Merge branch 'dev' into improve-dataset-delete

.github/workflows/release.yml

@@ -0,0 +1,154 @@
+name: release.yml
+on:
+  workflow_dispatch:
+    inputs:
+      flavour:
+        required: true
+        default: dev
+        type: choice
+        options:
+          - dev
+          - main
+        description: Dev or Main release
+      test_mode:
+        required: true
+        type: boolean
+        description: Aka Dry Run. If true, it won't affect public indices or repositories
+
+jobs:
+  release-github:
+    name: Create GitHub Release from ${{ inputs.flavour }}
+    outputs:
+      tag: ${{ steps.create_tag.outputs.tag }}
+      version: ${{ steps.create_tag.outputs.version }}
+    permissions:
+      contents: write
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Check out ${{ inputs.flavour }}
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.flavour }}
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+
+      - name: Create and push git tag
+        id: create_tag
+        env: 
+          TEST_MODE: ${{ inputs.test_mode }}
+        run: |
+          VERSION="$(uv version --short)"
+          TAG="v${VERSION}"
+
+          echo "Tag to create: ${TAG}"
+
+          git config user.name "github-actions[bot]"
+          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
+
+          echo "tag=${TAG}" >> "$GITHUB_OUTPUT"
+          echo "version=${VERSION}" >> "$GITHUB_OUTPUT"
+
+          if [ "$TEST_MODE" = "false" ]; then
+            git tag "${TAG}"
+            git push origin "${TAG}"
+          else 
+            echo "Test mode is enabled. Skipping tag creation and push."
+          fi
+
+      - name: Create GitHub Release
+        uses: softprops/action-gh-release@v2
+        with:
+          tag_name: ${{ steps.create_tag.outputs.tag }}
+          generate_release_notes: true
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+  
+  release-pypi-package:    
+    needs: release-github
+    name: Release PyPI Package from ${{ inputs.flavour }}
+    permissions:
+      contents: read
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Check out ${{ inputs.flavour }}
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.flavour }}
+      
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+      
+      - name: Install Python
+        run: uv python install
+      
+      - name: Install dependencies
+        run: uv sync --locked --all-extras
+      
+      - name: Build distributions
+        run: uv build
+      
+      - name: Publish ${{ inputs.flavour }} release to TestPyPI
+        if: ${{ inputs.test_mode }}
+        env:
+          UV_PUBLISH_TOKEN: ${{ secrets.TEST_PYPI_TOKEN }}
+        run: uv publish --publish-url https://test.pypi.org/legacy/
+      
+      - name: Publish ${{ inputs.flavour }} release to PyPI
+        if: ${{ !inputs.test_mode }}
+        env:
+          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }}
+        run: uv publish
+  
+  release-docker-image:    
+    needs: release-github
+    name: Release Docker Image from ${{ inputs.flavour }}
+    permissions:
+      contents: read
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Check out ${{ inputs.flavour }}
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.flavour }}
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Log in to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}      
+
+      - name: Build and push Dev Docker Image
+        if: ${{ inputs.flavour == 'dev' }}        
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          platforms: linux/amd64,linux/arm64
+          push: ${{ !inputs.test_mode }}
+          tags: cognee/cognee:${{ needs.release-github.outputs.version }}
+          labels: |
+            version=${{ needs.release-github.outputs.version }}
+            flavour=${{ inputs.flavour }}
+          cache-from: type=registry,ref=cognee/cognee:buildcache
+          cache-to: type=registry,ref=cognee/cognee:buildcache,mode=max
+
+      - name: Build and push Main Docker Image
+        if: ${{ inputs.flavour == 'main' }}
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          platforms: linux/amd64,linux/arm64
+          push: ${{ !inputs.test_mode }}
+          tags: | 
+            cognee/cognee:${{ needs.release-github.outputs.version }}
+            cognee/cognee:latest
+          labels: |
+            version=${{ needs.release-github.outputs.version }}
+            flavour=${{ inputs.flavour }}
+          cache-from: type=registry,ref=cognee/cognee:buildcache
+          cache-to: type=registry,ref=cognee/cognee:buildcache,mode=max

