fixed errors

.gitattributes

@@ -1,13 +1,3 @@
-<<<<<<< HEAD
-<<<<<<< HEAD
+
 faiss_index/index.faiss filter=lfs diff=lfs merge=lfs -text
 resources/Guide-to-Litiaton-in-India.pdf filter=lfs diff=lfs merge=lfs -text
-=======
-faiss_index/index.faiss filter=lfs diff=lfs merge=lfs -text
-resources/Guide-to-Litiaton-in-India.pdf filter=lfs diff=lfs merge=lfs -text
->>>>>>> e9cd6c40756e45d6beab9c9d3c151efcb2a657a0
-resources/Guide-to-Litigation-in-India.pdf filter=lfs diff=lfs merge=lfs -text
-=======
-faiss_index/index.faiss filter=lfs diff=lfs merge=lfs -text
-resources/Guide-to-Litiaton-in-India.pdf filter=lfs diff=lfs merge=lfs -text
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef

Dockerfile

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 FROM python:3.10-slim
 
 WORKDIR /app
@@ -19,40 +18,8 @@ ENV GOOGLE_API_KEY=$GOOGLE_API_KEY1
 ARG LANGCHAIN_API_KEY1
 ENV LANGCHAIN_API_KEY=$LANGCHAIN_API_KEY1
 
-<<<<<<< HEAD
 EXPOSE 8000
 
 ENTRYPOINT ["chainlit", "run"]
-=======
-EXPOSE 8501
 
-ENTRYPOINT ["streamlit", "run"]
->>>>>>> e9cd6c40756e45d6beab9c9d3c151efcb2a657a0
-
-=======
-FROM python:3.10-slim
-
-WORKDIR /app
-
-COPY . /app
-
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-        ca-certificates \
-        netbase \
-        && rm -rf /var/lib/apt/lists/*
-
-RUN pip3 install -r requirements.txt
-
-ARG GOOGLE_API_KEY1
-ENV GOOGLE_API_KEY=$GOOGLE_API_KEY1
-
-ARG LANGCHAIN_API_KEY1
-ENV LANGCHAIN_API_KEY=$LANGCHAIN_API_KEY1
-
-EXPOSE 8501
-
-ENTRYPOINT ["streamlit", "run"]
-
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef
 CMD ["app.py"]

LICENSE

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 MIT License
 
 Copyright (c) 2025 Rajarshi Roy
@@ -20,26 +19,3 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
-=======
-MIT License
-
-Copyright (c) 2025 Rajarshi Roy
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef

README.md

@@ -1,7 +1,4 @@
-<<<<<<< HEAD
-=======
 ---
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef
 title: Legal-Agent
 emoji: 🌖
 colorFrom: gray
@@ -10,8 +7,7 @@ sdk: docker
 sdk_version: 5.18.0
 app_file: app.py
 pinned: false
-<<<<<<< HEAD
-
+---
 
 # Legal Agent Codebase
 
@@ -68,11 +64,7 @@ The system follows a multi-stage workflow:
 
 The code is organized into several classes and functions:
 
-<<<<<<< HEAD
-* **`DocumentRetriever`:** Loads and interacts with the FAISS index, retrieving relevant documents based on a query.
-=======
 * **`FaissRetriever`:** Loads and interacts with the FAISS index, retrieving relevant documents based on a query.
->>>>>>> e9cd6c40756e45d6beab9c9d3c151efcb2a657a0
 
 * **`DocSummarizerPipeline`:** Summarizes retrieved documents using the Gemini model, generating a concise answer focused on the user's query.  It uses a carefully crafted prompt to ensure the response is structured and informative.
 
@@ -307,6 +299,3 @@ Rajarshi Roy - [[email protected]](mailto:[email protected])
 This project is licensed under the MIT License. Feel free to modify and distribute it as per the terms of the license.
 
