HarshaBattula
commited on
Commit
·
a9e9e50
1
Parent(s):
b91232a
adding gpt-3.5 based retrieval augmented system
Browse files- app.py +52 -0
- chain.py +70 -0
- db/chroma-collections.parquet +3 -0
- db/chroma-embeddings.parquet +3 -0
- db/index/id_to_uuid_d25f8acb-f4d6-4b67-b80a-9b85ac72b87c.pkl +3 -0
- db/index/index_d25f8acb-f4d6-4b67-b80a-9b85ac72b87c.bin +3 -0
- db/index/index_metadata_d25f8acb-f4d6-4b67-b80a-9b85ac72b87c.pkl +3 -0
- db/index/uuid_to_id_d25f8acb-f4d6-4b67-b80a-9b85ac72b87c.pkl +3 -0
- requirements.txt +7 -0
- retriever.py +41 -0
app.py
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import openai
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from langchain.vectorstores import Chroma
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from langchain.embeddings import OpenAIEmbeddings
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from retriever import *
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from chain import *
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import gradio as gr
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def chatbot(query):
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llm_response = qa_chain.run({"query": query})
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return llm_response
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def load_embeddings_database_from_disk(persistence_directory, embeddings_generator):
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"""
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Load a Chroma vector database from disk.
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This function loads a Chroma vector database from the specified directory on disk.
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It expects the same persistence_directory and embedding function as used when creating the database.
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Args:
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persistence_directory (str): The directory where the database is stored on disk.
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embeddings_generator (obj): The embeddings generator function that was used when creating the database.
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Returns:
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vector_database (obj): The loaded Chroma vector database.
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"""
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# Load the Chroma vector database from the persistence directory.
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# The embedding_function parameter should be the same as the one used when the database was created.
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vector_database = Chroma(persist_directory=persistence_directory, embedding_function=embeddings_generator)
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return vector_database
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# Specify the directory where the database will be stored when it's persisted.
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persistence_directory = 'db'
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# Create and persist the embeddings for the documents.
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embeddings_generator = OpenAIEmbeddings(openai_api_key = openai.api_key)
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# Load the Chroma vector database from disk.
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vector_database = load_embeddings_database_from_disk(persistence_directory, embeddings_generator)
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topk_documents = 2
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# Creating the retriever on top documents.
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retriever = initialize_document_retriever(topk_documents, vector_database)
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qa_chain = create_question_answering_chain(retriever)
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inputs = gr.inputs.Textbox(lines=7, label="Coversational Interface with Chat history")
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outputs = gr.outputs.Textbox(label="Reply")
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gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, title="Retrieval Augmented Question Answering",
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show_progress = True, theme="compact").launch(share = True, debug=True)
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chain.py
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from langchain.memory import ConversationBufferMemory
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from langchain import PromptTemplate
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from langchain.chat_models import ChatOpenAI
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from langchain.chains import RetrievalQA
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import openai
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openai.api_key = "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"
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def create_juniper_prompt_template():
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template = """You are a network engineer from Juniper Networks not a Language Model, use your knowledge, and the some pieces of context (delimited by <ctx></ctx>) to answer the user's question. \n Try to pretend as if you are a member of Juniper Networks. \nIf you don't know the answer, just say that you don't know, don't try to make up an answer.
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Do not indicate that you have access to any context.
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Use the chat history (delimited by <hs></hs>) to keep track of the conversation.
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\n----------------\n
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<ctx>
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{context}
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</ctx>
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\n----------------\n
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------
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<hs>
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{history}
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</hs>
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------
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{question}
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Answer:
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"""
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juniper_prompt_template = PromptTemplate(input_variables=["history", "context", "question"], template=template)
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return juniper_prompt_template
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def create_question_answering_chain(retriever):
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"""
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Create a retrieval question answering (QA) chain.
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This function initializes a QA chain that can be used to answer questions based on retrieved documents.
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It uses the OpenAI 'gpt-3.5-turbo' model for the language model (LLM), and a document retriever for finding
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relevant documents.
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Args:
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retriever (obj): The document retriever to use for finding relevant documents.
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Returns:
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qa_chain (obj): The initialized retrieval QA chain.
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"""
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# Initialize the OpenAI language model with specified temperature, model name, and API key.
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turbo_llm = ChatOpenAI(
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temperature=0,
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model_name='gpt-3.5-turbo',
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openai_api_key = openai.api_key
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)
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# Initialize the retrieval QA chain with the language model, chain type, document retriever,
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# and a flag indicating whether to return source documents.
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qa_chain = RetrievalQA.from_chain_type(
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llm=turbo_llm,
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chain_type='stuff',
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retriever=retriever,
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verbose=False,
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chain_type_kwargs={
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"verbose": False,
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"prompt": create_juniper_prompt_template(),
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"memory": ConversationBufferMemory(
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memory_key="history",
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input_key="question")
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}
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)
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return qa_chain
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db/chroma-collections.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:d11f275e47d9d5a2bb0acb41c5746868e7288b0436871abe793fbd1679064d5e
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size 557
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db/chroma-embeddings.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:9eac62ca72d3b72a738519d1fb159052c35a8b43284974729b737525c88d920c
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size 244539180
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db/index/id_to_uuid_d25f8acb-f4d6-4b67-b80a-9b85ac72b87c.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:874a7a333254e6fc9fc426e74068ae585acad068eb18f40ad8781b206f30a778
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size 641398
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db/index/index_d25f8acb-f4d6-4b67-b80a-9b85ac72b87c.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:eff35d3c91148d593e15d8d18684b1841f5008db2bd664418736fec5b93f2531
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size 124197200
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db/index/index_metadata_d25f8acb-f4d6-4b67-b80a-9b85ac72b87c.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:1da962bf05d46a551c527af9a099dbbcddd739579e064b36fed531e14faeb5dc
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size 105
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db/index/uuid_to_id_d25f8acb-f4d6-4b67-b80a-9b85ac72b87c.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:878e7943e4bbdd6b6b5963aa63441f165dce733a0d6a3304e839ce2231f63246
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size 749904
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requirements.txt
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langchain
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openai
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tiktoken
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chromadb
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langchain
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pypdf
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gradio
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retriever.py
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def initialize_document_retriever(top_k_documents, vector_database):
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"""
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Initialize a document retriever using a Chroma vector database.
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This function initializes a document retriever that can be used to find and retrieve the most relevant documents
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for a specified search query. The number of documents to retrieve is determined by the top_k_documents parameter.
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Args:
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top_k_documents (int): The number of top relevant documents to retrieve.
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vector_database (obj): The Chroma vector database to use for retrieving documents.
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Returns:
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document_retriever (obj): The initialized document retriever.
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"""
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# Initialize the document retriever with the Chroma vector database and the number of documents to retrieve.
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document_retriever = vector_database.as_retriever(
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search_kwargs = {"k": top_k_documents}
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)
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return document_retriever
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def retrieve_relevant_documents(search_query, document_retriever):
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"""
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Retrieve the most relevant documents for a given query.
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This function uses an initialized document retriever to find and retrieve the most relevant documents
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for a specified search query.
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Args:
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search_query (str): The search query for which to find and retrieve relevant documents.
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document_retriever (obj): The initialized document retriever.
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Returns:
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relevant_documents (list): The list of most relevant documents for the search query.
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"""
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# Retrieve the most relevant documents for the search query using the document retriever.
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relevant_documents = document_retriever.get_relevant_documents(search_query)
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return relevant_documents
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