File size: 69,540 Bytes
03375c9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 |
import os
import pickle
import streamlit as st
import json
from pathlib import Path
from typing import Annotated, List, TypedDict, Dict, Any, Literal, Optional, NotRequired
import operator
import numpy as np
from scipy.spatial.distance import cosine
from dotenv import load_dotenv
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langchain_community.tools.arxiv.tool import ArxivQueryRun
from langchain.schema.output_parser import StrOutputParser
from langchain_core.documents import Document
from langchain_core.vectorstores import VectorStore
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.retrievers import BM25Retriever
from langchain.retrievers import EnsembleRetriever
from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
from langchain_cohere import CohereRerank
from langgraph.graph import StateGraph, START, END
from langgraph.prebuilt import ToolNode
from pydantic import BaseModel, Field
import asyncio
import requests
from tavily import TavilyClient, AsyncTavilyClient
from langchain_community.retrievers import ArxivRetriever
from enum import Enum
from dataclasses import dataclass, fields
from langchain_core.runnables import RunnableConfig
from langchain.chat_models import init_chat_model
from langgraph.constants import Send
from langgraph.types import interrupt, Command
from IPython.display import Markdown, display
import uuid
# Debug function to print directory information at startup
def debug_startup_info():
"""Print debug information at startup to help identify file locations"""
print("=" * 50)
print("DEBUG STARTUP INFO")
print("=" * 50)
# Print current working directory
print(f"Current working directory: {os.getcwd()}")
# Check for the data directory
print("\nChecking for data directory:")
if os.path.exists("data"):
print("Found 'data' directory in current directory")
print(f"Contents: {os.listdir('data')}")
if os.path.exists("data/processed_data"):
print(f"Contents of data/processed_data: {os.listdir('data/processed_data')}")
# Check common paths that might exist in Hugging Face Spaces
common_paths = [
"/data",
"/repository",
"/app",
"/app/data",
"/repository/data",
"/app/repository",
"AB_AI_RAG_Agent/data"
]
print("\nChecking common paths:")
for path in common_paths:
if os.path.exists(path):
print(f"Found path: {path}")
print(f"Contents: {os.listdir(path)}")
# Check for processed_data subdirectory
processed_path = os.path.join(path, "processed_data")
if os.path.exists(processed_path):
print(f"Found processed_data at: {processed_path}")
print(f"Contents: {os.listdir(processed_path)}")
print("=" * 50)
# Run debug info at startup
debug_startup_info()
# Enable debugging for file paths
import os
DEBUG_FILE_PATHS = True
def debug_paths():
if DEBUG_FILE_PATHS:
print("Current working directory:", os.getcwd())
print("Files in /data:", os.listdir("/data") if os.path.exists("/data") else "Not found")
print("Files in /data/processed_data:", os.listdir("/data/processed_data") if os.path.exists("/data/processed_data") else "Not found")
for path in ["/repository", "/app", "/app/data"]:
if os.path.exists(path):
print(f"Files in {path}:", os.listdir(path))
# Load environment variables
load_dotenv()
# Check for required API keys
required_keys = ["COHERE_API_KEY", "ANTHROPIC_API_KEY", "TAVILY_API_KEY"]
missing_keys = [key for key in required_keys if not os.environ.get(key)]
if missing_keys:
st.error(f"Missing required API keys: {', '.join(missing_keys)}. Please set them as environment variables.")
st.stop()
# Custom vector store implementation
class CustomVectorStore(VectorStore):
def __init__(self, embedded_docs, embedding_model):
self.embedded_docs = embedded_docs
self.embedding_model = embedding_model
def similarity_search_with_score(self, query, k=5):
# Get the query embedding
query_embedding = self.embedding_model.embed_query(query)
# Calculate similarity scores
results = []
for doc in self.embedded_docs:
# Calculate cosine similarity (1 - cosine distance)
similarity = 1 - cosine(query_embedding, doc["embedding"])
results.append((doc, similarity))
# Sort by similarity score (highest first)
results.sort(key=lambda x: x[1], reverse=True)
# Convert to Document objects and return top k
documents_with_scores = []
for doc, score in results[:k]:
document = Document(
page_content=doc["text"],
metadata=doc["metadata"]
)
documents_with_scores.append((document, score))
return documents_with_scores
def similarity_search(self, query, k=5):
docs_with_scores = self.similarity_search_with_score(query, k)
return [doc for doc, _ in docs_with_scores]
@classmethod
def from_texts(cls, texts, embedding, metadatas=None, **kwargs):
"""Implement required abstract method from VectorStore base class."""
# Create embeddings for the texts
embeddings = embedding.embed_documents(texts)
# Create embedded docs format
embedded_docs = []
for i, (text, embedding_vector) in enumerate(zip(texts, embeddings)):
metadata = metadatas[i] if metadatas else {}
embedded_docs.append({
"text": text,
"embedding": embedding_vector,
"metadata": metadata
})
# Return an instance with the embedded docs
return cls(embedded_docs, embedding)
def find_processed_data():
"""Find the processed_data directory path"""
possible_paths = [
"data/processed_data",
"../data/processed_data",
"/data/processed_data",
"/app/data/processed_data",
"./data/processed_data",
"/repository/data/processed_data",
"AB_AI_RAG_Agent/data/processed_data"
]
for path in possible_paths:
if os.path.exists(path):
required_files = ["chunks.pkl", "bm25_retriever.pkl", "embedding_info.json", "embedded_docs.pkl"]
if all(os.path.exists(os.path.join(path, f)) for f in required_files):
print(f"Found processed_data at: {path}")
return path
raise FileNotFoundError("Could not find processed_data directory with required files")
@st.cache_resource
def initialize_vectorstore():
"""Initialize the hybrid retriever system with Cohere reranking"""
try:
# Find processed data directory
processed_data_path = find_processed_data()
# Load documents
with open(os.path.join(processed_data_path, "chunks.pkl"), "rb") as f:
documents = pickle.load(f)
# Load BM25 retriever
with open(os.path.join(processed_data_path, "bm25_retriever.pkl"), "rb") as f:
bm25_retriever = pickle.load(f)
bm25_retriever.k = 5
# Load embedding model info
with open(os.path.join(processed_data_path, "embedding_info.json"), "r") as f:
embedding_info = json.load(f)
# Load pre-computed embedded docs
with open(os.path.join(processed_data_path, "embedded_docs.pkl"), "rb") as f:
embedded_docs = pickle.load(f)
# Initialize embedding model
embedding_model = HuggingFaceEmbeddings(
model_name=embedding_info["model_name"]
)
# Create custom vectorstore using pre-computed embeddings
vectorstore = CustomVectorStore(embedded_docs, embedding_model)
qdrant_retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
# Create hybrid retriever
hybrid_retriever = EnsembleRetriever(
retrievers=[qdrant_retriever, bm25_retriever],
weights=[0.5, 0.5],
)
# Create Cohere reranker
cohere_rerank = CohereRerank(
model="rerank-english-v3.0",
top_n=5,
)
reranker = ContextualCompressionRetriever(
base_compressor=cohere_rerank,
base_retriever=hybrid_retriever
)
print("Successfully initialized retriever system")
return reranker, documents
except Exception as e:
st.error(f"Error initializing retrievers: {str(e)}")
st.stop()
# Define prompts
# Prompt to generate search queries to help with planning the report
report_planner_query_writer_instructions="""You are performing research for a report.
