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https://github.com/arc53/DocsGPT
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init
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@ -27,6 +27,7 @@ from langchain.prompts.chat import (
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)
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from pymongo import MongoClient
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from werkzeug.utils import secure_filename
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from langchain.llms import GPT4All
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from core.settings import settings
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from error import bad_request
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@ -195,6 +196,9 @@ def api_answer():
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llm = HuggingFaceHub(repo_id="bigscience/bloom", huggingfacehub_api_token=api_key)
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elif settings.LLM_NAME == "cohere":
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llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key)
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elif settings.LLM_NAME == "gpt4all":
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llm = GPT4All(model="/Users/alextu/Library/Application Support/nomic.ai/GPT4All/",
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backend='gpt4all-j-v1.3-groovy')
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else:
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raise ValueError("unknown LLM model")
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@ -210,6 +214,19 @@ def api_answer():
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# result = chain({"question": question, "chat_history": chat_history})
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# generate async with async generate method
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result = run_async_chain(chain, question, chat_history)
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elif settings.LLM_NAME == "gpt4all":
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question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
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doc_chain = load_qa_chain(llm, chain_type="map_reduce", combine_prompt=p_chat_combine)
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chain = ConversationalRetrievalChain(
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retriever=docsearch.as_retriever(k=2),
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question_generator=question_generator,
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combine_docs_chain=doc_chain,
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)
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chat_history = []
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# result = chain({"question": question, "chat_history": chat_history})
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# generate async with async generate method
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result = run_async_chain(chain, question, chat_history)
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else:
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qa_chain = load_qa_chain(llm=llm, chain_type="map_reduce",
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combine_prompt=chat_combine_template, question_prompt=q_prompt)
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@ -26,7 +26,8 @@ ecdsa==0.18.0
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entrypoints==0.4
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faiss-cpu==1.7.3
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filelock==3.9.0
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Flask==2.3.2
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Flask==2.2.3
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Flask-Cors==3.0.10
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frozenlist==1.3.3
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geojson==2.5.0
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greenlet==2.0.2
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@ -39,7 +40,8 @@ Jinja2==3.1.2
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jmespath==1.0.1
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joblib==1.2.0
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kombu==5.2.4
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langchain==0.0.126
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langchain==0.0.179
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loguru==0.6.0
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lxml==4.9.2
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MarkupSafe==2.1.2
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marshmallow==3.19.0
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