 I hope this README provides you with the necessary information to get started with the road to Generative AI with Google Gemini and Langchain.
-=======
----
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef

app copy.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 import streamlit as st
 from legal_agent.components.full_workflow import run_user_query
 
@@ -41,47 +40,3 @@ if st.button("Send"):
         st.experimental_rerun()
     else:
         st.error("Please enter a valid query.")
-=======
-import streamlit as st
-from legal_agent.components.full_workflow import run_user_query
-
-# Initialize session state for conversation history
-if "conversation" not in st.session_state:
-    st.session_state.conversation = []
-
-st.title("Legal Agent Chat App")
-
-
-# Display conversation history
-def display_conversation():
-    for chat in st.session_state.conversation:
-        if chat["role"] == "user":
-            st.markdown(f"**User:** {chat['content']}")
-        else:
-            st.markdown(f"**Legal Agent:** {chat['content']}")
-
-
-display_conversation()
-
-# Input area for new query
-user_input = st.text_input("Enter your legal query:")
-
-if st.button("Send"):
-    if user_input.strip():
-        # Append user message to conversation
-        st.session_state.conversation.append({"role": "user", "content": user_input})
-
-        # Run the legal agent query workflow
-        result = run_user_query(user_input)
-        agent_response = result.get("response", "No response received.")
-
-        # Append agent response to conversation
-        st.session_state.conversation.append(
-            {"role": "assistant", "content": agent_response}
-        )
-
-        # Clear the text input by rerunning the app
-        st.experimental_rerun()
-    else:
-        st.error("Please enter a valid query.")
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef

app.py

@@ -1,15 +1,5 @@
-<<<<<<< HEAD
 import chainlit as cl
 from legal_agent.components.full_workflow import run_user_query
-<<<<<<< HEAD
-import os
-=======
-
-# from QA_app.components.data_querying import user_query
-import chainlit as cl
-
-# user_query
->>>>>>> e9cd6c40756e45d6beab9c9d3c151efcb2a657a0
 
 
 async def user_query_func(user_question):
@@ -35,8 +25,6 @@ async def main(message: cl.Message):
     # response = await user_query_func("What happended to the birds")
     response = await user_query_func(user_question)
     print(user_question, "see")
-<<<<<<< HEAD
-=======
     # user_query = cl.make_async(user_query)
 
     # await user_query("What happended to the birds")
@@ -46,61 +34,6 @@ async def main(message: cl.Message):
     # callback_handler = LangchainCallbackHandler(stream_final_answer=True)
     # response = await cl.make_async(user_query)(user_question)
     # response = await cl.make_async(user_query)(user_question)
->>>>>>> e9cd6c40756e45d6beab9c9d3c151efcb2a657a0
 
     # await message.reply(response)
     await cl.Message(content=response["response"]).send()
-
-
-# # Run the Chainlit app
-# cl.run()
-=======
-import chainlit as cl
-from legal_agent.components.full_workflow import run_user_query
-
-# from QA_app.components.data_querying import user_query
-import chainlit as cl
-
-# user_query
-
-
-async def user_query_func(user_question):
-    response = run_user_query(user_question)
-    # Replace this with your actual logic for processing the user query
-    # It could involve interacting with an LLM, searching web documents, etc.
-    # For illustration purposes, let's just return a simple response
-    return response
-
-
-@cl.on_chat_start
-def start():
-    # user_query
-
-    print("Chat started!")
-
-
-@cl.on_message
-async def main(message: cl.Message):
-    # user_query
-    user_question = message.content
-    # response = user_query(user_question)
-    # response = await user_query_func("What happended to the birds")
-    response = await user_query_func(user_question)
-    print(user_question, "see")
-    # user_query = cl.make_async(user_query)
-
-    # await user_query("What happended to the birds")
-    # print(user_question, "see22222222")
-
-    # Use LangchainCallbackHandler to capture the final answer
-    # callback_handler = LangchainCallbackHandler(stream_final_answer=True)
-    # response = await cl.make_async(user_query)(user_question)
-    # response = await cl.make_async(user_query)(user_question)
-
-    # await message.reply(response)
-    await cl.Message(content=response["response"]).send()
-
-
-# # Run the Chainlit app
-# cl.run()
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef

chainlit.md

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 # Welcome to Game Recommendation APP! 🚀🤖
 
 **Objective: Let the Games Begin!**
@@ -7,14 +6,4 @@ Welcome to the Game Recommendation Party! 🎉 Our mission is simple: to make ga
 
 Picture this: a magical realm where every gamer finds their perfect match, where the journey to discovering new games is filled with laughter, surprises, and endless joy. Our objective? To sprinkle a dash of fun into the gaming universe and ignite the spark of adventure in every player's heart.
 
