langchain_gaia_demo.py

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
import json

# Initialize the chat model with Gaia's endpoint
llm = ChatOpenAI(
    model="llama3b",
    openai_api_key="gaia",
    openai_api_base="https://llama3b.gaia.domains/v1",
    temperature=0.7
)

# Create prompt templates for different tasks
translation_prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a translator. Translate the text from English to French."),
    ("human", "{text}")
])

summarization_prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a summarizer. Summarize the following French text in English in one sentence."),
    ("human", "{text}")
])

analysis_prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a text analyzer. Analyze the following summary and provide three key points in JSON format with 'key_points' as the key."),
    ("human", "{text}")
])

# Create individual chains
translation_chain = translation_prompt | llm | StrOutputParser()
summarization_chain = summarization_prompt | llm | StrOutputParser()
analysis_chain = analysis_prompt | llm | StrOutputParser()

# Create a combined chain
def process_text(input_text: str) -> dict:
    # First, translate to French
    french_text = translation_chain.invoke({"text": input_text})
    print(f"\nTranslated to French:\n{french_text}")
    
    # Then, summarize back to English
    summary = summarization_chain.invoke({"text": french_text})
    print(f"\nSummarized in English:\n{summary}")
    
    # Finally, analyze the summary
    analysis = analysis_chain.invoke({"text": summary})
    try:
        # Try to parse JSON response
        analysis_dict = json.loads(analysis)
    except json.JSONDecodeError:
        # If JSON parsing fails, return raw text
        analysis_dict = {"key_points": analysis}
    
    print(f"\nAnalysis:\n{json.dumps(analysis_dict, indent=2)}")
    
    return {
        "french_translation": french_text,
        "english_summary": summary,
        "analysis": analysis_dict
    }

# Example usage
if __name__ == "__main__":
    input_text = """
    The rise of artificial intelligence has transformed many industries, 
    creating new opportunities for innovation and efficiency. However, 
    it also presents challenges regarding ethics, privacy, and job displacement 
    that society must carefully address.
    """
    
    print("Original text:", input_text.strip())
    result = process_text(input_text)
    
    # Show how to access specific parts of the result
    print("\nAccessing specific parts of the result:")
    print(f"French translation first 50 chars: {result['french_translation'][:50]}...")
    print(f"English summary first 50 chars: {result['english_summary'][:50]}...")