Refactor Neo4j ingestion through configurable pipelines

docs/getting-started/configuration.md

@@ -192,6 +192,44 @@ OPERATION_TIMEOUT=300
 LARGE_DOCUMENT_TIMEOUT=600
 ```
 
+### Ingestion Pipelines
+
+Document ingestion now uses [LlamaIndex ingestion pipelines](https://docs.llamaindex.ai/) with pluggable connectors, transformations, and writers. The service ships with three pipelines (`manual_input`, `file`, `directory`), and you can override or extend them from configuration by providing a JSON-style mapping in your `.env` file:
+
+```bash
+INGESTION_PIPELINES='{
+  "file": {
+    "transformations": [
+      {
+        "class_path": "llama_index.core.node_parser.SimpleNodeParser",
+        "kwargs": {"chunk_size": 256, "chunk_overlap": 20}
+      },
+      {
+        "class_path": "codebase_rag.services.knowledge.pipeline_components.MetadataEnrichmentTransformation",
+        "kwargs": {"metadata": {"language": "python"}}
+      }
+    ]
+  },
+  "git": {
+    "connector": {
+      "class_path": "my_project.pipeline.GitRepositoryConnector",
+      "kwargs": {"branch": "main"}
+    },
+    "transformations": [
+      {
+        "class_path": "my_project.pipeline.CodeBlockParser",
+        "kwargs": {"max_tokens": 400}
+      }
+    ],
+    "writer": {
+      "class_path": "codebase_rag.services.knowledge.pipeline_components.Neo4jKnowledgeGraphWriter"
+    }
+  }
+}'
+```
+
+Each entry is merged with the defaults. This means you can change chunking behaviour, add metadata enrichment steps, or register new data sources by publishing your own connector class. At runtime the knowledge service builds and reuses the configured pipeline instances so changes only require a service restart.
+
 ### Neo4j Performance Tuning
 
 For large repositories:

src/codebase_rag/config/settings.py

@@ -7,7 +7,7 @@
 
 from pydantic_settings import BaseSettings
 from pydantic import Field
-from typing import Optional, Literal
+from typing import Optional, Literal, Dict, Any
 
 
 class Settings(BaseSettings):
@@ -99,6 +99,10 @@ class Settings(BaseSettings):
     # Document Processing Settings
     max_document_size: int = Field(default=10 * 1024 * 1024, description="Maximum document size in bytes (10MB)")
     max_payload_size: int = Field(default=50 * 1024 * 1024, description="Maximum task payload size for storage (50MB)")
+    ingestion_pipelines: Dict[str, Dict[str, Any]] = Field(
+        default_factory=dict,
+        description="Optional ingestion pipeline overrides",
+    )
 
     # API Settings
     cors_origins: list = Field(default=["*"], description="CORS allowed origins")