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fix: simplify code pipeline (#529)

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## Description
<!-- Provide a clear description of the changes in this PR -->

## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit


- **New Features**
  - Enhanced code search and dependency analysis for improved accuracy.
  - Introduced a new high-performance text embedding option.
  - Added an additional execution entry point for code graph processing.
- New optional parameters for flexible property selection in retrieval
functions.
- Introduced new classes for handling import statements, function
definitions, and class definitions.
  - Updated embedding engine selection based on configuration options.

- **Bug Fixes**
- Improved error handling in search operations and database queries for
a more stable user experience.
  - Enhanced error logging for source code parsing.

- **Refactor**
- Streamlined asynchronous processing and refactored internal dependency
extraction.
- Updated configuration and integration settings to enhance overall
reliability.
  - Restructured functions for simplified dependency handling.

- **Chores**
- Upgraded and reorganized dependency management with optional libraries
for extended functionality.
- Added new secret parameters for embedding configuration in workflow
settings.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Co-authored-by: vasilije <vas.markovic@gmail.com>
This commit is contained in:
Boris 2025-02-12 23:58:48 +01:00 • committed by GitHub
parent bb8cb692e0
commit f9e6dcf837
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28 changed files with 1488 additions and 831 deletions
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4
.github/workflows/test_code_graph_example.yml vendored
View file

@ -19,5 +19,9 @@ jobs:
secrets: secrets:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }} LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
EMBEDDING_PROVIDER: "fastembed"
EMBEDDING_MODEL: "sentence-transformers/all-MiniLM-L6-v2"
EMBEDDING_DIMENSIONS: 384
EMBEDDING_MAX_TOKENS: 256
GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }} GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }} GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}

2
cognee-mcp/pyproject.toml
View file

@ -7,7 +7,7 @@ requires-python = ">=3.10"
dependencies = [ dependencies = [
"cognee[codegraph]", "cognee[codegraph]",
"mcp==1.1.3", "mcp==1.2.1",
] ]
[[project.authors]] [[project.authors]]

8
cognee-mcp/src/client.py
View file

@ -37,9 +37,13 @@ async def run():
toolResult = await session.call_tool("prune", arguments={}) toolResult = await session.call_tool("prune", arguments={})
toolResult = await session.call_tool("cognify", arguments={"text": text}) toolResult = await session.call_tool(
"codify", arguments={"repo_path": "/Users/borisarzentar/Projects/django"}
)
toolResult = await session.call_tool("search", arguments={"search_query": "AI"}) toolResult = await session.call_tool(
"search", arguments={"search_type": "CODE", "search_query": "exceptions"}
)
print(f"Cognify result: {toolResult.content}") print(f"Cognify result: {toolResult.content}")

555
cognee-mcp/uv.lock generated
View file

@ -2,8 +2,7 @@ version = 1
requires-python = ">=3.10" requires-python = ">=3.10"
resolution-markers = [ resolution-markers = [
"python_full_version >= '3.13'", "python_full_version >= '3.13'",
"python_full_version >= '3.12.4' and python_full_version < '3.13'", "python_full_version == '3.12.*'",
"python_full_version >= '3.12' and python_full_version < '3.12.4'",
"python_full_version == '3.11.*'", "python_full_version == '3.11.*'",
"python_full_version < '3.11'", "python_full_version < '3.11'",
] ]
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[package.optional-dependencies] [package.optional-dependencies]
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wheels = [ wheels = [
{ url = "https://files.pythonhosted.org/packages/89/9b/599bcfc7064fbe5740919e78c5df18e5dceb0887e676256a1061bb5ae232/virtualenv-20.29.1-py3-none-any.whl", hash = "sha256:4e4cb403c0b0da39e13b46b1b2476e505cb0046b25f242bee80f62bf990b2779", size = 4282379 }, { url = "https://files.pythonhosted.org/packages/93/fa/849483d56773ae29740ae70043ad88e068f98a6401aa819b5d6bee604683/virtualenv-20.29.2-py3-none-any.whl", hash = "sha256:febddfc3d1ea571bdb1dc0f98d7b45d24def7428214d4fb73cc486c9568cce6a", size = 4301478 },
] ]
[[package]] [[package]]