<Report topic>
{topic}
</Report topic>
<Report organization>
{report_organization}
</Report organization>
<Task>
Your goal is to generate {number_of_queries} web search queries that will help gather information for planning the report sections.
The queries should:
1. Be related to the Report topic
2. Help satisfy the requirements specified in the report organization
Make the queries specific enough to find high-quality, relevant sources while covering the breadth needed for the report structure.
</Task>
"""
# Prompt to generate the report plan
report_planner_instructions="""I want a plan for a report that is concise and focused.
<Report topic>
The topic of the report is:
{topic}
</Report topic>
<Report organization>
The report should follow this organization:
{report_organization}
</Report organization>
<Context>
Here is context to use to plan the sections of the report:
{context}
</Context>
<Task>
Generate a list of sections for the report. Your plan should be tight and focused with NO overlapping sections or unnecessary filler.
For example, a good report structure might look like:
1/ intro
2/ overview of topic A
3/ overview of topic B
4/ comparison between A and B
5/ conclusion
Each section should have the fields:
- Name - Name for this section of the report.
- Description - Brief overview of the main topics covered in this section.
- Research - Whether to perform web research for this section of the report.
- Content - The content of the section, which you will leave blank for now.
Integration guidelines:
- Include examples and implementation details within main topic sections, not as separate sections
- Ensure each section has a distinct purpose with no content overlap
- Combine related concepts rather than separating them
Before submitting, review your structure to ensure it has no redundant sections and follows a logical flow.
</Task>
"""
# Query writer instructions
query_writer_instructions="""You are an expert technical writer crafting targeted web search queries that will gather comprehensive information for writing a technical report section.
<Report topic>
{topic}
</Report topic>
<Section topic>
{section_topic}
</Section topic>
<Task>
Your goal is to generate {number_of_queries} search queries that will help gather comprehensive information above the section topic.
The queries should:
1. Be related to the topic
2. Examine different aspects of the topic
Make the queries specific enough to find high-quality, relevant sources.
</Task>
"""
# Section writer instructions
section_writer_instructions = """You are an expert technical writer crafting one section of a technical report.
<Report topic>
{topic}
</Report topic>
<Section name>
{section_name}
</Section name>
<Section topic>
{section_topic}
</Section topic>
<Existing section content (if populated)>
{section_content}
</Existing section content>
<Source material>
{context}
</Source material>
<Guidelines for writing>
1. If the existing section content is not populated, write a new section from scratch.
2. If the existing section content is populated, write a new section that synthesizes the existing section content with the Source material. If there is a discrepancy between the existing section content and the Source material, use the existing section content as the primary source. The purpose of the Source material is to provide additional information and context to help fill the gaps in the existing section content.
</Guidelines for writing>
<Length and style>
- Strict 150-200 word limit
- No marketing language
- Technical focus
- Write in simple, clear language
- Start with your most important insight in **bold**
- Use short paragraphs (2-3 sentences max)
- Use ## for section title (Markdown format)
- Only use ONE structural element IF it helps clarify your point:
* Either a focused table comparing 2-3 key items (using Markdown table syntax)
* Or a short list (3-5 items) using proper Markdown list syntax:
- Use `*` or `-` for unordered lists
- Use `1.` for ordered lists
- Ensure proper indentation and spacing
</Length and style>
<Quality checks>
- Exactly 150-200 words (excluding title and sources)
- Careful use of only ONE structural element (table or list) and only if it helps clarify your point
- One specific example / case study
- Starts with bold insight
- No preamble prior to creating the section content
- If there is a discrepancy between the existing section content and the Source material, use the existing section content as the primary source. The purpose of the Source material is to provide additional information and context to help fill the gaps in the existing section content.
</Quality checks>
"""
# Instructions for section grading
section_grader_instructions = """Review a report section relative to the specified topic:
<Report topic>
{topic}
</Report topic>
<section topic>
{section_topic}
</section topic>
<section content>
{section}
</section content>
<search type>
{current_iteration}
</search type>
<task>
Evaluate whether the section content adequately addresses the section topic.
If the section content does not adequately address the section topic, generate {number_of_follow_up_queries} follow-up search queries to gather missing information. Note that if search type is 1, your follow-up search queries will be used to search Arxiv for academic papers. If search type is 2 or more, your follow-up search queries will be used to search Tavily for general web search.
</task>
<format>
grade: Literal["pass","fail"] = Field(
description="Evaluation result indicating whether the response meets requirements ('pass') or needs revision ('fail')."
)
follow_up_queries: List[SearchQuery] = Field(
description="List of follow-up search queries.",
)
</format>
"""
final_section_writer_instructions="""You are an expert technical writer crafting a section that synthesizes information from the rest of the report.
