Spaces:
Running
Running
Worked on passage finder
Browse files- Pipfile +2 -0
- Pipfile.lock +563 -347
- app2.py +187 -0
- prompts/quote_finder_prompts.py +28 -0
- src/passage_finder.py +236 -0
- src/utils/qdrant_manager.py +2 -1
Pipfile
CHANGED
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@@ -15,6 +15,8 @@ jupyter = "*"
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ipykernel = "*"
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langgraph = "*"
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streamlit = "*"
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[dev-packages]
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ipykernel = "*"
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langgraph = "*"
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streamlit = "*"
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fuzzywuzzy = "*"
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python-levenshtein = "*"
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[dev-packages]
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Pipfile.lock
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"referencing": {
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"hashes": [
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"sha256:25b42124a6c8b632a425174f24087783efb348a6f1e0008e63cd4466fedf703c",
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"rich": {
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"markers": "python_full_version >= '3.
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"version": "==13.
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"rpds-py": {
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"hashes": [
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"ruff": {
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"markers": "sys_platform != 'emscripten'",
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"version": "==0.6.
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"semantic-version": {
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"hashes": [
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"uvicorn": {
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"version": "==0.
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|
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"version": "==1.
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"develop": {}
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| 1 |
{
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| 2 |
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| 3 |
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| 27 |
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| 31 |
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| 34 |
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"sha256:6a2acde25be0cf9be23a8f6cbd31734536a264723fca860af3ae5e89d771cd71",
|
| 3631 |
+
"sha256:6a5185ad722ab4dd52d5fb1f30dcc73282eb1ed494906a92d1a228d3f89607b0",
|
| 3632 |
+
"sha256:6b7f6e699304717fdc265a7e1922561b02a93ceffdaefdc877acaf9b9f3080b8",
|
| 3633 |
+
"sha256:703b0f584fcf157ef87816a3c0ff868e8c9f3c370009a8b23b56255885528f10",
|
| 3634 |
+
"sha256:7055bbade838d68af73aea13f8c86588e4bcc00c2235b4b6d6edb0dbd174e246",
|
| 3635 |
+
"sha256:78f271722423b2d4851cf1f4fa1a1c4833a128d020062721ba35e1a87154a049",
|
| 3636 |
+
"sha256:7addd26594e588503bdef03908fc207206adac5bd90b6d4bc3e3cf33a829f57d",
|
| 3637 |
+
"sha256:81bad32c8f8b5897c909bf3468bf601f1b855d12f53b6af0271963ee67fff0d2",
|
| 3638 |
+
"sha256:82e692fb325013a18a5b73a4fed5a1edaa7c58144dc67ad9ef3d604eccd451ad",