-=======
-# Welcome to Game Recommendation APP! 🚀🤖
-
-**Objective: Let the Games Begin!**
-
-Welcome to the Game Recommendation Party! 🎉 Our mission is simple: to make gaming discovery as thrilling as playing the games themselves! With our vibrant and fun-loving Game Recommendation App, we're here to shake up the gaming world and inject a dose of excitement into every recommendation.
-
-Picture this: a magical realm where every gamer finds their perfect match, where the journey to discovering new games is filled with laughter, surprises, and endless joy. Our objective? To sprinkle a dash of fun into the gaming universe and ignite the spark of adventure in every player's heart.
-
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef
 So, gear up, grab your controller, and get ready to embark on an epic quest through the world of gaming like never before. Let's turn every recommendation into a memorable adventure, where the only limit is your imagination! 🚀✨

demo_app.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 # Importing Important libraries
 
 import streamlit as st
@@ -310,316 +309,3 @@ if st.session_state.get("run_id"):
 
         else:
             st.warning("Invalid feedback score.")
-=======
-# Importing Important libraries
-
-import streamlit as st
-from pathlib import Path
-
-import os
-
-import google.generativeai as genai
-
-from langchain_community.chat_message_histories.streamlit import (
-    StreamlitChatMessageHistory,
-)
-
-
-from datetime import datetime
-
-from langchain.memory.buffer import ConversationBufferMemory
-from langchain.schema.runnable import RunnableMap
-
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.prompts import MessagesPlaceholder
-
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.callbacks.tracers.langchain import wait_for_all_tracers
-
-import streamlit as st
-from streamlit_feedback import streamlit_feedback
-
-
-from langsmith import Client
-
-from langchain_core.tracers.context import collect_runs
-
-
-from dotenv import load_dotenv
-
-from langchain_community.vectorstores import FAISS
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-
-## Loading APIs
-
-load_dotenv()
-
-embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-
-gemini_api_key = os.getenv("GOOGLE_API_KEY")
-
-
-os.environ["LANGCHAIN_PROJECT"] = "GAME RECOMMENDATION"  # Set your custom project name
-
-os.environ["LANGCHAIN_TRACING_V2"] = "true"
-os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
-langchain_api_key = os.getenv("LANGCHAIN_API_KEY")
-
-# Update with your API URL if using a hosted instance of Langsmith.
-langchain_endpoint = os.environ["LANGCHAIN_ENDPOINT"] = (
-    "https://api.smith.langchain.com"
-)
-
-# Used LLM model
-
-
-# Adding an event loop
-import asyncio
-import aiohttp
-
-
-loop = asyncio.new_event_loop()
-asyncio.set_event_loop(loop)
-
-
-model = ChatGoogleGenerativeAI(
-    model="gemini-1.5-pro-latest",
-    api_key=gemini_api_key,
-    temperature=0.3,
-    convert_system_message_to_human=True,
-)
-
-
-# Configuring memory
-memory = ConversationBufferMemory(
-    chat_memory=StreamlitChatMessageHistory(key="langchain_messages"),
-    return_messages=True,
-    memory_key="chat_history",
-)
-
-
-# Load Vector DB
-new_db = FAISS.load_local(
-    "faiss_index", embeddings, allow_dangerous_deserialization=True
-)
-
-# Main retriever
-retriever = new_db.as_retriever()
-
-# Configuring Langsmith Client
-client = Client(api_url=langchain_endpoint, api_key=langchain_api_key)
-
-
-# Introducing try catch block in case you don't have a dataset with good feed back examples
-try:
-    # Getting best feedback examples to save in the memory context
-    examples = client.list_examples(
-        dataset_name="Feedbacks"
-    )  # Choose your dataset_name here
-
-    my_examples = []
-
-    for i in examples:
-        print(i.inputs)
-        print(i.outputs["output"]["content"])
-        print("\n\n--------\n\n")
-        my_examples.append(
-            (i.inputs["input"], {"output": i.outputs["output"]["content"]})
-        )
-except:
-    my_examples = []
-
-
-my_examples = my_examples[:2]
-
-# Configuring our runnablemap
-ingress = RunnableMap(
-    {
-        "input": lambda x: x["input"],
-        "chat_history": lambda x: memory.load_memory_variables(x)["chat_history"],
-        "time": lambda _: str(datetime.now()),
-        "context": lambda x: retriever.get_relevant_documents(x["input"]),
-        "examples": lambda x: my_examples,
-    }
-)
-
-# Making the prompt template
-prompt = ChatPromptTemplate.from_messages(
-    [
-        (
-            "system",
-            "Only discuss games. You are a GAME RECOMMENDATION system assistant. Be humble, greet users nicely, and answer their queries."
-            """
-            "Instructions": 
-            "Regardless of the input, always adhere to the context provided."
-            "You can only make conversations based on the provided context. If a response cannot be formed strictly using the context, politely say you dont have knowledge about that topic."
-            "Use the Context section to provide accurate answers, as if you knew this information innately."
-            "If unsure, state that you don't know."
-
-            "Context": {context}
-
-            "Examples of Human feedback": 
-            {examples},          
-            """,
-            # "system",
-            # "Only and Only talk about games, nothing else, your knowledge is constraint games"
-            # "You are a GAME RECOMMENDATION system assistant. You are humble AI. Greet the user nicely and answer their queries"
-            # """