53
cognee/api/v1/cognify/code_graph_pipeline.py
View file

@ -1,5 +1,6 @@
import asyncio import asyncio
import logging import logging
from uuid import NAMESPACE_OID, uuid5
from cognee.base_config import get_base_config from cognee.base_config import get_base_config
from cognee.modules.cognify.config import get_cognify_config from cognee.modules.cognify.config import get_cognify_config
@ -7,19 +8,18 @@ from cognee.modules.pipelines import run_tasks
from cognee.modules.pipelines.tasks.Task import Task from cognee.modules.pipelines.tasks.Task import Task
from cognee.modules.users.methods import get_default_user from cognee.modules.users.methods import get_default_user
from cognee.shared.data_models import KnowledgeGraph, MonitoringTool from cognee.shared.data_models import KnowledgeGraph, MonitoringTool
from cognee.shared.utils import render_graph
from cognee.tasks.documents import classify_documents, extract_chunks_from_documents from cognee.tasks.documents import classify_documents, extract_chunks_from_documents
from cognee.tasks.graph import extract_graph_from_data from cognee.tasks.graph import extract_graph_from_data
from cognee.tasks.ingestion import ingest_data from cognee.tasks.ingestion import ingest_data
from cognee.tasks.repo_processor import ( from cognee.tasks.repo_processor import (
enrich_dependency_graph,
expand_dependency_graph,
get_data_list_for_user, get_data_list_for_user,
get_non_py_files, get_non_py_files,
get_repo_file_dependencies, get_repo_file_dependencies,
) )
from cognee.tasks.repo_processor.get_source_code_chunks import get_source_code_chunks
from cognee.tasks.storage import add_data_points from cognee.tasks.storage import add_data_points
from cognee.tasks.summarization import summarize_code, summarize_text from cognee.tasks.summarization import summarize_text
from cognee.infrastructure.llm import get_max_chunk_tokens from cognee.infrastructure.llm import get_max_chunk_tokens
monitoring = get_base_config().monitoring_tool monitoring = get_base_config().monitoring_tool
@ -32,24 +32,25 @@ update_status_lock = asyncio.Lock()
@observe @observe
async def run_code_graph_pipeline(repo_path, include_docs=True): async def run_code_graph_pipeline(repo_path, include_docs=False):
import cognee import cognee
from cognee.infrastructure.databases.relational import create_db_and_tables from cognee.low_level import setup
await cognee.prune.prune_data() await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True) await cognee.prune.prune_system(metadata=True)
await create_db_and_tables() await setup()
cognee_config = get_cognify_config() cognee_config = get_cognify_config()
user = await get_default_user() user = await get_default_user()
detailed_extraction = False
tasks = [ tasks = [
Task(get_repo_file_dependencies), Task(get_repo_file_dependencies, detailed_extraction=detailed_extraction),
Task(enrich_dependency_graph), # Task(enrich_dependency_graph, task_config={"batch_size": 50}),
Task(expand_dependency_graph, task_config={"batch_size": 50}), # Task(expand_dependency_graph, task_config={"batch_size": 50}),
Task(get_source_code_chunks, task_config={"batch_size": 50}), # Task(get_source_code_chunks, task_config={"batch_size": 50}),
Task(summarize_code, task_config={"batch_size": 50}), # Task(summarize_code, task_config={"batch_size": 50}),
Task(add_data_points, task_config={"batch_size": 50}), Task(add_data_points, task_config={"batch_size": 100 if detailed_extraction else 500}),
] ]
if include_docs: if include_docs:
@ -69,19 +70,23 @@ async def run_code_graph_pipeline(repo_path, include_docs=True):
), ),
] ]
pipeline_run_status = None dataset_id = uuid5(NAMESPACE_OID, "codebase")
if include_docs: if include_docs:
non_code_pipeline_run = run_tasks(non_code_tasks, None, repo_path, "cognify_pipeline") non_code_pipeline_run = run_tasks(non_code_tasks, dataset_id, repo_path, "cognify_pipeline")
async for run_status in non_code_pipeline_run: async for run_status in non_code_pipeline_run:
pipeline_run_status = run_status yield run_status
from cognee.modules.data.methods import get_datasets async for run_status in run_tasks(tasks, dataset_id, repo_path, "cognify_code_pipeline"):
yield run_status
existing_datasets = await get_datasets(user.id)
code_pipeline_run = run_tasks(
tasks, existing_datasets[0].id, repo_path, "cognify_code_pipeline"
)
async for run_status in code_pipeline_run:
pipeline_run_status = run_status
return pipeline_run_status if __name__ == "__main__":
async def main():
async for data_points in run_code_graph_pipeline("REPO_PATH"):
print(data_points)
await render_graph()
asyncio.run(main())

18
cognee/api/v1/cognify/routers/get_code_pipeline_router.py
View file

@ -1,10 +1,8 @@
from fastapi import APIRouter from fastapi import APIRouter
from fastapi.responses import JSONResponse
from cognee.api.DTO import InDTO from cognee.api.DTO import InDTO
from cognee.api.v1.cognify.code_graph_pipeline import run_code_graph_pipeline from cognee.api.v1.cognify.code_graph_pipeline import run_code_graph_pipeline
from cognee.modules.retrieval.description_to_codepart_search import ( from cognee.modules.retrieval import code_graph_retrieval
code_description_to_code_part_search,
)
from fastapi.responses import JSONResponse
class CodePipelineIndexPayloadDTO(InDTO): class CodePipelineIndexPayloadDTO(InDTO):
@ -39,17 +37,15 @@ def get_code_pipeline_router() -> APIRouter:
else payload.full_input else payload.full_input
) )
retrieved_codeparts, __ = await code_description_to_code_part_search( retrieved_files = await code_graph_retrieval(query)
query, include_docs=False
)
return [ return [
{ {
"name": codepart.attributes["file_path"], "name": file_path,
"description": codepart.attributes["file_path"], "description": file_path,
"content": codepart.attributes["source_code"], "content": source_code,
} }
for codepart in retrieved_codeparts for file_path, source_code in retrieved_files.items()
] ]
except Exception as error: except Exception as error:
return JSONResponse(status_code=409, content={"error": str(error)}) return JSONResponse(status_code=409, content={"error": str(error)})

2
cognee/api/v1/search/routers/get_search_router.py
View file

@ -38,7 +38,7 @@ def get_search_router() -> APIRouter:
from cognee.api.v1.search import search as cognee_search from cognee.api.v1.search import search as cognee_search
try: try:
results = await cognee_search(payload.search_type, payload.query, user) results = await cognee_search(payload.query, payload.search_type, user)
return results return results
except Exception as error: except Exception as error:

