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Support Llama 3 and Improve Offline Chat Actors (#724)
- Add support for Llama 3 in Khoj offline mode - Make chat actors generate valid json with more local models - Fix offline chat actor tests
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commit
17a06f152c
4 changed files with 22 additions and 30 deletions
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@ -64,7 +64,7 @@ dependencies = [
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"pymupdf >= 1.23.5",
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"django == 4.2.10",
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"authlib == 1.2.1",
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"llama-cpp-python == 0.2.56",
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"llama-cpp-python == 0.2.64",
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"itsdangerous == 2.1.2",
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"httpx == 0.25.0",
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"pgvector == 0.2.4",
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@ -2,6 +2,7 @@ import glob
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import logging
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import math
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import os
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from typing import Any, Dict
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from huggingface_hub.constants import HF_HUB_CACHE
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@ -14,12 +15,16 @@ logger = logging.getLogger(__name__)
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def download_model(repo_id: str, filename: str = "*Q4_K_M.gguf", max_tokens: int = None):
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# Initialize Model Parameters
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# Use n_ctx=0 to get context size from the model
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kwargs = {"n_threads": 4, "n_ctx": 0, "verbose": False}
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kwargs: Dict[str, Any] = {"n_threads": 4, "n_ctx": 0, "verbose": False}
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# Decide whether to load model to GPU or CPU
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device = "gpu" if state.chat_on_gpu and state.device != "cpu" else "cpu"
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kwargs["n_gpu_layers"] = -1 if device == "gpu" else 0
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# Add chat format if known
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if "llama-3" in repo_id.lower():
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kwargs["chat_format"] = "llama-3"
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# Check if the model is already downloaded
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model_path = load_model_from_cache(repo_id, filename)
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chat_model = None
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@ -168,6 +168,7 @@ You are Khoj, an extremely smart and helpful search assistant with the ability t
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- Add as much context from the previous questions and answers as required into your search queries.
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- Break messages into multiple search queries when required to retrieve the relevant information.
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- Add date filters to your search queries from questions and answers when required to retrieve the relevant information.
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- Share relevant search queries as a JSON list of strings. Do not say anything else.
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Current Date: {current_date}
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User's Location: {location}
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@ -199,7 +200,7 @@ Khoj: ["Met in {location} on {yesterday_date} dt>='{yesterday_date}' dt<'{curren
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Chat History:
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{chat_history}
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What searches will you perform to answer the following question, using the chat history as reference? Respond with relevant search queries as list of strings.
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What searches will you perform to answer the following question, using the chat history as reference? Respond only with relevant search queries as a valid JSON list of strings.
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Q: {query}
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""".strip()
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)
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@ -370,7 +371,7 @@ AI: Learning to play the guitar is a great hobby. It can be a lot of fun and a g
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Q: What is the first element of the periodic table?
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Khoj: {{"source": ["general"]}}
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Now it's your turn to pick the data sources you would like to use to answer the user's question. Respond with data sources as a list of strings in a JSON object.
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Now it's your turn to pick the data sources you would like to use to answer the user's question. Provide the data sources as a list of strings in a JSON object. Do not say anything else.
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Chat History:
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{chat_history}
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@ -415,7 +416,7 @@ AI: Not too bad. How can I help you today?
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Q: What's the latest news on r/worldnews?
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Khoj: {{"links": ["https://www.reddit.com/r/worldnews/"]}}
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Now it's your turn to share actual webpage urls you'd like to read to answer the user's question.
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Now it's your turn to share actual webpage urls you'd like to read to answer the user's question. Provide them as a list of strings in a JSON object. Do not say anything else.
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History:
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{chat_history}
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@ -435,7 +436,7 @@ You are Khoj, an advanced google search assistant. You are tasked with construct
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- Official, up-to-date information about you, Khoj, is available at site:khoj.dev, github or pypi.
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What Google searches, if any, will you need to perform to answer the user's question?
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Provide search queries as a JSON list of strings
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Provide search queries as a list of strings in a JSON object.
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Current Date: {current_date}
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User's Location: {location}
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@ -482,7 +483,7 @@ AI: NASA's Saturn V rocket frequently makes lunar trips and has a large cargo ca
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Q: How many oranges would fit in NASA's Saturn V rocket?
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Khoj: {{"queries": ["volume of an orange", "volume of saturn v rocket"]}}
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Now it's your turn to construct Google search queries to answer the user's question.
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Now it's your turn to construct Google search queries to answer the user's question. Provide them as a list of strings in a JSON object. Do not say anything else.
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History:
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{chat_history}
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@ -92,29 +92,16 @@ def test_extract_question_with_date_filter_from_relative_year():
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)
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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@freeze_time("1984-04-02", ignore=["transformers"])
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def test_extract_question_includes_root_question(loaded_model):
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# Act
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response = extract_questions_offline("Which countries have I visited this year?", loaded_model=loaded_model)
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# Assert
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assert len(response) >= 1
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assert response[-1] == "Which countries have I visited this year?"
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_extract_multiple_explicit_questions_from_message(loaded_model):
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# Act
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response = extract_questions_offline("What is the Sun? What is the Moon?", loaded_model=loaded_model)
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responses = extract_questions_offline("What is the Sun? What is the Moon?", loaded_model=loaded_model)
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# Assert
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expected_responses = ["What is the Sun?", "What is the Moon?"]
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assert len(response) >= 2
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assert expected_responses[0] == response[-2]
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assert expected_responses[1] == response[-1]
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assert len(responses) >= 2
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assert ["the Sun" in response for response in responses]
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assert ["the Moon" in response for response in responses]
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# ----------------------------------------------------------------------------------------------------
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@ -159,13 +146,13 @@ def test_generate_search_query_using_question_from_chat_history(loaded_model):
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"son",
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"sons",
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"children",
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"family",
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]
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# Assert
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assert len(response) >= 1
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assert response[-1] == query, "Expected last question to be the user query, but got: " + response[-1]
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# Ensure the remaining generated search queries use proper nouns and chat history context
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for question in response[:-1]:
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for question in response:
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if "Barbara" in question:
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assert any([expected_relation in question for expected_relation in any_expected_with_barbara]), (
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"Expected search queries using proper nouns and chat history for context, but got: " + question
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@ -198,14 +185,13 @@ def test_generate_search_query_using_answer_from_chat_history(loaded_model):
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expected_responses = [
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"Barbara",
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"Robert",
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"daughter",
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"Anderson",
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]
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# Assert
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assert len(response) >= 1
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assert any([expected_response in response[0] for expected_response in expected_responses]), (
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"Expected chat actor to mention Darth Vader's daughter, but got: " + response[0]
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"Expected chat actor to mention person's by name, but got: " + response[0]
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)
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@ -461,7 +447,7 @@ My sister, Aiyla is married to Tolga. They have 3 kids, Yildiz, Ali and Ahmet.""
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response = "".join([response_chunk for response_chunk in response_gen])
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# Assert
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expected_responses = ["which sister", "Which sister", "which of your sister", "Which of your sister"]
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expected_responses = ["which sister", "Which sister", "which of your sister", "Which of your sister", "Which one"]
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assert any([expected_response in response for expected_response in expected_responses]), (
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"Expected chat actor to ask for clarification in response, but got: " + response
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)
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