<Report topic>
{topic}
</Report topic>
<Section name>
{section_name}
</Section name>
<Section topic>
{section_topic}
</Section topic>
<Available report content>
{context}
</Available report content>
<Task>
1. Section-Specific Approach:
For Introduction:
- Use # for report title (Markdown format)
- 50-100 word limit
- Write in simple and clear language
- Focus on the core motivation for the report in 1-2 paragraphs
- Use a clear narrative arc to introduce the report
- Include NO structural elements (no lists or tables)
- No sources section needed
For Conclusion/Summary:
- Use ## for section title (Markdown format)
- 100-150 word limit
- For comparative reports:
* Must include a focused comparison table using Markdown table syntax
* Table should distill insights from the report
* Keep table entries clear and concise
- For non-comparative reports:
* Only use ONE structural element IF it helps distill the points made in the report:
* Either a focused table comparing items present in the report (using Markdown table syntax)
* Or a short list using proper Markdown list syntax:
- Use `*` or `-` for unordered lists
- Use `1.` for ordered lists
- Ensure proper indentation and spacing
- End with specific next steps or implications
- No sources section needed
3. Writing Approach:
- Use concrete details over general statements
- Make every word count
- Focus on your single most important point
</Task>
<Quality Checks>
- For introduction: 50-100 word limit, # for report title, no structural elements, no sources section
- For conclusion: 100-150 word limit, ## for section title, only ONE structural element at most, no sources section
- Markdown format
- Do not include word count or any preamble in your response
</Quality Checks>"""
initial_AB_topic_check_instructions="""You are checking if a given topic is related to A/B testing (even vaguely e.g. statistics, A/B testing, experimentation, etc.).
<Topic>
{topic}
</Topic>
<Task>
Check if the topic is related to A/B testing (even vaguely, e.g. statistics, A/B testing, experimentation, etc.).
If the topic is related to A/B testing (even vaguely), return 'true'.
If the topic is not related to A/B testing, return 'false'.
</Task>
"""
class Section(BaseModel):
name: str = Field(
description="Name for this section of the report.",
)
description: str = Field(
description="Brief overview of the main topics and concepts to be covered in this section.",
)
research: bool = Field(
description="Whether to perform web research for this section of the report."
)
content: str = Field(
description="The content of the section."
)
sources: str = Field(
default="",
description="All sources used for this section"
)
class Sections(BaseModel):
sections: List[Section] = Field(
description="Sections of the report.",
)
class SearchQuery(BaseModel):
search_query: str = Field(None, description="Query for web search.")
class Queries(BaseModel):
queries: List[SearchQuery] = Field(
description="List of search queries.",
)
class Feedback(BaseModel):
grade: Literal["pass","fail"] = Field(
description="Evaluation result indicating whether the response meets requirements ('pass') or needs revision ('fail')."
)
follow_up_queries: List[SearchQuery] = Field(
description="List of follow-up search queries.",
)
class ReportStateInput(TypedDict):
topic: str # Report topic
class ReportStateOutput(TypedDict):
final_report: str # Final report
class ReportState(TypedDict):
topic: str # Report topic
sections: list[Section] # List of report sections
completed_sections: Annotated[list, operator.add] # Send() API key
report_sections_from_research: str # String of any completed sections from research to write final sections
final_report: str # Final report
ab_testing_check: NotRequired[bool] # Whether the topic is related to A/B testing
class SectionState(TypedDict):
topic: str # Report topic
section: Section # Report section
search_iterations: int # Number of search iterations done
search_queries: list[SearchQuery] # List of search queries
source_str: str # String of formatted source content from current iteration web search (for writer)
source_str_all: str # All accumulated sources (for user display)
report_sections_from_research: str # String of any completed sections from research to write final sections
completed_sections: list[Section] # Final key we duplicate in outer state for Send() API
class SectionOutputState(TypedDict):
completed_sections: list[Section] # Final key we duplicate in outer state for Send() API
# Initialize the AB Testing report system
@st.cache_resource
def initialize_report_system(_reranker):
"""Initialize the AB Testing report system"""
# Create a retriever reranker
reranker = _reranker
# Utilities and helpers
tavily_client = TavilyClient()
tavily_async_client = AsyncTavilyClient()
def get_config_value(value):
"""
Helper function to handle both string and enum cases of configuration values
"""
return value if isinstance(value, str) else value.value
# Helper function to get search parameters based on the search API and config
def get_search_params(search_api: str, search_api_config: Optional[Dict[str, Any]]) -> Dict[str, Any]:
"""
Filters the search_api_config dictionary to include only parameters accepted by the specified search API.
Args:
search_api (str): The search API identifier (e.g., "tavily").
search_api_config (Optional[Dict[str, Any]]): The configuration dictionary for the search API.
Returns:
Dict[str, Any]: A dictionary of parameters to pass to the search function.
"""
# Define accepted parameters for each search API
SEARCH_API_PARAMS = {
"rag": [], # RAG currently accepts no additional parameters
"arxiv": ["load_max_docs", "get_full_documents", "load_all_available_meta"],
"tavily": [] # Tavily currently accepts no additional parameters
}
# Get the list of accepted parameters for the given search API
accepted_params = SEARCH_API_PARAMS.get(search_api, [])
# If no config provided, return an empty dict
if not search_api_config:
return {}
# Filter the config to only include accepted parameters
return {k: v for k, v in search_api_config.items() if k in accepted_params}
def get_next_search_type(search_iterations):
if search_iterations == 0:
return "RAG search (internal A/B testing knowledge base)"
elif search_iterations == 1:
return "ArXiv web search (search academic papers on arXiv)"
else:
return "tavily web search (general web sources)"
def deduplicate_and_format_sources(search_response, max_tokens_per_source, include_raw_content=True, search_iterations=None, return_has_sources=False):
"""
Takes a list of search responses and formats them into a readable string.
Limits the raw_content to approximately max_tokens_per_source.