.github/workflows/test_llms.yml

@@ -84,3 +84,93 @@ jobs:
           EMBEDDING_DIMENSIONS: "3072"
           EMBEDDING_MAX_TOKENS: "8191"
         run: uv run python ./examples/python/simple_example.py
+
+  test-bedrock-api-key:
+    name: Run Bedrock API Key Test
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v4
+
+      - name: Cognee Setup
+        uses: ./.github/actions/cognee_setup
+        with:
+          python-version: '3.11.x'
+          extra-dependencies: "aws"
+
+      - name: Run Bedrock API Key Simple Example
+        env:
+          LLM_PROVIDER: "bedrock"
+          LLM_API_KEY: ${{ secrets.BEDROCK_API_KEY }}
+          LLM_MODEL: "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"
+          LLM_MAX_TOKENS: "16384"
+          AWS_REGION_NAME: "eu-west-1"
+          EMBEDDING_PROVIDER: "bedrock"
+          EMBEDDING_API_KEY: ${{ secrets.BEDROCK_API_KEY }}
+          EMBEDDING_MODEL: "amazon.titan-embed-text-v2:0"
+          EMBEDDING_DIMENSIONS: "1024"
+          EMBEDDING_MAX_TOKENS: "8191"
+        run: uv run python ./examples/python/simple_example.py
+
+  test-bedrock-aws-credentials:
+    name: Run Bedrock AWS Credentials Test
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v4
+
+      - name: Cognee Setup
+        uses: ./.github/actions/cognee_setup
+        with:
+          python-version: '3.11.x'
+          extra-dependencies: "aws"
+
+      - name: Run Bedrock AWS Credentials Simple Example
+        env:
+          LLM_PROVIDER: "bedrock"
+          LLM_MODEL: "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"
+          LLM_MAX_TOKENS: "16384"
+          AWS_REGION_NAME: "eu-west-1"
+          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          EMBEDDING_PROVIDER: "bedrock"
+          EMBEDDING_API_KEY: ${{ secrets.BEDROCK_API_KEY }}
+          EMBEDDING_MODEL: "amazon.titan-embed-text-v2:0"
+          EMBEDDING_DIMENSIONS: "1024"
+          EMBEDDING_MAX_TOKENS: "8191"
+        run: uv run python ./examples/python/simple_example.py
+
+  test-bedrock-aws-profile:
+    name: Run Bedrock AWS Profile Test
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v4
+
+      - name: Cognee Setup
+        uses: ./.github/actions/cognee_setup
+        with:
+          python-version: '3.11.x'
+          extra-dependencies: "aws"
+
+      - name: Configure AWS Profile
+        run: |
+          mkdir -p ~/.aws
+          cat > ~/.aws/credentials << EOF
+          [bedrock-test]
+          aws_access_key_id = ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws_secret_access_key = ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          EOF
+
+      - name: Run Bedrock AWS Profile Simple Example
+        env:
+          LLM_PROVIDER: "bedrock"
+          LLM_MODEL: "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"
+          LLM_MAX_TOKENS: "16384"
+          AWS_PROFILE_NAME: "bedrock-test"
+          AWS_REGION_NAME: "eu-west-1"
+          EMBEDDING_PROVIDER: "bedrock"
+          EMBEDDING_MODEL: "amazon.titan-embed-text-v2:0"
+          EMBEDDING_DIMENSIONS: "1024"
+          EMBEDDING_MAX_TOKENS: "8191"
+        run: uv run python ./examples/python/simple_example.py

cognee-mcp/src/test_client.py

@@ -3,7 +3,7 @@
 Test client for Cognee MCP Server functionality.
 
 This script tests all the tools and functions available in the Cognee MCP server,
-including cognify, codify, search, prune, status checks, and utility functions.
+including cognify, search, prune, status checks, and utility functions.
 
