diff --git a/.env.template b/.env.template
index 8e1bdd23f..ae2cb1338 100644
--- a/.env.template
+++ b/.env.template
@@ -243,13 +243,14 @@ LITELLM_LOG="ERROR"
 
 ########## Local LLM via Ollama ###############################################
 
+
 #LLM_API_KEY ="ollama"
 #LLM_MODEL="llama3.1:8b"
 #LLM_PROVIDER="ollama"
 #LLM_ENDPOINT="http://localhost:11434/v1"
 #EMBEDDING_PROVIDER="ollama"
 #EMBEDDING_MODEL="nomic-embed-text:latest"
-#EMBEDDING_ENDPOINT="http://localhost:11434/api/embeddings"
+#EMBEDDING_ENDPOINT="http://localhost:11434/api/embed"
 #EMBEDDING_DIMENSIONS=768
 #HUGGINGFACE_TOKENIZER="nomic-ai/nomic-embed-text-v1.5"
 
diff --git a/.github/workflows/dockerhub-mcp.yml b/.github/workflows/dockerhub-mcp.yml
index 3d4ef4fe1..5a1f28296 100644
--- a/.github/workflows/dockerhub-mcp.yml
+++ b/.github/workflows/dockerhub-mcp.yml
@@ -7,14 +7,29 @@ on:
 
 jobs:
   docker-build-and-push:
-    runs-on: ubuntu-latest
+    runs-on:
+      group: Default
+      labels:
+        - docker_build_runner
 
     steps:
+      - name: Check and free disk space before build
+        run: |
+          echo "=== Before cleanup ==="
+          df -h
+          echo "Removing unused preinstalled SDKs to free space..."
+          sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc || true
+          docker system prune -af || true
+          echo "=== After cleanup ==="
+          df -h
+
       - name: Checkout repository
         uses: actions/checkout@v4
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
+        with:
+          buildkitd-flags: --root /tmp/buildkit
 
       - name: Log in to Docker Hub
         uses: docker/login-action@v3
@@ -34,7 +49,7 @@ jobs:
 
       - name: Build and push
         id: build
-        uses: docker/build-push-action@v5
+        uses: docker/build-push-action@v6
         with:
           context: .
           platforms: linux/amd64,linux/arm64
@@ -45,5 +60,6 @@ jobs:
           cache-from: type=registry,ref=cognee/cognee-mcp:buildcache
           cache-to: type=registry,ref=cognee/cognee-mcp:buildcache,mode=max
 
+
       - name: Image digest
         run: echo ${{ steps.build.outputs.digest }}
diff --git a/AGENTS.md b/AGENTS.md
new file mode 100644
index 000000000..4cadfbdc1
--- /dev/null
+++ b/AGENTS.md
@@ -0,0 +1,132 @@
+## Repository Guidelines
+
+This document summarizes how to work with the cognee repository: how it’s organized, how to build, test, lint, and contribute. It mirrors our actual tooling and CI while providing quick commands for local development.
+
+## Project Structure & Module Organization
+
+- `cognee/`: Core Python library and API.
+  - `api/`: FastAPI application and versioned routers (add, cognify, memify, search, delete, users, datasets, responses, visualize, settings, sync, update, checks).
+  - `cli/`: CLI entry points and subcommands invoked via `cognee` / `cognee-cli`.
+  - `infrastructure/`: Databases, LLM providers, embeddings, loaders, and storage adapters.
+  - `modules/`: Domain logic (graph, retrieval, ontology, users, processing, observability, etc.).
+  - `tasks/`: Reusable tasks (e.g., code graph, web scraping, storage). Extend with new tasks here.
+  - `eval_framework/`: Evaluation utilities and adapters.
+  - `shared/`: Cross-cutting helpers (logging, settings, utils).
+  - `tests/`: Unit, integration, CLI, and end-to-end tests organized by feature.
+  - `__main__.py`: Entrypoint to route to CLI.
+- `cognee-mcp/`: Model Context Protocol server exposing cognee as MCP tools (SSE/HTTP/stdio). Contains its own README and Dockerfile.
+- `cognee-frontend/`: Next.js UI for local development and demos.
+- `distributed/`: Utilities for distributed execution (Modal, workers, queues).
+- `examples/`: Example scripts demonstrating the public APIs and features (graph, code graph, multimodal, permissions, etc.).
+- `notebooks/`: Jupyter notebooks for demos and tutorials.
+- `alembic/`: Database migrations for relational backends.
+
+Notes:
+- Co-locate feature-specific helpers under their respective package (`modules/`, `infrastructure/`, or `tasks/`).
+- Extend the system by adding new tasks, loaders, or retrievers rather than modifying core pipeline mechanisms.
+
+## Build, Test, and Development Commands
+
+Python (root) – requires Python >= 3.10 and < 3.14. We recommend `uv` for speed and reproducibility.
+
+- Create/refresh env and install dev deps:
+```bash
+uv sync --dev --all-extras --reinstall
+```
+
+- Run the CLI (examples):
+```bash
+uv run cognee-cli add "Cognee turns documents into AI memory."
+uv run cognee-cli cognify
+uv run cognee-cli search "What does cognee do?"
+uv run cognee-cli -ui   # Launches UI, backend API, and MCP server together
+```
+
+- Start the FastAPI server directly:
+```bash
+uv run python -m cognee.api.client
+```
+
+- Run tests (CI mirrors these commands):
+```bash
+uv run pytest cognee/tests/unit/ -v
+uv run pytest cognee/tests/integration/ -v
+```
+
+- Lint and format (ruff):
+```bash
+uv run ruff check .
+uv run ruff format .
+```
+
+- Optional static type checks (mypy):
+```bash
+uv run mypy cognee/
+```
+
+MCP Server (`cognee-mcp/`):
+
+- Install and run locally:
+```bash
+cd cognee-mcp
+uv sync --dev --all-extras --reinstall
+uv run python src/server.py               # stdio (default)
+uv run python src/server.py --transport sse
+uv run python src/server.py --transport http --host 127.0.0.1 --port 8000 --path /mcp
+```
+
+- API Mode (connect to a running Cognee API):
+```bash
+uv run python src/server.py --transport sse --api-url http://localhost:8000 --api-token YOUR_TOKEN
+```
+
+- Docker quickstart (examples): see `cognee-mcp/README.md` for full details
+```bash
+docker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main
+```
+
+Frontend (`cognee-frontend/`):
+```bash
+cd cognee-frontend
+npm install
+npm run dev     # Next.js dev server
+npm run lint    # ESLint
+npm run build && npm start
+```
+
+## Coding Style & Naming Conventions
+
+Python:
+- 4-space indentation, modules and functions in `snake_case`, classes in `PascalCase`.
+- Public APIs should be type-annotated where practical.
+- Use `ruff format` before committing; `ruff check` enforces import hygiene and style (line-length 100 configured in `pyproject.toml`).
+- Prefer explicit, structured error handling. Use shared logging utilities in `cognee.shared.logging_utils`.
+
+MCP server and Frontend:
+- Follow the local `README.md` and ESLint/TypeScript configuration in `cognee-frontend/`.
+
+## Testing Guidelines
+
+- Place Python tests under `cognee/tests/`.
+  - Unit tests: `cognee/tests/unit/`
+  - Integration tests: `cognee/tests/integration/`
+  - CLI tests: `cognee/tests/cli_tests/`
+- Name test files `test_*.py`. Use `pytest.mark.asyncio` for async tests.
+- Avoid external state; rely on test fixtures and the CI-provided env vars when LLM/embedding providers are required. See CI workflows under `.github/workflows/` for expected environment variables.
+- When adding public APIs, provide/update targeted examples under `examples/python/`.
+
+## Commit & Pull Request Guidelines
+
+- Use clear, imperative subjects (≤ 72 chars) and conventional commit styling in PR titles. Our CI validates semantic PR titles (see `.github/workflows/pr_lint`). Examples:
+  - `feat(graph): add temporal edge weighting`
+  - `fix(api): handle missing auth cookie`
+  - `docs: update installation instructions`
+- Reference related issues/discussions in the PR body and provide brief context.
+- PRs should describe scope, list local test commands run, and mention any impacts on MCP server or UI if applicable.
+- Sign commits and affirm the DCO (see `CONTRIBUTING.md`).
+
+## CI Mirrors Local Commands
+
+Our GitHub Actions run the same ruff checks and pytest suites shown above (`.github/workflows/basic_tests.yml` and related workflows). Use the commands in this document locally to minimize CI surprises.
+
+
diff --git a/README.md b/README.md
index 305bffdfe..d51a380b1 100644
--- a/README.md
+++ b/README.md
@@ -97,7 +97,7 @@ Hosted platform:
 
