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import requests
import json
import ast
def prompt_generator(question: str, db_knn: dict) -> tuple[str, str, str]:
context = ""
references = ""
for i in range(len(db_knn['matches'])):
data = db_knn['matches'][i]['metadata']['data']
context += (data + "\n")
data = ast.literal_eval(data)
line_number = ""
if data['type'] == "function" or data['type'] == "class":
line_number = f"#L{data['lineno'][0]}-L{data['lineno'][1]}"
references += ("<https://github.com/fury-gl/fury/blob/" + data['path'] + line_number + ">").replace("fury-0.10.0", "v0.10.0")
if data.get("function_name"):
references += f"\tFunction Name: {data.get('function_name')}"
elif data.get("class_name"):
references += f"\tClass Name: {data.get('class_name')}"
elif data['type'] == 'rst':
references += f"\tDocumentation: {data['path'].split("/")[-1]}"
elif data['type'] == 'documentation_examples':
references += f"\tDocumentation: {data['path'].split("/")[-1]}"
references += "\n"
prompt = f"""
You are a senior developer. Answer the users question based on the context provided.
Question: {question}
Context: {context}
"""
return prompt, context, references
def groq_llm_output(question: str, db_knn: dict, llm: str, stream: bool) -> tuple[str, str]:
"""
Returns output from the LLM using the given user-question and retrived context
"""
URL_LLM = 'https://robinroy03-fury-bot.hf.space'
prompt, context, references = prompt_generator(question, db_knn)
obj = {
'model': llm,
'prompt': prompt,
'stream': stream
}
response = requests.post(URL_LLM + "/api/groq/generate", json=obj)
response_json = json.loads(response.text)
return (response_json['choices'][0]['message']['content'], references)
def google_llm_output(question: str, db_knn: dict, llm: str, stream: bool) -> tuple[str, str]:
URL_LLM = 'https://robinroy03-fury-bot.hf.space'
prompt, context, references = prompt_generator(question, db_knn)
obj = {
'model': llm,
'prompt': prompt,
'stream': stream
}
response = requests.post(URL_LLM + "/api/google/generate", json=obj)
response_json = json.loads(response.text)
return (response_json['candidates'][0]['content']['parts'][0]['text'], references)
def embedding_output(message: str) -> list:
"""
Returns embeddings for the given message
rtype: list of embeddings. Length depends on the model.
"""
URL_EMBEDDING = 'https://robinroy03-fury-embeddings-endpoint.hf.space'
response = requests.post(URL_EMBEDDING + "/embedding", json={"text": message})
response_json = json.loads(response.text)
return response_json['output']
def db_output(embedding: list, knn: int) -> dict:
"""
Returns the KNN results.
rtype: JSON
"""
URL_DB = 'https://robinroy03-fury-db-endpoint.hf.space'
response = requests.post(URL_DB + "/query", json={"embeddings": embedding, "knn": knn})
response_json = json.loads(response.text)
return response_json
def ollama_llm_output(question: str, db_knn: dict, llm: str, stream: bool) -> tuple[str, str]:
URL_LLM = 'https://robinroy03-ollama-server-backend.hf.space'
# URL_LLM = "http://localhost:11434"
prompt, context, references = prompt_generator(question, db_knn)
obj = {
"model": llm,
"prompt": question,
"stream": stream
}
try:
response = requests.post(URL_LLM + "/api/generate", json=obj)
except Exception as e:
print(e)
return {"error": e}
response_json = json.loads(response.text)
return response_json, references |