < \"3.11.0\""}
 
 [package.extras]
-docs = ["Sphinx (>=5.3.0,<5.4.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-asyncio (>=0.3.0,<0.4.0)"]
-test = ["flake8 (>=6.1,<7.0)", "uvloop (>=0.15.3)"]
+docs = ["Sphinx (>=8.1.3,<8.2.0)", "sphinx-rtd-theme (>=1.2.2)"]
+gssauth = ["gssapi", "sspilib"]
+test = ["distro (>=1.9.0,<1.10.0)", "flake8 (>=6.1,<7.0)", "flake8-pyi (>=24.1.0,<24.2.0)", "gssapi", "k5test", "mypy (>=1.8.0,<1.9.0)", "sspilib", "uvloop (>=0.15.3)"]
 
 [[package]]
 name = "attrs"
@@ -7632,4 +7641,4 @@ weaviate = ["weaviate-client"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9.0,<3.12"
-content-hash = "f5874af8364839dd2a362b6b3209c4aae108f30dcc27be43d0d07f7b28160eda"
+content-hash = "474ae44ef721bf9b2d34d1cd139cddf42542ef9167895960784b6e88214dd1e6"

pyproject.toml

@@ -66,7 +66,7 @@ anthropic = "^0.26.1"
 sentry-sdk = {extras = ["fastapi"], version = "^2.9.0"}
 fastapi-users = {version = "*", extras = ["sqlalchemy"]}
 alembic = "^1.13.3"
-asyncpg = "^0.29.0"
+asyncpg = "0.30.0"
 pgvector = "^0.3.5"
 psycopg2 = {version = "^2.9.10", optional = true}
 llama-index-core = {version = "^0.11.22", optional = true}