|
| 3639 |
+
"sha256:84bbcdcf393139f0abc9f642bf03f00cac31010f3034faa03224a9ef0bb74323",
|
| 3640 |
+
"sha256:86c438ce920e089c8c2388c7dcc8ab30dfe13c09b8af3d306bcabb46a053d6f7",
|
| 3641 |
+
"sha256:8be8cdfe20787e6a5fcbd010f8066227e2bb9058331a4eccddec6c0db2bb85b2",
|
| 3642 |
+
"sha256:8c723c91c94a3bc8033dd2696a0f53e5d5f8496186013167bddc3fb5d9df46a3",
|
| 3643 |
+
"sha256:8ca53632007c69ddcdefe1e8cbc3920dd88825e618153795b57e6ebcc92e752a",
|
| 3644 |
+
"sha256:8f722f30366474a99745533cc4015b1781ee54b08de73260b2bbe13316079851",
|
| 3645 |
+
"sha256:942c80a832a79c3707cca46bd12ab8aa58fddb34b1626d42b05aa8f0bcefc206",
|
| 3646 |
+
"sha256:94a993f976cdcb2dc1b855d8b89b792893220db8862d1a619efa7451817c836b",
|
| 3647 |
+
"sha256:95c6737f28069153c399d875317f226bbdea939fd48a6349a3b03da6829fb550",
|
| 3648 |
+
"sha256:9915300fe5a0aa663c01363db37e4ae8e7c15996ebe2c6cce995e7033ff6457f",
|
| 3649 |
+
"sha256:9a18595e6a2ee0826bf7dfdee823b6ab55c9b70e8f80f8b77c37e694288f5de1",
|
| 3650 |
+
"sha256:9c8854b9f80693d20cec797d8e48a848c2fb273eb6f2587b57763ccba3f3bd4b",
|
| 3651 |
+
"sha256:9cec42a20eae8bebf81e9ce23fb0d0c729fc54cf00643eb251ce7c0215ad49fe",
|
| 3652 |
+
"sha256:9d2e1626be8712333a9f71270366f4a132f476ffbe83b689dd6dc0d114796c74",
|
| 3653 |
+
"sha256:9d74f3c335cfe9c21ea78988e67f18eb9822f5d31f88b41aec3a1ec5ecd32da5",
|
| 3654 |
+
"sha256:9fb4134cc6e005b99fa29dbc86f1ea0a298440ab6b07c6b3ee09232a3b48f495",
|
| 3655 |
+
"sha256:a0ae6637b173d0c40b9c1462e12a7a2000a71a3258fa88756a34c7d38926911c",
|
| 3656 |
+
"sha256:a31d21089894942f7d9a8df166b495101b7258ff11ae0abec58e32daf8088813",
|
| 3657 |
+
"sha256:a3442c31c11088e462d44a644a454d48110f0588de830921fd201060ff19612a",
|
| 3658 |
+
"sha256:ab9524e45ee809a083338a749af3b53cc7efec458c3ad084361c1dbf7aaf82a2",
|
| 3659 |
+
"sha256:b1481c048fe787f65e34cb06f7d6824376d5d99f1231eae4778bbe5c3831076d",
|
| 3660 |
+
"sha256:b8c837ab90c455f3ea8e68bee143472ee87828bff19ba19776e16ff961425b57",
|
| 3661 |
+
"sha256:bbf2c3f04ff50f16404ce70f822cdc59760e5e2d7965905f0e700270feb2bbfc",
|
| 3662 |
+
"sha256:bbf9c2a589be7414ac4a534d54e4517d03f1cbb142c0041191b729c2fa23f320",
|
| 3663 |
+
"sha256:bcd5bf4132e6a8d3eb54b8d56885f3d3a38ecd7ecae8426ecf7d9673b270de43",
|
| 3664 |
+
"sha256:c14c16831b565707149c742d87a6203eb5597f4329278446d5c0ae7a1a43928e",
|
| 3665 |
+
"sha256:c49f3e379177f4477f929097f7ed4b0622a586b0aa40c07ac8c0f8e40659a1ac",
|
| 3666 |
+
"sha256:c92b89bffc660f1274779cb6fbb290ec1f90d6dfe14492523a0667f10170de26",
|
| 3667 |
+
"sha256:cd66152561632ed4b2a9192e7f8e5a1d41e28f58120b4761622e0355f0fe034c",
|
| 3668 |
+
"sha256:cf1ad338620249f8dd6d4b6a91a69d1f265387df3697ad5dc996305cf6c26fb2",
|
| 3669 |
+
"sha256:d07b52c8c450f9366c34aa205754355e933922c79135125541daae6cbf31c799",
|
| 3670 |
+
"sha256:d0d12fe78dcf60efa205e9a63f395b5d343e801cf31e5e1dda0d2c1fb618073d",
|
| 3671 |
+
"sha256:d4ee1d240b84e2f213565f0ec08caef27a0e657d4c42859809155cf3a29d1735",
|
| 3672 |
+
"sha256:d959fe96e5c2712c1876d69af0507d98f0b0e8d81bee14cfb3f6737470205419",
|
| 3673 |
+
"sha256:dcaef817e13eafa547cdfdc5284fe77970b891f731266545aae08d6cce52161e",
|
| 3674 |
+
"sha256:df4e82e68f43a07735ae70a2d84c0353e58e20add20ec0af611f32cd5ba43fb4",
|
| 3675 |
+
"sha256:ec8cfe2295f3e5e44c51f57272afbd69414ae629ec7c6b27f5a410efc78b70a0",
|
| 3676 |
+
"sha256:ec9dd328016d8d25702a24ee274932aebf6be9787ed1c28d021945d264235b3c",
|
| 3677 |
+
"sha256:ef9b85fa1bc91c4db24407e7c4da93a5822a73dd4513d67b454ca7064e8dc6a3",
|
| 3678 |
+
"sha256:f3bf60444269345d712838bb11cc4eadaf51ff1a364ae39ce87a5ca8ad3bb2c8",
|
| 3679 |
+
"sha256:f452cc1436151387d3d50533523291d5f77c6bc7913c116eb985304abdbd9ec9",
