config.yml

# Can be "smart" or "fast". Default is fast
llm_chain:
  default: "fast"  

#Re-ranking module enable or disable config
reranking:
  enabled: false  # Set to true to enable re-ranking
  method: "standard"
  model_name: "cross-encoder/ms-marco-MiniLM-L-6-v2"
  top_k: 10

#initial document retrieval config
retrieval:
  initial_k: 20

# File extension for data loading
file_extension: "**/*.java"

# Document processing
chunk_size: 1000
chunk_overlap: 200

# Tree of thoughts depth. default is 3
n_ideas : 3

# LLM Configuration
llm:
  model: "qwen2.5:latest"
  num_ctx: 8048
  temperature: 0.4
  top_p: 0.9

# RAG Configuration
max_iterations: 3

# Prompt Template - Do not modify below {chat_history}, {context} and {question} syntax below prompt
prompt_template: |
  You are a security-focused software engineer reviews the code and expert in coding knowledge. You will be provided with code snippets and a specific question. Your goal is to analyze the code thoroughly and provide a detailed assessment by adhering the specified query:
  
  Follow below Instructions strictly:
  Do not make any assumption and examples, Always follow and check the fact and context references before answering the questions.

  Contextual Awareness: Utilize the provided chat history and code context to understand the purpose and environment of the code snippet.

  Always Say "I need more information" if you need any additional context and if you can't find anything just mention "cannot find" or "I couldn't find" in response.

  **Provide a detailed and comprehensive answer. Format your answer in Markdown, using headings, bullet points, and code blocks where appropriate.**

  Chat History:
  {chat_history}

  Context:
  {context}

  Question:
  {question}

  Answer:


vector_store:
  persist_directory: './qdrant_data'
  collection_name: 'contexi_collection'