 ### 📦 Installation
 
-You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager.
+You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager..
 
 Cognee supports Python 3.10 to 3.12
 
diff --git a/cognee-mcp/README.md b/cognee-mcp/README.md
index d14bc9fa1..9ac8b4973 100644
--- a/cognee-mcp/README.md
+++ b/cognee-mcp/README.md
@@ -110,6 +110,47 @@ If you'd rather run cognee-mcp in a container, you have two options:
       # For stdio transport (default)
       docker run -e TRANSPORT_MODE=stdio --env-file ./.env --rm -it cognee/cognee-mcp:main
       ```
+      
+      **Installing optional dependencies at runtime:**
+      
+      You can install optional dependencies when running the container by setting the `EXTRAS` environment variable:
+      ```bash
+      # Install a single optional dependency group at runtime
+      docker run \
+        -e TRANSPORT_MODE=http \
+        -e EXTRAS=aws \
+        --env-file ./.env \
+        -p 8000:8000 \
+        --rm -it cognee/cognee-mcp:main
+      
+      # Install multiple optional dependency groups at runtime (comma-separated)
+      docker run \
+        -e TRANSPORT_MODE=sse \
+        -e EXTRAS=aws,postgres,neo4j \
+        --env-file ./.env \
+        -p 8000:8000 \
+        --rm -it cognee/cognee-mcp:main
+      ```
+      
+      **Available optional dependency groups:**
+      - `aws` - S3 storage support
+      - `postgres` / `postgres-binary` - PostgreSQL database support
+      - `neo4j` - Neo4j graph database support
+      - `neptune` - AWS Neptune support
+      - `chromadb` - ChromaDB vector store support
+      - `scraping` - Web scraping capabilities
+      - `distributed` - Modal distributed execution
+      - `langchain` - LangChain integration
+      - `llama-index` - LlamaIndex integration
+      - `anthropic` - Anthropic models
+      - `groq` - Groq models
+      - `mistral` - Mistral models
+      - `ollama` / `huggingface` - Local model support
+      - `docs` - Document processing
+      - `codegraph` - Code analysis
+      - `monitoring` - Sentry & Langfuse monitoring
+      - `redis` - Redis support
+      - And more (see [pyproject.toml](https://github.com/topoteretes/cognee/blob/main/pyproject.toml) for full list)
 2. **Pull from Docker Hub** (no build required):
    ```bash
    # With HTTP transport (recommended for web deployments)
@@ -119,6 +160,17 @@ If you'd rather run cognee-mcp in a container, you have two options:
    # With stdio transport (default)
    docker run -e TRANSPORT_MODE=stdio --env-file ./.env --rm -it cognee/cognee-mcp:main
    ```
+   
+   **With runtime installation of optional dependencies:**
+   ```bash
+   # Install optional dependencies from Docker Hub image
+   docker run \
+     -e TRANSPORT_MODE=http \
+     -e EXTRAS=aws,postgres \
+     --env-file ./.env \
+     -p 8000:8000 \
+     --rm -it cognee/cognee-mcp:main
+   ```
 
