Question -Answer System

The chat file is based on the LangChain framework, combining vector databases with large language models to build a question-answering system. It can provide retrieval-based answers (RAG, Retrieval-Augmented Generation) or model-generated responses based on the user's input.

Key Functionalities:

1. Imported Libraries:

· Chroma: Used for managing a vector database, supporting persistent storage and similarity-based searches.

· ChatPromptTemplate: Used to create and format dialogue prompt templates.

· get_model and get_embedding_function: These functions, from the custom model_selector module, are responsible for fetching the model and embedding function.

· Path and sys: Handle paths to ensure correct import of configurations and modules.

· config: From the config module, imports RAG_PROMPT_TEMPLATE and CHROMA_PATH, which refer to the dialogue template and the path for vector database storage.

2. generate_response Function:

This is the core function of the system, generating a response based on user input. It combines vector database search with a language model's generation capabilities.

Parameters:

· prompt: The user's input question or dialogue.

· tools: Optional parameter, used to specify if certain tools are employed (not implemented in the given code).

· model: The name of the model to use, defaulting to "default".

· use_self_model: Specifies whether to generate responses using a custom model. If False, only retrieval-based document chunks are returned. If True, the function combines the language model's generated dialogue.

· use_local_model: Specifies whether to use a local model for inference.

3. Vector Database Query:

· The function first calls get_embedding_function() to retrieve the embedding function and uses it along with the CHROMA_PATH to initialize a Chroma vector database.

· Through db.similarity_search_with_relevance_scores(prompt, k=3), the database performs a similarity search and returns the document chunks most relevant to the user's input, along with relevance scores.

4. Two Response Modes:

Retrieval Mode (use_self_model=False):

If custom model generation is not used, the function returns only the most relevant document chunks and their reference information.

· If the highest similarity score is greater than or equal to 0.7, relevant document chunks and their sources are returned.

· Otherwise, empty document chunks and references are returned.

Dialogue Mode (use_self_model=True):

If a custom model is used to generate dialogue, it further combines the retrieved document chunks with the model's generated answer.

· First, it calls get_model to fetch the specified model and generates a response from the model.

· If relevant retrieval results exist (score >= 0.7), the document chunks are formatted as context and used along with the model's response to create a final answer using a template.

· If no highly relevant results are found, it returns the model-generated answer directly.

5. Return Structure:

The function returns a dictionary containing the generated answer (answer) or the retrieved document chunks (chunks), as well as their reference sources (references).