-            #     Use the information from the Context section to provide accurate answers but act as if you knew this information innately.
-            #     If unsure, simply state that you don't know.
-            #     Context: {context}
-            #     Here are some impressive examples of Human feedback, Do your best to try to generate these type of answer format for the specific format of questions
-            #     The examples are listed below :
-            #     {examples}
-            #     Assistant:""",
-        ),
-        MessagesPlaceholder(variable_name="chat_history"),
-        ("human", "{input}"),
-    ]
-)
-
-llm = model
-
-# Our final chain
-chain = ingress | prompt | llm
-
-
-# Initialize State
-if "trace_link" not in st.session_state:
-    st.session_state.trace_link = None
-if "run_id" not in st.session_state:
-    st.session_state.run_id = None
-
-
-# Sidebar to give option for Clearing message history
-if st.sidebar.button("Clear message history"):
-    print("Clearing message history")
-    memory.clear()
-    st.session_state.trace_link = None
-    st.session_state.run_id = None
-
-
-# When we get response from the Chatbot, then only we can see this Trace link
-if st.session_state.trace_link:
-    st.sidebar.markdown(
-        f'<a href="{st.session_state.trace_link}" target="_blank"><button>Latest Trace: 🛠️</button></a>',
-        unsafe_allow_html=True,
-    )
-
-st.header("Hey Gamers, I am a Game Recommender 🤖", divider="rainbow")
-
-for msg in st.session_state.langchain_messages:
-    avatar = "🤖" if msg.type == "ai" else None
-    with st.chat_message(msg.type, avatar=avatar):
-        st.markdown(msg.content)
-
-
-# The main chatbot configuration to get desired out and create runs for Langsmith
-if prompt := st.chat_input(placeholder="Ask me a question!"):
-    st.chat_message("user").write(prompt)
-    with st.chat_message("assistant", avatar="🤖"):
-        message_placeholder = st.empty()
-        full_response = ""
-        print("in chat here")
-
-        # Getting the input
-        input_dict = {"input": prompt}
-
-        # Displaying the response from chatbot and collecting runs
-        with collect_runs() as cb:
-            for chunk in chain.stream(input_dict, config={"tags": ["Streamlit Chat"]}):
-                full_response += chunk.content
-                message_placeholder.markdown(full_response + "▌")
-        memory.save_context(input_dict, {"output": full_response})
-
-        # storing the run id in streamlit session
-        ## Since the runnable sequence would come after retriever I have chosen `1` instead on `0`
-        run_id = cb.traced_runs[1].id
-
-        st.session_state.run_id = run_id
-
-        wait_for_all_tracers()
-        # Requires langsmith >= 0.0.19
-
-        # Getting the Trace link
-        url = client.share_run(run_id)
-
-        st.session_state.trace_link = url
-
-        message_placeholder.markdown(full_response)
-
-# Checking if we have messages in chat
-has_chat_messages = len(st.session_state.get("langchain_messages", [])) > 0
-
-
-# Only show the feedback toggle if there are chat messages
-if has_chat_messages:
-    feedback_option = (
-        "faces" if st.toggle(label="`Thumbs` ⇄ `Faces`", value=False) else "thumbs"
-    )
-
-else:
-    pass
-
-if st.session_state.get("run_id"):
-    feedback = streamlit_feedback(
-        feedback_type=feedback_option,  # Use the selected feedback option
-        optional_text_label="[Optional] Please provide an explanation",  # Adding a label for optional text input
-        key=f"feedback_{st.session_state.run_id}",
-        align="flex-start",
-    )
-
-    # Define score mappings for both "thumbs" and "faces" feedback systems
-    score_mappings = {
-        "thumbs": {"👍": 1, "👎": 0},
-        "faces": {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0},
-    }
-
-    # Get the score mapping based on the selected feedback option
-    scores = score_mappings[feedback_option]
-
-    if feedback:
-        # Get the score from the selected feedback option's score mapping
-        score = scores.get(feedback["score"])
-
-        if score is not None:
-            # Formulate feedback type string incorporating the feedback option and score value
-            feedback_type_str = f"{feedback_option} {feedback['score']}"
-
-            # Record the feedback with the formulated feedback type string and optional comment
-            feedback_record = client.create_feedback(
-                st.session_state.run_id,
-                feedback_type_str,  # Updated feedback type
-                score=score,
-                comment=feedback.get("text"),
-            )
-            st.session_state.feedback = {
-                "feedback_id": str(feedback_record.id),
-                "score": score,
-            }
-
-            # # Incase you want to add this run with feedback to simultaneously add to a dataset
-            # run_id = st.session_state.get("run_id")
-            # selected_runs = client.list_runs(id=[run_id])
-
-            # for run in tqdm(selected_runs):
-
-            #     # print(run, "lets see")
-            #     print(run.inputs)
-            #     print(run.outputs)
-            #     print(run.extra)
-            #     print(run.feedback_stats)
-
-            #     client.create_examples(
-            #         inputs=[run.inputs],
-            #         outputs=[run.outputs],
-            #         feedback_stats=[run.feedback_stats],
-            #         dataset_id=<your-dataset-id>,
-            #     )
-
-        else:
-            st.warning("Invalid feedback score.")
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef

