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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+db/chroma_db/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,18 @@
+# Byte-compiled / cache files
+__pycache__/
+*.py[cod]
+*.so
+*.swp
+*.swo
+
+# Virtual environment
+venv/
+.env
+*.log
+
+# Jupyter Notebook checkpoints
+.ipynb_checkpoints/
+
+# PyCharm project files
+.idea/
+*.iml

Dockerfile

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+FROM python:3.12.4-bullseye
+
+WORKDIR /app
+
+COPY requirements.txt requirements.txt
+RUN pip install -r requirements.txt
+
+COPY . .
+
+CMD ["python", "app.py"]

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Union
+import os
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.chains.history_aware_retriever import create_history_aware_retriever
+from langchain.chains.retrieval import create_retrieval_chain
+from langchain_chroma import Chroma
+from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
+from dotenv import load_dotenv
+from starlette.middleware.cors import CORSMiddleware
+
+load_dotenv()
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+# Define the persistent directory
+current_dir = os.path.dirname(os.path.abspath(__file__))
+persistent_directory = os.path.join(current_dir, "db", "chroma_db")
+
+# Initialize embeddings
+embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", api_key=GOOGLE_API_KEY)
+
+# Load the existing vector store with the embedding function
+db = Chroma(persist_directory=persistent_directory, embedding_function=embeddings)
+
+# Create a retriever for querying the vector store
+retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 5})
+
+# Initialize the LLM
+llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", api_key=GOOGLE_API_KEY)
+
+# Contextualize question prompt
+contextualize_q_system_prompt = (
+    "Given a chat history and the latest user question "
+    "which might reference context in the chat history, "
+    "formulate a standalone question which can be understood "
+    "without the chat history. Do NOT answer the question, just "
+    "reformulate it if needed and otherwise return it as is."
+)
+
+contextualize_q_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", contextualize_q_system_prompt),
+        MessagesPlaceholder("chat_history"),
+        ("human", "{input}"),
+    ]
+)
+
+# Create a history-aware retriever
+history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
+
+# Answer question prompt
+# Update this prompt to reflect your desired behavior (e.g., act as "you")
+qa_system_prompt = (
+    "You are an assistant that acts as me. Use the following pieces of retrieved context "
+    "to answer the question. If you don't know the answer, just say that you don't know. "
+    "Use three sentences maximum and keep the answer concise. Always respond as if you are me."
+    "\n\n"
+    "{context}"
+)
+
+qa_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", qa_system_prompt),
+        MessagesPlaceholder("chat_history"),
+        ("human", "{input}"),
+    ]
+)
+
+# Create a chain to combine documents for question answering
+question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
+
+# Create a retrieval chain that combines the history-aware retriever and the question answering chain
+rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
+
+app = FastAPI()
+
+# Global chat history
+chat_history = []
+
+class ChatRequest(BaseModel):
+    input: str
+
+class ChatResponse(BaseModel):
+    answer: str
+# Enable CORS to allow frontend access
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Home route to check if FastAPI is running
+@app.get("/")
+async def root():
+    return {"message": "FastAPI Server is Running!"}
+@app.post("/start")
+async def start_chat():
+    global chat_history
+    chat_history = []  # Reset chat history
+    return {"message": "Chat session started. Chat history has been reset."}
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(chat_request: ChatRequest):
+    global chat_history
+
+    query = chat_request.input
+
+    if query.lower() == "exit":
+        raise HTTPException(status_code=400, detail="Use /start to reset the chat session.")
+
+    # Filter out SystemMessage, keeping only HumanMessage and AIMessage
+    filtered_chat_history = [
+        msg for msg in chat_history if isinstance(msg, HumanMessage) or isinstance(msg, AIMessage)
+    ]
+
+    # Invoke the RAG chain
+    result = rag_chain.invoke({"input": query, "chat_history": filtered_chat_history})
+
+    # Update the chat history
+    chat_history.append(HumanMessage(content=query))
+    chat_history.append(AIMessage(content=result['answer']))
+
+    return ChatResponse(answer=result['answer'])
+
+# Run the FastAPI app
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8080)

db/chroma_db/chroma.sqlite3

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db/chroma_db/dcfa2b4e-85ee-416d-aba8-0010eceea7cf/data_level0.bin

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db/chroma_db/dcfa2b4e-85ee-416d-aba8-0010eceea7cf/link_lists.bin

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