65
cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py Normal file
View file

@ -0,0 +1,65 @@
import logging
from typing import List, Optional
from fastembed import TextEmbedding
import litellm
import os
from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine
from cognee.infrastructure.databases.exceptions.EmbeddingException import EmbeddingException
from cognee.infrastructure.llm.tokenizer.TikToken import TikTokenTokenizer
litellm.set_verbose = False
logger = logging.getLogger("FastembedEmbeddingEngine")
class FastembedEmbeddingEngine(EmbeddingEngine):
model: str
dimensions: int
mock: bool
MAX_RETRIES = 5
def __init__(
self,
model: Optional[str] = "openai/text-embedding-3-large",
dimensions: Optional[int] = 3072,
max_tokens: int = 512,
):
self.model = model
self.dimensions = dimensions
self.max_tokens = max_tokens
self.tokenizer = self.get_tokenizer()
# self.retry_count = 0
self.embedding_model = TextEmbedding(model_name=model)
enable_mocking = os.getenv("MOCK_EMBEDDING", "false")
if isinstance(enable_mocking, bool):
enable_mocking = str(enable_mocking).lower()
self.mock = enable_mocking in ("true", "1", "yes")
async def embed_text(self, text: List[str]) -> List[List[float]]:
try:
if self.mock:
return [[0.0] * self.dimensions for _ in text]
else:
embeddings = self.embedding_model.embed(
text,
batch_size=len(text),
parallel=0,
)
return list(embeddings)
except Exception as error:
logger.error(f"Embedding error in FastembedEmbeddingEngine: {str(error)}")
raise EmbeddingException(f"Failed to index data points using model {self.model}")
def get_vector_size(self) -> int:
return self.dimensions
def get_tokenizer(self):
logger.debug("Loading tokenizer for FastembedEmbeddingEngine...")
tokenizer = TikTokenTokenizer(model="gpt-4o", max_tokens=self.max_tokens)
logger.debug("Tokenizer loaded for for FastembedEmbeddingEngine")
return tokenizer

12
cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py
View file

@ -1,13 +1,23 @@
from cognee.infrastructure.databases.vector.embeddings.config import get_embedding_config from cognee.infrastructure.databases.vector.embeddings.config import get_embedding_config
from cognee.infrastructure.llm.config import get_llm_config from cognee.infrastructure.llm.config import get_llm_config
from .EmbeddingEngine import EmbeddingEngine from .EmbeddingEngine import EmbeddingEngine
from .LiteLLMEmbeddingEngine import LiteLLMEmbeddingEngine
def get_embedding_engine() -> EmbeddingEngine: def get_embedding_engine() -> EmbeddingEngine:
config = get_embedding_config() config = get_embedding_config()
llm_config = get_llm_config() llm_config = get_llm_config()
if config.embedding_provider == "fastembed":
from .FastembedEmbeddingEngine import FastembedEmbeddingEngine
return FastembedEmbeddingEngine(
model=config.embedding_model,
dimensions=config.embedding_dimensions,
max_tokens=config.embedding_max_tokens,
)
from .LiteLLMEmbeddingEngine import LiteLLMEmbeddingEngine
return LiteLLMEmbeddingEngine( return LiteLLMEmbeddingEngine(
# If OpenAI API is used for embeddings, litellm needs only the api_key. # If OpenAI API is used for embeddings, litellm needs only the api_key.
provider=config.embedding_provider, provider=config.embedding_provider,

35
cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py
View file

@ -150,24 +150,31 @@ class LanceDBAdapter(VectorDBInterface):
query_vector = (await self.embedding_engine.embed_text([query_text]))[0] query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
connection = await self.get_connection() connection = await self.get_connection()
collection = await connection.open_table(collection_name)
collection_size = await collection.count_rows() try:
collection = await connection.open_table(collection_name)
results = await collection.vector_search(query_vector).limit(collection_size).to_pandas() collection_size = await collection.count_rows()
result_values = list(results.to_dict("index").values()) results = (
await collection.vector_search(query_vector).limit(collection_size).to_pandas()
normalized_values = normalize_distances(result_values)
return [
ScoredResult(
id=parse_id(result["id"]),
payload=result["payload"],
score=normalized_values[value_index],
) )
for value_index, result in enumerate(result_values)
] result_values = list(results.to_dict("index").values())
normalized_values = normalize_distances(result_values)
return [
ScoredResult(
id=parse_id(result["id"]),
payload=result["payload"],
score=normalized_values[value_index],
)
for value_index, result in enumerate(result_values)
]
except ValueError:
# Ignore if collection doesn't exist
return []
async def search( async def search(
self, self,