Args:
search_responses: List of search response dicts, each containing:
- query: str
- results: List of dicts with fields:
- title: str
- url: str
- content: str
- raw_content: str|None
- score: float
max_tokens_per_source: int
include_raw_content: bool
search_iterations: int, optional
If 0, deduplicate by title (for RAG results) and show only title
Otherwise, deduplicate by URL (for web/arxiv results) and show title + URL
return_has_sources: bool, optional
If True, returns (formatted_string, has_sources_bool)
If False, returns just formatted_string
Returns:
str OR tuple:
- If return_has_sources=False: formatted string
- If return_has_sources=True: (formatted_string, has_sources_bool)
"""
# Collect all results
sources_list = []
for response in search_response:
sources_list.extend(response['results'])
if not sources_list:
empty_result = ""
return (empty_result, False) if return_has_sources else empty_result
# Deduplicate by title if search_iterations == 0 (RAG), otherwise by URL
if search_iterations == 0:
unique_sources = {source['title']: source for source in sources_list}
else:
unique_sources = {source['url']: source for source in sources_list}
# Check if we have unique sources after deduplication
has_unique_sources = bool(unique_sources)
if not unique_sources:
empty_result = ""
return (empty_result, False) if return_has_sources else empty_result
# Format output
formatted_text = ""
for i, source in enumerate(unique_sources.values(), 1):
formatted_text += f"#### {source['title']}\n\n"
# Only show URL if not RAG results (search_iterations != 0)
if search_iterations != 0:
formatted_text += f"#### URL: {source['url']}\n\n"
if include_raw_content:
# Using rough estimate of 4 characters per token
char_limit = max_tokens_per_source * 4
# Handle None raw_content
raw_content = source.get('raw_content', '')
if raw_content is None:
raw_content = ''
print(f"Warning: No raw_content found for source {source['url']}")
if len(raw_content) > char_limit:
raw_content = raw_content[:char_limit] + "... [truncated]"
formatted_text += f"#### Full source content limited to {max_tokens_per_source} tokens \n\n"
final_result = formatted_text.strip()
return (final_result, has_unique_sources) if return_has_sources else final_result
def format_sections(sections: list[Section]) -> str:
""" Format a list of sections into a string """
formatted_str = ""
for idx, section in enumerate(sections, 1):
formatted_str += f"""
{'='*60} # divider line of 60 equal signs
Section {idx}: {section.name}
{'='*60} # divider line of 60 equal signs
Description:
{section.description}
Requires Research:
{section.research}
Content:
{section.content if section.content else '[Not yet written]'}
"""
return formatted_str
async def tavily_search_async(search_queries):
"""
Performs concurrent web searches using the Tavily API.
Args:
search_queries (List[SearchQuery]): List of search queries to process
Returns:
List[dict]: List of search responses from Tavily API, one per query. Each response has format:
{
'query': str, # The original search query
'follow_up_questions': None,
'answer': None,
'images': list,
'results': [ # List of search results
{
'title': str, # Title of the webpage
'url': str, # URL of the result
'content': str, # Summary/snippet of content
'score': float, # Relevance score
'raw_content': str|None # Full page content if available
},
...
]
}
"""
search_tasks = []
for query in search_queries:
search_tasks.append(
tavily_async_client.search(
query,
max_results=5,
include_raw_content=True,
topic="general"
)
)
# Execute all searches concurrently
search_docs = await asyncio.gather(*search_tasks)
return search_docs
async def arxiv_search_async(search_queries, load_max_docs=5, get_full_documents=False, load_all_available_meta=True):
"""
Performs concurrent searches on arXiv using the ArxivRetriever.
Args:
search_queries (List[str]): List of search queries or article IDs
load_max_docs (int, optional): Maximum number of documents to return per query. Default is 5.
get_full_documents (bool, optional): Whether to fetch full text of documents. Default is True.
load_all_available_meta (bool, optional): Whether to load all available metadata. Default is True.
Returns:
List[dict]: List of search responses from arXiv, one per query. Each response has format:
{
'query': str, # The original search query
'follow_up_questions': None,
'answer': None,
'images': [],
'results': [ # List of search results
{
'title': str, # Title of the paper
'url': str, # URL (Entry ID) of the paper
'content': str, # Formatted summary with metadata
'score': float, # Relevance score (approximated)
'raw_content': str|None # Full paper content if available
},
...
]
}
"""
# Debug: Log the start of ArXiv search
print(f"[DEBUG] Starting ArXiv search with {len(search_queries)} queries: {[str(q) for q in search_queries]}")
async def process_single_query(query):
print(f"[DEBUG] Processing ArXiv query: {query}")
try:
# Debug: Log retriever creation
print(f"[DEBUG] Creating ArxivRetriever with params: load_max_docs={load_max_docs}, get_full_documents={get_full_documents}, load_all_available_meta={load_all_available_meta}")
# Create retriever for each query
retriever = ArxivRetriever(
load_max_docs=load_max_docs,
get_full_documents=get_full_documents,
load_all_available_meta=load_all_available_meta
)
print(f"[DEBUG] ArxivRetriever created successfully")
# Run the synchronous retriever in a thread pool
loop = asyncio.get_event_loop()
print(f"[DEBUG] About to invoke retriever for query: {query}")
docs = await loop.run_in_executor(None, lambda: retriever.invoke(query))
print(f"[DEBUG] ArXiv query '{query}' returned {len(docs)} documents")
# Debug: Log document details
if docs:
print(f"[DEBUG] First document metadata keys: {list(docs[0].metadata.keys())}")
print(f"[DEBUG] First document has page_content: {bool(docs[0].page_content)}")
else:
print(f"[DEBUG] no documents returned for query: {query}")
results = []
# Assign decreasing scores based on the order
base_score = 1.0
score_decrement = 1.0 / (len(docs) + 1) if docs else 0
for i, doc in enumerate(docs):
# Normalize metadata keys to lowercase with underscores
normalized_metadata = {k.lower().replace(' ', '_'): v for k, v in doc.metadata.items()}
print(f"[DEBUG] Processing doc {i+1}: {normalized_metadata.get('title', 'No title')}")
# Extract metadata using consistent lowercase keys
url = normalized_metadata.get('entry_id', '')
title = normalized_metadata.get('title', '')
authors = normalized_metadata.get('authors', '')
published = normalized_metadata.get('published')
# Handle summary with fallback to page_content
summary = normalized_metadata.get('summary', '')
if not summary and doc.page_content:
summary = doc.page_content.strip()
# Build content with guaranteed fields
content_parts = []
if summary:
content_parts.append(f"Summary: {summary}")
if authors:
content_parts.append(f"Authors: {authors}")
# Add publication information
if published:
published_str = published.isoformat() if hasattr(published, 'isoformat') else str(published)
content_parts.append(f"Published: {published_str}")
# Add additional metadata if available
primary_category = normalized_metadata.get('primary_category', '')
