Merge branch 'dev' into COG-650-replace-pylint

README.md

@@ -17,7 +17,9 @@ Try it in a Google Colab  <a href="https://colab.research.google.com/drive/1g-Qn
 
 If you have questions, join our  <a href="https://discord.gg/NQPKmU5CCg">Discord</a> community
 
-
+<div align="center">
+<img src="assets/cognee_benefits.png" alt="why cognee" width="80%" />
+</div>
 ## 📦 Installation
 
 You can install Cognee using either **pip** or **poetry**.

cognee/api/v1/cognify/code_graph_pipeline.py

@@ -3,6 +3,8 @@ import logging
 from pathlib import Path
 
 from cognee.base_config import get_base_config
+from cognee.infrastructure.databases.vector.embeddings import \
+    get_embedding_engine
 from cognee.modules.cognify.config import get_cognify_config
 from cognee.modules.pipelines import run_tasks
 from cognee.modules.pipelines.tasks.Task import Task
@@ -15,8 +17,10 @@ from cognee.tasks.ingestion import ingest_data_with_metadata
 from cognee.tasks.repo_processor import (enrich_dependency_graph,
                                          expand_dependency_graph,
                                          get_data_list_for_user,
-                                         get_non_code_files,
+                                         get_non_py_files,
                                          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
 
 monitoring = get_base_config().monitoring_tool
@@ -28,6 +32,7 @@ from cognee.tasks.summarization import summarize_code, summarize_text
 logger = logging.getLogger("code_graph_pipeline")
 update_status_lock = asyncio.Lock()
 
+
 @observe
 async def run_code_graph_pipeline(repo_path, include_docs=True):
     import os
@@ -46,20 +51,23 @@ async def run_code_graph_pipeline(repo_path, include_docs=True):
     await cognee.prune.prune_system(metadata=True)
     await create_db_and_tables()
 
+    embedding_engine = get_embedding_engine()
+
     cognee_config = get_cognify_config()
     user = await get_default_user()
 
     tasks = [
         Task(get_repo_file_dependencies),
-        Task(enrich_dependency_graph, task_config={"batch_size": 50}),
+        Task(enrich_dependency_graph),
         Task(expand_dependency_graph, task_config={"batch_size": 50}),
+        Task(get_source_code_chunks, embedding_model=embedding_engine.model, task_config={"batch_size": 50}),
         Task(summarize_code, task_config={"batch_size": 50}),
         Task(add_data_points, task_config={"batch_size": 50}),
     ]
 
     if include_docs:
         non_code_tasks = [
-            Task(get_non_code_files, task_config={"batch_size": 50}),
+            Task(get_non_py_files, task_config={"batch_size": 50}),
             Task(ingest_data_with_metadata, dataset_name="repo_docs", user=user),
             Task(get_data_list_for_user, dataset_name="repo_docs", user=user),
             Task(classify_documents),
@@ -71,7 +79,7 @@ async def run_code_graph_pipeline(repo_path, include_docs=True):
                 task_config={"batch_size": 50}
             ),
         ]
-
+        
     if include_docs:
         async for result in run_tasks(non_code_tasks, repo_path):
             yield result

cognee/infrastructure/databases/exceptions/EmbeddingException.py

@@ -0,0 +1,3 @@
+class EmbeddingException(Exception):
+    """Custom exception for handling embedding-related errors."""
+    pass

cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py

@@ -5,17 +5,19 @@ from typing import List, Optional
 import litellm
 import os
 from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine
+from cognee.infrastructure.databases.exceptions.EmbeddingException import EmbeddingException
 
 litellm.set_verbose = False
 logger = logging.getLogger("LiteLLMEmbeddingEngine")
 
+
 class LiteLLMEmbeddingEngine(EmbeddingEngine):
     api_key: str
     endpoint: str
     api_version: str
     model: str
     dimensions: int
-    mock:bool
+    mock: bool
 
     def __init__(
         self,
@@ -33,7 +35,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
 
         enable_mocking = os.getenv("MOCK_EMBEDDING", "false")
         if isinstance(enable_mocking, bool):
-            enable_mocking= str(enable_mocking).lower()
+            enable_mocking = str(enable_mocking).lower()
         self.mock = enable_mocking in ("true", "1", "yes")
 
