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507d13c 99e964c 507d13c 99e964c 507d13c 99e964c 507d13c 99e964c 507d13c 99e964c 507d13c 99e964c 507d13c 99e964c 507d13c | 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 | from modal import Stub, Image, Secret, asgi_app, method
from urllib.request import urlretrieve
from fastapi import FastAPI
from typing import List, Dict
image = Image.debian_slim("3.11").pip_install(
"cohere",
"gradio==3.50.2",
"pinecone-client",
)
stub = Stub("secsplorer", image=image)
web_app = FastAPI()
@stub.function(
secrets=[Secret.from_name("cohere-api-key"), Secret.from_name("pinecone-api-key")]
)
@asgi_app()
def fastapi_app():
import cohere
import pinecone
import os
import uuid
import gradio as gr
from gradio.routes import mount_gradio_app
print("Connecting to cohere client")
co = cohere.Client(os.environ["COHERE_API_KEY"])
print("Done")
pinecone.init(api_key=os.environ["PINECONE_API_KEY"], environment="us-west1-gcp")
index = pinecone.Index(index_name="td-sec-embeddings")
def retrieve(
index: pinecone.Index, query: str, co: cohere.Client
) -> List[Dict[str, str]]:
"""
Retrieves documents based on the given query.
Parameters:
query (str): The query to retrieve documents for.
Returns:
List[Dict[str, str]]: A list of dictionaries representing the retrieved documents, with 'title', 'snippet', and 'url' keys.
"""
docs_retrieved = []
print(f"Calling retrieve for '{query}'")
print("Embedding the query")
query_emb = co.embed(
texts=[query], model="embed-english-v3.0", input_type="search_query"
).embeddings
print("Querying pinecone")
res = index.query(query_emb, top_k=10, include_metadata=True)
print("Preparing to rerank")
docs_to_rerank = [match["metadata"] for match in res["matches"]]
rerank_results = co.rerank(
query=query,
documents=docs_to_rerank,
top_n=3,
model="rerank-english-v2.0",
)
docs_retrieved = []
for hit in rerank_results:
docs_retrieved.append(docs_to_rerank[hit.index])
print("Returning retrieved docs")
return docs_retrieved
class Chatbot:
def __init__(self, co: cohere.Client, index: pinecone.Index):
self.index = index
self.conversation_id = str(uuid.uuid4())
self.co = co
def generate_response(self, message: str):
"""
Generates a response to the user's message.
Parameters:
message (str): The user's message.
Yields:
Event: A response event generated by the chatbot.
Returns:
List[Dict[str, str]]: A list of dictionaries representing the retrieved documents.
"""
# Generate search queries (if any)
response = self.co.chat(message=message, search_queries_only=True)
# If there are search queries, retrieve documents and respond
if response.search_queries:
print("Retrieving information")
documents = self.retrieve_docs(response)
response = self.co.chat(
message=message,
documents=documents,
conversation_id=self.conversation_id,
stream=True,
)
for event in response:
yield event
# If there is no search query, directly respond
else:
response = self.co.chat(
message=message, conversation_id=self.conversation_id, stream=True
)
for event in response:
yield event
def retrieve_docs(self, response) -> List[Dict[str, str]]:
"""
Retrieves documents based on the search queries in the response.
Parameters:
response: The response object containing search queries.
Returns:
List[Dict[str, str]]: A list of dictionaries representing the retrieved documents.
"""
# Get the query(s)
queries = []
for search_query in response.search_queries:
queries.append(search_query["text"])
# Retrieve documents for each query
retrieved_docs = []
for query in queries:
retrieved_docs.extend(retrieve(self.index, query, self.co))
return retrieved_docs
chatbot = Chatbot(co, index)
def chat_function(message, history):
flag = False
reply = ""
for event in chatbot.generate_response(message):
if event.event_type == "text-generation":
reply += str(event.text)
yield reply
# Citations
if event.event_type == "citation-generation":
if not flag:
reply += "\n\nCITATIONS:\n\n"
yield reply
flag = True
reply += str(event.citations) + "\n"
yield reply
interface = gr.ChatInterface(chat_function).queue()
print("All ready!")
return mount_gradio_app(app=web_app, blocks=interface, path="/")
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