Create rag.py

rag.py

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+from langchain_community.document_loaders import Docx2txtLoader
+from langchain_community.document_transformers import DoctranTextTranslator
+from langchain_core.documents import Document
+from dotenv import load_dotenv
+from langchain_community.retrievers import BM25Retriever
+import openai
+
+# Set your OpenAI API key
+# openai.api_key = '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'
+
+from openai import OpenAI
+from os import getenv
+from langchain_core.documents import Document
+from langchain_text_splitters import CharacterTextSplitter
+
+def setup_retriever():
+    knowledge_file = "E:/Backup_K20pro/Download/treesat_benchmark/Canopy species list and uses (PT)_pt_en.docx"
+    loader = Docx2txtLoader(knowledge_file) 
+    data = loader.load()
+    text_splitter = CharacterTextSplitter(
+    separator="-",
+    chunk_size=600,
+    chunk_overlap=0,
+    length_function=len,
+    is_separator_regex=False,
+)
+    chunks = text_splitter.split_documents(data)
+# print(chunks)
+    retriever = BM25Retriever.from_documents(chunks)
+    retriever.k = 1
+    return retriever
+
+retriever = setup_retriever()
+species = 'Byrsonima'
+species_info = retriever.invoke(f"Scientific name:{species}")
+print(species_info)
+# qa_translator = DoctranTextTranslator(language="english")
+# translated_document = qa_translator.transform_documents(data)
+# print(translated_document)
+
+
+info = 'Scientific name:Licaniasp.\n\nFamily:Chrysobalanaceae\n\nPopular name:They are generally known as caripé or macucu, among other generic names.\n\nHabitat:LicaniaIt is a large genus, with dozens of species distributed in all Amazon habitats, species that are difficult to identify without fertile material.\n\nUses:Some species can be edible, others known as caripé had (have) their bark roasted (presence of silica), macerated and together with clay used in the preparation of ceramics by indigenous populations.'
+# gets API Key from environment variable OPENAI_API_KEY
+def setup_client():
+    client = OpenAI(
+  base_url="https://openrouter.ai/api/v1",
+  api_key=getenv("OPENROUTER_API_KEY"),
+)
+    
+    return client
+
+client = setup_client()
+
+def generate_image(species_info, client):
+    completion = client.chat.completions.create(
+  model="openai/gpt-3.5-turbo",
+  messages=[
+    {
+      "role": "user",
+
+      "content": f"Using the trees species information provided below, Using the information in the 'Uses:' section. Generate 1 useful and informative unicode image to be used to be placed on a drone panoramic image. Tree species info:{species_info}",
+    },
+  ],
+)
+    return completion.choices[0].message.content
+
+
+if __name__ == '__main__':
+    ans = generate_image(species_info, client)
+    print(ans)
+# # Define the prompt and parameters for the request
+# prompt = "Once upon a time"
+# response = openai.Completion.create(
+#     engine="gpt-3.5-turbo",  # Use the GPT-3.5 model
+#     prompt=prompt,
+#     max_tokens=50,  # Adjust the number of tokens based on your requirement
+#     n=1,
+#     stop=None,
+#     temperature=0.7,
+# )
+
+# # Print the generated text
+# generated_text = response.choices[0].text.strip()
+# print(generated_text)