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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
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