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Update pipeline.py
Browse files- pipeline.py +16 -3
pipeline.py
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@@ -8,7 +8,7 @@ from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.vectorstores import FAISS
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from langchain.chains import RetrievalQA
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from smolagents import CodeAgent, DuckDuckGoSearchTool, ManagedAgent, LiteLLMModel
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#
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# Import the chain builders from our separate files
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from classification_chain import get_classification_chain
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@@ -22,8 +22,21 @@ if not os.environ.get("GEMINI_API_KEY"):
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if not os.environ.get("GROQ_API_KEY"):
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os.environ["GROQ_API_KEY"] = getpass.getpass("Enter your GROQ API Key: ")
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# 2) Load spaCy model for NER
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# Function to extract the main topic using NER
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def extract_main_topic(query: str) -> str:
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from langchain.vectorstores import FAISS
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from langchain.chains import RetrievalQA
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from smolagents import CodeAgent, DuckDuckGoSearchTool, ManagedAgent, LiteLLMModel
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import subprocess # Import subprocess to run shell commands
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# Import the chain builders from our separate files
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from classification_chain import get_classification_chain
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if not os.environ.get("GROQ_API_KEY"):
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os.environ["GROQ_API_KEY"] = getpass.getpass("Enter your GROQ API Key: ")
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# 2) Load spaCy model for NER and download the spaCy model if not already installed
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def install_spacy_model():
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try:
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# Check if the model is already installed
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spacy.load("en_core_web_sm")
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print("spaCy model 'en_core_web_sm' is already installed.")
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except OSError:
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# If model is not installed, download it using subprocess
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print("Downloading spaCy model 'en_core_web_sm'...")
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subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"], check=True)
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print("spaCy model 'en_core_web_sm' downloaded successfully.")
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# Call the function to install the spaCy model if needed
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install_spacy_model()
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# Function to extract the main topic using NER
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def extract_main_topic(query: str) -> str:
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