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import pinecone
from colorama import Fore, Style
from autogpt.llm_utils import create_embedding_with_ada
from autogpt.logs import logger
from autogpt.memory.base import MemoryProviderSingleton
class PineconeMemory(MemoryProviderSingleton):
def __init__(self, cfg):
pinecone_api_key = cfg.pinecone_api_key
pinecone_region = cfg.pinecone_region
pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)
dimension = 1536
metric = "cosine"
pod_type = "p1"
table_name = "auto-gpt"
# this assumes we don't start with memory.
# for now this works.
# we'll need a more complicated and robust system if we want to start with
# memory.
self.vec_num = 0
try:
pinecone.whoami()
except Exception as e:
logger.typewriter_log(
"FAILED TO CONNECT TO PINECONE",
Fore.RED,
Style.BRIGHT + str(e) + Style.RESET_ALL,
)
logger.double_check(
"Please ensure you have setup and configured Pinecone properly for use."
+ f"You can check out {Fore.CYAN + Style.BRIGHT}"
"https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup"
f"{Style.RESET_ALL} to ensure you've set up everything correctly."
)
exit(1)
if table_name not in pinecone.list_indexes():
pinecone.create_index(
table_name, dimension=dimension, metric=metric, pod_type=pod_type
)
self.index = pinecone.Index(table_name)
def add(self, data):
vector = create_embedding_with_ada(data)
# no metadata here. We may wish to change that long term.
self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
_text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
self.vec_num += 1
return _text
def get(self, data):
return self.get_relevant(data, 1)
def clear(self):
self.index.delete(deleteAll=True)
return "Obliviated"
def get_relevant(self, data, num_relevant=5):
"""
Returns all the data in the memory that is relevant to the given data.
:param data: The data to compare to.
:param num_relevant: The number of relevant data to return. Defaults to 5
"""
query_embedding = create_embedding_with_ada(data)
results = self.index.query(
query_embedding, top_k=num_relevant, include_metadata=True
)
sorted_results = sorted(results.matches, key=lambda x: x.score)
return [str(item["metadata"]["raw_text"]) for item in sorted_results]
def get_stats(self):
return self.index.describe_index_stats()
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