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Update app.py
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app.py
CHANGED
@@ -326,34 +326,10 @@ def format_kadi_api_doc_context(docs):
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def rag_workflow(query):
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The vectorstore consists of the source code and documentation of a Python library, which enables users to
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programmatically interact with a REST-like API of a software system. The library methods have descriptive
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docstrings. Your task is to rewrite the query in a way that aligns with the language and structure of the
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library's methods and documentation, ensuring optimal retrieval of relevant information.
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Guidelines for rewriting the query:
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1. Identify the main action the user wants to perform (e.g., "Upload a file to a record," "Get users of a group").
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2. Remove conversational elements like greetings or pleasantries (e.g., "Hello Chatbot", "I need you to help me with").
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3. Exclude specific variable values (e.g., "ID of my record is '31'") unless essential to the intent.
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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").
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5. Given the query the user might need more than one method to achieve his goal.
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Examples:
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- 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."
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- Rewritten query: "create records, add metadata to record"
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- 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'."
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Rewritten query: "add a record to a collection"
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- User query: "I have a folder with files in my local disc and i need a python script with which i can upload the files to a record. The id of the record is 324. The path to the folder is: r"C:\Users\Anwender\Documents\League of Legends"
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Rewritten query: "upload files to a record"
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Based on these examples and guidelines, rewrite the following user query to align more effectively with the keywords used in the docstrings.
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Original query:
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{query}
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"""
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rewritten_query_response = llm.invoke(
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rewritten_query = rewritten_query_response.content.strip()
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kadi_apy_docs = retrieve_within_kadiApy_docs (vector_store, query, k = 5)
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def rag_workflow(query):
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rewritten_query_response = llm.invoke("Hello")
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rewritten_query = rewritten_query_response.content.strip()
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kadi_apy_docs = retrieve_within_kadiApy_docs (vector_store, query, k = 5)
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