longchain assignment

8_Assignment_langchain-projects/clinical_workflow_assistant/main.py

+from langgraph.graph import StateGraph, END
+
+def symptom_state():
+    return {"symptoms": None, "api_result": None}
+
+# Node 1: Get user symptoms
+def get_symptoms(state):
+    symptoms = input("What symptoms are you experiencing? ")
+    return {"symptoms": symptoms}
+
+# Node 2: Validate symptoms
+def validate_symptoms(state):
+    symptoms = state["symptoms"]
+    if len(symptoms.strip().split()) < 3:
+        print("Please provide more details.")
+        return "get_symptoms"
+    return "call_api"
+
+# Node 3: Simulate API call
+def call_medical_api(state):
+    symptoms = state["symptoms"]
+    result = f"Simulated advice for symptoms: {symptoms}"
+    return {"api_result": result}
+
+# Node 4: Return advice
+def return_advice(state):
+    print("\n=== Diagnosis Summary ===")
+    print(f"Symptoms: {state['symptoms']}")
+    print(f"Advice: {state['api_result']}")
+    return END
+
+# Build graph
+builder = StateGraph(symptom_state)
+builder.add_node("get_symptoms", get_symptoms)
+builder.add_node("validate", validate_symptoms)
+builder.add_node("call_api", call_medical_api)
+builder.add_node("return_advice", return_advice)
+
+builder.set_entry_point("get_symptoms")
+builder.add_edge("get_symptoms", "validate")
+builder.add_conditional_edges("validate", validate_symptoms)
+builder.add_edge("call_api", "return_advice")
+
+app = builder.compile()
+app.invoke({})
+

8_Assignment_langchain-projects/contract_review_agent/contract_review.py

+from langchain.chains import LLMChain, SequentialChain
+from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI  # Works with DeepSeek's OpenAI-compatible API
+from langchain.agents import Tool, AgentExecutor
+from langchain.agents import create_react_agent
+from langchain import hub
+from dotenv import load_dotenv
+import os
+
+# Load environment variables
+load_dotenv()
+
+# Initialize DeepSeek LLM
+llm = ChatOpenAI(
+    model="deepseek-chat",  # Verify correct model name with DeepSeek's documentation
+    openai_api_key=os.getenv("DEEPSEEK_API_KEY"),
+    base_url="https://api.deepseek.com/v1",  # Confirm this endpoint with DeepSeek
+    temperature=0
+)
+
+# Document loader function
+def load_document(file_path):
+    if file_path.endswith('.pdf'):
+        loader = PyPDFLoader(file_path)
+    elif file_path.endswith('.docx'):
+        loader = Docx2txtLoader(file_path)
+    else:
+        raise ValueError("Unsupported file format")
+    
+    return loader.load()
+
+# Text splitter configuration
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=1000,
+    chunk_overlap=200
+)
+
+# Prompt templates
+obligations_prompt = PromptTemplate(
+    input_variables=["text"],
+    template="""
+    Analyze the following contract text and extract all obligations for each party.
+    Return the results in a structured format with party names and their obligations.
+    
+    Contract text: {text}
+    """
+)
+
+dates_prompt = PromptTemplate(
+    input_variables=["text"],
+    template="""
+    Extract all important dates from the following contract text including:
+    - Effective dates
+    - Termination dates
+    - Delivery deadlines
+    - Payment due dates
+    - Other significant timelines
+    
+    Contract text: {text}
+    """
+)
+
+risks_prompt = PromptTemplate(
+    input_variables=["text"],
+    template="""
+    Identify potential risks in the following contract text including:
+    - Unfavorable terms
+    - Ambiguous language
+    - Unbalanced obligations
+    - Unreasonable liabilities
+    - Other potential risk factors
+    
+    Contract text: {text}
+    """
+)
+
+# Create analysis chains
+obligations_chain = LLMChain(llm=llm, prompt=obligations_prompt, output_key="obligations")
+dates_chain = LLMChain(llm=llm, prompt=dates_prompt, output_key="dates")
+risks_chain = LLMChain(llm=llm, prompt=risks_prompt, output_key="risks")
+
+# Sequential chain to run all analyses
+contract_chain = SequentialChain(
+    chains=[obligations_chain, dates_chain, risks_chain],
+    input_variables=["text"],
+    output_variables=["obligations", "dates", "risks"],
+    verbose=True
+)
+
+# Tools for the agent
+tools = [
+    Tool(
+        name="Contract Analyzer",
+        func=lambda text: contract_chain({"text": text}),
+        description="Useful for analyzing contracts and extracting obligations, dates, and risks"
+    )
+]
+
+# Create agent
+agent_prompt = hub.pull("hwchase17/react")
+agent = create_react_agent(llm, tools, agent_prompt)
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+# Main analysis function
+def analyze_contract(file_path):
+    # Load and split document
+    docs = load_document(file_path)
+    texts = text_splitter.split_documents(docs)
+    
+    # Process each chunk (for large documents)
+    full_results = {"obligations": [], "dates": [], "risks": []}
+    for text in texts:
+        result = agent_executor.invoke({"input": f"Analyze this contract text: {text.page_content}"})
+        
+        # Aggregate results
+        for key in full_results:
+            if key in result and result[key] not in full_results[key]:
+                full_results[key].append(result[key])
+    
+    return full_results
+
+if __name__ == "__main__":
+    file_path = input("Enter path to contract file (PDF or DOCX): ").strip()
+    try:
+        results = analyze_contract(file_path)
+        
+        print("\n=== Contract Analysis Results ===")
+        print("\nObligations:")
+        for obligation in results["obligations"]:
+            print(f"- {obligation}")
+            
+        print("\nImportant Dates:")
+        for date in results["dates"]:
+            print(f"- {date}")
+            
+        print("\nPotential Risks:")
+        for risk in results["risks"]:
+            print(f"- {risk}")
+    except Exception as e:
+        print(f"\nError: {str(e)}")
+        print("Please check:")
+        print("- File path is correct")
+        print("- File is PDF or DOCX format")
+        print("- DeepSeek API key is valid")