 ### **Important: Docker vs Direct Usage**
 **Docker uses environment variables**, not command line arguments:
diff --git a/cognee-mcp/entrypoint.sh b/cognee-mcp/entrypoint.sh
index 2f122bbfd..cf7d19f0a 100644
--- a/cognee-mcp/entrypoint.sh
+++ b/cognee-mcp/entrypoint.sh
@@ -4,6 +4,42 @@ set -e  # Exit on error
 echo "Debug mode: $DEBUG"
 echo "Environment: $ENVIRONMENT"
 
+# Install optional dependencies if EXTRAS is set
+if [ -n "$EXTRAS" ]; then
+    echo "Installing optional dependencies: $EXTRAS"
+    
+    # Get the cognee version that's currently installed
+    COGNEE_VERSION=$(uv pip show cognee | grep "Version:" | awk '{print $2}')
+    echo "Current cognee version: $COGNEE_VERSION"
+    
+    # Build the extras list for cognee
+    IFS=',' read -ra EXTRA_ARRAY <<< "$EXTRAS"
+    # Combine base extras from pyproject.toml with requested extras
+    ALL_EXTRAS=""
+    for extra in "${EXTRA_ARRAY[@]}"; do
+        # Trim whitespace
+        extra=$(echo "$extra" | xargs)
+        # Add to extras list if not already present
+        if [[ ! "$ALL_EXTRAS" =~ (^|,)"$extra"(,|$) ]]; then
+            if [ -z "$ALL_EXTRAS" ]; then
+                ALL_EXTRAS="$extra"
+            else
+                ALL_EXTRAS="$ALL_EXTRAS,$extra"
+            fi
+        fi
+    done
+    
+    echo "Installing cognee with extras: $ALL_EXTRAS"
+    echo "Running: uv pip install 'cognee[$ALL_EXTRAS]==$COGNEE_VERSION'"
+    uv pip install "cognee[$ALL_EXTRAS]==$COGNEE_VERSION"
+    
+    # Verify installation
+    echo ""
+    echo "✓ Optional dependencies installation completed"
+else
+    echo "No optional dependencies specified"
+fi
+
 # Set default transport mode if not specified
 TRANSPORT_MODE=${TRANSPORT_MODE:-"stdio"}
 echo "Transport mode: $TRANSPORT_MODE"
diff --git a/cognee-mcp/pyproject.toml b/cognee-mcp/pyproject.toml
index c3327b67f..f37bf337c 100644
--- a/cognee-mcp/pyproject.toml
+++ b/cognee-mcp/pyproject.toml
@@ -9,7 +9,7 @@ dependencies = [
     # For local cognee repo usage remove comment bellow and add absolute path to cognee. Then run `uv sync --reinstall` in the mcp folder on local cognee changes.
     #"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j] @ file:/Users/igorilic/Desktop/cognee",
     # TODO: Remove gemini from optional dependecnies for new Cognee version after 0.3.4
-    "cognee[postgres,codegraph,huggingface,docs,neo4j]==0.3.7",
+    "cognee[postgres,docs,neo4j]==0.3.7",
     "fastmcp>=2.10.0,<3.0.0",
     "mcp>=1.12.0,<2.0.0",
     "uv>=0.6.3,<1.0.0",
diff --git a/cognee-mcp/src/client.py b/cognee-mcp/src/client.py
index 2d6bdfe18..952503ee7 100755
--- a/cognee-mcp/src/client.py
+++ b/cognee-mcp/src/client.py
@@ -37,12 +37,10 @@ async def run():
 
             toolResult = await session.call_tool("prune", arguments={})
 
-            toolResult = await session.call_tool(
-                "codify", arguments={"repo_path": "SOME_REPO_PATH"}
-            )
+            toolResult = await session.call_tool("cognify", arguments={})
 
             toolResult = await session.call_tool(
-                "search", arguments={"search_type": "CODE", "search_query": "exceptions"}
+                "search", arguments={"search_type": "GRAPH_COMPLETION"}
             )
 
             print(f"Cognify result: {toolResult.content}")
diff --git a/cognee-mcp/uv.lock b/cognee-mcp/uv.lock
index daa88edef..a6a6fb511 100644
--- a/cognee-mcp/uv.lock
+++ b/cognee-mcp/uv.lock
@@ -718,19 +718,10 @@ wheels = [
 ]
 
 [package.optional-dependencies]
-codegraph = [
-    { name = "fastembed", marker = "python_full_version < '3.13'" },
-    { name = "transformers" },
-    { name = "tree-sitter" },
-    { name = "tree-sitter-python" },
-]
 docs = [
     { name = "lxml" },
     { name = "unstructured", extra = ["csv", "doc", "docx", "epub", "md", "odt", "org", "pdf", "ppt", "pptx", "rst", "rtf", "tsv", "xlsx"] },
 ]
-huggingface = [
-    { name = "transformers" },
-]
 neo4j = [
     { name = "neo4j" },
 ]
@@ -745,7 +736,7 @@ name = "cognee-mcp"
 version = "0.4.0"
 source = { editable = "." }
 dependencies = [
-    { name = "cognee", extra = ["codegraph", "docs", "huggingface", "neo4j", "postgres"] },
+    { name = "cognee", extra = ["docs", "neo4j", "postgres"] },
     { name = "fastmcp" },
     { name = "httpx" },
     { name = "mcp" },
@@ -759,7 +750,7 @@ dev = [
 
 [package.metadata]
 requires-dist = [
-    { name = "cognee", extras = ["postgres", "codegraph", "huggingface", "docs", "neo4j"], specifier = "==0.3.7" },
+    { name = "cognee", extras = ["postgres", "docs", "neo4j"], specifier = "==0.3.7" },
     { name = "fastmcp", specifier = ">=2.10.0,<3.0.0" },
     { name = "httpx", specifier = ">=0.27.0,<1.0.0" },
     { name = "mcp", specifier = ">=1.12.0,<2.0.0" },
@@ -6038,57 +6029,6 @@ wheels = [
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 ]
 