 Usage:
     # Set your OpenAI API key first
@@ -23,6 +23,7 @@ import tempfile
 import time
 from contextlib import asynccontextmanager
 from cognee.shared.logging_utils import setup_logging
+from logging import ERROR, INFO
 
 from mcp import ClientSession, StdioServerParameters
 from mcp.client.stdio import stdio_client
@@ -35,7 +36,7 @@ from src.server import (
     load_class,
 )
 
-# Set timeout for cognify/codify to complete in
+# Set timeout for cognify to complete in
 TIMEOUT = 5 * 60  # 5 min  in seconds
 
 
@@ -151,12 +152,9 @@ DEBUG = True
 
                 expected_tools = {
                     "cognify",
-                    "codify",
                     "search",
                     "prune",
                     "cognify_status",
-                    "codify_status",
-                    "cognee_add_developer_rules",
                     "list_data",
                     "delete",
                 }
@@ -247,106 +245,6 @@ DEBUG = True
             }
             print(f"❌ {test_name} test failed: {e}")
 
-    async def test_codify(self):
-        """Test the codify functionality using MCP client."""
-        print("\n🧪 Testing codify functionality...")
-        try:
-            async with self.mcp_server_session() as session:
-                codify_result = await session.call_tool(
-                    "codify", arguments={"repo_path": self.test_repo_dir}
-                )
-
-                start = time.time()  # mark the start
-                while True:
-                    try:
-                        # Wait a moment
-                        await asyncio.sleep(5)
-
-                        # Check if codify processing is finished
-                        status_result = await session.call_tool("codify_status", arguments={})
-                        if hasattr(status_result, "content") and status_result.content:
-                            status_text = (
-                                status_result.content[0].text
-                                if status_result.content
-                                else str(status_result)
-                            )
-                        else:
-                            status_text = str(status_result)
-
-                        if str(PipelineRunStatus.DATASET_PROCESSING_COMPLETED) in status_text:
-                            break
-                        elif time.time() - start > TIMEOUT:
-                            raise TimeoutError("Codify did not complete in 5min")
-                    except DatabaseNotCreatedError:
-                        if time.time() - start > TIMEOUT:
-                            raise TimeoutError("Database was not created in 5min")
-
-                self.test_results["codify"] = {
-                    "status": "PASS",
-                    "result": codify_result,
-                    "message": "Codify executed successfully",
-                }
-                print("✅ Codify test passed")
-
-        except Exception as e:
-            self.test_results["codify"] = {
-                "status": "FAIL",
-                "error": str(e),
-                "message": "Codify test failed",
-            }
-            print(f"❌ Codify test failed: {e}")
-
-    async def test_cognee_add_developer_rules(self):
-        """Test the cognee_add_developer_rules functionality using MCP client."""
-        print("\n🧪 Testing cognee_add_developer_rules functionality...")
-        try:
-            async with self.mcp_server_session() as session:
-                result = await session.call_tool(
-                    "cognee_add_developer_rules", arguments={"base_path": self.test_data_dir}
-                )
-
-                start = time.time()  # mark the start
-                while True:
-                    try:
-                        # Wait a moment
-                        await asyncio.sleep(5)
-
-                        # Check if developer rule cognify processing is finished
-                        status_result = await session.call_tool("cognify_status", arguments={})
-                        if hasattr(status_result, "content") and status_result.content:
-                            status_text = (
-                                status_result.content[0].text
-                                if status_result.content
-                                else str(status_result)
-                            )
-                        else:
-                            status_text = str(status_result)
-
-                        if str(PipelineRunStatus.DATASET_PROCESSING_COMPLETED) in status_text:
-                            break
-                        elif time.time() - start > TIMEOUT:
-                            raise TimeoutError(
-                                "Cognify of developer rules did not complete in 5min"
-                            )
-                    except DatabaseNotCreatedError:
-                        if time.time() - start > TIMEOUT:
-                            raise TimeoutError("Database was not created in 5min")
-
-                self.test_results["cognee_add_developer_rules"] = {
-                    "status": "PASS",
-                    "result": result,
-                    "message": "Developer rules addition executed successfully",
-                }
-                print("✅ Developer rules test passed")
-
-        except Exception as e:
-            self.test_results["cognee_add_developer_rules"] = {
-                "status": "FAIL",
-                "error": str(e),
-                "message": "Developer rules test failed",
-            }
-            print(f"❌ Developer rules test failed: {e}")
-
     async def test_search_functionality(self):
         """Test the search functionality with different search types using MCP client."""
         print("\n🧪 Testing search functionality...")
@@ -359,7 +257,11 @@ DEBUG = True
         # Go through all Cognee search types
         for search_type in SearchType:
             # Don't test these search types
-            if search_type in [SearchType.NATURAL_LANGUAGE, SearchType.CYPHER]:
+            if search_type in [
+                SearchType.NATURAL_LANGUAGE,
+                SearchType.CYPHER,
+                SearchType.TRIPLET_COMPLETION,
+            ]:
                 break
             try:
                 async with self.mcp_server_session() as session:
@@ -681,9 +583,6 @@ class TestModel:
             test_name="Cognify2",
         )
 
