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import os
import gradio as gr
import weaviate
from langchain import LLMChain
from langchain.chains import SequentialChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
collection_name = "Chunk"
MODEL = "gpt-3.5-turbo"
LANGUAGE = "en" # nl / en
llm = ChatOpenAI(temperature=0.0, openai_api_key=os.environ["OPENAI_API_KEY"])
def get_answer_given_the_context(llm, prompt, context) -> SequentialChain:
template = f"""
Provide an answer to the prompt given the context.
<PROMPT>
{prompt}
<CONTEXT>
{context}
"""
prompt_get_skills_intersection = ChatPromptTemplate.from_template(template=template)
skills_match_chain = LLMChain(
llm=llm,
prompt=prompt_get_skills_intersection,
output_key="answer",
)
chain = SequentialChain(
chains=[skills_match_chain],
input_variables=["prompt", "context"],
output_variables=[
skills_match_chain.output_key,
],
verbose=False,
)
return chain({"prompt": prompt, "context": context})["answer"]
def predict(prompt):
client = weaviate.Client(
url=os.environ["WEAVIATE_URL"],
auth_client_secret=weaviate.AuthApiKey(api_key=os.environ["WEAVIATE_API_KEY"]),
additional_headers={"X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]},
)
search_result = (
client.query.get(class_name=collection_name, properties=["text"])
.with_near_text({"concepts": prompt})
# .with_generate(single_prompt="{text}")
.with_limit(5)
.do()
)
context_list = [
element["text"] for element in search_result["data"]["Get"]["Chunk"]
]
context = "\n".join(context_list)
return get_answer_given_the_context(llm=llm, prompt=prompt, context=context)
iface = gr.Interface(
fn=predict, # the function to wrap
inputs="text", # the input type
outputs="text", # the output type
examples=[
[f"what is the process of raising an incident?"],
[f"What is Cx0 program management?"],
[
f"What is process for identifying risksthat can impact the desired outcomes of a project?"
],
[f"What is the release management process?"],
],
)
if __name__ == "__main__":
iface.launch()
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