Merge branch 'dev' into fix-only-context

.github/ISSUE_TEMPLATE/documentation.yml

@@ -71,3 +71,4 @@ body:
           required: true
         - label: I have specified the location of the documentation issue
           required: true
+

.github/core-team.txt

@@ -0,0 +1,12 @@
+# Core team GitHub logins (one per line). Lines may begin with @; case-insensitive.
+borisarzentar
+daukadolt
+dexters1
+hajdul88
+hande-k
+lxobr
+pazone
+siillee
+vasilije1990
+
+

.github/workflows/label-core-team.yml

@@ -0,0 +1,76 @@
+name: Label PRs from core team
+
+on:
+  pull_request_target:
+    types: [opened, reopened, synchronize, ready_for_review, edited]
+
+permissions:
+  contents: read
+  issues: write
+
+jobs:
+  label-core-team:
+    if: ${{ !github.event.pull_request.draft }}
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out base repository
+        uses: actions/checkout@v4
+        with:
+          repository: ${{ github.repository }}
+          ref: ${{ github.event.pull_request.base.ref }}
+
+      - name: Determine if PR author is a core team member
+        id: check_core
+        shell: bash
+        run: |
+          AUTHOR="${{ github.event.pull_request.user.login }}"
+          LIST_FILE=".github/core-team.txt"
+
+          if [ ! -f "$LIST_FILE" ]; then
+            echo "core=false" >> "$GITHUB_OUTPUT"
+            exit 0
+          fi
+
+          # Normalize author to lowercase and strip leading '@'
+          AUTHOR_NORM="$(echo "$AUTHOR" | tr '[:upper:]' '[:lower:]' | sed 's/^@//')"
+
+          # Compare against normalized list values (ignore comments/blank lines)
+          if awk -v author="$AUTHOR_NORM" '
+            BEGIN { found=0 }
+            {
+              line=$0
+              sub(/^[ \t]+|[ \t]+$/, "", line)
+              if (line ~ /^#/ || line == "") next
+              sub(/^@/, "", line)
+              line=tolower(line)
+              if (line == author) { found=1; exit }
+            }
+            END { exit(found ? 0 : 1) }
+          ' "$LIST_FILE"; then
+            echo "core=true" >> "$GITHUB_OUTPUT"
+          else
+            echo "core=false" >> "$GITHUB_OUTPUT"
+          fi
+
+      - name: Add core-team label
+        if: steps.check_core.outputs.core == 'true'
+        uses: actions/github-script@v7
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          script: |
+            const label = 'core-team';
+            const { owner, repo } = context.repo;
+            const prNumber = context.payload.pull_request.number;
+            try {
+              await github.rest.issues.addLabels({
+                owner,
+                repo,
+                issue_number: prNumber,
+                labels: [label],
+              });
+              core.info(`Label '${label}' added to PR #${prNumber}`);
+            } catch (error) {
+              core.warning(`Failed to add label: ${error.message}`);
+            }
+
+

README.md

@@ -178,7 +178,7 @@ You can also cognify your files and query using cognee UI.
 
 ### Running the UI
 
-Try cognee UI by running ``` cognee-cli -ui ``` command on your terminal.
+Try cognee UI by setting LLM_API_KEY and running ``` cognee-cli -ui ``` command on your terminal.
 
 ## Understand our architecture
 

cognee/infrastructure/databases/graph/kuzu/adapter.py

@@ -1277,7 +1277,6 @@ class KuzuAdapter(GraphDBInterface):
             A tuple containing a list of filtered node properties and a list of filtered edge
             properties.
         """
-
         where_clauses = []
         params = {}
 
@@ -1288,16 +1287,50 @@ class KuzuAdapter(GraphDBInterface):
                 params[param_name] = values
 
         where_clause = " AND ".join(where_clauses)
-        nodes_query = f"MATCH (n:Node) WHERE {where_clause} RETURN properties(n)"
+        nodes_query = (
+            f"MATCH (n:Node) WHERE {where_clause} RETURN n.id, {{properties: n.properties}}"
+        )
         edges_query = f"""
         MATCH (n1:Node)-[r:EDGE]->(n2:Node)
         WHERE {where_clause.replace("n.", "n1.")} AND {where_clause.replace("n.", "n2.")}
-        RETURN properties(r)
+        RETURN n1.id, n2.id, r.relationship_name, r.properties
         """
         nodes, edges = await asyncio.gather(
             self.query(nodes_query, params), self.query(edges_query, params)
         )
-        return ([n[0] for n in nodes], [e[0] for e in edges])
+        formatted_nodes = []
+        for n in nodes:
+            if n[0]:
+                node_id = str(n[0])
+                props = n[1]
+                if props.get("properties"):
+                    try:
+                        additional_props = json.loads(props["properties"])
+                        props.update(additional_props)
+                        del props["properties"]
+                    except json.JSONDecodeError:
+                        logger.warning(f"Failed to parse properties JSON for node {node_id}")
+                formatted_nodes.append((node_id, props))
+        if not formatted_nodes:
+            logger.warning("No nodes found in the database")
+            return [], []
+
+        formatted_edges = []
+        for e in edges:
+            if e and len(e) >= 3:
+                source_id = str(e[0])
+                target_id = str(e[1])
+                rel_type = str(e[2])
+                props = {}
+                if len(e) > 3 and e[3]:
+                    try:
+                        props = json.loads(e[3])
+                    except (json.JSONDecodeError, TypeError):
+                        logger.warning(
+                            f"Failed to parse edge properties for {source_id}->{target_id}"
+                        )
+                formatted_edges.append((source_id, target_id, rel_type, props))
+        return formatted_nodes, formatted_edges
 
     async def get_graph_metrics(self, include_optional=False) -> Dict[str, Any]:
         """

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

@@ -29,6 +29,9 @@ observe = get_observe()
 
 logger = get_logger()
 
+# litellm to drop unsupported params, e.g., reasoning_effort when not supported by the model.
+litellm.drop_params = True
+
 
 class OpenAIAdapter(LLMInterface):
     """
@@ -132,39 +135,13 @@ class OpenAIAdapter(LLMInterface):
                 api_version=self.api_version,
                 response_model=response_model,
                 max_retries=self.MAX_RETRIES,
-                extra_body={"reasoning_effort": "minimal"},
+                reasoning_effort="minimal",
             )
         except (
             ContentFilterFinishReasonError,
             ContentPolicyViolationError,
             InstructorRetryException,
-        ) as error:
-            if (
-                isinstance(error, InstructorRetryException)
-                and "content management policy" not in str(error).lower()
-            ):
-                logger.debug(
-                    "LLM Model does not support reasoning_effort parameter, trying call without the parameter."
-                )
-                return await self.aclient.chat.completions.create(
-                    model=self.model,
-                    messages=[
-                        {
-                            "role": "user",
-                            "content": f"""{text_input}""",
-                        },
-                        {
-                            "role": "system",
-                            "content": system_prompt,
-                        },
-                    ],
-                    api_key=self.api_key,
-                    api_base=self.endpoint,
-                    api_version=self.api_version,
-                    response_model=response_model,
-                    max_retries=self.MAX_RETRIES,
-                )
-
+        ):
             if not (self.fallback_model and self.fallback_api_key):
                 raise ContentPolicyFilterError(
                     f"The provided input contains content that is not aligned with our content policy: {text_input}"
@@ -246,6 +223,7 @@ class OpenAIAdapter(LLMInterface):
             api_base=self.endpoint,
             api_version=self.api_version,
             response_model=response_model,
+            reasoning_effort="minimal",
             max_retries=self.MAX_RETRIES,
         )