-[[package]]
-name = "tree-sitter"
-version = "0.24.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/a7/a2/698b9d31d08ad5558f8bfbfe3a0781bd4b1f284e89bde3ad18e05101a892/tree-sitter-0.24.0.tar.gz", hash = "sha256:abd95af65ca2f4f7eca356343391ed669e764f37748b5352946f00f7fc78e734", size = 168304 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/08/9a/bd627a02e41671af73222316e1fcf87772c7804dc2fba99405275eb1f3eb/tree_sitter-0.24.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f3f00feff1fc47a8e4863561b8da8f5e023d382dd31ed3e43cd11d4cae445445", size = 140890 },
-    { url = "https://files.pythonhosted.org/packages/5b/9b/b1ccfb187f8be78e2116176a091a2f2abfd043a06d78f80c97c97f315b37/tree_sitter-0.24.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f9691be48d98c49ef8f498460278884c666b44129222ed6217477dffad5d4831", size = 134413 },
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diff --git a/cognee/infrastructure/engine/models/Edge.py b/cognee/infrastructure/engine/models/Edge.py
index 5ad9c84dd..59f01a9ab 100644
--- a/cognee/infrastructure/engine/models/Edge.py
+++ b/cognee/infrastructure/engine/models/Edge.py
@@ -1,4 +1,4 @@
-from pydantic import BaseModel
+from pydantic import BaseModel, field_validator
 from typing import Optional, Any, Dict
 
 
@@ -18,9 +18,21 @@ class Edge(BaseModel):
 
         # Mixed usage
         has_items: (Edge(weight=0.5, weights={"confidence": 0.9}), list[Item])
+
+        # With edge_text for rich embedding representation
+        contains: (Edge(relationship_type="contains", edge_text="relationship_name: contains; entity_description: Alice"), Entity)
     """
 
     weight: Optional[float] = None
     weights: Optional[Dict[str, float]] = None
     relationship_type: Optional[str] = None
     properties: Optional[Dict[str, Any]] = None
+    edge_text: Optional[str] = None
+
+    @field_validator("edge_text", mode="before")
+    @classmethod
+    def ensure_edge_text(cls, v, info):
+        """Auto-populate edge_text from relationship_type if not explicitly provided."""
+        if v is None and info.data.get("relationship_type"):
+            return info.data["relationship_type"]
+        return v
diff --git a/cognee/infrastructure/files/utils/guess_file_type.py b/cognee/infrastructure/files/utils/guess_file_type.py
index f30bd5963..4e3ff6824 100644
--- a/cognee/infrastructure/files/utils/guess_file_type.py
+++ b/cognee/infrastructure/files/utils/guess_file_type.py
@@ -22,42 +22,6 @@ class FileTypeException(Exception):
         self.message = message
 
 
-class TxtFileType(filetype.Type):
-    """
-    Represents a text file type with specific MIME and extension properties.
-
-    Public methods:
-    - match: Determines whether a given buffer matches the text file type.
-    """
-
-    MIME = "text/plain"
-    EXTENSION = "txt"
-
-    def __init__(self):
-        super(TxtFileType, self).__init__(mime=TxtFileType.MIME, extension=TxtFileType.EXTENSION)
-
-    def match(self, buf):
-        """
-        Determine if the given buffer contains text content.
-
-        Parameters:
-        -----------
-
-            - buf: The buffer to check for text content.
-
-        Returns:
-        --------
-
-            Returns True if the buffer is identified as text content, otherwise False.
-        """
-        return is_text_content(buf)
-
-
-txt_file_type = TxtFileType()
-
-filetype.add_type(txt_file_type)
-
-
 def guess_file_type(file: BinaryIO) -> filetype.Type:
     """
     Guess the file type from the given binary file stream.
diff --git a/cognee/infrastructure/loaders/LoaderEngine.py b/cognee/infrastructure/loaders/LoaderEngine.py
index 725f37b14..f9511e7c5 100644
--- a/cognee/infrastructure/loaders/LoaderEngine.py
+++ b/cognee/infrastructure/loaders/LoaderEngine.py
@@ -1,6 +1,7 @@
 import filetype
 from typing import Dict, List, Optional, Any
 from .LoaderInterface import LoaderInterface
+from cognee.infrastructure.files.utils.guess_file_type import guess_file_type
 from cognee.shared.logging_utils import get_logger
 
 logger = get_logger(__name__)
@@ -80,7 +81,7 @@ class LoaderEngine:
         """
         from pathlib import Path
 
-        file_info = filetype.guess(file_path)
+        file_info = guess_file_type(file_path)
 
         path_extension = Path(file_path).suffix.lstrip(".")
 
diff --git a/cognee/modules/chunking/models/DocumentChunk.py b/cognee/modules/chunking/models/DocumentChunk.py
index 9f8c57486..e024bf00b 100644
--- a/cognee/modules/chunking/models/DocumentChunk.py
+++ b/cognee/modules/chunking/models/DocumentChunk.py
@@ -1,6 +1,7 @@
 from typing import List, Union
 
 from cognee.infrastructure.engine import DataPoint
+from cognee.infrastructure.engine.models.Edge import Edge
 from cognee.modules.data.processing.document_types import Document
 from cognee.modules.engine.models import Entity
 from cognee.tasks.temporal_graph.models import Event
@@ -31,6 +32,6 @@ class DocumentChunk(DataPoint):
     chunk_index: int
     cut_type: str
     is_part_of: Document
-    contains: List[Union[Entity, Event]] = None
+    contains: List[Union[Entity, Event, tuple[Edge, Entity]]] = None
 