|
| 3680 |
+
"sha256:f7917697bcaa3bc3e83db91aa3a0e448bf5cde43c84b7fc1ae2427d2417c0224",
|
| 3681 |
+
"sha256:f90575e9fe3aae2c1e686393a9689c724cd00045275407f71771ae5d690ccf38",
|
| 3682 |
+
"sha256:fb382fd7b4377363cc9f13ba7c819c3c78ed97c36a82f16f3f92f108c787cbbf",
|
| 3683 |
+
"sha256:fb9f59f3848edf186a76446eb8bcf4c900fe147cb756fbbd730ef43b2e67c6a7",
|
| 3684 |
+
"sha256:fc2931ac9ce9c61c9968989ec831d3a5e6fcaaff9474e7cfa8de80b7aff5a093"
|
| 3685 |
+
],
|
| 3686 |
+
"markers": "python_version >= '3.8'",
|
| 3687 |
+
"version": "==1.13.1"
|
| 3688 |
}
|
| 3689 |
},
|
| 3690 |
"develop": {}
|
app2.py
ADDED
|
@@ -0,0 +1,187 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
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|
|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import gradio as gr
|
| 2 |
+
import os
|
| 3 |
+
from langchain_core.messages import HumanMessage
|
| 4 |
+
import src.srf_bot as sb
|
| 5 |
+
import prompts.system_prompts as sp
|
| 6 |
+
|
| 7 |
+
# Initialize chatbot
|
| 8 |
+
chatbot = sb.SRFChatbot()
|
| 9 |
+
|
| 10 |
+
# Define the respond function
|
| 11 |
+
def respond(query, history, custom_instructions, use_custom_instructions, selected_system_prompt):
|
| 12 |
+
formatted_query = [HumanMessage(content=query)]
|
| 13 |
+
|
| 14 |
+
# Set the system instructions based on user input or dropdown
|
| 15 |
+
if use_custom_instructions:
|
| 16 |
+
chatbot.config["system_prompt"] = custom_instructions # Use custom instructions
|
| 17 |
+
else:
|
| 18 |
+
chatbot.config["system_prompt"] = sp.system_prompt_templates[selected_system_prompt] # Use selected from dropdown
|
| 19 |
+
|
| 20 |
+
# Invoke the graph with properly formatted input
|
| 21 |
+
result = chatbot.graph.invoke({"messages": formatted_query}, chatbot.config)
|
| 22 |
+
|
| 23 |
+
# Get the passages from the graph and append to history if documents exist
|
| 24 |
+
state = chatbot.graph.get_state(config=chatbot.config).values
|
| 25 |
+
documents = state.get("documents")
|
| 26 |
+
passages = ''
|
| 27 |
+
if documents and len(documents) > 0:
|
| 28 |
+
for d in documents:
|
| 29 |
+
passages += f'<b>{d.metadata["publication_name"]} - {d.metadata["chapter_name"]}</b>\n{d.page_content}\n\n'
|
| 30 |
+
history.append((f'Passages: {query}', passages))
|
| 31 |
+
|
| 32 |
+
# Extract the assistant's response and append to history
|
| 33 |
+
response = result["messages"][-1].content
|
| 34 |
+
system_message_dropdown = state.get("system_message_dropdown")
|
| 35 |
+
history.append((query, f"<i>[{system_message_dropdown}]</i>\n" + response))
|
| 36 |
+
|
| 37 |
+
return history
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
# Gradio interface with black and grey color scheme
|
| 41 |
+
with gr.Blocks(css="""
|
| 42 |
+
.gradio-container {
|
| 43 |
+
background-color: #F0F0F0;
|
| 44 |
+
font-family: 'Arial', sans-serif;
|
| 45 |
+
}
|
| 46 |
+
h1, h2, p {
|
| 47 |
+
color: black;
|
| 48 |
+
}
|
| 49 |
+
h1 {
|
| 50 |
+
font-size: 32px;
|
| 51 |
+
text-align: left;
|
| 52 |
+
}
|
| 53 |
+
h2 {
|
| 54 |
+
font-size: 24px;
|
| 55 |
+
}
|
| 56 |
+
p {
|
| 57 |
+
font-size: 18px;
|
| 58 |
+
margin-bottom: 15px;
|
| 59 |
+
}
|
| 60 |
+
.gr-button {
|
| 61 |
+
background-color: #333333;
|
| 62 |
+
color: white;
|
| 63 |
+
font-size: 18px;
|
| 64 |
+
padding: 10px;
|
| 65 |
+
}
|
| 66 |
+
.gr-textbox textarea {
|
| 67 |
+
font-size: 18px;
|
| 68 |
+
color: black;
|
| 69 |
+
}
|
| 70 |
+
.gr-dropdown {
|
| 71 |
+
font-size: 18px;
|
| 72 |
+
color: black;
|
| 73 |
+
}
|
| 74 |
+
.source-box {
|
| 75 |
+
background-color: white;