48
cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py
View file

@ -187,31 +187,37 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
if query_text and not query_vector: if query_text and not query_vector:
query_vector = (await self.embedding_engine.embed_text([query_text]))[0] query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
# Get PGVectorDataPoint Table from database try:
PGVectorDataPoint = await self.get_table(collection_name) # Get PGVectorDataPoint Table from database
PGVectorDataPoint = await self.get_table(collection_name)
# Use async session to connect to the database # Use async session to connect to the database
async with self.get_async_session() as session: async with self.get_async_session() as session:
# Find closest vectors to query_vector # Find closest vectors to query_vector
closest_items = await session.execute( closest_items = await session.execute(
select( select(
PGVectorDataPoint, PGVectorDataPoint,
PGVectorDataPoint.c.vector.cosine_distance(query_vector).label("similarity"), PGVectorDataPoint.c.vector.cosine_distance(query_vector).label(
).order_by("similarity") "similarity"
) ),
).order_by("similarity")
)
vector_list = [] vector_list = []
# Extract distances and find min/max for normalization # Extract distances and find min/max for normalization
for vector in closest_items: for vector in closest_items:
# TODO: Add normalization of similarity score # TODO: Add normalization of similarity score
vector_list.append(vector) vector_list.append(vector)
# Create and return ScoredResult objects # Create and return ScoredResult objects
return [ return [
ScoredResult(id=parse_id(str(row.id)), payload=row.payload, score=row.similarity) ScoredResult(id=parse_id(str(row.id)), payload=row.payload, score=row.similarity)
for row in vector_list for row in vector_list
] ]
except EntityNotFoundError:
# Ignore if collection does not exist
return []
async def search( async def search(
self, self,

50
cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py
View file

@ -155,30 +155,34 @@ class QDrantAdapter(VectorDBInterface):
client = self.get_qdrant_client() client = self.get_qdrant_client()
results = await client.search( try:
collection_name=collection_name, results = await client.search(
query_vector=models.NamedVector( collection_name=collection_name,
name="text", query_vector=models.NamedVector(
vector=query_vector name="text",
if query_vector is not None vector=query_vector
else (await self.embed_data([query_text]))[0], if query_vector is not None
), else (await self.embed_data([query_text]))[0],
with_vectors=with_vector, ),
) with_vectors=with_vector,
await client.close()
return [
ScoredResult(
id=parse_id(result.id),
payload={
**result.payload,
"id": parse_id(result.id),
},
score=1 - result.score,
) )
for result in results
] return [
ScoredResult(
id=parse_id(result.id),
payload={
**result.payload,
"id": parse_id(result.id),
},
score=1 - result.score,
)
for result in results
]
except ValueError:
# Ignore if the collection doesn't exist
return []
finally:
await client.close()
async def search( async def search(
self, self,

33
cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py
View file

@ -172,6 +172,7 @@ class WeaviateAdapter(VectorDBInterface):
with_vector: bool = False, with_vector: bool = False,
) -> List[ScoredResult]: ) -> List[ScoredResult]:
import weaviate.classes as wvc import weaviate.classes as wvc
import weaviate.exceptions
if query_text is None and query_vector is None: if query_text is None and query_vector is None:
raise ValueError("One of query_text or query_vector must be provided!") raise ValueError("One of query_text or query_vector must be provided!")
@ -179,21 +180,25 @@ class WeaviateAdapter(VectorDBInterface):
if query_vector is None: if query_vector is None:
query_vector = (await self.embed_data([query_text]))[0] query_vector = (await self.embed_data([query_text]))[0]
search_result = self.get_collection(collection_name).query.hybrid( try:
query=None, search_result = self.get_collection(collection_name).query.hybrid(
vector=query_vector, query=None,
include_vector=with_vector, vector=query_vector,
return_metadata=wvc.query.MetadataQuery(score=True), include_vector=with_vector,
) return_metadata=wvc.query.MetadataQuery(score=True),
return [
ScoredResult(
id=parse_id(str(result.uuid)),
payload=result.properties,
score=1 - float(result.metadata.score),
) )
for result in search_result.objects
] return [
ScoredResult(
id=parse_id(str(result.uuid)),
payload=result.properties,
score=1 - float(result.metadata.score),
)
for result in search_result.objects
]
except weaviate.exceptions.UnexpectedStatusCodeError:
# Ignore if the collection doesn't exist
return []
async def search( async def search(
self, self,

1
cognee/modules/retrieval/__init__.py
View file

@ -0,0 +1 @@
from .code_graph_retrieval import code_graph_retrieval

19
cognee/modules/retrieval/brute_force_triplet_search.py
View file

@ -50,7 +50,11 @@ def format_triplets(edges):
async def brute_force_triplet_search( async def brute_force_triplet_search(
query: str, user: User = None, top_k=5, collections=None query: str,
user: User = None,
top_k: int = 5,
collections: List[str] = None,
properties_to_project: List[str] = None,
) -> list: ) -> list:
if user is None: if user is None:
user = await get_default_user() user = await get_default_user()
@ -58,12 +62,18 @@ async def brute_force_triplet_search(
if user is None: if user is None:
raise PermissionError("No user found in the system. Please create a user.") raise PermissionError("No user found in the system. Please create a user.")
retrieved_results = await brute_force_search(query, user, top_k, collections=collections) retrieved_results = await brute_force_search(
query, user, top_k, collections=collections, properties_to_project=properties_to_project
)
return retrieved_results return retrieved_results
async def brute_force_search( async def brute_force_search(
query: str, user: User, top_k: int, collections: List[str] = None query: str,
user: User,
top_k: int,
collections: List[str] = None,
properties_to_project: List[str] = None,
) -> list: ) -> list:
""" """
Performs a brute force search to retrieve the top triplets from the graph. Performs a brute force search to retrieve the top triplets from the graph.
@ -113,7 +123,8 @@ async def brute_force_search(
await memory_fragment.project_graph_from_db( await memory_fragment.project_graph_from_db(
graph_engine, graph_engine,
node_properties_to_project=["id", "description", "name", "type", "text"], node_properties_to_project=properties_to_project
or ["id", "description", "name", "type", "text"],
edge_properties_to_project=["relationship_name"], edge_properties_to_project=["relationship_name"],
) )