-        await self.test_codify()
-        await self.test_cognee_add_developer_rules()
-
         # Test list_data and delete functionality
         await self.test_list_data()
         await self.test_delete()
@@ -739,7 +638,5 @@ async def main():
 
 
 if __name__ == "__main__":
-    from logging import ERROR
-
     logger = setup_logging(log_level=ERROR)
     asyncio.run(main())

cognee/api/v1/add/add.py

@@ -155,7 +155,7 @@ async def add(
         - LLM_API_KEY: API key for your LLM provider (OpenAI, Anthropic, etc.)
 
         Optional:
-        - LLM_PROVIDER: "openai" (default), "anthropic", "gemini", "ollama", "mistral"
+        - LLM_PROVIDER: "openai" (default), "anthropic", "gemini", "ollama", "mistral", "bedrock"
         - LLM_MODEL: Model name (default: "gpt-5-mini")
         - DEFAULT_USER_EMAIL: Custom default user email
         - DEFAULT_USER_PASSWORD: Custom default user password

cognee/api/v1/cognify/cognify.py

@@ -53,6 +53,7 @@ async def cognify(
     custom_prompt: Optional[str] = None,
     temporal_cognify: bool = False,
     data_per_batch: int = 20,
+    **kwargs
 ):
     """
     Transform ingested data into a structured knowledge graph.
@@ -223,6 +224,7 @@ async def cognify(
             config=config,
             custom_prompt=custom_prompt,
             chunks_per_batch=chunks_per_batch,
+            **kwargs,
         )
 
     # By calling get pipeline executor we get a function that will have the run_pipeline run in the background or a function that we will need to wait for
@@ -251,6 +253,7 @@ async def get_default_tasks(  # TODO: Find out a better way to do this (Boris's
     config: Config = None,
     custom_prompt: Optional[str] = None,
     chunks_per_batch: int = 100,
+    **kwargs,
 ) -> list[Task]:
     if config is None:
         ontology_config = get_ontology_env_config()
@@ -288,6 +291,7 @@ async def get_default_tasks(  # TODO: Find out a better way to do this (Boris's
             config=config,
             custom_prompt=custom_prompt,
             task_config={"batch_size": chunks_per_batch},
+            **kwargs,
         ),  # Generate knowledge graphs from the document chunks.
         Task(
             summarize_text,

cognee/api/v1/cognify/routers/get_cognify_router.py

@@ -42,7 +42,9 @@ class CognifyPayloadDTO(InDTO):
         default="", description="Custom prompt for entity extraction and graph generation"
     )
     ontology_key: Optional[List[str]] = Field(
-        default=None, description="Reference to one or more previously uploaded ontologies"
+        default=None,
+        examples=[[]],
+        description="Reference to one or more previously uploaded ontologies",
     )
 
