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| class RAGChain: | |
| def __init__(self, llm, vector_store): | |
| """ | |
| Initialize the RAGChain with an LLM instance and a vector store. | |
| """ | |
| self.llm = llm | |
| self.vector_store = vector_store | |
| def rewrite_query(self, query): | |
| """ | |
| Rewrite the user's query to align with the language and structure of the library's methods and documentation. | |
| """ | |
| rewrite_prompt = ( | |
| f"""You are an intelligent assistant that helps users rewrite their queries. | |
| The vectorstore consists of the source code and documentation of a Python library, which enables users to | |
| programmatically interact with a REST-like API of a software system. The library methods have descriptive | |
| docstrings. Your task is to rewrite the query in a way that aligns with the language and structure of the | |
| library's methods and documentation, ensuring optimal retrieval of relevant information. | |
| Guidelines for rewriting the query: | |
| 1. Identify the main action the user wants to perform (e.g., "Upload a file to a record," "Get users of a group"). | |
| 2. Remove conversational elements like greetings or pleasantries (e.g., "Hello Chatbot", "I need you to help me with"). | |
| 3. Exclude specific variable values (e.g., "ID of my record is '31'") unless essential to the intent. | |
| 4. Rephrase the query to match the format and keywords used in the docstrings, focusing on verbs and objects relevant to the action (e.g., "Add a record to a collection"). | |
| 5. Given the query the user might need more than one action to achieve his goal. In this case the rewritten query has more than one action. | |
| Examples: | |
| - User query: "Create a Python script with a method that facilitates the creation of records. This method should accept an array of identifiers as a parameter and allow metadata to be added to each record." | |
| - Rewritten query: "create records, add metadata to record" | |
| - User query: "Hi, can you help me write Python code to add a record to a collection? The record ID is '45', and the collection ID is '12'." | |
| Rewritten query: "add a record to a collection" | |
| - User query: I need a python script with which i create a new record with the title: "Hello World" and then link the record to a given collection. | |
| Rewritten query: "create a new record with title" , "link a record to a collection" | |
| Based on these examples and guidelines, rewrite the following user query to align more effectively with the keywords used in the docstrings. | |
| Do not include any addition comments, explanations, or text. | |
| Original query: | |
| {query} | |
| """ | |
| ) | |
| return self.llm.invoke(rewrite_prompt) | |
| def predict_library_usage(self, query): | |
| """ | |
| Use the LLM to predict the relevant library for the user's query. | |
| """ | |
| prompt = ( | |
| f"""The query is: '{query}'. | |
| Based on the user's query, assist them by determining which technical document they should read to interact with the software named 'Kadi4Mat'. | |
| There are two different technical documents to choose from: | |
| - Document 1: Provides information on how to use a Python library to interact with the HTTP API of 'Kadi4Mat'. | |
| - Document 2: Provides information on how to use a Python library to implement custom CLI commands to interact with 'Kadi4Mat'. | |
| Your task is to select the single most likely option. | |
| If Document 1 is the best choice, respond with 'kadi_apy/lib/'. | |
| If Document 2 is the best choice, respond with 'kadi_apy/cli/'. | |
| Respond with only the exact corresponding option and do not include any additional comments, explanations, or text." | |
| """ | |
| ) | |
| return self.llm.predict(prompt) | |
| def retrieve_contexts(self, query, k, filter = None): | |
| """ | |
| Retrieve relevant documents and source code based on the query and library usage prediction. | |
| """ | |
| context = self.vector_store.similarity_search(query = query, k=k, filter=filter) | |
| return context | |
| def format_documents(self, documents): | |
| formatted_docs = [] | |
| for i, doc in enumerate(documents, start=1): | |
| formatted_docs.append(f"Snippet {i}: \n") | |
| formatted_docs.append("\n") | |
| all_metadata = doc.metadata | |
| metadata_str = ", ".join(f"{key}: {value}" for key, value in all_metadata.items()) | |
| print("\n") | |
| print("------------------------------Beneath is retrieved doc------------------------------------------------") | |
| print(metadata_str) | |
| formatted_docs.append(metadata_str) | |
| print("\n") | |
| formatted_docs.append("\n") | |
| formatted_docs.append(doc.page_content) | |
| print(doc.page_content) | |
| print("\n\n") | |
| print("------------------------------End of retrived doc------------------------------------------------") | |
| formatted_docs.append("\n\n\n") | |
| return formatted_docs | |
| def generate_response(self, query, doc_context, code_context): | |
| """ | |
| Generate a response using the retrieved contexts and the LLM. | |
| """ | |
| prompt = f"""You are an expert python developer. You are assisting in generating code for users who want to programmatically | |
| make use of a python library named 'kadiAPY' to interact with the API of a software. It provides an object-oriented | |
| approach for interfacing with the API. | |
| You are given "Documentation Snippets" and "Code Snippets" | |
| "Documentation Snippets:" Contains a collection of potentially useful snippets, including code examples and documentation excerpts of "kadiAPY" | |
| "Code Snippets:" Contains potentially useful snippets from the source code of "kadiAPY" | |
| Based on the retrieved snippets and the guidelines answer the "query". | |
| General Guidelines: | |
| - If no related information is found from the snippets to answer the query, reply that you do not know. | |
| Guidelines when generating code: | |
| - First display the full code and then follow with a well structured explanation of the generated code. | |
| Documentation Snippets: | |
| {doc_context} | |
| Code Snippets: | |
| {code_context} | |
| Query: | |
| {query} | |
| """ | |
| return self.llm.invoke(prompt).content |