if primary_category:
content_parts.append(f"Primary Category: {primary_category}")
categories = normalized_metadata.get('categories', [])
if categories:
if isinstance(categories, list):
content_parts.append(f"Categories: {', '.join(categories)}")
else:
content_parts.append(f"Categories: {categories}")
comment = normalized_metadata.get('comment', '')
if comment:
content_parts.append(f"Comment: {comment}")
journal_ref = normalized_metadata.get('journal_ref', '')
if journal_ref:
content_parts.append(f"Journal Reference: {journal_ref}")
doi = normalized_metadata.get('doi', '')
if doi:
content_parts.append(f"DOI: {doi}")
# Get PDF link if available in the links
links = normalized_metadata.get('links', [])
if links:
for link in links:
if 'pdf' in str(link).lower():
content_parts.append(f"PDF: {link}")
break
# Join all content parts with newlines
content = "\n".join(content_parts)
result = {
'title': title,
'url': url,
'content': content,
'score': base_score - (i * score_decrement),
'raw_content': doc.page_content if get_full_documents else None
}
results.append(result)
print(f"[DEBUG] Query '{query}' processed successfully, returning {len(results)} results")
return {
'query': query,
'follow_up_questions': None,
'answer': None,
'images': [],
'results': results
}
except Exception as e:
# Handle exceptions gracefully
print(f"[DEBUG ERROR] Error processing arXiv query '{query}': {str(e)}")
print(f"[DEBUG ERROR] Exception type: {type(e).__name__}")
import traceback
print(f"[DEBUG ERROR] Full traceback: {traceback.format_exc()}")
return {
'query': query,
'follow_up_questions': None,
'answer': None,
'images': [],
'results': [],
'error': str(e)
}
# Process queries sequentially with delay to respect arXiv rate limit (1 request per 3 seconds)
search_docs = []
for i, query in enumerate(search_queries):
try:
# Add delay between requests (3 seconds per ArXiv's rate limit)
if i > 0: # Don't delay the first request
print(f"[DEBUG] Adding 4-second delay before processing query {i+1}")
await asyncio.sleep(4.0)
result = await process_single_query(query)
search_docs.append(result)
print(f"[DEBUG] Completed processing query {i+1}/{len(search_queries)}")
except Exception as e:
# Handle exceptions gracefully
print(f"[DEBUG ERROR] Error processing arXiv query '{query}': {str(e)}")
search_docs.append({
'query': query,
'follow_up_questions': None,
'answer': None,
'images': [],
'results': [],
'error': str(e)
})
# Add additional delay if we hit a rate limit error
if "429" in str(e) or "Too Many Requests" in str(e):
print("[DEBUG] ArXiv rate limit exceeded. Adding additional delay...")
await asyncio.sleep(7.0) # Add a longer delay if we hit a rate limit
print(f"[DEBUG] ArXiv search completed. Total results across all queries: {sum(len(doc.get('results', [])) for doc in search_docs)}")
return search_docs
async def rag_search_async(search_queries):
"""
Performs concurrent RAG searches of our thorough A/B testing collection using the reranker.
Args:
search_queries (List[SearchQuery]): List of search queries to process
Returns:
List[dict]: List of search responses from RAG, one per query. Each response has format:
{
'query': str, # The original search query
'follow_up_questions': None,
'answer': None,
'images': list,
'results': [ # List of search results
{
'title': str, # Title in format "Kohavi: {title}, Section: {section}"
'url': str, # None for RAG results
'content': str, # None for RAG results
'score': float, # None for RAG results
'raw_content': str|None # Chunk's page_content
},
...
]
}
"""
async def single_rag_search(query):
# Retrieve documents. It's a best practice to return contexts in ascending order
docs_descending = reranker.get_relevant_documents(query)
docs = docs_descending[::-1]
# Format each document as a result
results = []
for doc in docs:
source_path = doc.metadata.get("source", "")
filename = source_path.split("/")[-1] if "/" in source_path else source_path
# Remove .pdf extension if present
if filename.endswith('.pdf'):
filename = filename[:-4]
section = doc.metadata.get("section_title", "unknown")
title = f"Kohavi: {filename}, Section: {section}"
results.append({
'title': title,
'url': None,
'content': None,
'score': None,
'raw_content': doc.page_content
})
return {
'query': query,
'follow_up_questions': None,
'answer': None,
'images': [],
'results': results
}
# Create tasks for concurrent execution
search_tasks = [single_rag_search(query) for query in search_queries]
# Execute all searches concurrently
search_responses = await asyncio.gather(*search_tasks)
return search_responses
DEFAULT_REPORT_STRUCTURE = """Use this structure to create a report on the user-provided topic:
1. Introduction (no research needed - REQUIRED)
- Brief overview of the topic area
- Set research=false for this section
2. Main Body Sections:
- Each section should focus on a sub-topic of the user-provided topic
- These sections require research
3. Conclusion (no research needed - REQUIRED)
- Aim for 1 structural element (either a list of table) that distills the main body sections
- Provide a concise summary of the report
- Set research=false for this section
IMPORTANT: Always include at least one Introduction section and one Conclusion section with research=false."""
# Enum classes in Python create sets of named constants with unique values
class SearchAPI(Enum):
TAVILY = "tavily"
ARXIV = "arxiv"
RAG = "rag"
class PlannerProvider(Enum):
ANTHROPIC = "anthropic"
OPENAI = "openai"
class WriterProvider(Enum):
ANTHROPIC = "anthropic"
OPENAI = "openai"
# Dataclasses automatically generate boilerplate code for classes that primarily store data
# Dataclasses automatically create __init__, __repr__, __eq__ methods
@dataclass(kw_only=True)
class Configuration:
"""The configurable fields for the chatbot."""
report_structure: str = DEFAULT_REPORT_STRUCTURE # Defaults to the default report structure
### SET THESE NUMBERS HIGHER FOR A LARGER / MORE DETAILED REPORT - YOU MAY RUN INTO RATE LIMITING ISSUES
number_of_queries: int = 1 # Number of search queries to generate per iteration
max_search_depth: int = 3 # Maximum number of reflection + search iterations
### UNCOMMENT BELOW IF RUN INTO RATE LIMIT ISSUES
# planner_provider: PlannerProvider = PlannerProvider.OPENAI # Defaults to OpenAI as provider
# planner_model: str = "o3-mini" # Defaults to o3-mini, add "-thinking" to enable thinking mode
# writer_provider: WriterProvider = WriterProvider.OPENAI # Defaults to OpenAI as provider
#writer_model: str = "o3-mini" # Defaults to o3-mini
### COMMENT BELOW IF RUN INTO RATE LIMIT ISSUES
planner_provider: PlannerProvider = PlannerProvider.ANTHROPIC # Defaults to Anthropic as provider
planner_model: str = "claude-opus-4-20250514" # Defaults to claude-opus-4-20250514
writer_provider: WriterProvider = WriterProvider.ANTHROPIC # Defaults to Anthropic as provider
writer_model: str = "claude-sonnet-4-20250514" # Defaults to claude-sonnet-4-20250514
search_api: SearchAPI = SearchAPI.TAVILY # Default to TAVILY
search_api_config: Optional[Dict[str, Any]] = None
@classmethod
def from_runnable_config(
cls, config: Optional[RunnableConfig] = None
) -> "Configuration":
"""Create a Configuration instance from a RunnableConfig."""
