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92 lines
3.4 KiB
Python
92 lines
3.4 KiB
Python
import os
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from application.retriever.base import BaseRetriever
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from application.core.settings import settings
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from application.vectorstore.vector_creator import VectorCreator
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from application.llm.llm_creator import LLMCreator
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from application.utils import count_tokens
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class ClassicRAG(BaseRetriever):
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def __init__(self, question, source, chat_history, prompt, chunks=2, gpt_model='docsgpt'):
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self.question = question
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self.vectorstore = self._get_vectorstore(source=source)
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self.chat_history = chat_history
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self.prompt = prompt
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self.chunks = chunks
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self.gpt_model = gpt_model
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def _get_vectorstore(self, source):
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if "active_docs" in source:
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if source["active_docs"].split("/")[0] == "default":
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vectorstore = ""
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elif source["active_docs"].split("/")[0] == "local":
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vectorstore = "indexes/" + source["active_docs"]
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else:
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vectorstore = "vectors/" + source["active_docs"]
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if source["active_docs"] == "default":
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vectorstore = ""
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else:
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vectorstore = ""
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vectorstore = os.path.join("application", vectorstore)
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return vectorstore
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def _get_data(self):
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if self.chunks == 0:
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docs = []
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else:
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docsearch = VectorCreator.create_vectorstore(
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settings.VECTOR_STORE,
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self.vectorstore,
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settings.EMBEDDINGS_KEY
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)
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docs_temp = docsearch.search(self.question, k=self.chunks)
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docs = [
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{
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"title": i.metadata['title'].split('/')[-1] if i.metadata else i.page_content,
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"text": i.page_content
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}
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for i in docs_temp
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]
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if settings.LLM_NAME == "llama.cpp":
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docs = [docs[0]]
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return docs
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def gen(self):
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docs = self._get_data()
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# join all page_content together with a newline
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docs_together = "\n".join([doc["text"] for doc in docs])
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p_chat_combine = self.prompt.replace("{summaries}", docs_together)
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messages_combine = [{"role": "system", "content": p_chat_combine}]
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for doc in docs:
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yield {"source": doc}
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if len(self.chat_history) > 1:
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tokens_current_history = 0
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# count tokens in history
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self.chat_history.reverse()
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for i in self.chat_history:
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if "prompt" in i and "response" in i:
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tokens_batch = count_tokens(i["prompt"]) + count_tokens(i["response"])
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if tokens_current_history + tokens_batch < settings.TOKENS_MAX_HISTORY:
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tokens_current_history += tokens_batch
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messages_combine.append({"role": "user", "content": i["prompt"]})
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messages_combine.append({"role": "system", "content": i["response"]})
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messages_combine.append({"role": "user", "content": self.question})
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llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=settings.API_KEY)
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completion = llm.gen_stream(model=self.gpt_model,
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messages=messages_combine)
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for line in completion:
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yield {"answer": str(line)}
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def search(self):
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return self._get_data()
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