45
cognee/modules/retrieval/code_graph_retrieval.py
View file

@ -1,18 +1,35 @@
from cognee.modules.retrieval.description_to_codepart_search import ( from cognee.low_level import DataPoint
code_description_to_code_part_search, from cognee.modules.graph.utils.convert_node_to_data_point import get_all_subclasses
) from .brute_force_triplet_search import brute_force_triplet_search
async def code_graph_retrieval(query, include_docs=False): async def code_graph_retrieval(query: str) -> dict[str, str]:
retrieved_codeparts, __ = await code_description_to_code_part_search( subclasses = get_all_subclasses(DataPoint)
query, include_docs=include_docs
vector_index_collections = []
for subclass in subclasses:
index_fields = subclass.model_fields["metadata"].default.get("index_fields", [])
for field_name in index_fields:
vector_index_collections.append(f"{subclass.__name__}_{field_name}")
found_triplets = await brute_force_triplet_search(
query,
top_k=5,
collections=vector_index_collections or None,
properties_to_project=["id", "file_path", "source_code"],
) )
return [ retrieved_files = {}
{
"name": codepart.attributes["file_path"], for triplet in found_triplets:
"description": codepart.attributes["file_path"], if triplet.node1.attributes["source_code"]:
"content": codepart.attributes["source_code"], retrieved_files[triplet.node1.attributes["file_path"]] = triplet.node1.attributes[
} "source_code"
for codepart in retrieved_codeparts ]
] if triplet.node2.attributes["source_code"]:
retrieved_files[triplet.node2.attributes["file_path"]] = triplet.node2.attributes[
"source_code"
]
return retrieved_files

2
cognee/modules/retrieval/description_to_codepart_search.py
View file

@ -86,7 +86,7 @@ async def code_description_to_code_part(
) )
code_summaries = await vector_engine.search( code_summaries = await vector_engine.search(
"code_summary_text", query_text=query, limit=top_k "CodeSummary_text", query_text=query, limit=top_k
) )
if not code_summaries: if not code_summaries:
logging.warning("No results found for query: '%s' by user: %s", query, user.id) logging.warning("No results found for query: '%s' by user: %s", query, user.id)

45
cognee/shared/CodeGraphEntities.py
View file

@ -1,25 +1,50 @@
from typing import List, Optional from typing import List, Optional
from cognee.infrastructure.engine import DataPoint from cognee.low_level import DataPoint
class Repository(DataPoint): class Repository(DataPoint):
path: str path: str
metadata: dict = {"index_fields": []}
class ImportStatement(DataPoint):
name: str
start_point: tuple
end_point: tuple
source_code: str
file_path: Optional[str] = None
metadata: dict = {"index_fields": ["name", "source_code"]}
class FunctionDefinition(DataPoint):
name: str
start_point: tuple
end_point: tuple
source_code: str
file_path: Optional[str] = None
metadata: dict = {"index_fields": ["name", "source_code"]}
class ClassDefinition(DataPoint):
name: str
start_point: tuple
end_point: tuple
source_code: str
file_path: Optional[str] = None
metadata: dict = {"index_fields": ["name", "source_code"]}
class CodeFile(DataPoint): class CodeFile(DataPoint):
extracted_id: str # actually file path file_path: str
source_code: Optional[str] = None source_code: Optional[str] = None
part_of: Optional[Repository] = None part_of: Optional[Repository] = None
depends_on: Optional[List["CodeFile"]] = None depends_on: Optional[List["ImportStatement"]] = []
depends_directly_on: Optional[List["CodeFile"]] = None provides_function_definition: Optional[List["FunctionDefinition"]] = []
contains: Optional[List["CodePart"]] = None provides_class_definition: Optional[List["ClassDefinition"]] = []
metadata: dict = {"index_fields": []} metadata: dict = {"index_fields": ["source_code"]}
class CodePart(DataPoint): class CodePart(DataPoint):
file_path: str # file path file_path: str
# part_of: Optional[CodeFile] = None
source_code: Optional[str] = None source_code: Optional[str] = None
metadata: dict = {"index_fields": []} metadata: dict = {"index_fields": []}
@ -28,10 +53,8 @@ class SourceCodeChunk(DataPoint):
code_chunk_of: Optional[CodePart] = None code_chunk_of: Optional[CodePart] = None
source_code: Optional[str] = None source_code: Optional[str] = None
previous_chunk: Optional["SourceCodeChunk"] = None previous_chunk: Optional["SourceCodeChunk"] = None
metadata: dict = {"index_fields": ["source_code"]} metadata: dict = {"index_fields": ["source_code"]}
CodeFile.model_rebuild() CodeFile.model_rebuild()
CodePart.model_rebuild()
SourceCodeChunk.model_rebuild() SourceCodeChunk.model_rebuild()

2
cognee/tasks/repo_processor/expand_dependency_graph.py
View file

@ -30,7 +30,7 @@ def _add_code_parts_nodes_and_edges(code_file: CodeFile, part_type, code_parts)
id=part_node_id, id=part_node_id,
type=part_type, type=part_type,
# part_of = code_file, # part_of = code_file,
file_path=code_file.extracted_id[len(code_file.part_of.path) + 1 :], file_path=code_file.file_path[len(code_file.part_of.path) + 1 :],
source_code=code_part, source_code=code_part,
) )
) )

