from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI

# Build the knowledge store
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
vectorstore = Chroma.from_documents(
    documents=your_documents,
    embedding=embeddings,
    persist_directory="./chroma_db"
)

# Create retrieval chain
retriever = vectorstore.as_retriever(
    search_type="mmr",          # Max Marginal Relevance for diversity
    search_kwargs={"k": 6, "fetch_k": 20}
)

qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4o"),
    chain_type="stuff",
    retriever=retriever,
    return_source_documents=True
)

from langchain.retrievers import EnsembleRetriever
from langchain.retrievers import BM25Retriever

bm25_retriever = BM25Retriever.from_documents(documents, k=4)
vector_retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

ensemble_retriever = EnsembleRetriever(
    retrievers=[bm25_retriever, vector_retriever],
    weights=[0.4, 0.6]
)

from sentence_transformers import CrossEncoder

reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-12-v2")

def rerank(query: str, documents: list[str], top_k: int = 3) -> list[str]:
    pairs = [(query, doc) for doc in documents]
    scores = reranker.predict(pairs)
    ranked = sorted(zip(scores, documents), reverse=True)
    return [doc for _, doc in ranked[:top_k]]

# Using Microsoft GraphRAG
from graphrag.query.structured_search.local_search.search import LocalSearch
from graphrag.query.structured_search.global_search.search import GlobalSearch

# Local search: entity-focused queries
local_searcher = LocalSearch(
    llm=llm,
    context_builder=local_context_builder,
    token_encoder=token_encoder
)

# Global search: thematic/summary queries across entire corpus
global_searcher = GlobalSearch(
    llm=llm,
    context_builder=global_context_builder
)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model, TaskType
from trl import SFTTrainer

# Configure LoRA
lora_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=16,                    # rank
    lora_alpha=32,
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.05,
    bias="none"
)

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3-8B",
    load_in_4bit=True,       # QLoRA: 4-bit quantization
    device_map="auto"
)
model = get_peft_model(model, lora_config)

# Training data format (instruction following)
training_data = [
    {
        "instruction": "What is our refund policy?",
        "input": "",
        "output": "Our refund policy allows returns within 30 days of purchase..."
    },
    # ... thousands more examples
]

trainer = SFTTrainer(
    model=model,
    train_dataset=formatted_dataset,
    max_seq_length=2048,
    dataset_text_field="text"
)
trainer.train()

import anthropic

# Testing retrieval at different positions in a 500K token context
def test_needle_retrieval(client, needle: str, haystack: str, position: str):
    """
    position: 'start', 'middle', 'end'
    """
    if position == "start":
        content = needle + "\n\n" + haystack
    elif position == "end":
        content = haystack + "\n\n" + needle
    else:  # middle
        mid = len(haystack) // 2
        content = haystack[:mid] + "\n\n" + needle + "\n\n" + haystack[mid:]
    
    response = client.messages.create(
        model="claude-3-7-sonnet-20260219",
        max_tokens=1024,
        messages=[{
            "role": "user",
            "content": f"{content}\n\nWhat is the magic number mentioned in the document?"
        }]
    )
    return response.content[0].text

# Analyzing an entire codebase in one shot
def analyze_codebase_security(codebase_contents: str) -> str:
    """
    For a ~50K token codebase, long-context beats RAG:
    - No chunking artifacts
    - Cross-file reasoning works naturally
    - Setup is trivial
    """
    client = anthropic.Anthropic()
    
    prompt = f"""Analyze the following codebase for security vulnerabilities.
Pay special attention to SQL injection, XSS, authentication flaws, and secrets in code.

<codebase>
{codebase_contents}
</codebase>

Provide a prioritized list of security issues with file locations and remediation steps."""

    response = client.messages.create(
        model="claude-3-7-sonnet-20260219",
        max_tokens=4096,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text

from openai import OpenAI

client = OpenAI()

TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "retrieve_from_rag",
            "description": "Search the knowledge base for relevant factual information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "The search query"}
                },
                "required": ["query"]
            }
        }
    },
    {
        "type": "function", 
        "function": {
            "name": "retrieve_conversation_history",
            "description": "Get relevant past conversation turns",
            "parameters": {
                "type": "object",
                "properties": {
                    "user_id": {"type": "string"},
                    "topic": {"type": "string"}
                },
                "required": ["user_id", "topic"]
            }
        }
    }
]

def memory_agent_response(user_message: str, user_id: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a helpful assistant. Use the available tools to retrieve relevant information before responding."},
            {"role": "user", "content": user_message}
        ],
        tools=TOOLS,
        tool_choice="auto"
    )
    
    # Handle tool calls, execute them, and continue the conversation
    # ...
    return final_response