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from dotenv import load_dotenv
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import os
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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from llama_index.llms.huggingface import HuggingFaceInferenceAPI
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from llama_index.core.settings import Settings
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from llama_index.core import SimpleDirectoryReader
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from llama_index.core.node_parser import SentenceWindowNodeParser
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from pinecone import Pinecone
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from llama_index.core import VectorStoreIndex, StorageContext
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from llama_index.vector_stores.pinecone import PineconeVectorStore
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from llama_index.core.postprocessor import MetadataReplacementPostProcessor
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from llama_index.core.postprocessor import SentenceTransformerRerank
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def initialize_llm():
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load_dotenv()
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HF_TOKEN = os.getenv("HUGGINGFACE_API_KEY")
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Settings.llm = HuggingFaceInferenceAPI(model_name="mistralai/Mixtral-8x7B-Instruct-v0.1", model_kwargs={"temperature": 0.1}, token=HF_TOKEN)
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Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
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def initialize_pinecone():
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load_dotenv()
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api_key = os.environ["PINECONE_API_KEY"]
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index_name = "chatbot"
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pc = Pinecone(api_key=api_key)
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pinecone_index = pc.Index(index_name)
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vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
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index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
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return index
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def create_query_engine(index):
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postproc = MetadataReplacementPostProcessor(target_metadata_key="window")
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rerank = SentenceTransformerRerank(top_n=2, model="BAAI/bge-reranker-base")
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query_engine = index.as_query_engine(similarity_top_k = 6,vector_store_query_mode="hybrid",alpha=0.5,node_postprocessors = [postproc, rerank],
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)
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return query_engine
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def generate_prompt(context, query):
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prompt_template = """
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You are a highly informed representative of KYC SUD CONSULTING.
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Your role is to assist users by answering questions about our company with accurate and helpful information.
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Use the provided context to guide your responses effectively.
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If a question falls outside your knowledge or the context provided, simply state that you don't have the information.
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Given the following context:
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{context_str}
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Answer the following question in different words while keeping the meaning intact:
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{query_str}
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Response:
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"""
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return prompt_template.format(context_str=context, query_str=query)
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def get_response(query_engine, sentence):
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retrieved_context = query_engine.query(sentence)
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prompt = generate_prompt(retrieved_context, sentence)
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response = Settings.llm.complete(prompt)
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return response.text |