3
cognee/tasks/repo_processor/extract_code_parts.py
View file

@ -1,5 +1,4 @@
from typing import Dict, List from typing import Dict, List
import parso
import logging import logging
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -48,6 +47,8 @@ def extract_code_parts(source_code: str) -> Dict[str, List[str]]:
return parts_dict return parts_dict
try: try:
import parso
module = parso.parse(source_code) module = parso.parse(source_code)
except Exception as e: except Exception as e:
logger.error(f"Error parsing source code: {e}") logger.error(f"Error parsing source code: {e}")

238
cognee/tasks/repo_processor/get_local_dependencies.py
View file

@ -1,154 +1,116 @@
import argparse from typing import AsyncGenerator
import asyncio from uuid import NAMESPACE_OID, uuid5
import sys import tree_sitter_python as tspython
from contextlib import contextmanager from tree_sitter import Language, Node, Parser
from pathlib import Path
from pickle import UnpicklingError
from typing import List, Dict, Optional
import aiofiles import aiofiles
import jedi
import parso
from parso.tree import BaseNode
import logging import logging
from cognee.low_level import DataPoint
from cognee.shared.CodeGraphEntities import (
CodeFile,
ImportStatement,
FunctionDefinition,
ClassDefinition,
)
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
PY_LANGUAGE = Language(tspython.language())
source_code_parser = Parser(PY_LANGUAGE)
@contextmanager
def add_sys_path(path): async def get_source_code(file_path: str):
original_sys_path = sys.path.copy()
sys.path.insert(0, path)
try: try:
yield async with aiofiles.open(file_path, "r", encoding="utf-8") as f:
finally: source_code = await f.read()
sys.path = original_sys_path return source_code
except Exception as error:
logger.error(f"Error reading file {file_path}: {str(error)}")
def _get_code_entities(node: parso.tree.NodeOrLeaf) -> List[Dict[str, any]]: return None
"""
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 KeyError as e:
# TODO: See if there is a way to handle KeyError properly
logger.error(f"Failed to analyze code entity {code_entity['name']}: {e}")
return
except UnpicklingError as e:
# TODO: See if there is a way to handle UnpicklingError properly
logger.error(f"Failed to analyze code entity {code_entity['name']}: {e}")
return
except EOFError as e:
# TODO: See if there is a way to handle EOFError properly
logger.error(f"Failed to analyze code entity {code_entity['name']}: {e}")
return
except AttributeError as e:
# TODO: See if there is a way to handle AttributeError properly
logger.error(f"Failed to analyze code entity {code_entity['name']}: {e}")
except AssertionError as e:
# TODO: See if there is a way to handle AttributeError properly
logger.error(f"Failed to analyze code entity {code_entity['name']}: {e}")
except ValueError as e:
# TODO: See if there is a way to handle ValueError properly
logger.error(f"Failed to analyze code entity {code_entity['name']}: {e}")
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}")
raise 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( async def get_local_script_dependencies(
script_path: str, repo_path: Optional[str] = None repo_path: str, script_path: str, detailed_extraction: bool = False
) -> List[str]: ) -> CodeFile:
""" source_code = await get_source_code(script_path)
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: relative_file_path = script_path[len(repo_path) + 1 :]
return await _extract_dependencies(script_path)
try: if not detailed_extraction:
repo_path = Path(repo_path).resolve(strict=True) code_file_node = CodeFile(
except (FileNotFoundError, PermissionError) as e: id=uuid5(NAMESPACE_OID, script_path),
logger.warning(f"Error resolving repo path: {e}. Proceeding without repo_path.") source_code=source_code,
return await _extract_dependencies(script_path) file_path=relative_file_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) return code_file_node
with add_sys_path(str(repo_path)): code_file_node = CodeFile(
dependencies = await _extract_dependencies(script_path) id=uuid5(NAMESPACE_OID, script_path),
source_code=None,
file_path=relative_file_path,
)
return [path for path in dependencies if path.startswith(str(repo_path))] source_code_tree = source_code_parser.parse(bytes(source_code, "utf-8"))
async for part in extract_code_parts(source_code_tree.root_node):
part.file_path = relative_file_path
if isinstance(part, FunctionDefinition):
code_file_node.provides_function_definition.append(part)
if isinstance(part, ClassDefinition):
code_file_node.provides_class_definition.append(part)
if isinstance(part, ImportStatement):
code_file_node.depends_on.append(part)
return code_file_node
def find_node(nodes: list[Node], condition: callable) -> Node:
for node in nodes:
if condition(node):
return node
return None
async def extract_code_parts(tree_root: Node) -> AsyncGenerator[DataPoint, None]:
for child_node in tree_root.children:
if child_node.type == "import_statement":
module_node = child_node.children[1]
yield ImportStatement(
name=module_node.text,
start_point=child_node.start_point,
end_point=child_node.end_point,
source_code=child_node.text,
)
if child_node.type == "import_from_statement":
module_node = child_node.children[1]
yield ImportStatement(
name=module_node.text,
start_point=child_node.start_point,
end_point=child_node.end_point,
source_code=child_node.text,
)
if child_node.type == "function_definition":
function_name_node = find_node(
child_node.children, lambda node: node.type == "identifier"
)
yield FunctionDefinition(
name=function_name_node.text,
start_point=child_node.start_point,
end_point=child_node.end_point,
source_code=child_node.text,
)
if child_node.type == "class_definition":
class_name_node = find_node(child_node.children, lambda node: node.type == "identifier")
yield ClassDefinition(
name=class_name_node.text,
start_point=child_node.start_point,
end_point=child_node.end_point,
source_code=child_node.text,
)

