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b80958f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | # source:https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_community.llms import Ollama
from fastapi import FastAPI
import requests
from pydantic import BaseModel
from langchain.chains import create_retrieval_chain
from dotenv import load_dotenv
import os
load_dotenv()
token= os.getenv("TOKEN")
app = FastAPI()
class QueryInput(BaseModel):
query: str
# build the retrieval and augmented generator chain here
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
db = FAISS.load_local("./vectorstore/agriquery_faiss_index", embeddings, allow_dangerous_deserialization=True)
llm = Ollama(model="llama3", base_url="http://localhost:11434")
retriever = db.as_retriever()
system_prompt = (
"You are an agriultural research assistant."
"Use the given context to answer the question."
"If you don't know the answer, say you don't know."
"Context: {context}"
)
prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
("human", "{input}"),
]
)
question_answer_chain = create_stuff_documents_chain(llm,prompt)
chain = create_retrieval_chain(retriever, question_answer_chain)
@app.post("/query")
async def query_handler(input: QueryInput):
result = chain.invoke({"input": input.query})
answer = result['answer'].replace("\\n", "\n").strip()
return {"answer": answer} |