fix

Author: nickprock

.env.example

@@ -40,6 +40,7 @@ TIMEOUT=300
 ## CHUNKING CONFIG
 CONTEXT_CHUNK_SIZE=1000
 TIKTOKEN_MODEL=gpt-4
+ENABLE_BATCH_PROCESSING=true
 
 ## QDRANT
 QDRANT_MODE=memory # or cloud or docker

README.md

@@ -61,6 +61,7 @@ crewai install
 *	`TIMEOUT`: The LLM timeout (optional).
 * `CONTEXT_CHUNK_SIZE`: the chunks dimension if your repo is large 
 * `TIKTOKEN_MODEL`: [`tiktoken`](https://github.com/openai/tiktoken) is the OpenAI tokenizer. It counts tokens corresponding to a specific model in the OpenAI API. This is usually a good approximation for all LLMs.
+* `ENABLE_BATCH_PROCESSING`: Default *true*. If *false* you force the crew to **NOT USE** chunking.
 *	`QDRANT_MODE`: The Qdrant mode (e.g., `memory`, `cloud`, `docker`).
 *	`QDRANT_HOST`: The Qdrant host (required for cloud mode).
 *	`QDRANT_API_KEY`: The Qdrant API key (required for cloud mode).

src/code_explainer/crew.py

@@ -71,7 +71,7 @@ def prepare_inputs(self, inputs):
             if chunks:
                 inputs["code_chunks"] = chunks
                 inputs["total_chunks"] = len(chunks)
-                print(f"Codice diviso in {len(chunks)} chunk per l'elaborazione")
+                # print(f"Code divided into {len(chunks)} chunks for processing")
         return inputs
 
     def _read_codebase(self, code_path: str) -> Dict[str, str]:

streamlit_app.py

@@ -220,6 +220,7 @@ def run_crew(repository_url, local_path, diagram_type, output_format, sonarqube_
                 llm_config,
                 local_dir,
                 output_dir,
+                chunking_config
             )
 
         # Visualization