 

cognee/infrastructure/files/storage/s3_config.py

@@ -9,6 +9,8 @@ class S3Config(BaseSettings):
     aws_access_key_id: Optional[str] = None
     aws_secret_access_key: Optional[str] = None
     aws_session_token: Optional[str] = None
+    aws_profile_name: Optional[str] = None
+    aws_bedrock_runtime_endpoint: Optional[str] = None
     model_config = SettingsConfigDict(env_file=".env", extra="allow")
 
 

cognee/infrastructure/llm/LLMGateway.py

@@ -11,7 +11,7 @@ class LLMGateway:
 
     @staticmethod
     def acreate_structured_output(
-        text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> Coroutine:
         llm_config = get_llm_config()
         if llm_config.structured_output_framework.upper() == "BAML":
@@ -31,7 +31,7 @@ class LLMGateway:
 
             llm_client = get_llm_client()
             return llm_client.acreate_structured_output(
-                text_input=text_input, system_prompt=system_prompt, response_model=response_model
+                text_input=text_input, system_prompt=system_prompt, response_model=response_model, **kwargs
             )
 
     @staticmethod

cognee/infrastructure/llm/extraction/knowledge_graph/extract_content_graph.py

@@ -10,7 +10,7 @@ from cognee.infrastructure.llm.config import (
 
 
 async def extract_content_graph(
-    content: str, response_model: Type[BaseModel], custom_prompt: Optional[str] = None
+    content: str, response_model: Type[BaseModel], custom_prompt: Optional[str] = None, **kwargs
 ):
     if custom_prompt:
         system_prompt = custom_prompt
@@ -30,7 +30,7 @@ async def extract_content_graph(
         system_prompt = render_prompt(prompt_path, {}, base_directory=base_directory)
 
     content_graph = await LLMGateway.acreate_structured_output(
-        content, system_prompt, response_model
+        content, system_prompt, response_model, **kwargs
     )
 
     return content_graph

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/anthropic/adapter.py

@@ -52,7 +52,7 @@ class AnthropicAdapter(LLMInterface):
         reraise=True,
     )
     async def acreate_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a response from a user query.

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/bedrock/__init__.py

@@ -0,0 +1,5 @@
+"""Bedrock LLM adapter module."""
+
+from .adapter import BedrockAdapter
+
+__all__ = ["BedrockAdapter"]