configurable = (
config["configurable"] if config and "configurable" in config else {}
)
values: dict[str, Any] = {
f.name: os.environ.get(f.name.upper(), configurable.get(f.name))
for f in fields(cls)
if f.init
}
return cls(**{k: v for k, v in values.items() if v})
# Nodes
async def generate_report_plan(state: ReportState, config: RunnableConfig):
""" Generate the report plan """
# Inputs
topic = state["topic"]
# Get configuration
configurable = Configuration.from_runnable_config(config)
report_structure = configurable.report_structure
number_of_queries = configurable.number_of_queries
# We want to use tavily as the search API for generating the report plan
search_api = "tavily"
# Convert JSON object to string if necessary
if isinstance(report_structure, dict):
report_structure = str(report_structure)
# Set writer model (model used for query writing and section writing)
writer_provider = get_config_value(configurable.writer_provider)
writer_model_name = get_config_value(configurable.writer_model)
writer_model = init_chat_model(model=writer_model_name, model_provider=writer_provider)
# Forces the model to generate valid JSON matching the Queries schema, which
# makes it easier to process the results systemically
structured_llm = writer_model.with_structured_output(Queries)
# Format system instructions
system_instructions_query = report_planner_query_writer_instructions.format(topic=topic, report_organization=report_structure, number_of_queries=number_of_queries)
# Generate queries
results = structured_llm.invoke([SystemMessage(content=system_instructions_query),
HumanMessage(content="Generate search queries that will help with planning the sections of the report.")])
# Web search
query_list = [query.search_query for query in results.queries]
search_api_config = configurable.search_api_config or {}
params_to_pass = get_search_params(search_api, search_api_config)
# Search the web with parameters
if search_api == "tavily":
search_results = await tavily_search_async(query_list, **params_to_pass)
source_str = deduplicate_and_format_sources(search_results, max_tokens_per_source=1500, include_raw_content=False)
elif search_api == "arxiv":
search_results = await arxiv_search_async(query_list, **params_to_pass)
source_str = deduplicate_and_format_sources(search_results, max_tokens_per_source=1500, include_raw_content=False)
else:
raise ValueError(f"Unsupported search API: {search_api}")
# Format system instructions
system_instructions_sections = report_planner_instructions.format(topic=topic, report_organization=report_structure, context=source_str)
# Set the planner
planner_provider = get_config_value(configurable.planner_provider)
planner_model = get_config_value(configurable.planner_model)
# Report planner instructions
planner_message = """Generate the sections of the report. Your response must include a 'sections' field containing a list of sections.
Each section must have: name, description, plan, research, and content fields."""
# Run the planner
planner_llm = init_chat_model(
model=planner_model,
model_provider=planner_provider,
max_tokens=32_000,
thinking={"type": "enabled", "budget_tokens": 24_000}
)
# Forces the model to generate valid JSON matching the Sections schema, which
# makes it easier to process the results systemically
structured_llm = planner_llm.with_structured_output(Sections)
report_sections = structured_llm.invoke([SystemMessage(content=system_instructions_sections),
HumanMessage(content=planner_message)])
# Get sections
sections = report_sections.sections
return Command(goto=[Send("build_section_with_web_research", {"topic": topic, "section": s, "search_iterations": 0}) for s in sections if s.research], update={"sections": sections})
def generate_queries(state: SectionState, config: RunnableConfig):
""" Generate search queries for a report section to query our A/B testing RAG collection """
# Get state
topic = state["topic"]
section = state["section"]
# Get configuration
configurable = Configuration.from_runnable_config(config)
number_of_queries = configurable.number_of_queries
# Generate queries
writer_provider = get_config_value(configurable.writer_provider)
writer_model_name = get_config_value(configurable.writer_model)
writer_model = init_chat_model(model=writer_model_name, model_provider=writer_provider)
structured_llm = writer_model.with_structured_output(Queries)
# Format system instructions
system_instructions = query_writer_instructions.format(topic=topic,
section_topic=section.description,
number_of_queries=number_of_queries)
# Generate queries
queries = structured_llm.invoke([SystemMessage(content=system_instructions),
HumanMessage(content="Generate search queries on the provided topic.")])
