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| from llama_index.core import VectorStoreIndex | |
| from llama_index.vector_stores.pinecone import PineconeVectorStore | |
| from pinecone import Pinecone | |
| from research_assistant_app.constants import gemini_api_key, pinecone_api_key | |
| import google.generativeai as genai | |
| pc = Pinecone(api_key=pinecone_api_key) | |
| pinecone_index = pc.Index( | |
| "ai-research-assistant" | |
| ) # `ai-research-assistant` is the index name | |
| vector_store = PineconeVectorStore(pinecone_index=pinecone_index) | |
| from llama_index.core.retrievers import VectorIndexRetriever | |
| from llama_index.core.query_engine import RetrieverQueryEngine | |
| from llama_index.core import PromptTemplate | |
| def get_vector_retriever(Pinecone_vector_store): | |
| # Instantiate VectorStoreIndex object from your vector_store object | |
| vector_index = VectorStoreIndex.from_vector_store( | |
| vector_store=Pinecone_vector_store | |
| ) | |
| print(vector_index, "check indexes") | |
| # Grab 5 search results | |
| retriever = VectorIndexRetriever(index=vector_index, similarity_top_k=5) | |
| # Pass in your retriever from above, which is configured to return the top 5 results | |
| query_engine = RetrieverQueryEngine(retriever=retriever) | |
| return query_engine, vector_index | |
| def get_full_prompt_template(cur_instr: str, prompt_tmpl): | |
| tmpl_str = prompt_tmpl.get_template() | |
| new_tmpl_str = cur_instr + "\n" + tmpl_str | |
| new_tmpl = PromptTemplate(new_tmpl_str) | |
| return new_tmpl | |
| def proper_prompting(my_query_enginge, my_vector_index): | |
| QA_PROMPT_KEY = "response_synthesizer:text_qa_template" | |
| # get the base qa prompt (without any instruction prefix) | |
| base_qa_prompt = my_query_enginge.get_prompts()[QA_PROMPT_KEY] | |
| initial_instr = """\ | |
| You are a QA assistant specifically designed to help in reaserch work as and research assistant. | |
| --------------------- | |
| Context information is below. Given the context information and not prior knowledge, \ | |
| "{context_str}\n" | |
| --------------------- | |
| answer the query. \ | |
| It is very important that If the context is not relevant, | |
| please answer the question by using your own knowledge about the topic | |
| """ | |
| # this is the "initial" prompt template | |
| # implicitly used in the first stage of the loop during prompt optimization | |
| # here we explicitly capture it so we can use it for evaluation | |
| old_qa_prompt = get_full_prompt_template(initial_instr, base_qa_prompt) | |
| old_qa_prompt | |
| # Use the custom prompt when querying | |
| # genai.configure(api_key=gemini_api_key) | |
| query_engine = my_vector_index.as_query_engine(text_qa_template=old_qa_prompt) | |
| return query_engine | |
| ## This will be the main function that we would call for querying | |
| def user_query(qus): | |
| genai.configure(api_key=gemini_api_key) | |
| my_query_enginge, my_vector_index = get_vector_retriever(vector_store) | |
| query_engine = proper_prompting(my_query_enginge, my_vector_index) | |
| response = query_engine.query(qus) | |
| return response.response | |