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/bedrock/adapter.py

@@ -0,0 +1,153 @@
+import litellm
+import instructor
+from typing import Type
+from pydantic import BaseModel
+from litellm.exceptions import ContentPolicyViolationError
+from instructor.exceptions import InstructorRetryException
+
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
+from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import (
+    LLMInterface,
+)
+from cognee.infrastructure.llm.exceptions import (
+    ContentPolicyFilterError,
+    MissingSystemPromptPathError,
+)
+from cognee.infrastructure.files.storage.s3_config import get_s3_config
+from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import (
+    rate_limit_async,
+    rate_limit_sync,
+    sleep_and_retry_async,
+    sleep_and_retry_sync,
+)
+from cognee.modules.observability.get_observe import get_observe
+
+observe = get_observe()
+
+
+class BedrockAdapter(LLMInterface):
+    """
+    Adapter for AWS Bedrock API with support for three authentication methods:
+    1. API Key (Bearer Token)
+    2. AWS Credentials (access key + secret key)
+    3. AWS Profile (boto3 credential chain)
+    """
+
+    name = "Bedrock"
+    model: str
+    api_key: str
+    default_instructor_mode = "json_schema_mode"
+
+    MAX_RETRIES = 5
+
+    def __init__(
+        self,
+        model: str,
+        api_key: str = None,
+        max_completion_tokens: int = 16384,
+        streaming: bool = False,
+        instructor_mode: str = None,
+    ):
+        self.instructor_mode = instructor_mode if instructor_mode else self.default_instructor_mode
+
+        self.aclient = instructor.from_litellm(
+            litellm.acompletion, mode=instructor.Mode(self.instructor_mode)
+        )
+        self.client = instructor.from_litellm(litellm.completion)
+        self.model = model
+        self.api_key = api_key
+        self.max_completion_tokens = max_completion_tokens
+        self.streaming = streaming
+
+    def _create_bedrock_request(
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+    ) -> dict:
+        """Create Bedrock request with authentication."""
+
+        request_params = {
+            "model": self.model,
+            "custom_llm_provider": "bedrock",
+            "drop_params": True,
+            "messages": [
+                {"role": "user", "content": text_input},
+                {"role": "system", "content": system_prompt},
+            ],
+            "response_model": response_model,
+            "max_retries": self.MAX_RETRIES,
+            "max_completion_tokens": self.max_completion_tokens,
+            "stream": self.streaming,
+        }
+
+        s3_config = get_s3_config()
+
+        # Add authentication parameters
+        if self.api_key:
+            request_params["api_key"] = self.api_key
+        elif s3_config.aws_access_key_id and s3_config.aws_secret_access_key:
+            request_params["aws_access_key_id"] = s3_config.aws_access_key_id
+            request_params["aws_secret_access_key"] = s3_config.aws_secret_access_key
+            if s3_config.aws_session_token:
+                request_params["aws_session_token"] = s3_config.aws_session_token
+        elif s3_config.aws_profile_name:
+            request_params["aws_profile_name"] = s3_config.aws_profile_name
+
+        if s3_config.aws_region:
+            request_params["aws_region_name"] = s3_config.aws_region
+
+        # Add optional parameters
+        if s3_config.aws_bedrock_runtime_endpoint:
+            request_params["aws_bedrock_runtime_endpoint"] = s3_config.aws_bedrock_runtime_endpoint
+
+        return request_params
+
+    @observe(as_type="generation")
+    @sleep_and_retry_async()
+    @rate_limit_async
+    async def acreate_structured_output(
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+    ) -> BaseModel:
+        """Generate structured output from AWS Bedrock API."""
+
+        try:
+            request_params = self._create_bedrock_request(text_input, system_prompt, response_model)
+            return await self.aclient.chat.completions.create(**request_params)
+
+        except (
+            ContentPolicyViolationError,
+            InstructorRetryException,
+        ) as error:
+            if (
+                isinstance(error, InstructorRetryException)
+                and "content management policy" not in str(error).lower()
+            ):
+                raise error
+
+            raise ContentPolicyFilterError(
+                f"The provided input contains content that is not aligned with our content policy: {text_input}"
+            )
+
+    @observe
+    @sleep_and_retry_sync()
+    @rate_limit_sync
+    def create_structured_output(
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+    ) -> BaseModel:
+        """Generate structured output from AWS Bedrock API (synchronous)."""
+
+        request_params = self._create_bedrock_request(text_input, system_prompt, response_model)
+        return self.client.chat.completions.create(**request_params)
+
+    def show_prompt(self, text_input: str, system_prompt: str) -> str:
+        """Format and display the prompt for a user query."""
+        if not text_input:
+            text_input = "No user input provided."
+        if not system_prompt:
+            raise MissingSystemPromptPathError()
+        system_prompt = LLMGateway.read_query_prompt(system_prompt)
+
+        formatted_prompt = (
+            f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n"""
+            if system_prompt
+            else None
+        )
+        return formatted_prompt

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py

@@ -80,7 +80,7 @@ class GeminiAdapter(LLMInterface):
         reraise=True,
     )
     async def acreate_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a response from a user query.

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py

@@ -80,7 +80,7 @@ class GenericAPIAdapter(LLMInterface):
         reraise=True,
     )
     async def acreate_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a response from a user query.

