Merge branch 'dev' into COG-748

.github/workflows/ruff_format.yaml

@@ -0,0 +1,11 @@
+name: ruff format
+on: [ pull_request ]
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v2
+        with:
+          args: "format --check"

.github/workflows/ruff_lint.yaml

@@ -0,0 +1,9 @@
+name: ruff lint
+on: [ pull_request ]
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v2

.pre-commit-config.yaml

@@ -0,0 +1,20 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.2.0
+    hooks:
+    -   id: trailing-whitespace
+    -   id: end-of-file-fixer
+    -   id: check-yaml
+    -   id: check-added-large-files
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version.
+  rev: v0.8.3
+  hooks:
+    # Run the linter.
+    - id: ruff
+      types_or: [ python, pyi ]
+    # Run the formatter.
+    - id: ruff-format
+      types_or: [ python, pyi ]

README.md

@@ -17,6 +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
 
@@ -193,93 +196,14 @@ if __name__ == '__main__':
 When you run this script, you will see step-by-step messages in the console that help you trace the execution flow and understand what the script is doing at each stage.
 A version of this example is here: `examples/python/simple_example.py`
 
-### Create your own memory store
+### Understand our architecture
 
 cognee framework consists of tasks that can be grouped into pipelines.
 Each task can be an independent part of business logic, that can be tied to other tasks to form a pipeline.
 These tasks persist data into your memory store enabling you to search for relevant context of past conversations, documents, or any other data you have stored.
-
-
-### Example: Classify your documents
-
-Here is an example of how it looks for a default cognify pipeline:
-
-1. To prepare the data for the pipeline run, first we need to add it to our metastore and normalize it:
-
-Start with:
-```
-text = """Natural language processing (NLP) is an interdisciplinary
-       subfield of computer science and information retrieval"""
-
-await cognee.add(text) # Add a new piece of information
-```
-
-2. In the next step we make a task. The task can be any business logic we need, but the important part is that it should be encapsulated in one function.
-
-Here we show an example of creating a naive LLM classifier that takes a Pydantic model and then stores the data in both the graph and vector stores after analyzing each chunk.
-We provided just a snippet for reference, but feel free to check out the implementation in our repo. 
-
-```
-async def chunk_naive_llm_classifier(
-    data_chunks: list[DocumentChunk],
-    classification_model: Type[BaseModel]
-):
-    # Extract classifications asynchronously
-    chunk_classifications = await asyncio.gather(
-        *(extract_categories(chunk.text, classification_model) for chunk in data_chunks)
-    )
-
-    # Collect classification data points using a set to avoid duplicates
-    classification_data_points = {
-        uuid5(NAMESPACE_OID, cls.label.type)
-        for cls in chunk_classifications
-    } | {
-        uuid5(NAMESPACE_OID, subclass.value)
-        for cls in chunk_classifications
-        for subclass in cls.label.subclass
-    }
-
-    vector_engine = get_vector_engine()
-    collection_name = "classification"
-
-    # Define the payload schema
-    class Keyword(BaseModel):
-        uuid: str
-        text: str
-        chunk_id: str
-        document_id: str
-
-    # Ensure the collection exists and retrieve existing data points
-    if not await vector_engine.has_collection(collection_name):
-        await vector_engine.create_collection(collection_name, payload_schema=Keyword)
-        existing_points_map = {}
-    else:
-        existing_points_map = {}
-    return data_chunks
-
-...
-
-```
-
-We have many tasks that can be used in your pipelines, and you can also create your  tasks to fit your business logic.
-
-
-3. Once we have our tasks, it is time to group them into a pipeline.
-This simplified snippet demonstrates how tasks can be added to a pipeline, and how they can pass the information forward from one to another. 
-
-```
-            
-
-Task(
-    chunk_naive_llm_classifier,
-    classification_model = cognee_config.classification_model,
-)
-
-pipeline = run_tasks(tasks, documents)
-
-```
-
-To see the working code, check cognee.api.v1.cognify default pipeline in our repo.
+<div align="center">
+<img src="assets/cognee_diagram.png" alt="cognee concept diagram" width="50%" />
+</div>
 
