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