8_Assignment_langchain-projects/contract_review_agent/extractor.py

+from langchain_community.document_loaders import PyPDFLoader
+#from langchain_community.llms import OpenAI
+from langchain_openai import OpenAI
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+# Load and split PDF
+loader = PyPDFLoader("contract.pdf")
+pages = loader.load_and_split()
+
+splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+docs = splitter.split_documents(pages)
+
+# Prompt Template
+with open("prompts/extract_prompt.txt", "r") as f:
+    prompt_template = f.read()
+
+prompt = PromptTemplate(input_variables=["document"], template=prompt_template)
+llm = OpenAI(temperature=0)
+chain = LLMChain(llm=llm, prompt=prompt)
+
+# Process top 3 chunks
+for i, chunk in enumerate(docs[:3]):
+    print(f"\n--- Chunk {i+1} ---")
+    result = chain.run(document=chunk.page_content)
+    print(result)
+

8_Assignment_langchain-projects/contract_review_agent/prompts/extract_prompt.txt

+You are a legal contract analyzer. Extract the following from the given contract text:
+
+1. Obligations of the parties
+2. Important dates (deadlines, effective dates, etc.)
+3. Any potential risks or liabilities
+
+Document:
+{document}
+
+Return the output in this JSON format:
+{{
+  "obligations": [...],
+  "dates": [...],
+  "risks": [...]
+}}
+

8_Assignment_langchain-projects/requirements.txt

+langchain
+openai
+pypdf
+chromadb
+tiktoken
+

8_Assignment_langchain-projects/venv/bin/python

+python3
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8_Assignment_langchain-projects/venv/bin/python3

+/usr/bin/python3
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8_Assignment_langchain-projects/venv/bin/python3.10

+python3
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8_Assignment_langchain-projects/venv/lib64

+lib
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8_Assignment_langchain-projects/venv/pyvenv.cfg

+home = /usr/bin
+include-system-site-packages = false
+version = 3.10.17