|
| 76 |
+
padding: 10px;
|
| 77 |
+
border-radius: 8px;
|
| 78 |
+
margin-top: 20px;
|
| 79 |
+
color: black;
|
| 80 |
+
border: 1px solid #D0D0D0;
|
| 81 |
+
}
|
| 82 |
+
|
| 83 |
+
@media (prefers-color-scheme: dark) {
|
| 84 |
+
.gradio-container {
|
| 85 |
+
background-color: #1e1e1e;
|
| 86 |
+
color: white;
|
| 87 |
+
}
|
| 88 |
+
h1, h2, p {
|
| 89 |
+
color: white;
|
| 90 |
+
}
|
| 91 |
+
.gr-textbox textarea {
|
| 92 |
+
background-color: #333333;
|
| 93 |
+
color: white;
|
| 94 |
+
}
|
| 95 |
+
.gr-button {
|
| 96 |
+
background-color: #555555;
|
| 97 |
+
color: white;
|
| 98 |
+
}
|
| 99 |
+
.gr-dropdown {
|
| 100 |
+
background-color: #333333;
|
| 101 |
+
color: white;
|
| 102 |
+
}
|
| 103 |
+
.source-box {
|
| 104 |
+
background-color: #333333;
|
| 105 |
+
color: white;
|
| 106 |
+
border: 1px solid #555555;
|
| 107 |
+
}
|
| 108 |
+
}
|
| 109 |
+
|
| 110 |
+
@media (max-width: 600px) {
|
| 111 |
+
.gr-row { flex-direction: column !important; }
|
| 112 |
+
.gr-column { width: 100% !important; }
|
| 113 |
+
}
|
| 114 |
+
""") as demo:
|
| 115 |
+
|
| 116 |
+
# Title
|
| 117 |
+
gr.Markdown("# SRF Chatbot")
|
| 118 |
+
|
| 119 |
+
with gr.Row(elem_classes="gr-row"):
|
| 120 |
+
with gr.Column(scale=4, elem_classes="gr-column"):
|
| 121 |
+
# Chatbot interface
|
| 122 |
+
chatbot_output = gr.Chatbot(height=600) # Increased height for longer chat interface
|
| 123 |
+
user_input = gr.Textbox(placeholder="Type your question here...", label="Your Question", value="What is the meaning of life?")
|
| 124 |
+
submit_button = gr.Button("Submit")
|
| 125 |
+
|
| 126 |
+
with gr.Column(scale=1, elem_classes="gr-column"):
|
| 127 |
+
# Dropdown to select system prompts
|
| 128 |
+
system_prompt_dropdown = gr.Dropdown(
|
| 129 |
+
choices=list(sp.system_prompt_templates.keys()),
|
| 130 |
+
label="Select Chatbot Instructions",
|
| 131 |
+
value=list(sp.system_prompt_templates.keys())[0],
|
| 132 |
+
elem_classes="gr-dropdown"
|
| 133 |
+
)
|
| 134 |
+
|
| 135 |
+
# Display the selected system prompt
|
| 136 |
+
system_prompt_display = gr.Textbox(
|
| 137 |
+
value=sp.system_prompt_templates[list(sp.system_prompt_templates.keys())[0]],
|
| 138 |
+
label="Current Chatbot Instructions",
|
| 139 |
+
lines=5,
|
| 140 |
+
interactive=False
|
| 141 |
+
)
|
| 142 |
+
|
| 143 |
+
# Custom instructions input
|
| 144 |
+
custom_instructions = gr.Textbox(
|
| 145 |
+
placeholder="Write your own instructions here...",
|
| 146 |
+
label="Or Write Your Own Instructions",
|
| 147 |
+
lines=5
|
| 148 |
+
)
|
| 149 |
+
|
| 150 |
+
# Toggle between using dropdown or custom instructions
|
| 151 |
+
use_custom_instructions = gr.Checkbox(
|
| 152 |
+
label="Use Custom Instructions",
|
| 153 |
+
value=False
|
| 154 |
+
)
|
| 155 |
+
|
| 156 |
+
# Sources box
|
| 157 |
+
gr.Markdown("""
|
| 158 |
+
<div class="source-box">
|
| 159 |
+
<strong>Available sources:</strong>
|
| 160 |
+
<ul>
|
| 161 |
+
<li>Journey to Self-Realization</li>
|
| 162 |
+
<li>The Second Coming of Christ</li>
|
| 163 |
+
<li>Autobiography of a Yogi</li>
|
| 164 |
+
</ul>
|
| 165 |
+
</div>
|
| 166 |
+
""")
|
| 167 |
+
|
| 168 |
+
# Update system prompt display when a new prompt is selected
|
| 169 |
+
system_prompt_dropdown.change(
|
| 170 |
+
fn=lambda x: sp.system_prompt_templates[x],
|
| 171 |
+
inputs=[system_prompt_dropdown],
|
| 172 |
+
outputs=[system_prompt_display]
|
| 173 |
+
)
|
| 174 |
+
|
| 175 |
+
# Submit button logic to handle chatbot conversation
|
| 176 |
+
submit_button.click(
|
| 177 |
+
fn=respond,
|
| 178 |
+