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py

@@ -24,6 +24,7 @@ class LLMProvider(Enum):
     - CUSTOM: Represents a custom provider option.
     - GEMINI: Represents the Gemini provider.
     - MISTRAL: Represents the Mistral AI provider.
+    - BEDROCK: Represents the AWS Bedrock provider.
     """
 
     OPENAI = "openai"
@@ -32,6 +33,7 @@ class LLMProvider(Enum):
     CUSTOM = "custom"
     GEMINI = "gemini"
     MISTRAL = "mistral"
+    BEDROCK = "bedrock"
 
 
 def get_llm_client(raise_api_key_error: bool = True):
@@ -154,7 +156,7 @@ def get_llm_client(raise_api_key_error: bool = True):
         )
 
     elif provider == LLMProvider.MISTRAL:
-        if llm_config.llm_api_key is None:
+        if llm_config.llm_api_key is None and raise_api_key_error:
             raise LLMAPIKeyNotSetError()
 
         from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.mistral.adapter import (
@@ -169,5 +171,21 @@ def get_llm_client(raise_api_key_error: bool = True):
             instructor_mode=llm_config.llm_instructor_mode.lower(),
         )
 
+    elif provider == LLMProvider.BEDROCK:
+        # if llm_config.llm_api_key is None and raise_api_key_error:
+        #     raise LLMAPIKeyNotSetError()
+
+        from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.bedrock.adapter import (
+            BedrockAdapter,
+        )
+
+        return BedrockAdapter(
+            model=llm_config.llm_model,
+            api_key=llm_config.llm_api_key,
+            max_completion_tokens=max_completion_tokens,
+            streaming=llm_config.llm_streaming,
+            instructor_mode=llm_config.llm_instructor_mode.lower(),
+        )
+
     else:
         raise UnsupportedLLMProviderError(provider)

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py

@@ -69,7 +69,7 @@ class MistralAdapter(LLMInterface):
         reraise=True,
     )
     async def acreate_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a response from the user query.

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/ollama/adapter.py

@@ -76,7 +76,7 @@ class OllamaAPIAdapter(LLMInterface):
         reraise=True,
     )
     async def acreate_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a structured output from the LLM using the provided text and system prompt.
@@ -123,7 +123,7 @@ class OllamaAPIAdapter(LLMInterface):
         before_sleep=before_sleep_log(logger, logging.DEBUG),
         reraise=True,
     )
-    async def create_transcript(self, input_file: str) -> str:
+    async def create_transcript(self, input_file: str, **kwargs) -> str:
         """
         Generate an audio transcript from a user query.
 
@@ -162,7 +162,7 @@ class OllamaAPIAdapter(LLMInterface):
         before_sleep=before_sleep_log(logger, logging.DEBUG),
         reraise=True,
     )
-    async def transcribe_image(self, input_file: str) -> str:
+    async def transcribe_image(self, input_file: str, **kwargs) -> str:
         """
         Transcribe content from an image using base64 encoding.
 

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py

@@ -112,7 +112,7 @@ class OpenAIAdapter(LLMInterface):
         reraise=True,
     )
     async def acreate_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a response from a user query.
@@ -154,6 +154,7 @@ class OpenAIAdapter(LLMInterface):
                     api_version=self.api_version,
                     response_model=response_model,
                     max_retries=self.MAX_RETRIES,
+                    **kwargs,
                 )
         except (
             ContentFilterFinishReasonError,
@@ -180,6 +181,7 @@ class OpenAIAdapter(LLMInterface):
                         # api_base=self.fallback_endpoint,
                         response_model=response_model,
                         max_retries=self.MAX_RETRIES,
+                        **kwargs,
                     )
             except (
                 ContentFilterFinishReasonError,
@@ -205,7 +207,7 @@ class OpenAIAdapter(LLMInterface):
         reraise=True,
     )
     def create_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a response from a user query.
@@ -245,6 +247,7 @@ class OpenAIAdapter(LLMInterface):
             api_version=self.api_version,
             response_model=response_model,
             max_retries=self.MAX_RETRIES,
+            **kwargs,
         )
 
     @retry(
@@ -254,7 +257,7 @@ class OpenAIAdapter(LLMInterface):
         before_sleep=before_sleep_log(logger, logging.DEBUG),
         reraise=True,
     )
-    async def create_transcript(self, input):
+    async def create_transcript(self, input, **kwargs):
         """
         Generate an audio transcript from a user query.
 