     metadata: dict = {"index_fields": ["text"]}
diff --git a/cognee/modules/graph/cognee_graph/CogneeGraph.py b/cognee/modules/graph/cognee_graph/CogneeGraph.py
index 9703928f0..cb7562422 100644
--- a/cognee/modules/graph/cognee_graph/CogneeGraph.py
+++ b/cognee/modules/graph/cognee_graph/CogneeGraph.py
@@ -171,8 +171,10 @@ class CogneeGraph(CogneeAbstractGraph):
             embedding_map = {result.payload["text"]: result.score for result in edge_distances}
 
             for edge in self.edges:
-                relationship_type = edge.attributes.get("relationship_type")
-                distance = embedding_map.get(relationship_type, None)
+                edge_key = edge.attributes.get("edge_text") or edge.attributes.get(
+                    "relationship_type"
+                )
+                distance = embedding_map.get(edge_key, None)
                 if distance is not None:
                     edge.attributes["vector_distance"] = distance
 
diff --git a/cognee/modules/graph/utils/expand_with_nodes_and_edges.py b/cognee/modules/graph/utils/expand_with_nodes_and_edges.py
index 3b01f5af4..c68eb494d 100644
--- a/cognee/modules/graph/utils/expand_with_nodes_and_edges.py
+++ b/cognee/modules/graph/utils/expand_with_nodes_and_edges.py
@@ -1,5 +1,6 @@
 from typing import Optional
 
+from cognee.infrastructure.engine.models.Edge import Edge
 from cognee.modules.chunking.models import DocumentChunk
 from cognee.modules.engine.models import Entity, EntityType
 from cognee.modules.engine.utils import (
@@ -243,10 +244,26 @@ def _process_graph_nodes(
             ontology_relationships,
         )
 
-        # Add entity to data chunk
         if data_chunk.contains is None:
             data_chunk.contains = []
-        data_chunk.contains.append(entity_node)
+
+        edge_text = "; ".join(
+            [
+                "relationship_name: contains",
+                f"entity_name: {entity_node.name}",
+                f"entity_description: {entity_node.description}",
+            ]
+        )
+
+        data_chunk.contains.append(
+            (
+                Edge(
+                    relationship_type="contains",
+                    edge_text=edge_text,
+                ),
+                entity_node,
+            )
+        )
 
 
 def _process_graph_edges(
diff --git a/cognee/modules/graph/utils/resolve_edges_to_text.py b/cognee/modules/graph/utils/resolve_edges_to_text.py
index eb5bedd2c..5deb13ba8 100644
--- a/cognee/modules/graph/utils/resolve_edges_to_text.py
+++ b/cognee/modules/graph/utils/resolve_edges_to_text.py
@@ -1,71 +1,70 @@
+import string
 from typing import List
+from collections import Counter
+
 from cognee.modules.graph.cognee_graph.CogneeGraphElements import Edge
+from cognee.modules.retrieval.utils.stop_words import DEFAULT_STOP_WORDS
+
+
+def _get_top_n_frequent_words(
+    text: str, stop_words: set = None, top_n: int = 3, separator: str = ", "
+) -> str:
+    """Concatenates the top N frequent words in text."""
+    if stop_words is None:
+        stop_words = DEFAULT_STOP_WORDS
+
+    words = [word.lower().strip(string.punctuation) for word in text.split()]
+    words = [word for word in words if word and word not in stop_words]
+
+    top_words = [word for word, freq in Counter(words).most_common(top_n)]
+    return separator.join(top_words)
+
+
+def _create_title_from_text(text: str, first_n_words: int = 7, top_n_words: int = 3) -> str:
+    """Creates a title by combining first words with most frequent words from the text."""
+    first_words = text.split()[:first_n_words]
+    top_words = _get_top_n_frequent_words(text, top_n=top_n_words)
+    return f"{' '.join(first_words)}... [{top_words}]"
+
+
+def _extract_nodes_from_edges(retrieved_edges: List[Edge]) -> dict:
+    """Creates a dictionary of nodes with their names and content."""
+    nodes = {}
+
+    for edge in retrieved_edges:
+        for node in (edge.node1, edge.node2):
+            if node.id in nodes:
+                continue
+
+            text = node.attributes.get("text")
+            if text:
+                name = _create_title_from_text(text)
+                content = text
+            else:
+                name = node.attributes.get("name", "Unnamed Node")
+                content = node.attributes.get("description", name)
+
+            nodes[node.id] = {"node": node, "name": name, "content": content}
+
+    return nodes
 
 
 async def resolve_edges_to_text(retrieved_edges: List[Edge]) -> str:
-    """
-    Converts retrieved graph edges into a human-readable string format.
+    """Converts retrieved graph edges into a human-readable string format."""
+    nodes = _extract_nodes_from_edges(retrieved_edges)
 