inputs=[user_input, chatbot_output, custom_instructions, use_custom_instructions, system_prompt_dropdown],
|
| 179 |
+
outputs=[chatbot_output]
|
| 180 |
+
)
|
| 181 |
+
|
| 182 |
+
# Access the secrets
|
| 183 |
+
username = os.getenv("USERNAME")
|
| 184 |
+
password = os.getenv("PASSWORD")
|
| 185 |
+
|
| 186 |
+
# Launch the interface
|
| 187 |
+
demo.launch(share=True, auth=(username, password), debug=True)
|
prompts/quote_finder_prompts.py
ADDED
|
@@ -0,0 +1,28 @@
|
|
|
|
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|
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|
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|
|
|
|
|
| 1 |
+
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
|
| 2 |
+
|
| 3 |
+
system_template = '''The following are passages from the books and teachings of Paramhansa Yogananda, his disciples, or other
|
| 4 |
+
sources related to the Self-Realization Fellowship / Yogoda Satsanga Society.
|
| 5 |
+
Extract the most relevant quotes from the given passage that directly address the user's original query and are the most helpful and/or insipriational to the user.
|
| 6 |
+
Ensure that quotes are verbatim and provide specific information related to the query.
|
| 7 |
+
'''
|
| 8 |
+
|
| 9 |
+
system_message = SystemMessagePromptTemplate.from_template(system_template)
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
human_message_template = '''Here is the original query: {query} \n\n
|
| 13 |
+
|
| 14 |
+
Here is the passage: {passage}
|
| 15 |
+
|
| 16 |
+
Now think step-by-step and follow the instructions carefully:
|
| 17 |
+
1. Read the original query carefully.
|
| 18 |
+
2. Analyze the given passage and identify sections that directly address the query.
|
| 19 |
+
3. Extract the most relevant quotes, ensuring they are verbatim from the passage.
|
| 20 |
+
4. Rank the quotes based on their relevance and importance to the query.
|
| 21 |
+
5. Return the top 3 most relevant quotes in the specified format.'''
|
| 22 |
+
|
| 23 |
+
human_message = HumanMessagePromptTemplate.from_template(human_message_template)
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
quote_finder_prompt = ChatPromptTemplate.from_messages([
|
| 27 |
+
system_message, human_message
|
| 28 |
+
])
|
src/passage_finder.py
ADDED
|
@@ -0,0 +1,236 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import sys
|
| 2 |
+
import os
|
| 3 |
+
import uuid
|
| 4 |
+
from dotenv import load_dotenv
|
| 5 |
+
from typing import Annotated, List, Tuple
|
| 6 |
+
from typing_extensions import TypedDict
|
| 7 |
+
from langchain.tools import tool, BaseTool
|
| 8 |
+
from langchain.schema import Document
|
| 9 |
+
from langgraph.graph import StateGraph, START, END, MessagesState
|
| 10 |
+
from langgraph.graph.message import add_messages
|
| 11 |
+
from langgraph.prebuilt import ToolNode, tools_condition
|
| 12 |
+
from langgraph.checkpoint.memory import MemorySaver
|
| 13 |
+
from langchain_openai import ChatOpenAI
|
| 14 |
+
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, AIMessagePromptTemplate, HumanMessagePromptTemplate
|
| 15 |
+
# from langchain.schema import SystemMessage, HumanMessage, AIMessage, ToolMessage
|
| 16 |
+
from langchain_core.messages import HumanMessage, AIMessage, ToolMessage, SystemMessage
|
| 17 |
+
from langchain.retrievers.multi_query import MultiQueryRetriever
|
| 18 |
+
import json
|
| 19 |
+
from langchain_core.messages import BaseMessage
|
| 20 |
+
from pydantic import BaseModel
|
| 21 |
+
import re
|
| 22 |
+
from fuzzywuzzy import fuzz
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
sys.path.append(os.path.abspath('..'))