demo_chaillit_app.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 # from QA_app.components.data_querying import user_query
 from chainlit import on_chat_start, on_message, LangchainCallbackHandler
 import chainlit as cl
@@ -57,63 +56,3 @@ async def main(message: cl.Message):
 
 # # Run the Chainlit app
 # cl.run()
-=======
-# from QA_app.components.data_querying import user_query
-from chainlit import on_chat_start, on_message, LangchainCallbackHandler
-import chainlit as cl
-from main_app_deploy.components.data_querying import my_query
-
-
-import os
-
-os.environ["LITERAL_API_KEY"] = os.getenv("LITERAL_API_KEY")
-
-os.environ["LANGCHAIN_PROJECT"] = "GAME RECOMMENDATION"
-
-os.environ["LANGCHAIN_TRACING_V2"] = "true"
-os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
-
-
-# user_query
-
-
-async def user_query_func(user_question):
-    response = my_query(user_question)
-    # Replace this with your actual logic for processing the user query
-    # It could involve interacting with an LLM, searching web documents, etc.
-    # For illustration purposes, let's just return a simple response
-    return response
-
-
-@cl.on_chat_start
-def start():
-    # user_query
-
-    print("Chat started!")
-
-
-@cl.on_message
-async def main(message: cl.Message):
-    # user_query
-    user_question = message.content
-    # response = user_query(user_question)
-    # response = await user_query_func("What happended to the birds")
-    response = await user_query_func(user_question)
-    print(user_question, "see")
-    # user_query = cl.make_async(user_query)
-
-    # await user_query("What happended to the birds")
-    # print(user_question, "see22222222")
-
-    # Use LangchainCallbackHandler to capture the final answer
-    # callback_handler = LangchainCallbackHandler(stream_final_answer=True)
-    # response = await cl.make_async(user_query)(user_question)
-    # response = await cl.make_async(user_query)(user_question)
-
-    # await message.reply(response)
-    await cl.Message(content=response).send()
-
-
-# # Run the Chainlit app
-# cl.run()
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef

demo_main.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 from main_app_deploy.components.data_querying import my_query
 import os
 