100
cognee/tasks/repo_processor/get_repo_file_dependencies.py
View file

@ -1,26 +1,17 @@
import asyncio import asyncio
import math
import os import os
from concurrent.futures import ProcessPoolExecutor
# from concurrent.futures import ProcessPoolExecutor
from typing import AsyncGenerator from typing import AsyncGenerator
from uuid import NAMESPACE_OID, uuid5 from uuid import NAMESPACE_OID, uuid5
import aiofiles from cognee.infrastructure.engine import DataPoint
from cognee.shared.CodeGraphEntities import CodeFile, Repository from cognee.shared.CodeGraphEntities import CodeFile, Repository
from cognee.tasks.repo_processor.get_local_dependencies import get_local_script_dependencies from cognee.tasks.repo_processor.get_local_dependencies import get_local_script_dependencies
async def get_py_path_and_source(file_path): async def get_source_code_files(repo_path):
try:
async with aiofiles.open(file_path, "r", encoding="utf-8") as f:
source_code = await f.read()
return file_path, source_code
except Exception as e:
print(f"Error reading file {file_path}: {e}")
return file_path, None
async def get_py_files_dict(repo_path):
"""Get .py files and their source code""" """Get .py files and their source code"""
if not os.path.exists(repo_path): if not os.path.exists(repo_path):
return {} return {}
@ -32,26 +23,16 @@ async def get_py_files_dict(repo_path):
if file.endswith(".py") if file.endswith(".py")
) )
py_files_dict = {} source_code_files = set()
for file_path in py_files_paths: for file_path in py_files_paths:
absolute_path = os.path.abspath(file_path) file_path = os.path.abspath(file_path)
if os.path.getsize(absolute_path) == 0: if os.path.getsize(file_path) == 0:
continue continue
relative_path, source_code = await get_py_path_and_source(absolute_path) source_code_files.add(file_path)
py_files_dict[relative_path] = {"source_code": source_code}
return py_files_dict return list(source_code_files)
def get_edge(
file_path: str, dependency: str, repo_path: str, relative_paths: bool = False
) -> tuple:
if relative_paths:
file_path = os.path.relpath(file_path, repo_path)
dependency = os.path.relpath(dependency, repo_path)
return (file_path, dependency, {"relation": "depends_directly_on"})
def run_coroutine(coroutine_func, *args, **kwargs): def run_coroutine(coroutine_func, *args, **kwargs):
@ -62,60 +43,43 @@ def run_coroutine(coroutine_func, *args, **kwargs):
return result return result
async def get_repo_file_dependencies(repo_path: str) -> AsyncGenerator[list, None]: async def get_repo_file_dependencies(
repo_path: str, detailed_extraction: bool = False
) -> AsyncGenerator[DataPoint, None]:
"""Generate a dependency graph for Python files in the given repository path.""" """Generate a dependency graph for Python files in the given repository path."""
if not os.path.exists(repo_path): if not os.path.exists(repo_path):
raise FileNotFoundError(f"Repository path {repo_path} does not exist.") raise FileNotFoundError(f"Repository path {repo_path} does not exist.")
py_files_dict = await get_py_files_dict(repo_path) source_code_files = await get_source_code_files(repo_path)
repo = Repository( repo = Repository(
id=uuid5(NAMESPACE_OID, repo_path), id=uuid5(NAMESPACE_OID, repo_path),
path=repo_path, path=repo_path,
) )
yield [repo] yield repo
with ProcessPoolExecutor(max_workers=12) as executor: chunk_size = 100
loop = asyncio.get_event_loop() number_of_chunks = math.ceil(len(source_code_files) / chunk_size)
chunk_ranges = [
(
chunk_number * chunk_size,
min((chunk_number + 1) * chunk_size, len(source_code_files)) - 1,
)
for chunk_number in range(number_of_chunks)
]
for start_range, end_range in chunk_ranges:
# with ProcessPoolExecutor(max_workers=12) as executor:
tasks = [ tasks = [
loop.run_in_executor( get_local_script_dependencies(repo_path, file_path, detailed_extraction)
executor, for file_path in source_code_files[start_range:end_range]
run_coroutine,
get_local_script_dependencies,
os.path.join(repo_path, file_path),
repo_path,
)
for file_path, metadata in py_files_dict.items()
if metadata.get("source_code") is not None
] ]
results = await asyncio.gather(*tasks) results: list[CodeFile] = await asyncio.gather(*tasks)
code_files = [] for source_code_file in results:
for (file_path, metadata), dependencies in zip(py_files_dict.items(), results): source_code_file.part_of = repo
source_code = metadata.get("source_code")
code_files.append( yield source_code_file
CodeFile(
id=uuid5(NAMESPACE_OID, file_path),
source_code=source_code,
extracted_id=file_path,
part_of=repo,
depends_on=[
CodeFile(
id=uuid5(NAMESPACE_OID, dependency),
extracted_id=dependency,
part_of=repo,
source_code=py_files_dict.get(dependency, {}).get("source_code"),
)
for dependency in dependencies
]
if dependencies
else None,
)
)
yield code_files