|
| 27 |
+
|
| 28 |
+
|
| 29 |
+
import src.utils.qdrant_manager as qm
|
| 30 |
+
import prompts.system_prompts as sp
|
| 31 |
+
import prompts.quote_finder_prompts as qfp
|
| 32 |
+
load_dotenv('/Users/nadaa/Documents/code/py_innovations/srf_chatbot_v2/.env')
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
class AgentState(TypedDict):
|
| 36 |
+
messages: Annotated[list[BaseMessage], add_messages]
|
| 37 |
+
documents: list[Document]
|
| 38 |
+
query: str
|
| 39 |
+
final_response: str
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
class ToolManager:
|
| 43 |
+
def __init__(self, collection_name="openai_large_chunks_1500char"):
|
| 44 |
+
self.tools = []
|
| 45 |
+
self.qdrant = qm.QdrantManager(collection_name=collection_name)
|
| 46 |
+
self.vectorstore = self.qdrant.get_vectorstore()
|
| 47 |
+
self.add_tools()
|
| 48 |
+
|
| 49 |
+
def get_tools(self):
|
| 50 |
+
return self.tools
|
| 51 |
+
|
| 52 |
+
def add_tools(self):
|
| 53 |
+
@tool
|
| 54 |
+
def vector_search(query: str, k: int = 5) -> list[Document]:
|
| 55 |
+
"""Useful for simple queries. This tool will search a vector database for passages from the teachings of Paramhansa Yogananda and other publications from the Self Realization Fellowship (SRF).
|
| 56 |
+
The user has the option to specify the number of passages they want the search to return, otherwise the number of passages will be set to the default value."""
|
| 57 |
+
retriever = self.vectorstore.as_retriever(search_kwargs={"k": k})
|
| 58 |
+
documents = retriever.invoke(query)
|
| 59 |
+
return documents
|
| 60 |
+
|
| 61 |
+
@tool
|
| 62 |
+
def multiple_query_vector_search(query: str, k: int = 5) -> list[Document]:
|
| 63 |
+
"""Useful when the user's query is vague, complex, or involves multiple concepts.
|
| 64 |
+
This tool will write multiple versions of the user's query and search the vector database for relevant passages.
|
| 65 |
+
Use this tool when the user asks for an in depth answer to their question."""
|
| 66 |
+
|
| 67 |
+
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.5)
|
| 68 |
+
retriever_from_llm = MultiQueryRetriever.from_llm(retriever=self.vectorstore.as_retriever(), llm=llm)
|
| 69 |
+
documents = retriever_from_llm.invoke(query)
|
| 70 |
+
return documents
|
| 71 |
+
|
| 72 |
+
self.tools.append(vector_search)
|
| 73 |
+
self.tools.append(multiple_query_vector_search)
|
| 74 |
+
|
| 75 |
+
class BasicToolNode:
|
| 76 |
+
"""A node that runs the tools requested in the last AIMessage."""
|
| 77 |
+
|
| 78 |
+
def __init__(self, tools: list) -> None:
|
| 79 |
+
self.tools_by_name = {tool.name: tool for tool in tools}
|
| 80 |
+
|
| 81 |
+
def __call__(self, inputs: dict):
|
| 82 |
+
if messages := inputs.get("messages", []):
|
| 83 |
+
message = messages[-1]
|
| 84 |
+
else:
|
| 85 |
+
raise ValueError("No message found in input")
|
| 86 |
+
outputs = []
|
| 87 |
+
documents = []
|
| 88 |
+
for tool_call in message.tool_calls:
|
| 89 |
+
tool_result = self.tools_by_name[tool_call["name"]].invoke(
|
| 90 |
+
tool_call["args"]
|
| 91 |
+
)
|
| 92 |
+
outputs.append(
|
| 93 |
+
ToolMessage(
|
| 94 |
+
content=str(tool_result),
|
| 95 |
+
name=tool_call["name"],
|
| 96 |
+
tool_call_id=tool_call["id"],
|
| 97 |
+
)
|
| 98 |
+
)
|
| 99 |
+
documents += tool_result
|
| 100 |
+
|
| 101 |
+
return {"messages": outputs, "documents": documents}
|
| 102 |
+
|
| 103 |
+
# Create the Pydantic Model for the quote finder
|
| 104 |
+
class Quote(BaseModel):
|
| 105 |
+
'''Most relevant quotes to the user's query strictly pulled verbatim from the context provided.'''