     MAX_RETRIES = 5
@@ -43,7 +45,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
         async def exponential_backoff(attempt):
             wait_time = min(10 * (2 ** attempt), 60)  # Max 60 seconds
             await asyncio.sleep(wait_time)
-      
+
         try:
             if self.mock:
                 response = {
@@ -56,10 +58,10 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
             else:
                 response = await litellm.aembedding(
                     self.model,
-                    input = text,
-                    api_key = self.api_key,
-                    api_base = self.endpoint,
-                    api_version = self.api_version
+                    input=text,
+                    api_key=self.api_key,
+                    api_base=self.endpoint,
+                    api_version=self.api_version
                 )
 
                 self.retry_count = 0
@@ -71,7 +73,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
                 if len(text) == 1:
                     parts = [text]
                 else:
-                    parts = [text[0:math.ceil(len(text)/2)], text[math.ceil(len(text)/2):]]
+                    parts = [text[0:math.ceil(len(text) / 2)], text[math.ceil(len(text) / 2):]]
 
                 parts_futures = [self.embed_text(part) for part in parts]
                 embeddings = await asyncio.gather(*parts_futures)
@@ -95,6 +97,9 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
 
             return await self.embed_text(text)
 
+        except (litellm.exceptions.BadRequestError, litellm.llms.OpenAI.openai.OpenAIError):
+            raise EmbeddingException("Failed to index data points.")
+
         except Exception as error:
             logger.error("Error embedding text: %s", str(error))
             raise error

cognee/shared/CodeGraphEntities.py

@@ -1,5 +1,4 @@
 from typing import List, Optional
-
 from cognee.infrastructure.engine import DataPoint
 
 
@@ -7,7 +6,7 @@ class Repository(DataPoint):
     __tablename__ = "Repository"
     path: str
     _metadata: dict = {
-        "index_fields": ["source_code"],
+        "index_fields": [],
         "type": "Repository"
     }
 
@@ -19,29 +18,31 @@ class CodeFile(DataPoint):
     depends_on: Optional[List["CodeFile"]] = None
     depends_directly_on: Optional[List["CodeFile"]] = None
     contains: Optional[List["CodePart"]] = None
-
     _metadata: dict = {
-        "index_fields": ["source_code"],
+        "index_fields": [],
         "type": "CodeFile"
     }
 
 class CodePart(DataPoint):
     __tablename__ = "codepart"
-    # part_of: Optional[CodeFile]
-    source_code: str
-    
+    # part_of: Optional[CodeFile] = None
+    source_code: Optional[str] = None
     _metadata: dict = {
-        "index_fields": ["source_code"],
+        "index_fields": [],
         "type": "CodePart"
     }
 
-class CodeRelationship(DataPoint):
-    source_id: str
-    target_id: str
-    relation: str  # depends on or depends directly
+class SourceCodeChunk(DataPoint):
+    __tablename__ = "sourcecodechunk"
+    code_chunk_of: Optional[CodePart] = None
+    source_code: Optional[str] = None
+    previous_chunk: Optional["SourceCodeChunk"] = None
+
     _metadata: dict = {
-        "type": "CodeRelationship"
+        "index_fields": ["source_code"],
+        "type": "SourceCodeChunk"
     }
 
 CodeFile.model_rebuild()
 CodePart.model_rebuild()
+SourceCodeChunk.model_rebuild()

cognee/shared/data_models.py

@@ -210,7 +210,6 @@ class SummarizedClass(BaseModel):
     decorators: Optional[List[str]] = None
 
 class SummarizedCode(BaseModel):
-    file_name: str
     high_level_summary: str
     key_features: List[str]
     imports: List[str] = []

cognee/tasks/repo_processor/get_repo_file_dependencies.py

@@ -71,7 +71,7 @@ async def get_repo_file_dependencies(repo_path: str) -> AsyncGenerator[list, Non
         path = repo_path,
     )
 
-    yield repo
+    yield [repo]
 
     with ProcessPoolExecutor(max_workers = 12) as executor:
         loop = asyncio.get_event_loop()
@@ -90,10 +90,11 @@ async def get_repo_file_dependencies(repo_path: str) -> AsyncGenerator[list, Non
 
         results = await asyncio.gather(*tasks)
 
+        code_files = []
         for (file_path, metadata), dependencies in zip(py_files_dict.items(), results):
             source_code = metadata.get("source_code")
 
-            yield CodeFile(
+            code_files.append(CodeFile(
                 id = uuid5(NAMESPACE_OID, file_path),
                 source_code = source_code,
                 extracted_id = file_path,
@@ -106,4 +107,6 @@ async def get_repo_file_dependencies(repo_path: str) -> AsyncGenerator[list, Non
                         source_code = py_files_dict.get(dependency, {}).get("source_code"),
                     ) for dependency in dependencies
                 ] if dependencies else None,
-            )
+            ))
+
+        yield code_files