4
cognee/tasks/repo_processor/get_source_code_chunks.py
View file

@ -2,8 +2,6 @@ import logging
from typing import AsyncGenerator, Generator from typing import AsyncGenerator, Generator
from uuid import NAMESPACE_OID, uuid5 from uuid import NAMESPACE_OID, uuid5
import parso
from cognee.infrastructure.databases.vector import get_vector_engine from cognee.infrastructure.databases.vector import get_vector_engine
from cognee.infrastructure.engine import DataPoint from cognee.infrastructure.engine import DataPoint
from cognee.shared.CodeGraphEntities import CodeFile, CodePart, SourceCodeChunk from cognee.shared.CodeGraphEntities import CodeFile, CodePart, SourceCodeChunk
@ -46,6 +44,8 @@ def _get_subchunk_token_counts(
return _get_naive_subchunk_token_counts(source_code, max_subchunk_tokens) return _get_naive_subchunk_token_counts(source_code, max_subchunk_tokens)
try: try:
import parso
module = parso.parse(source_code) module = parso.parse(source_code)
except Exception as e: except Exception as e:
logger.error(f"Error parsing source code: {e}") logger.error(f"Error parsing source code: {e}")

8
cognee/tasks/repo_processor/top_down_repo_parse.py
View file

@ -1,7 +1,5 @@
import os import os
import jedi
import parso
from tqdm import tqdm from tqdm import tqdm
import logging import logging
@ -60,6 +58,8 @@ def _parse_node(node):
def extract_importable_objects_with_positions_from_source_code(source_code): def extract_importable_objects_with_positions_from_source_code(source_code):
"""Extract top-level objects in a Python source code string with their positions (line/column).""" """Extract top-level objects in a Python source code string with their positions (line/column)."""
try: try:
import parso
tree = parso.parse(source_code) tree = parso.parse(source_code)
except Exception as e: except Exception as e:
logger.error(f"Error parsing source code: {e}") logger.error(f"Error parsing source code: {e}")
@ -137,6 +137,8 @@ def parse_file_with_references(project, file_path):
return [] return []
try: try:
import jedi
script = jedi.Script(path=file_path, project=project) script = jedi.Script(path=file_path, project=project)
except Exception as e: except Exception as e:
logger.error(f"Error initializing Jedi Script: {e}") logger.error(f"Error initializing Jedi Script: {e}")
@ -156,6 +158,8 @@ def parse_file_with_references(project, file_path):
def parse_repo(repo_path): def parse_repo(repo_path):
"""Parse a repository to extract object names, types, and references for all Python files.""" """Parse a repository to extract object names, types, and references for all Python files."""
try: try:
import jedi
project = jedi.Project(path=repo_path) project = jedi.Project(path=repo_path)
except Exception as e: except Exception as e:
logger.error(f"Error creating Jedi project for repository at {repo_path}: {e}") logger.error(f"Error creating Jedi project for repository at {repo_path}: {e}")

4
cognee/tasks/summarization/models.py
View file

@ -2,7 +2,7 @@ from typing import Union
from cognee.infrastructure.engine import DataPoint from cognee.infrastructure.engine import DataPoint
from cognee.modules.chunking.models import DocumentChunk from cognee.modules.chunking.models import DocumentChunk
from cognee.shared.CodeGraphEntities import CodeFile, CodePart, SourceCodeChunk from cognee.shared.CodeGraphEntities import CodeFile, CodePart
class TextSummary(DataPoint): class TextSummary(DataPoint):
@ -14,6 +14,6 @@ class TextSummary(DataPoint):
class CodeSummary(DataPoint): class CodeSummary(DataPoint):
text: str text: str
summarizes: Union[CodeFile, CodePart, SourceCodeChunk] summarizes: Union[CodeFile, CodePart]
metadata: dict = {"index_fields": ["text"]} metadata: dict = {"index_fields": ["text"]}

4
notebooks/cognee_llama_index.ipynb
View file

@ -12,9 +12,7 @@
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": "%pip install llama-index-core\n"
"%pip install llama-index-core"
]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",

947
poetry.lock generated
View file

File diff suppressed because it is too large Load diff

12
pyproject.toml
View file

@ -78,11 +78,12 @@ httpx = "0.27.0"
bokeh="^3.6.2" bokeh="^3.6.2"
nltk = "3.9.1" nltk = "3.9.1"
google-generativeai = {version = "^0.8.4", optional = true} google-generativeai = {version = "^0.8.4", optional = true}
parso = {version = "^0.8.4", optional = true}
jedi = {version = "^0.19.2", optional = true}
plotly = "^6.0.0"
mistral-common = {version = "^1.5.2", optional = true} mistral-common = {version = "^1.5.2", optional = true}
gdown = "^5.2.0" fastembed = {version = "^0.4.2", optional = true}
tree-sitter = {version = "^0.24.0", optional = true}
tree-sitter-python = {version = "^0.23.6", optional = true}
plotly = {version = "^6.0.0", optional = true}
gdown = {version = "^5.2.0", optional = true}
[tool.poetry.extras] [tool.poetry.extras]
@ -103,7 +104,8 @@ falkordb = ["falkordb"]
groq = ["groq"] groq = ["groq"]
milvus = ["pymilvus"] milvus = ["pymilvus"]
docs = ["unstructured"] docs = ["unstructured"]
codegraph = ["parso", "jedi"] codegraph = ["fastembed", "tree-sitter", "tree-sitter-python"]
evals = ["plotly", "gdown"]
[tool.poetry.group.dev.dependencies] [tool.poetry.group.dev.dependencies]
pytest = "^7.4.0" pytest = "^7.4.0"