@@ -281,6 +284,7 @@ class OpenAIAdapter(LLMInterface):
                 api_base=self.endpoint,
                 api_version=self.api_version,
                 max_retries=self.MAX_RETRIES,
+                **kwargs,
             )
 
         return transcription
@@ -292,7 +296,7 @@ class OpenAIAdapter(LLMInterface):
         before_sleep=before_sleep_log(logger, logging.DEBUG),
         reraise=True,
     )
-    async def transcribe_image(self, input) -> BaseModel:
+    async def transcribe_image(self, input, **kwargs) -> BaseModel:
         """
         Generate a transcription of an image from a user query.
 
@@ -337,4 +341,5 @@ class OpenAIAdapter(LLMInterface):
             api_version=self.api_version,
             max_completion_tokens=300,
             max_retries=self.MAX_RETRIES,
+            **kwargs,
         )

cognee/modules/settings/get_settings.py

@@ -16,6 +16,7 @@ class ModelName(Enum):
     anthropic = "anthropic"
     gemini = "gemini"
     mistral = "mistral"
+    bedrock = "bedrock"
 
 
 class LLMConfig(BaseModel):
@@ -77,6 +78,10 @@ def get_settings() -> SettingsDict:
             "value": "mistral",
             "label": "Mistral",
         },
+        {
+            "value": "bedrock",
+            "label": "Bedrock",
+        },
     ]
 
     return SettingsDict.model_validate(
@@ -157,6 +162,20 @@ def get_settings() -> SettingsDict:
                             "label": "Mistral Large 2.1",
                         },
                     ],
+                    "bedrock": [
+                        {
+                            "value": "eu.anthropic.claude-sonnet-4-5-20250929-v1:0",
+                            "label": "Claude 4.5 Sonnet",
+                        },
+                        {
+                            "value": "eu.anthropic.claude-haiku-4-5-20251001-v1:0",
+                            "label": "Claude 4.5 Haiku",
+                        },
+                        {
+                            "value": "eu.amazon.nova-lite-v1:0",
+                            "label": "Amazon Nova Lite",
+                        },
+                    ],
                 },
             },
             vector_db={

cognee/tasks/graph/extract_graph_from_data.py

@@ -97,6 +97,7 @@ async def extract_graph_from_data(
     graph_model: Type[BaseModel],
     config: Config = None,
     custom_prompt: Optional[str] = None,
+    **kwargs,
 ) -> List[DocumentChunk]:
     """
     Extracts and integrates a knowledge graph from the text content of document chunks using a specified graph model.
@@ -111,7 +112,7 @@ async def extract_graph_from_data(
 
     chunk_graphs = await asyncio.gather(
         *[
-            extract_content_graph(chunk.text, graph_model, custom_prompt=custom_prompt)
+            extract_content_graph(chunk.text, graph_model, custom_prompt=custom_prompt, **kwargs)
             for chunk in data_chunks
         ]
     )

cognee/tasks/memify/get_triplet_datapoints.py

@@ -1,5 +1,6 @@
 from typing import AsyncGenerator, Dict, Any, List, Optional
 from cognee.infrastructure.databases.graph.get_graph_engine import get_graph_engine
+from cognee.modules.engine.utils import generate_node_id
 from cognee.shared.logging_utils import get_logger
 from cognee.modules.graph.utils.convert_node_to_data_point import get_all_subclasses
 from cognee.infrastructure.engine import DataPoint
@@ -155,7 +156,12 @@ def _process_single_triplet(
 
     embeddable_text = f"{start_node_text}-›{relationship_text}-›{end_node_text}".strip()
 
-    triplet_obj = Triplet(from_node_id=start_node_id, to_node_id=end_node_id, text=embeddable_text)
+    relationship_name = relationship.get("relationship_name", "")
+    triplet_id = generate_node_id(str(start_node_id) + str(relationship_name) + str(end_node_id))
+
+    triplet_obj = Triplet(
+        id=triplet_id, from_node_id=start_node_id, to_node_id=end_node_id, text=embeddable_text
+    )
 
     return triplet_obj, None