return {"search_queries": queries.queries}
async def search_rag_and_web(state: SectionState, config: RunnableConfig):
""" Search A/B testing RAG collection and web with dual source tracking """
# Get state
search_queries = state["search_queries"]
search_iterations = state["search_iterations"]
existing_source_str_all = state.get("source_str_all", "") # All previous sources
# Get configuration and choose search API based on iteration
configurable = Configuration.from_runnable_config(config)
if search_iterations == 0:
search_api = "rag"
elif search_iterations == 1:
search_api = "arxiv"
else:
search_api = "tavily"
# Execute search
query_list = [query.search_query for query in search_queries]
search_api_config = configurable.search_api_config or {}
params_to_pass = get_search_params(search_api, search_api_config)
if search_api == "rag":
search_results = await rag_search_async(query_list)
elif search_api == "arxiv":
search_results = await arxiv_search_async(query_list, **params_to_pass)
elif search_api == "tavily":
search_results = await tavily_search_async(query_list)
else:
raise ValueError(f"Unsupported search API: {search_api}")
# Format current iteration sources and check if there are any
# Use return_has_sources=True to get both the formatted string and the boolean
current_source_str, has_sources = deduplicate_and_format_sources(
search_results,
max_tokens_per_source=1500,
include_raw_content=True,
search_iterations=search_iterations,
return_has_sources=True
)
# Only add iteration header and sources if there are actually sources to display
if has_sources:
iteration_header = f"{'='*80}\n### SEARCH ITERATION {search_iterations + 1} - {search_api.upper()} RESULTS\n{'='*80}\n\n"
# Accumulate all sources for user display
if existing_source_str_all:
accumulated_source_str = existing_source_str_all + "\n\n" + iteration_header + current_source_str
else:
accumulated_source_str = iteration_header + current_source_str
else:
# No sources found, don't add header, keep existing sources
accumulated_source_str = existing_source_str_all
current_source_str = "" # No sources for writer
return {
"source_str": current_source_str, # Only current iteration for writer
"source_str_all": accumulated_source_str, # All sources for user display
"search_iterations": search_iterations + 1
}
def write_section(state: SectionState, config: RunnableConfig) -> Command[Literal[END, "search_rag_and_web"]]:
""" Write a section of the report """
# Get state
topic = state["topic"]
section = state["section"]
source_str = state["source_str"]
search_iterations = state["search_iterations"]
# Get configuration
configurable = Configuration.from_runnable_config(config)
# Get configuration
configurable = Configuration.from_runnable_config(config)
# Format system instructions
system_instructions = section_writer_instructions.format(topic=topic,
section_name=section.name,
section_topic=section.description,
context=source_str,
section_content=section.content)
# Generate section
writer_provider = get_config_value(configurable.writer_provider)
writer_model_name = get_config_value(configurable.writer_model)
writer_model = init_chat_model(model=writer_model_name, model_provider=writer_provider)
section_content = writer_model.invoke([SystemMessage(content=system_instructions),
HumanMessage(content="Generate a report section based on the existing section content (if any) and the provided sources.")])
# Write content to the section object
section.content = section_content.content
# Grade prompt
section_grader_message = """Grade the report and consider follow-up questions for missing information.
If the grade is 'pass', return empty strings for all follow-up queries.
If the grade is 'fail', provide specific search queries to gather missing information."""
section_grader_instructions_formatted = section_grader_instructions.format(topic=topic,
section_topic=section.description,
section=section.content,
number_of_follow_up_queries=configurable.number_of_queries,
current_iteration=search_iterations)
# Use planner model for reflection
planner_provider = get_config_value(configurable.planner_provider)
planner_model = get_config_value(configurable.planner_model)
reflection_llm = init_chat_model(
model=planner_model,
model_provider=planner_provider,
max_tokens=32_000,
thinking={"type": "enabled", "budget_tokens": 24_000}
)
reflection_model = reflection_llm.with_structured_output(Feedback)
feedback = reflection_model.invoke([SystemMessage(content=section_grader_instructions_formatted),
HumanMessage(content=section_grader_message)])
# If the section is passing or max depth reached
if feedback.grade == "pass" or state["search_iterations"] >= configurable.max_search_depth:
# Store sources in the section object
section.sources = state.get("source_str_all", "")
return Command(
update={
"completed_sections": [section]
},
goto=END
)
else:
return Command(
update={"search_queries": feedback.follow_up_queries, "section": section},
goto="search_rag_and_web"
)
def write_final_sections(state: SectionState, config: RunnableConfig):
""" Write final sections of the report, which do not require RAG or web search and use the completed sections as context """
# Get configuration
configurable = Configuration.from_runnable_config(config)
# Get state
topic = state["topic"]
section = state["section"]
completed_report_sections = state["report_sections_from_research"]
# Format system instructions
system_instructions = final_section_writer_instructions.format(topic=topic, section_name=section.name, section_topic=section.description, context=completed_report_sections)
# Generate section
writer_provider = get_config_value(configurable.writer_provider)
writer_model_name = get_config_value(configurable.writer_model)
writer_model = init_chat_model(model=writer_model_name, model_provider=writer_provider)
section_content = writer_model.invoke([SystemMessage(content=system_instructions),
HumanMessage(content="Generate a report section based on the provided sources.")])
# Write content to section
section.content = section_content.content
# Write the updated section to completed sections
return {"completed_sections": [section]}
def gather_completed_sections(state: ReportState):
""" Gather completed sections from research and format them as context for writing the final sections """
# Get original section order and completed sections
original_sections = state["sections"]
completed_sections = state["completed_sections"]
# Create mapping of completed sections by name
completed_by_name = {s.name: s for s in completed_sections}
# Sort completed sections by original report order
ordered_completed_sections = []
for original_section in original_sections:
if original_section.name in completed_by_name:
ordered_completed_sections.append(completed_by_name[original_section.name])
# Create sections without sources in correct order
sections_without_sources = []
for section in ordered_completed_sections:
temp_section = Section(
name=section.name,
description=section.description,
research=section.research,
content=section.content,
sources=""
)
sections_without_sources.append(temp_section)
# Format in original report order
completed_report_sections = format_sections(sections_without_sources)
return {"report_sections_from_research": completed_report_sections}
def initiate_final_section_writing(state: ReportState):
""" Write any final sections using the Send API to parallelize the process """
# Kick off section writing in parallel via Send() API for any sections that do not require research
return Command(goto=[Send("write_final_sections", {"topic": state["topic"], "section": s, "report_sections_from_research": state["report_sections_from_research"]}) for s in state["sections"] if not s.research ])
def compile_final_report(state: ReportState):
""" Compile the final report with section-grouped sources only for research sections """
# Get sections and sources
sections = state["sections"]
completed_sections = {s.name: s.content for s in state["completed_sections"]}
# Update sections with completed content while maintaining original order
for section in sections:
section.content = completed_sections[section.name]
# Compile main report
main_report = "\n\n".join([s.content for s in sections])
# Add sources section with organization by research sections only
research_sections_with_sources = [s for s in state["completed_sections"] if s.research and s.sources]
if research_sections_with_sources:
sources_section = "\n\n## Sources Used\n\n"
# Iterate through sections in original order and add sources if they exist
for section in sections:
if section.research:
# Find the completed section with sources
completed_section = next((s for s in state["completed_sections"] if s.name == section.name), None)
if completed_section and completed_section.sources:
sources_section += f"### Sources for Section: {section.name}\n\n"
sources_section += completed_section.sources + "\n\n"
final_report_with_sources = main_report + sources_section
else:
final_report_with_sources = main_report
return {"final_report": final_report_with_sources}
def initial_AB_topic_check(state: ReportState, config):
""" Checks if the topic is related to A/B testing """
# Get the topic
topic = state["topic"]
# Get configuration
configurable = Configuration.from_runnable_config(config)
# Format system instructions
system_instructions = initial_AB_topic_check_instructions.format(topic=topic)
# initial check human message
initial_AB_topic_check_message = """Check if the topic is related to A/B testing (even vaguely e.g. statistics, A/B testing, experimentation, etc.). If the topic is related to A/B testing (even vaguely), return 'true'. If the topic is not related to A/B testing, return 'false'. """
# Use planner model for reflection
planner_provider = get_config_value(configurable.planner_provider)
planner_model = get_config_value(configurable.planner_model)
reflection_model = init_chat_model(
model=planner_model,
model_provider=planner_provider,
max_tokens=32_000,
thinking={"type": "enabled", "budget_tokens": 24_000}
)
feedback = reflection_model.invoke([SystemMessage(content=system_instructions),
HumanMessage(content=initial_AB_topic_check_message)])
# Extract the response and determine if it's explicitly NOT A/B testing related
response_content = str(feedback.content).lower().strip()
is_explicitly_not_ab_testing = "false" in response_content
# Update state with the result
updated_state = state.copy()
updated_state["ab_testing_check"] = not is_explicitly_not_ab_testing # True unless explicitly false
# Only if explicitly NOT A/B testing related, set the final message
if is_explicitly_not_ab_testing:
return {
"ab_testing_check": False,
"final_report": "I'm trained to only generate reports related to A/B testing. Thus, unfortunately, I can't make this report."
