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Browse files- chatAgentPdf/utils/__pycache__/ask_human.cpython-310.pyc +0 -0
- chatAgentPdf/utils/__pycache__/model_params.cpython-310.pyc +0 -0
- chatAgentPdf/utils/__pycache__/prompts.cpython-310.pyc +0 -0
- chatAgentPdf/utils/ask_human.py +32 -0
- chatAgentPdf/utils/model_params.py +51 -0
- chatAgentPdf/utils/prompts.py +49 -0
chatAgentPdf/utils/__pycache__/ask_human.cpython-310.pyc
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Binary file (1.18 kB). View file
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chatAgentPdf/utils/__pycache__/model_params.cpython-310.pyc
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Binary file (1.01 kB). View file
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chatAgentPdf/utils/__pycache__/prompts.cpython-310.pyc
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chatAgentPdf/utils/ask_human.py
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"""
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Custom Langchain tool to ask human
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"""
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import time
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import streamlit as st
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from langchain.tools.base import BaseTool
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class CustomAskHumanTool(BaseTool):
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"""Tool that asks user for input."""
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name = "AskHuman"
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description = """Use this tool if you don't find a specific answer using KendraRetrievalTool.\
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Ask the human to clarify the question or provide the missing information.\
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The input should be a question for the human."""
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def _run(
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self,
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query: str,
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run_manager=None,
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) -> str:
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if "user_answer" not in st.session_state:
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answer_container = st.chat_message("assistant", avatar="🦜")
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answer_container.write(query)
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answer = st.text_input("Enter your answer", key="user_answer")
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while answer == "":
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time.sleep(1)
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return st.session_state["user_answer"]
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chatAgentPdf/utils/model_params.py
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"""
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Utilities for modeling
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"""
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def get_model_params(
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model_id: str,
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params: dict,
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) -> dict:
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"""
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Set up a dictionary with model parameters named appropriately for Bedrock
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Parameters
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----------
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model_id : str
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Model name
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params : dict
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Inference parameters
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Returns
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-------
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dict
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_description_
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"""
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model_params = {}
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# name parameters based on the model id
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if model_id.startswith("amazon"):
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model_params = {
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"maxTokenCount": params["answer_length"],
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"stopSequences": params["stop_words"],
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"temperature": params["temperature"],
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"topP": params["top_p"],
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}
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elif model_id.startswith("anthropic"):
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model_params = {
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"max_tokens_to_sample": params["answer_length"],
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"stop_sequences": params["stop_words"],
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"temperature": params["temperature"],
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"top_p": params["top_p"],
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}
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elif model_id.startswith("ai21"):
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model_params = {
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"maxTokens": params["answer_length"],
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"stopSequences": params["stop_words"],
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"temperature": params["temperature"],
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"topP": params["top_p"],
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}
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return model_params
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chatAgentPdf/utils/prompts.py
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"""
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Custom Langchain prompt templates
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"""
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from langchain.prompts import PromptTemplate
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def create_qa_prompt() -> PromptTemplate:
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"""
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Prompt for retrieval QA chain
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"""
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template = """\n\nHuman: Use the following pieces of context to answer the question at the end.
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{context}
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Question: {question}
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\n\nAssistant:
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Answer:"""
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return PromptTemplate(template=template, input_variables=["context", "question"])
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def create_agent_prompt() -> PromptTemplate:
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"""
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Prompt for the agent
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"""
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prefix = """\n\nHuman: Answer the following questions as best you can. You have access to the following tools:"""
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format_instructions = """Use the following format:
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Question: the input question you must answer
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Thought: you should always think about what to do
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Action: the action to take, should be one of [{tool_names}]
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Action Input: the input to the action
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Observation: the result of the action
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... (this Thought/Action/Action Input/Observation can repeat N times)
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Thought: I now know the final answer
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Final Answer: the final answer to the original input question"""
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suffix = """Begin!
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Question: {input}
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\n\nAssistant:
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Thought: {agent_scratchpad}
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"""
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return prefix, format_instructions, suffix
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