-    Parameters:
-    -----------
-
-        - retrieved_edges (list): A list of edges retrieved from the graph.
-
-    Returns:
-    --------
-
-        - str: A formatted string representation of the nodes and their connections.
-    """
-
-    def _get_nodes(retrieved_edges: List[Edge]) -> dict:
-        def _get_title(text: str, first_n_words: int = 7, top_n_words: int = 3) -> str:
-            def _top_n_words(text, stop_words=None, top_n=3, separator=", "):
-                """Concatenates the top N frequent words in text."""
-                if stop_words is None:
-                    from cognee.modules.retrieval.utils.stop_words import DEFAULT_STOP_WORDS
-
-                    stop_words = DEFAULT_STOP_WORDS
-
-                import string
-
-                words = [word.lower().strip(string.punctuation) for word in text.split()]
-
-                if stop_words:
-                    words = [word for word in words if word and word not in stop_words]
-
-                from collections import Counter
-
-                top_words = [word for word, freq in Counter(words).most_common(top_n)]
-
-                return separator.join(top_words)
-
-            """Creates a title, by combining first words with most frequent words from the text."""
-            first_words = text.split()[:first_n_words]
-            top_words = _top_n_words(text, top_n=first_n_words)
-            return f"{' '.join(first_words)}... [{top_words}]"
-
-        """Creates a dictionary of nodes with their names and content."""
-        nodes = {}
-        for edge in retrieved_edges:
-            for node in (edge.node1, edge.node2):
-                if node.id not in nodes:
-                    text = node.attributes.get("text")
-                    if text:
-                        name = _get_title(text)
-                        content = text
-                    else:
-                        name = node.attributes.get("name", "Unnamed Node")
-                        content = node.attributes.get("description", name)
-                    nodes[node.id] = {"node": node, "name": name, "content": content}
-        return nodes
-
-    nodes = _get_nodes(retrieved_edges)
     node_section = "\n".join(
         f"Node: {info['name']}\n__node_content_start__\n{info['content']}\n__node_content_end__\n"
         for info in nodes.values()
     )
-    connection_section = "\n".join(
-        f"{nodes[edge.node1.id]['name']} --[{edge.attributes['relationship_type']}]--> {nodes[edge.node2.id]['name']}"
-        for edge in retrieved_edges
-    )
+
+    connections = []
+    for edge in retrieved_edges:
+        source_name = nodes[edge.node1.id]["name"]
+        target_name = nodes[edge.node2.id]["name"]
+        edge_label = edge.attributes.get("edge_text") or edge.attributes.get("relationship_type")
+        connections.append(f"{source_name} --[{edge_label}]--> {target_name}")
+
+    connection_section = "\n".join(connections)
+
     return f"Nodes:\n{node_section}\n\nConnections:\n{connection_section}"
diff --git a/cognee/modules/retrieval/utils/brute_force_triplet_search.py b/cognee/modules/retrieval/utils/brute_force_triplet_search.py
index 1ef7545c2..f8bdbb97d 100644
--- a/cognee/modules/retrieval/utils/brute_force_triplet_search.py
+++ b/cognee/modules/retrieval/utils/brute_force_triplet_search.py
@@ -71,7 +71,7 @@ async def get_memory_fragment(
         await memory_fragment.project_graph_from_db(
             graph_engine,
             node_properties_to_project=properties_to_project,
-            edge_properties_to_project=["relationship_name"],
+            edge_properties_to_project=["relationship_name", "edge_text"],
             node_type=node_type,
             node_name=node_name,
         )
diff --git a/cognee/tasks/storage/index_data_points.py b/cognee/tasks/storage/index_data_points.py
index 902789c80..b0ec3a5b4 100644
--- a/cognee/tasks/storage/index_data_points.py
+++ b/cognee/tasks/storage/index_data_points.py
@@ -8,47 +8,58 @@ logger = get_logger("index_data_points")
 
 
 async def index_data_points(data_points: list[DataPoint]):
-    created_indexes = {}
-    index_points = {}
+    """Index data points in the vector engine by creating embeddings for specified fields.
+
+    Process:
+    1. Groups data points into a nested dict: {type_name: {field_name: [points]}}
+    2. Creates vector indexes for each (type, field) combination on first encounter
+    3. Batches points per (type, field) and creates async indexing tasks
+    4. Executes all indexing tasks in parallel for efficient embedding generation
+
+    Args:
+        data_points: List of DataPoint objects to index. Each DataPoint's metadata must
+                     contain an 'index_fields' list specifying which fields to embed.
+
+    Returns:
+        The original data_points list.
+    """
+    data_points_by_type = {}
 
     vector_engine = get_vector_engine()
 
     for data_point in data_points:
         data_point_type = type(data_point)
+        type_name = data_point_type.__name__
 
         for field_name in data_point.metadata["index_fields"]:
             if getattr(data_point, field_name, None) is None:
                 continue
 
-            index_name = f"{data_point_type.__name__}_{field_name}"
+            if type_name not in data_points_by_type:
+                data_points_by_type[type_name] = {}
 
-            if index_name not in created_indexes:
-                await vector_engine.create_vector_index(data_point_type.__name__, field_name)
-                created_indexes[index_name] = True
-
-            if index_name not in index_points:
-                index_points[index_name] = []
+            if field_name not in data_points_by_type[type_name]:
+                await vector_engine.create_vector_index(type_name, field_name)
+                data_points_by_type[type_name][field_name] = []
 
             indexed_data_point = data_point.model_copy()
             indexed_data_point.metadata["index_fields"] = [field_name]
-            index_points[index_name].append(indexed_data_point)
+            data_points_by_type[type_name][field_name].append(indexed_data_point)
 
-    tasks: list[asyncio.Task] = []
     batch_size = vector_engine.embedding_engine.get_batch_size()
 
-    for index_name_and_field, points in index_points.items():
-        first = index_name_and_field.index("_")
-        index_name = index_name_and_field[:first]
-        field_name = index_name_and_field[first + 1 :]
+    batches = (
+        (type_name, field_name, points[i : i + batch_size])
+        for type_name, fields in data_points_by_type.items()
+        for field_name, points in fields.items()
+        for i in range(0, len(points), batch_size)
+    )
 
-        # Create embedding requests per batch to run in parallel later
-        for i in range(0, len(points), batch_size):
-            batch = points[i : i + batch_size]
-            tasks.append(
-                asyncio.create_task(vector_engine.index_data_points(index_name, field_name, batch))
-            )
+    tasks = [
+        asyncio.create_task(vector_engine.index_data_points(type_name, field_name, batch_points))
+        for type_name, field_name, batch_points in batches
+    ]
 