cognee/tasks/repo_processor/get_source_code_chunks.py

@@ -0,0 +1,164 @@
+import logging
+from typing import AsyncGenerator, Generator
+from uuid import NAMESPACE_OID, uuid5
+
+import parso
+import tiktoken
+
+from cognee.infrastructure.engine import DataPoint
+from cognee.shared.CodeGraphEntities import CodeFile, CodePart, SourceCodeChunk
+
+logger = logging.getLogger("task:get_source_code_chunks")
+
+
+def _count_tokens(tokenizer: tiktoken.Encoding, source_code: str) -> int:
+    return len(tokenizer.encode(source_code))
+
+
+def _get_naive_subchunk_token_counts(
+        tokenizer: tiktoken.Encoding, source_code: str, max_subchunk_tokens: int = 8000
+) -> list[tuple[str, int]]:
+    """Splits source code into subchunks of up to max_subchunk_tokens and counts tokens."""
+
+    token_ids = tokenizer.encode(source_code)
+    subchunk_token_counts = []
+
+    for start_idx in range(0, len(token_ids), max_subchunk_tokens):
+        subchunk_token_ids = token_ids[start_idx: start_idx + max_subchunk_tokens]
+        token_count = len(subchunk_token_ids)
+        subchunk = ''.join(
+            tokenizer.decode_single_token_bytes(token_id).decode('utf-8', errors='replace')
+            for token_id in subchunk_token_ids
+        )
+        subchunk_token_counts.append((subchunk, token_count))
+
+    return subchunk_token_counts
+
+
+def _get_subchunk_token_counts(
+        tokenizer: tiktoken.Encoding,
+        source_code: str,
+        max_subchunk_tokens: int = 8000,
+        depth: int = 0,
+        max_depth: int = 100
+) -> list[tuple[str, int]]:
+    """Splits source code into subchunk and counts tokens for each subchunk."""
+    if depth > max_depth:
+        return _get_naive_subchunk_token_counts(tokenizer, source_code, max_subchunk_tokens)
+
+    try:
+        module = parso.parse(source_code)
+    except Exception as e:
+        logger.error(f"Error parsing source code: {e}")
+        return []
+
+    if not module.children:
+        logger.warning("Parsed module has no children (empty or invalid source code).")
+        return []
+
+    # Handle cases with only one real child and an EndMarker to prevent infinite recursion.
+    if len(module.children) <= 2:
+        module = module.children[0]
+
+    subchunk_token_counts = []
+    for child in module.children:
+        subchunk = child.get_code()
+        token_count = _count_tokens(tokenizer, subchunk)
+
+        if token_count == 0:
+            continue
+
+        if token_count <= max_subchunk_tokens:
+            subchunk_token_counts.append((subchunk, token_count))
+            continue
+
+        if child.type == 'string':
+            subchunk_token_counts.extend(_get_naive_subchunk_token_counts(tokenizer, subchunk, max_subchunk_tokens))
+            continue
+
+        subchunk_token_counts.extend(
+            _get_subchunk_token_counts(tokenizer, subchunk, max_subchunk_tokens, depth=depth + 1, max_depth=max_depth)
+        )
+
+    return subchunk_token_counts
+
+
+def _get_chunk_source_code(
+        code_token_counts: list[tuple[str, int]], overlap: float, max_tokens: int
+) -> tuple[list[tuple[str, int]], str]:
+    """Generates a chunk of source code from tokenized subchunks with overlap handling."""
+    current_count = 0
+    cumulative_counts = []
+    current_source_code = ''
+
+    for i, (child_code, token_count) in enumerate(code_token_counts):
+        current_count += token_count
+        cumulative_counts.append(current_count)
+        if current_count > max_tokens:
+            break
+        current_source_code += f"\n{child_code}"
+
+    if current_count <= max_tokens:
+        return [], current_source_code.strip()
+
+    cutoff = 1
+    for i, cum_count in enumerate(cumulative_counts):
+        if cum_count > (1 - overlap) * max_tokens:
+            break
+        cutoff = i
+
+    return code_token_counts[cutoff:], current_source_code.strip()
+
+
+def get_source_code_chunks_from_code_part(
+        code_file_part: CodePart,
+        max_tokens: int = 8192,
+        overlap: float = 0.25,
+        granularity: float = 0.1,
+        model_name: str = "text-embedding-3-large"
+) -> Generator[SourceCodeChunk, None, None]:
+    """Yields source code chunks from a CodePart object, with configurable token limits and overlap."""
+    if not code_file_part.source_code:
+        logger.error(f"No source code in CodeFile {code_file_part.id}")
+        return
+
+    tokenizer = tiktoken.encoding_for_model(model_name)
+    max_subchunk_tokens = max(1, int(granularity * max_tokens))
+    subchunk_token_counts = _get_subchunk_token_counts(tokenizer, code_file_part.source_code, max_subchunk_tokens)
+
+    previous_chunk = None
+    while subchunk_token_counts:
+        subchunk_token_counts, chunk_source_code = _get_chunk_source_code(subchunk_token_counts, overlap, max_tokens)
+        if not chunk_source_code:
+            continue
+        current_chunk = SourceCodeChunk(
+            id=uuid5(NAMESPACE_OID, chunk_source_code),
+            code_chunk_of=code_file_part,
+            source_code=chunk_source_code,
+            previous_chunk=previous_chunk
+        )
+        yield current_chunk
+        previous_chunk = current_chunk
+
+
+async def get_source_code_chunks(data_points: list[DataPoint], embedding_model="text-embedding-3-large") -> \
+        AsyncGenerator[list[DataPoint], None]:
+    """Processes code graph datapoints, create SourceCodeChink datapoints."""
+    # TODO: Add support for other embedding models, with max_token mapping
+    for data_point in data_points:
+        try:
+            yield data_point
+            if not isinstance(data_point, CodeFile):
+                continue
+            if not data_point.contains:
+                logger.warning(f"CodeFile {data_point.id} contains no code parts")
+                continue
+            for code_part in data_point.contains:
+                try:
+                    yield code_part
+                    for source_code_chunk in get_source_code_chunks_from_code_part(code_part, model_name=embedding_model):
+                        yield source_code_chunk
+                except Exception as e:
+                    logger.error(f"Error processing code part: {e}")
+        except Exception as e:
+            logger.error(f"Error processing data point: {e}")