@@ -10,16 +9,3 @@ os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
 ans = my_query("can you reccomend me some fantasy games? about 2 games")
 
 print(ans)
-=======
-from main_app_deploy.components.data_querying import my_query
-import os
-
-os.environ["LANGCHAIN_PROJECT"] = "GAME RECOMMENDATION"
-
-os.environ["LANGCHAIN_TRACING_V2"] = "true"
-os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
-
-ans = my_query("can you reccomend me some fantasy games? about 2 games")
-
-print(ans)
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef

requirements.txt

@@ -4,13 +4,9 @@ chromadb
 duckduckgo_search==6.3.4
 fastapi
 faiss-cpu
-<<<<<<< HEAD
-google-generativeai
-=======
 # flashrank[listwise]
 google-generativeai
 # google-play-scraper
->>>>>>> e9cd6c40756e45d6beab9c9d3c151efcb2a657a0
 IPython
 ipykernel
 langchain==0.3.7
@@ -18,19 +14,6 @@ langchain-community==0.3.7
 langchain_core
 langchain-docling
 langchain_google_genai==2.0.4
-<<<<<<< HEAD
-langgraph==0.2.48
-langserve
-langsmith
-numpy
-pandas>=1.3.6
-pypdf
-python-dotenv
-regex
-streamlit
-umap-learn
-unstructured[pdf]==0.7.12
-=======
 # langchain_huggingface
 langgraph==0.2.48
 langserve
@@ -45,5 +28,4 @@ streamlit
 unstructured[pdf]==0.7.12
 # sse_starlette
 # uvicorn
->>>>>>> e9cd6c40756e45d6beab9c9d3c151efcb2a657a0
 -e .

setup.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 import setuptools
 
 
@@ -29,35 +28,3 @@ setuptools.setup(
     package_dir={"": "src"},
     packages=setuptools.find_packages(where="src"),
 )
-=======
-import setuptools
-
-
-with open("README.md", "r", encoding="utf-8") as f:
-    long_description = f.read()
-
-
-__version__ = "0.0.0.1"
-
-REPO_NAME = "legal-agent"
-AUTHOR_USER_NAME = "Rajarshi12321"
-SRC_REPO = "legal_agent"
-AUTHOR_EMAIL = "[email protected]"
-
-
-setuptools.setup(
-    name=SRC_REPO,
-    version=__version__,
-    author=AUTHOR_USER_NAME,
-    author_email=AUTHOR_EMAIL,
-    description="A small python package for game recommendation using RAG model",
-    long_description=long_description,
-    long_description_content="text/markdown",
-    url=f"https://github.com/{AUTHOR_USER_NAME}/{REPO_NAME}",
-    project_urls={
-        "Bug Tracker": f"https://github.com/{AUTHOR_USER_NAME}/{REPO_NAME}/issues",
-    },
-    package_dir={"": "src"},
-    packages=setuptools.find_packages(where="src"),
-)
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef

template.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 import os
 from pathlib import Path
 import logging
@@ -34,40 +33,3 @@ for filepath in list_of_files:
 
     else:
         logging.info(f"{filename} is already exists")
-=======
-import os
-from pathlib import Path
-import logging
-
-# logging string
-logging.basicConfig(level=logging.INFO, format="[%(asctime)s]: %(message)s:")
-
-project_name = "legal_agent"
-
-list_of_files = [
-    ".github/workflows/.gitkeep",
-    f"src/{project_name}/__init__.py",
-    f"src/{project_name}/components/__init__.py",
-    f"src/{project_name}/utils/__init__.py",
-    "requirements.txt",
-    "setup.py",
-    "research/trials.ipynb",
-]
-
-
-for filepath in list_of_files:
-    filepath = Path(filepath)
-    filedir, filename = os.path.split(filepath)
-
-    if filedir != "":
-        os.makedirs(filedir, exist_ok=True)
-        logging.info(f"Creating directory; {filedir} for the file: {filename}")
-
-    if (not os.path.exists(filepath)) or (os.path.getsize(filepath) == 0):
-        with open(filepath, "w") as f:
-            pass
-            logging.info(f"Creating empty file: {filepath}")
-
-    else:
-        logging.info(f"{filename} is already exists")
->>>>>>> 77c2a5e7f373b9fd0f02cb3a080cbf240a85f4ef