-    # Run all embedding requests in parallel
     await asyncio.gather(*tasks)
 
     return data_points
diff --git a/cognee/tasks/storage/index_graph_edges.py b/cognee/tasks/storage/index_graph_edges.py
index 4fa8cfc75..03b5a25a5 100644
--- a/cognee/tasks/storage/index_graph_edges.py
+++ b/cognee/tasks/storage/index_graph_edges.py
@@ -1,17 +1,44 @@
-import asyncio
+from collections import Counter
+from typing import Optional, Dict, Any, List, Tuple, Union
 
 from cognee.modules.engine.utils.generate_edge_id import generate_edge_id
 from cognee.shared.logging_utils import get_logger
-from collections import Counter
-from typing import Optional, Dict, Any, List, Tuple, Union
-from cognee.infrastructure.databases.vector import get_vector_engine
 from cognee.infrastructure.databases.graph import get_graph_engine
 from cognee.modules.graph.models.EdgeType import EdgeType
 from cognee.infrastructure.databases.graph.graph_db_interface import EdgeData
+from cognee.tasks.storage.index_data_points import index_data_points
 
 logger = get_logger()
 
 
+def _get_edge_text(item: dict) -> str:
+    """Extract edge text for embedding - prefers edge_text field with fallback."""
+    if "edge_text" in item:
+        return item["edge_text"]
+
+    if "relationship_name" in item:
+        return item["relationship_name"]
+
+    return ""
+
+
+def create_edge_type_datapoints(edges_data) -> list[EdgeType]:
+    """Transform raw edge data into EdgeType datapoints."""
+    edge_texts = [
+        _get_edge_text(item)
+        for edge in edges_data
+        for item in edge
+        if isinstance(item, dict) and "relationship_name" in item
+    ]
+
+    edge_types = Counter(edge_texts)
+
+    return [
+        EdgeType(id=generate_edge_id(edge_id=text), relationship_name=text, number_of_edges=count)
+        for text, count in edge_types.items()
+    ]
+
+
 async def index_graph_edges(
     edges_data: Union[List[EdgeData], List[Tuple[str, str, str, Optional[Dict[str, Any]]]]] = None,
 ):
@@ -23,24 +50,17 @@ async def index_graph_edges(
     the `relationship_name` field.
 
     Steps:
-    1. Initialize the vector engine and graph engine.
-    2. Retrieve graph edge data and count relationship types (`relationship_name`).
-    3. Create vector indexes for `relationship_name` if they don't exist.
-    4. Transform the counted relationships into `EdgeType` objects.
-    5. Index the transformed data points in the vector engine.
+    1. Initialize the graph engine if needed and retrieve edge data.
+    2. Transform edge data into EdgeType datapoints.
+    3. Index the EdgeType datapoints using the standard indexing function.
 
     Raises:
-        RuntimeError: If initialization of the vector engine or graph engine fails.
+        RuntimeError: If initialization of the graph engine fails.
 
     Returns:
         None
     """
     try:
-        created_indexes = {}
-        index_points = {}
-
-        vector_engine = get_vector_engine()
-
         if edges_data is None:
             graph_engine = await get_graph_engine()
             _, edges_data = await graph_engine.get_graph_data()
@@ -51,47 +71,7 @@ async def index_graph_edges(
         logger.error("Failed to initialize engines: %s", e)
         raise RuntimeError("Initialization error") from e
 
-    edge_types = Counter(
-        item.get("relationship_name")
-        for edge in edges_data
-        for item in edge
-        if isinstance(item, dict) and "relationship_name" in item
-    )
-
-    for text, count in edge_types.items():
-        edge = EdgeType(
-            id=generate_edge_id(edge_id=text), relationship_name=text, number_of_edges=count
-        )
-        data_point_type = type(edge)
-
-        for field_name in edge.metadata["index_fields"]:
-            index_name = f"{data_point_type.__name__}.{field_name}"
-
-            if index_name not in created_indexes:
-                await vector_engine.create_vector_index(data_point_type.__name__, field_name)
-                created_indexes[index_name] = True
-
-            if index_name not in index_points:
-                index_points[index_name] = []
-
-            indexed_data_point = edge.model_copy()
-            indexed_data_point.metadata["index_fields"] = [field_name]
-            index_points[index_name].append(indexed_data_point)
-
-    # Get maximum batch size for embedding model
-    batch_size = vector_engine.embedding_engine.get_batch_size()
-    tasks: list[asyncio.Task] = []
-
-    for index_name, indexable_points in index_points.items():
-        index_name, field_name = index_name.split(".")
-
-        # Create embedding tasks to run in parallel later
-        for start in range(0, len(indexable_points), batch_size):
-            batch = indexable_points[start : start + batch_size]
-
-            tasks.append(vector_engine.index_data_points(index_name, field_name, batch))
-
-    # Start all embedding tasks and wait for completion
-    await asyncio.gather(*tasks)
+    edge_type_datapoints = create_edge_type_datapoints(edges_data)
+    await index_data_points(edge_type_datapoints)
 
     return None
diff --git a/cognee/tests/test_edge_ingestion.py b/cognee/tests/test_edge_ingestion.py
index 5b23f7819..0d1407fab 100755
--- a/cognee/tests/test_edge_ingestion.py
+++ b/cognee/tests/test_edge_ingestion.py
@@ -52,6 +52,33 @@ async def test_edge_ingestion():
 
     edge_type_counts = Counter(edge_type[2] for edge_type in graph[1])
 