cognee/tasks/storage/index_data_points.py

@@ -1,6 +1,10 @@
+import logging
+
+from cognee.infrastructure.databases.exceptions.EmbeddingException import EmbeddingException
 from cognee.infrastructure.databases.vector import get_vector_engine
 from cognee.infrastructure.engine import DataPoint
 
+logger = logging.getLogger("index_data_points")
 
 async def index_data_points(data_points: list[DataPoint]):
     created_indexes = {}
@@ -30,7 +34,10 @@ async def index_data_points(data_points: list[DataPoint]):
 
     for index_name, indexable_points in index_points.items():
         index_name, field_name = index_name.split(".")
-        await vector_engine.index_data_points(index_name, field_name, indexable_points)
+        try:
+            await vector_engine.index_data_points(index_name, field_name, indexable_points)
+        except EmbeddingException as e:
+            logger.warning(f"Failed to index data points for {index_name}.{field_name}: {e}")
 
     return data_points
 

cognee/tasks/summarization/models.py

@@ -1,6 +1,8 @@
+from typing import Union
+
 from cognee.infrastructure.engine import DataPoint
 from cognee.modules.chunking.models import DocumentChunk
-from cognee.shared.CodeGraphEntities import CodeFile
+from cognee.shared.CodeGraphEntities import CodeFile, CodePart, SourceCodeChunk
 
 
 class TextSummary(DataPoint):
@@ -17,7 +19,7 @@ class TextSummary(DataPoint):
 class CodeSummary(DataPoint):
     __tablename__ = "code_summary"
     text: str
-    made_from: CodeFile
+    summarizes: Union[CodeFile, CodePart, SourceCodeChunk]
 
     _metadata: dict = {
         "index_fields": ["text"],

cognee/tasks/summarization/summarize_code.py

@@ -1,10 +1,10 @@
 import asyncio
 from typing import AsyncGenerator, Union
 from uuid import uuid5
-from typing import Type
 
 from cognee.infrastructure.engine import DataPoint
 from cognee.modules.data.extraction.extract_summary import extract_code_summary
+
 from .models import CodeSummary
 
 
@@ -21,7 +21,7 @@ async def summarize_code(
     )
 
     file_summaries_map = {
-        code_data_point.extracted_id: str(file_summary)
+        code_data_point.id: str(file_summary)
         for code_data_point, file_summary in zip(code_data_points, file_summaries)
     }
 
@@ -35,6 +35,6 @@ async def summarize_code(
 
         yield CodeSummary(
             id=uuid5(node.id, "CodeSummary"),
-            made_from=node,
-            text=file_summaries_map[node.extracted_id],
+            summarizes=node,
+            text=file_summaries_map[node.id],
         )

examples/python/code_graph_example.py

@@ -11,6 +11,6 @@ async def main(repo_path, include_docs):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--repo_path", type=str, required=True, help="Path to the repository")
-    parser.add_argument("--include_docs", type=bool, default=True, help="Whether or not to process non-code files")
+    parser.add_argument("--include_docs", type=lambda x: x.lower() in ("true", "1"), default=True, help="Whether or not to process non-code files")
     args = parser.parse_args()
     asyncio.run(main(args.repo_path, args.include_docs))