|
| 106 |
+
quote: str
|
| 107 |
+
|
| 108 |
+
class QuoteList(BaseModel):
|
| 109 |
+
quotes: List[Quote]
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
class QuoteFinder:
|
| 113 |
+
def __init__(self, model: str = 'gpt-4o-mini', temperature: float = 0.5):
|
| 114 |
+
self.quotes_prompt = qfp.quote_finder_prompt
|
| 115 |
+
self.llm = ChatOpenAI(model=model, temperature=temperature)
|
| 116 |
+
self.llm_with_quotes_output = self.llm.with_structured_output(QuoteList)
|
| 117 |
+
self.quote_finder_chain = self.quotes_prompt | self.llm_with_quotes_output
|
| 118 |
+
|
| 119 |
+
|
| 120 |
+
def find_quotes_per_document(self, state: AgentState):
|
| 121 |
+
docs = state["documents"]
|
| 122 |
+
query = state["query"]
|
| 123 |
+
for doc in docs:
|
| 124 |
+
passage = doc.page_content
|
| 125 |
+
quotes = self.quote_finder_chain.invoke({"query": query, "passage": passage})
|
| 126 |
+
doc.metadata["quotes"] = quotes
|
| 127 |
+
|
| 128 |
+
return {"documents": docs}
|
| 129 |
+
|
| 130 |
+
def _highlight_quotes(self, document, quotes):
|
| 131 |
+
highlighted_content = document.page_content
|
| 132 |
+
matched_quotes = []
|
| 133 |
+
for quote in quotes.quotes:
|
| 134 |
+
# Fuzzy match the quote in the document
|
| 135 |
+
best_match = None
|
| 136 |
+
best_ratio = 0
|
| 137 |
+
for i in range(len(highlighted_content)):
|
| 138 |
+
substring = highlighted_content[i:i+len(quote.quote)]
|
| 139 |
+
ratio = fuzz.ratio(substring.lower(), quote.quote.lower())
|
| 140 |
+
if ratio > best_ratio:
|
| 141 |
+
best_ratio = ratio
|
| 142 |
+
best_match = substring
|
| 143 |
+
|
| 144 |
+
if best_match and best_ratio > 90: # Adjust threshold as needed
|
| 145 |
+
# Escape special regex characters in the best match
|
| 146 |
+
escaped_match = re.escape(best_match)
|
| 147 |
+
# Replace the matched text with highlighted version
|
| 148 |
+
highlighted_content = re.sub(
|
| 149 |
+
escaped_match,
|
| 150 |
+
f"<b>{best_match}</b>",
|
| 151 |
+
highlighted_content,
|
| 152 |
+
flags=re.IGNORECASE
|
| 153 |
+
)
|
| 154 |
+
matched_quotes.append(quote)
|
| 155 |
+
|
| 156 |
+
return highlighted_content, matched_quotes
|
| 157 |
+
|
| 158 |
+
|
| 159 |
+
def highlight_quotes_in_document(self, state: AgentState):
|
| 160 |
+
docs = state["documents"]
|
| 161 |
+
for doc in docs:
|
| 162 |
+
quotes = doc.metadata["quotes"]
|
| 163 |
+
annotated_passage, matched_quotes = self._highlight_quotes(doc, quotes)
|
| 164 |
+
doc.metadata["highlighted_content"] = annotated_passage
|
| 165 |
+
doc.metadata["matched_quotes"] = matched_quotes
|
| 166 |
+
|
| 167 |
+
return {"documents": docs}
|
| 168 |
+
|
| 169 |
+
|
| 170 |
+
def final_response_formatter(self, state: AgentState):
|
| 171 |
+
docs = state["documents"]
|
| 172 |
+
final_response = ""
|
| 173 |
+
for doc in docs:
|
| 174 |
+
final_response += doc.metadata["publication_name"] + ": " + doc.metadata["chapter_name"] + "\n" + doc.metadata["highlighted_content"] + "\n\n"
|
| 175 |
+
|
| 176 |
+
return {"final_response": final_response}
|
| 177 |
+
|
| 178 |
+
|
| 179 |
+
class PassageFinder:
|
| 180 |
+
def __init__(
|
| 181 |
+
self,
|
| 182 |
+
model: str = 'gpt-4o-mini',
|
| 183 |
+
temperature: float = 0.5,
|
| 184 |
+
):
