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4321589 | 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 | #retrieval
def retrieve_documents (query, db, k=3):
results = db.similarity_search(query, k=k)
return results
# Context + Citation builder
def build_context(docs):
context = ""
sources = []
for doc in docs:
context += doc.page_content + "\n\n"
source_info = f"{doc.metadata['source']} - page {doc.metadata['page']}"
if source_info not in sources:
sources.append(source_info)
return context, sources
#LLM answer generator
from openai import OpenAI
from src.prompts import SYSTEM_PROMPT
client = OpenAI()
#def generate_answer(query, context, sources):
#response = client.chat.completions.create(
#model="gpt-4o-mini",
#messages=[
# {"role": "system", "content": SYSTEM_PROMPT},
# {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"}
# ]
# )
#answer = response.choices[0].message.content
#final_answer = answer + "\n\nSources:\n" + "\n".join(sources)
# return final_answer
#Update Answer Generator
#Now modify generate_answer in:
#def generate_answer(query, context, sources, memory):
#history_messages = format_chat_history(memory)
#response = client.chat.completions.create(
#model="gpt-4o-mini",
#messages=[
#{"role": "system", "content": SYSTEM_PROMPT},
#*history_messages,
# {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"}
# ]
# )
#answer = response.choices[0].message.content
# final_answer = answer + "\n\nSources:\n" + "\n".join(sources)
#return final_answer
#Format Memory for LLM
#We need to convert memory into messages.
# So we Add this to rag_pipeline.py:
def format_chat_history(memory):
messages = []
for item in memory:
messages.append({"role": "user", "content": item["user"]})
messages.append({"role": "assistant", "content": item["bot"]})
return messages
#Ticket github modification code
tools = [
{
"type": "function",
"function": {
"name": "create_support_ticket",
"description": "Create a support ticket when user has an issue",
"parameters": {
"type": "object",
"properties": {
"title": {"type": "string"},
"description": {"type": "string"}
},
"required": ["title", "description"]
}
}
}
]
#updated answer_generator for ticketing
import json
from src.ticketing import create_github_issue
def generate_answer(query, context, sources, memory):
history_messages = format_chat_history(memory)
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
*history_messages,
{"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"}
],
tools=tools,
tool_choice="auto"
)
message = response.choices[0].message
# If model decides to call function
if message.tool_calls:
tool_call = message.tool_calls[0]
if tool_call.function.name == "create_support_ticket":
args = json.loads(tool_call.function.arguments)
issue_url = create_github_issue(
title=args["title"],
description=args["description"]
)
return f"✅ Support ticket created: {issue_url}"
# Normal response
answer = message.content
final_answer = answer + "\n\nSources:\n" + "\n".join(sources)
return final_answer |