}
else:
return {
"ab_testing_check": True
}
def route_after_ab_check(state: ReportState):
"""Route to either generate_report_plan or end based on A/B testing check"""
# Only end if we explicitly determined it's NOT A/B testing related
if state.get("ab_testing_check", True): # Default to True (continue) if check is missing
return "generate_report_plan"
else:
return END
section_builder = StateGraph(SectionState, output=SectionOutputState)
section_builder.add_node("generate_queries", generate_queries)
section_builder.add_node("search_rag_and_web", search_rag_and_web)
section_builder.add_node("write_section", write_section)
# Add edges
section_builder.add_edge(START, "generate_queries")
section_builder.add_edge("generate_queries", "search_rag_and_web")
section_builder.add_edge("search_rag_and_web", "write_section")
# Outer graph --
# Add nodes
builder = StateGraph(ReportState, input=ReportStateInput, output=ReportStateOutput, config_schema=Configuration)
builder.add_node("initial_AB_topic_check", initial_AB_topic_check)
builder.add_node("generate_report_plan", generate_report_plan)
builder.add_node("build_section_with_web_research", section_builder.compile())
builder.add_node("gather_completed_sections", gather_completed_sections)
builder.add_node("write_final_sections", write_final_sections)
builder.add_node("compile_final_report", compile_final_report)
builder.add_node("initiate_final_section_writing", initiate_final_section_writing)
# Add edges
builder.add_edge(START, "initial_AB_topic_check") # Start with AB check
builder.add_conditional_edges("initial_AB_topic_check", route_after_ab_check, ["generate_report_plan", END]) # Conditional routing
builder.add_edge("build_section_with_web_research", "gather_completed_sections")
builder.add_edge("gather_completed_sections", "initiate_final_section_writing")
builder.add_edge("write_final_sections", "compile_final_report")
builder.add_edge("compile_final_report", END)
return builder.compile()
def start_new_report(topic, report_placeholder):
"""Start a new report generation process"""
with st.spinner("Generating comprehensive report...This may take about 3-7 minutes."):
# Create input state
input_state = {"topic": topic}
# Run graph to completion
try:
config = {}
# Use asyncio.run to handle async function
result = asyncio.run(run_graph_to_completion(input_state, config))
if result.get("ab_testing_check") == False:
# Not AB testing related
response = result.get("final_report", "This topic is not related to A/B testing.")
report_placeholder.markdown(response)
return response
else:
# AB testing related - show final report
final_report = result.get("final_report", "")
if final_report:
final_content = f"## 📄 Final Report\n\n{final_report}"
report_placeholder.markdown(final_content)
return final_content
else:
error_msg = "No report was generated."
report_placeholder.error(error_msg)
return None
except Exception as e:
error_msg = f"Error generating report: {str(e)}"
report_placeholder.error(error_msg)
return None
async def run_graph_to_completion(input_state, config):
"""Run the graph to completion"""
result = await report_system.ainvoke(input_state, config)
return result
# Streamlit interface
st.markdown(
"<h1>📊 A/B<sub><span style='color:green;'>AI</span></sub></h1>",
unsafe_allow_html=True
)
st.markdown("""
A/B<sub><span style='color:green;'>AI</span></sub> is a specialized agent that generates comprehensive reports on your provided A/B testing topics using a thorough collection of Ron Kohavi's work, including his book, papers, and LinkedIn posts. For each section of the report, if A/B<sub><span style='color:green;'>AI</span></sub> can't answer your questions using this collection, it will then search Arxiv. If that's not enough, it will finally search the web. It provides ALL sources, section by section. It has been trained to only write based on the sources it retrieves. Let's begin!
""", unsafe_allow_html=True)
# Initialize the system
try:
# Show loading indicator
loading_placeholder = st.empty()
with loading_placeholder.container():
import time
for dots in [".", "..", "..."]:
loading_placeholder.text(f"Loading{dots}")
time.sleep(0.2)
# Initialize components (but hide the details)
vectorstore, chunks = initialize_vectorstore()
report_system = initialize_report_system(vectorstore)
# Clear loading indicator
loading_placeholder.empty()
except Exception as e:
st.error(f"Error initializing the system: {str(e)}")
st.stop()
# Initialize session state for chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat history
for i, message in enumerate(st.session_state.messages):
if message["role"] == "user":
st.chat_message("user").write(message["content"])
else:
with st.chat_message("assistant"):
st.write(message["content"])
# Chat input
query = st.chat_input("Please give me a topic on anything regarding A/B Testing...")
if query:
# Display user message
st.chat_message("user").write(query)
st.session_state.messages.append({"role": "user", "content": query})
# Create assistant container immediately
with st.chat_message("assistant"):
report_placeholder = st.empty()
# Start new report generation with placeholder
final_content = start_new_report(query, report_placeholder)
# Add to session state only after completion
if final_content:
st.session_state.messages.append({
"role": "assistant",
"content": final_content
})
|