Update utils.py
Browse files
utils.py
CHANGED
@@ -31,22 +31,23 @@ from pygments.formatters import HtmlFormatter
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from langchain.chains import LLMChain, RetrievalQA
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from langgraph.graph import END, StateGraph
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from
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from
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from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
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from langchain.document_loaders.generic import GenericLoader
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from langchain.document_loaders.parsers import OpenAIWhisperParser
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from langchain.schema import AIMessage, HumanMessage
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from
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from
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from
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from
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from langchain.retrievers.tavily_search_api import TavilySearchAPIRetriever
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from typing import Dict, TypedDict
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from langchain_core.messages import BaseMessage
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.prompts import PromptTemplate
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from langchain import hub
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from langchain.output_parsers.openai_tools import PydanticToolsParser
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from langchain.prompts import PromptTemplate
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@@ -59,7 +60,7 @@ from langchain_core.pydantic_v1 import BaseModel, Field
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from langchain_core.runnables import RunnablePassthrough
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from langchain_core.utils.function_calling import convert_to_openai_tool
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from
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from chromadb.errors import InvalidDimensionException
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import io
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from PIL import Image, ImageDraw, ImageOps, ImageFont
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@@ -398,7 +399,7 @@ def rag_chain(llm, prompt, retriever):
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if (len(filtered_docs)>0):
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llm_chain = LLMChain(llm = llm, prompt = RAG_CHAIN_PROMPT)
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result = llm_chain.run({"context":
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else:
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#Normale Abfrage, da keine relevanten Dokumente gefunden
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llm_chain = LLMChain(llm = llm, prompt = LLM_CHAIN_PROMPT)
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from langchain.chains import LLMChain, RetrievalQA
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from langgraph.graph import END, StateGraph
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.document_loaders import PyPDFLoader, , WebBaseLoader, UnstructuredWordDocumentLoader, DirectoryLoader
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from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
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from langchain.document_loaders.generic import GenericLoader
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from langchain.document_loaders.parsers import OpenAIWhisperParser
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from langchain.schema import AIMessage, HumanMessage
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from langchain_community.llms import HuggingFaceHub
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from langchain_community.llms import HuggingFaceTextGenInference
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from langchain_community.embeddings import HuggingFaceInstructEmbeddings, HuggingFaceEmbeddings, HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
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from langchain_community.tools import DuckDuckGoSearchRun
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from langchain.retrievers.tavily_search_api import TavilySearchAPIRetriever
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from typing import Dict, TypedDict
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from langchain_core.messages import BaseMessage
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.prompts import PromptTemplate
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from langchain import hub
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from langchain.output_parsers.openai_tools import PydanticToolsParser
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from langchain.prompts import PromptTemplate
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from langchain_core.runnables import RunnablePassthrough
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from langchain_core.utils.function_calling import convert_to_openai_tool
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_community.vectorstores import Chroma
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from chromadb.errors import InvalidDimensionException
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import io
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from PIL import Image, ImageDraw, ImageOps, ImageFont
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if (len(filtered_docs)>0):
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llm_chain = LLMChain(llm = llm, prompt = RAG_CHAIN_PROMPT)
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result = llm_chain.run({"context": filtered_docs, "question": neu_prompt})
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else:
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#Normale Abfrage, da keine relevanten Dokumente gefunden
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llm_chain = LLMChain(llm = llm, prompt = LLM_CHAIN_PROMPT)
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