|
| 185 |
+
|
| 186 |
+
self.llm = ChatOpenAI(model=model, temperature=temperature)
|
| 187 |
+
self.tools = ToolManager().get_tools()
|
| 188 |
+
self.llm_with_tools = self.llm.bind_tools(self.tools)
|
| 189 |
+
self.quote_finder = QuoteFinder()
|
| 190 |
+
|
| 191 |
+
# Build the graph
|
| 192 |
+
self.graph = self.build_graph()
|
| 193 |
+
|
| 194 |
+
|
| 195 |
+
def get_configurable(self):
|
| 196 |
+
# This thread id is used to keep track of the chatbot's conversation
|
| 197 |
+
self.thread_id = str(uuid.uuid4())
|
| 198 |
+
return {"configurable": {"thread_id": self.thread_id}}
|
| 199 |
+
|
| 200 |
+
|
| 201 |
+
def chatbot(self, state: AgentState):
|
| 202 |
+
messages = state["messages"]
|
| 203 |
+
original_query = messages[0].content
|
| 204 |
+
return {"messages": [self.llm_with_tools.invoke(messages)], "query": original_query}
|
| 205 |
+
|
| 206 |
+
def build_graph(self):
|
| 207 |
+
# Add chatbot state
|
| 208 |
+
graph_builder = StateGraph(AgentState)
|
| 209 |
+
|
| 210 |
+
# Add nodes
|
| 211 |
+
tool_node = BasicToolNode(tools=self.tools)
|
| 212 |
+
# tool_node = ToolNode(self.tools)
|
| 213 |
+
graph_builder.add_node("tools", tool_node)
|
| 214 |
+
graph_builder.add_node("chatbot", self.chatbot)
|
| 215 |
+
graph_builder.add_node("quote_finder", self.quote_finder.find_quotes_per_document)
|
| 216 |
+
graph_builder.add_node("quote_highlighter", self.quote_finder.highlight_quotes_in_document)
|
| 217 |
+
graph_builder.add_node("final_response_formatter", self.quote_finder.final_response_formatter)
|
| 218 |
+
|
| 219 |
+
# Add a conditional edge wherein the chatbot can decide whether or not to go to the tools
|
| 220 |
+
graph_builder.add_conditional_edges(
|
| 221 |
+
"chatbot",
|
| 222 |
+
tools_condition,
|
| 223 |
+
)
|
| 224 |
+
|
| 225 |
+
# Add fixed edges
|
| 226 |
+
graph_builder.add_edge(START, "chatbot")
|
| 227 |
+
graph_builder.add_edge("tools", "quote_finder")
|
| 228 |
+
graph_builder.add_edge("quote_finder", "quote_highlighter")
|
| 229 |
+
graph_builder.add_edge("quote_highlighter", "final_response_formatter")
|
| 230 |
+
graph_builder.add_edge("final_response_formatter", END)
|
| 231 |
+
|
| 232 |
+
# Instantiate the memory saver
|
| 233 |
+
memory = MemorySaver()
|
| 234 |
+
|
| 235 |
+
# Compile the graph
|
| 236 |
+
return graph_builder.compile(checkpointer=memory)
|
src/utils/qdrant_manager.py
CHANGED
|
@@ -7,9 +7,10 @@ from langchain.schema import Document
|
|
| 7 |
from langchain_openai import OpenAIEmbeddings
|
| 8 |
from qdrant_client import QdrantClient
|
| 9 |
from qdrant_client.models import Distance, VectorParams
|
| 10 |
-
|
| 11 |
from config import settings
|
| 12 |
|
|
|
|
| 13 |
# Qdrant Collections Params
|
| 14 |
|
| 15 |
openai_embeddings = OpenAIEmbeddings(model=settings.EMBEDDINGS_MODEL_NAME)
|
|
|
|
| 7 |
from langchain_openai import OpenAIEmbeddings
|
| 8 |
from qdrant_client import QdrantClient
|
| 9 |
from qdrant_client.models import Distance, VectorParams
|
| 10 |
+
from dotenv import load_dotenv
|
| 11 |
from config import settings
|
| 12 |
|
| 13 |
+
load_dotenv()
|
| 14 |
# Qdrant Collections Params
|
| 15 |
|
| 16 |
openai_embeddings = OpenAIEmbeddings(model=settings.EMBEDDINGS_MODEL_NAME)
|