rag_langchain.py

import os

from dotenv import load_dotenv
from gptcache import Cache
from gptcache.manager.factory import manager_factory
from gptcache.processor.pre import get_prompt
from langchain.globals import set_debug
from langchain.retrievers import ContextualCompressionRetriever
from langchain_cohere import CohereRerank, CohereEmbeddings
from langchain_community.cache import GPTCache
from langchain_core.language_models import BaseChatModel
from langchain_core.prompts import PromptTemplate
from langchain_core.retrievers import BaseRetriever
from langchain_google_genai import ChatGoogleGenerativeAI, HarmCategory, HarmBlockThreshold
from langchain_openai import ChatOpenAI

from agent.Agent import Agent
from agent.agents import deepinfra_chat, \
    together_ai_chat, groq_chat, cohere_llm
from emdedd.Embedding import Embedding
from emdedd.MongoEmbedding import EmbeddingDbConnection, MongoEmbedding
from prompt.prompt_store import PromptStore
from rag import vanilla_rag_chain, rag_chain

load_dotenv()

# set_verbose(True)
set_debug(True)


class LangChainRAG:
    embedding: Embedding
    llms: dict[str, BaseChatModel]
    retriever: BaseRetriever
    prompt_template: PromptTemplate
    config: dict
    semantic_cache: GPTCache
    prompt_store = PromptStore()

    def __init__(self, config):
        self.config = config
        self.semantic_cache = GPTCache(_init_gptcache)
        self.embedding = MongoEmbedding(
            db=EmbeddingDbConnection(
                connection=os.environ["DB_CONN_EMBED"],
                database=os.environ["MONGODB_DB_NAME_ZPL_EMBED"],
                collection="zpl-2402-cohere",
                index="knnVector-cosine-index"
            ),
            embedding=CohereEmbeddings(model="embed-multilingual-v3.0")
        )

        self.llms = {
            "gpt-4o 128k": ChatOpenAI(
                model_name="gpt-4o",
                temperature=config["temperature"],
                openai_api_key=os.environ["OPENAI_API_KEY"],
                openai_organization=os.environ["OPENAI_ORGANIZATION_ID"]
            ),
            "llama-3 70B deepinfra 8k": deepinfra_chat("meta-llama/Meta-Llama-3-70B-Instruct",
                                                       self.config["temperature"]),
            "llama-3 8B deepinfra 8k": deepinfra_chat("meta-llama/Meta-Llama-3-8B-Instruct",
                                                      self.config["temperature"]),
            "Mixtral-8x22B-Instruct-v0.1 deepinfra 32k": deepinfra_chat("mistralai/Mixtral-8x22B-Instruct-v0.1",
                                                                        self.config["temperature"]),
            "gemini-pro 128k": ChatGoogleGenerativeAI(
                model="gemini-pro",
                convert_system_message_to_human=True,
                safety_settings={
                    HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
                    HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
                    HarmCategory.HARM_CATEGORY_DEROGATORY: HarmBlockThreshold.BLOCK_NONE,
                    HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE,
                },
                transport="rest",
                stopSequence=["%%%%"],
                temperature=config["temperature"],
                cache=self.semantic_cache
            ),
            "Mistral (7B) Instruct v0.3 together.ai 32k": together_ai_chat(
                model="mistralai/Mistral-7B-Instruct-v0.3",
                temperature=config["temperature"]
            ),
            "OpenHermes-2.5 Mistral 7B together.ai 32k": together_ai_chat(
                model="teknium/OpenHermes-2p5-Mistral-7B",
                temperature=config["temperature"]
            ),
            "chat_groq_llm": groq_chat("mixtral-8x7b-32768"),
            "chat_groq_llama3_70": groq_chat("llama3-70b-8192"),
            "command_r_plus": cohere_llm(),

        }

        self.retriever = ContextualCompressionRetriever(
            base_compressor=CohereRerank(model="rerank-multilingual-v3.0", top_n=os.getenv("retrieve_documents")),
            base_retriever=self.embedding.get_vector_store().as_retriever(
                search_type="similarity",
                search_kwargs={"k": config["retrieve_documents"] * 10}
            )
        )

    def get_llms(self):
        return self.llms.keys()

    async def rag_chain(self, query, llm_choice):
        print("Using " + llm_choice)

        # answer, check_result, context_doc = rag_with_rerank_check_rewrite_hyde(
        # answer, check_result, context_doc = rag_with_rerank_check_multi_query_retriever(
        # answer, check_result, context_doc = vanilla_rag_chain(
        answer, check_result, context_doc = await rag_chain(
            Agent(embedding=self.embedding, llm=self.llms[llm_choice]),
            query,
            self.config["retrieve_documents"],
            self.prompt_store.get_by_name(self.config["prompt_id"]).text,
            self.prompt_store.get_by_name(self.config["check_prompt_id"]).text
        )

        return answer, check_result, context_doc


def _init_gptcache(cache_obj: Cache, llm: str):
    cache_obj.init(
        pre_embedding_func=get_prompt,
        data_manager=manager_factory(data_dir=f"map_cache"),
        # data_manager=get_data_manager(
        #     cache_base=CacheBase("mongo", url="mongodb://localhost:27017/"),
        #     vector_base=Chromadb(
        #         persist_directory="./chromadb/cache",
        #     ),
        # )
    )