 
 ## Vector retrieval, Graphs and LLMs
@@ -338,11 +262,7 @@ pip install cognee
 
 ## Vector & Graph Databases Implementation State
 
-<style>
-  table {
-    width: 100%;
-  }
-</style>
+
 
 | Name     | Type               | Current state     | Known Issues |
 |----------|--------------------|-------------------|--------------|
@@ -353,4 +273,4 @@ pip install cognee
 | NetworkX | Graph              | Stable &#x2705;   |              |
 | FalkorDB | Vector/Graph       | Unstable &#x274C; |              |
 | PGVector | Vector             | Stable &#x2705;   |              |
-| Milvus   | Vector             | Stable &#x2705;   |              |
+| Milvus   | Vector             | Stable &#x2705;   |              |

cognee-mcp/README.md

@@ -1,57 +1,85 @@
 # cognee MCP server
 
+
+
+
+### Installing Manually
 A MCP server project
+=======
+1. Clone the [cognee](www.github.com/topoteretes/cognee) repo
 
-Create a boilerplate server:
 
-```jsx
-uvx create-mcp-server
-```
 
-1. The command will ask you to name your server, e.g. mcp_cognee
+2. Install dependencies
 
+```
+pip install uv
+```
+```
+brew install postgresql
+```
 
-2. Answer “Y” to connect with Claude
-Then run
+```
+brew install rust
+```
 
 ```jsx
-cd mcp_cognee
+cd cognee-mcp
 uv sync --dev --all-extras
 ```
 
-Activate the venv with
+3. Activate the venv with
 
 ```jsx
 source .venv/bin/activate
 ```
 
-This should already add the new server to your Claude config, but if not, add these lines manually:
+4. Add the new server to your Claude config:
+
+The file should be located here: ~/Library/Application\ Support/Claude/
+You need to create claude_desktop_config.json in this folder if it doesn't exist
 
 ```
-"mcpcognee": {
-      "command": "uv",
-      "args": [
+
+
+{
+	"mcpServers": {
+		"cognee": {
+			"command": "/Users/{user}/cognee/.venv/bin/uv",
+			"args": [
         "--directory",
-        "/Users/your_username/mcp/mcp_cognee",
+        "/Users/{user}/cognee/cognee-mcp",
         "run",
-        "mcpcognee"
+        "cognee"
       ],
       "env": {
         "ENV": "local",
         "TOKENIZERS_PARALLELISM": "false",
-        "LLM_API_KEY": "add_your_api_key_here",
-        "GRAPH_DATABASE_PROVIDER": "neo4j",
-        "GRAPH_DATABASE_URL": "bolt://localhost:7687",
-        "GRAPH_DATABASE_USERNAME": "add_username_here",
-        "GRAPH_DATABASE_PASSWORD": "add_pwd_here",
-        "VECTOR_DB_PROVIDER": "lancedb",
-        "DB_PROVIDER": "sqlite",
-        "DB_NAME": "postgres"
+        "LLM_API_KEY": "sk-"
       }
+		},
+    "filesystem": {
+      "command": "npx",
+      "args": [
+        "-y",
+        "@modelcontextprotocol/server-filesystem",
+        "/Users/{user}/Desktop",
+        "/Users/{user}/Projects"
+      ]
     }
+	}
+}
 ```
 
-Then, edit the pyproject.toml in your new folder so that it includes packages from the cognee requirements. Use the pyproject.toml in your cognee library for this, but match the syntax of the automatically generated pyproject.toml so that it is compatible with uv.
+Restart your Claude desktop.
+
+### Installing via Smithery
+
+To install Cognee for Claude Desktop automatically via [Smithery](https://smithery.ai/server/cognee):
+
+```bash
+npx -y @smithery/cli install cognee --client claude
+```
 
 Define cognify tool in server.py
-Restart your Claude desktop.
+Restart your Claude desktop.

cognee-mcp/mcpcognee/__init__.py → cognee-mcp/cognee_mcp/__init__.py

@@ -8,7 +8,7 @@ def main():
     asyncio.run(server.main())
 
 # Optionally expose other important items at package level
-__all__ = ['main', 'server']
+__all__ = ["main", "server"]
 
 if __name__ == "__main__":
     main()