+    "Tests edge_text presence and format"
+    contains_edges = [edge for edge in graph[1] if edge[2] == "contains"]
+    assert len(contains_edges) > 0, "Expected at least one contains edge for edge_text verification"
+
+    edge_properties = contains_edges[0][3]
+    assert "edge_text" in edge_properties, "Expected edge_text in edge properties"
+
+    edge_text = edge_properties["edge_text"]
+    assert "relationship_name: contains" in edge_text, (
+        f"Expected 'relationship_name: contains' in edge_text, got: {edge_text}"
+    )
+    assert "entity_name:" in edge_text, f"Expected 'entity_name:' in edge_text, got: {edge_text}"
+    assert "entity_description:" in edge_text, (
+        f"Expected 'entity_description:' in edge_text, got: {edge_text}"
+    )
+
+    all_edge_texts = [
+        edge[3].get("edge_text", "") for edge in contains_edges if "edge_text" in edge[3]
+    ]
+    expected_entities = ["dave", "ana", "bob", "dexter", "apples", "cognee"]
+    found_entity = any(
+        any(entity in text.lower() for entity in expected_entities) for text in all_edge_texts
+    )
+    assert found_entity, (
+        f"Expected to find at least one entity name in edge_text: {all_edge_texts[:3]}"
+    )
+
     "Tests the presence of basic nested edges"
     for basic_nested_edge in basic_nested_edges:
         assert edge_type_counts.get(basic_nested_edge, 0) >= 1, (
diff --git a/cognee/tests/unit/infrastructure/databases/test_index_data_points.py b/cognee/tests/unit/infrastructure/databases/test_index_data_points.py
new file mode 100644
index 000000000..21a5695de
--- /dev/null
+++ b/cognee/tests/unit/infrastructure/databases/test_index_data_points.py
@@ -0,0 +1,27 @@
+import pytest
+from unittest.mock import AsyncMock, patch, MagicMock
+from cognee.tasks.storage.index_data_points import index_data_points
+from cognee.infrastructure.engine import DataPoint
+
+
+class TestDataPoint(DataPoint):
+    name: str
+    metadata: dict = {"index_fields": ["name"]}
+
+
+@pytest.mark.asyncio
+async def test_index_data_points_calls_vector_engine():
+    """Test that index_data_points creates vector index and indexes data."""
+    data_points = [TestDataPoint(name="test1")]
+
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.get_batch_size = MagicMock(return_value=100)
+
+    with patch.dict(
+        index_data_points.__globals__,
+        {"get_vector_engine": lambda: mock_vector_engine},
+    ):
+        await index_data_points(data_points)
+
+    assert mock_vector_engine.create_vector_index.await_count >= 1
+    assert mock_vector_engine.index_data_points.await_count >= 1
diff --git a/cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py b/cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py
index 48bbc53e3..cee0896c2 100644
--- a/cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py
+++ b/cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py
@@ -5,8 +5,7 @@ from cognee.tasks.storage.index_graph_edges import index_graph_edges
 
 @pytest.mark.asyncio
 async def test_index_graph_edges_success():
-    """Test that index_graph_edges uses the index datapoints and creates vector index."""
-    # Create the mocks for the graph and vector engines.
+    """Test that index_graph_edges retrieves edges and delegates to index_data_points."""
     mock_graph_engine = AsyncMock()
     mock_graph_engine.get_graph_data.return_value = (
         None,
@@ -15,26 +14,23 @@ async def test_index_graph_edges_success():
             [{"relationship_name": "rel2"}],
         ],
     )
-    mock_vector_engine = AsyncMock()
-    mock_vector_engine.embedding_engine.get_batch_size = MagicMock(return_value=100)
+    mock_index_data_points = AsyncMock()
 
-    # Patch the globals of the function so that when it does:
-    #   vector_engine = get_vector_engine()
-    #   graph_engine = await get_graph_engine()
-    # it uses the mocked versions.
     with patch.dict(
         index_graph_edges.__globals__,
         {
             "get_graph_engine": AsyncMock(return_value=mock_graph_engine),
-            "get_vector_engine": lambda: mock_vector_engine,
+            "index_data_points": mock_index_data_points,
         },
     ):
         await index_graph_edges()
 
-    # Assertions on the mock calls.
     mock_graph_engine.get_graph_data.assert_awaited_once()
-    assert mock_vector_engine.create_vector_index.await_count == 1
-    assert mock_vector_engine.index_data_points.await_count == 1
+    mock_index_data_points.assert_awaited_once()
+
+    call_args = mock_index_data_points.call_args[0][0]
+    assert len(call_args) == 2
+    assert all(hasattr(item, "relationship_name") for item in call_args)
 
 
 @pytest.mark.asyncio
@@ -42,20 +38,22 @@ async def test_index_graph_edges_no_relationships():
     """Test that index_graph_edges handles empty relationships correctly."""
     mock_graph_engine = AsyncMock()
     mock_graph_engine.get_graph_data.return_value = (None, [])
-    mock_vector_engine = AsyncMock()
+    mock_index_data_points = AsyncMock()
 
     with patch.dict(
         index_graph_edges.__globals__,
         {
             "get_graph_engine": AsyncMock(return_value=mock_graph_engine),
-            "get_vector_engine": lambda: mock_vector_engine,
+            "index_data_points": mock_index_data_points,
         },
     ):
         await index_graph_edges()
 
     mock_graph_engine.get_graph_data.assert_awaited_once()
-    mock_vector_engine.create_vector_index.assert_not_awaited()
-    mock_vector_engine.index_data_points.assert_not_awaited()
+    mock_index_data_points.assert_awaited_once()
+
+    call_args = mock_index_data_points.call_args[0][0]
+    assert len(call_args) == 0
 
 
 @pytest.mark.asyncio
diff --git a/pyproject.toml b/pyproject.toml
index 749d915a6..15a666517 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "cognee"
 
-version = "0.3.7.dev1"
+version = "0.3.8"
 description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning."
 authors = [
     { name = "Vasilije Markovic" },
diff --git a/uv.lock b/uv.lock
index 45f2221fa..65ee4b4ca 100644
--- a/uv.lock
+++ b/uv.lock
@@ -929,7 +929,7 @@ wheels = [
 
 [[package]]
 name = "cognee"
-version = "0.3.7.dev1"
+version = "0.3.8"
 source = { editable = "." }
 dependencies = [
     { name = "aiofiles" },