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This reverts commit 5a42280.
TransformerOptimus
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,22 +1,12 @@ | ||
| from sqlalchemy.orm import Session | ||
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| from superagi.models.agent_execution_feed import AgentExecutionFeed | ||
| from superagi.vector_store.base import VectorStore | ||
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| class ToolResponseQueryManager: | ||
| def __init__(self, session: Session, agent_execution_id: int,memory:VectorStore): | ||
| def __init__(self, session: Session, agent_execution_id: int): | ||
| self.session = session | ||
| self.agent_execution_id = agent_execution_id | ||
| self.memory=memory | ||
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| def get_last_response(self, tool_name: str = None): | ||
| return AgentExecutionFeed.get_last_tool_response(self.session, self.agent_execution_id, tool_name) | ||
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| def get_relevant_response(self, query: str,metadata:dict, top_k: int = 5): | ||
| documents = self.memory.get_matching_text(query, metadata=metadata) | ||
| relevant_responses = "" | ||
| for document in documents["documents"]: | ||
| relevant_responses += document.text_content | ||
| return relevant_responses |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,171 +0,0 @@ | ||
| import json | ||
| import re | ||
| import uuid | ||
| from typing import Any, List, Iterable, Mapping | ||
| from typing import Optional, Pattern | ||
| import traceback | ||
| import numpy as np | ||
| import redis | ||
| from redis.commands.search.field import TagField, VectorField | ||
| from redis.commands.search.indexDefinition import IndexDefinition, IndexType | ||
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| from superagi.config.config import get_config | ||
| from superagi.lib.logger import logger | ||
| from superagi.vector_store.base import VectorStore | ||
| from superagi.vector_store.document import Document | ||
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| DOC_PREFIX = "doc:" | ||
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| CONTENT_KEY = "content" | ||
| METADATA_KEY = "metadata" | ||
| VECTOR_SCORE_KEY = "vector_score" | ||
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| class Redis(VectorStore): | ||
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| def delete_embeddings_from_vector_db(self, ids: List[str]) -> None: | ||
| pass | ||
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| def add_embeddings_to_vector_db(self, embeddings: dict) -> None: | ||
| pass | ||
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| def get_index_stats(self) -> dict: | ||
| pass | ||
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| DEFAULT_ESCAPED_CHARS = r"[,.<>{}\[\]\\\"\':;!@#$%^&*()\-+=~\/ ]" | ||
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| def __init__(self, index: Any, embedding_model: Any): | ||
| """ | ||
| Args: | ||
| index: An instance of a Redis index. | ||
| embedding_model: An instance of a BaseEmbedding model. | ||
| vector_group_id: vector group id used to index similar vectors. | ||
| """ | ||
| redis_url = get_config('REDIS_URL') | ||
| self.redis_client = redis.Redis.from_url("redis://" + redis_url + "/0", decode_responses=True) | ||
| # self.redis_client = redis.Redis(host=redis_host, port=redis_port) | ||
| self.index = index | ||
| self.embedding_model = embedding_model | ||
| self.content_key = "content", | ||
| self.metadata_key = "metadata" | ||
| self.index = index | ||
| self.vector_key = "content_vector" | ||
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| def build_redis_key(self, prefix: str) -> str: | ||
| """Build a redis key with a prefix.""" | ||
| return f"{prefix}:{uuid.uuid4().hex}" | ||
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| def add_texts(self, texts: Iterable[str], | ||
| metadatas: Optional[List[dict]] = None, | ||
| embeddings: Optional[List[List[float]]] = None, | ||
| ids: Optional[list[str]] = None, | ||
| **kwargs: Any) -> List[str]: | ||
| pipe = self.redis_client.pipeline() | ||
| prefix = DOC_PREFIX + str(self.index) | ||
| keys = [] | ||
| for i, text in enumerate(texts): | ||
| id = ids[i] if ids else self.build_redis_key(prefix) | ||
| metadata = metadatas[i] if metadatas else {} | ||
| embedding = self.embedding_model.get_embedding(text) | ||
| embedding_arr = np.array(embedding, dtype=np.float32) | ||
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| pipe.hset(id, mapping={CONTENT_KEY: text, self.vector_key: embedding_arr.tobytes(), | ||
| METADATA_KEY: json.dumps(metadata)}) | ||
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| keys.append(id) | ||
| pipe.execute() | ||
| return keys | ||
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| def get_matching_text(self, query: str, top_k: int = 5, metadata: Optional[dict] = None, **kwargs: Any) -> List[Document]: | ||
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| embed_text = self.embedding_model.get_embedding(query) | ||
| from redis.commands.search.query import Query | ||
| hybrid_fields = self._convert_to_redis_filters(metadata) | ||
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| base_query = f"{hybrid_fields}=>[KNN {top_k} @{self.vector_key} $vector AS vector_score]" | ||
| return_fields = [METADATA_KEY,CONTENT_KEY, "vector_score","id"] | ||
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| query = ( | ||
| Query(base_query) | ||
| .return_fields(*return_fields) | ||
| .sort_by("vector_score") | ||
| .paging(0, top_k) | ||
| .dialect(2) | ||
| ) | ||
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| params_dict: Mapping[str, str] = { | ||
| "vector": np.array(embed_text) | ||
| .astype(dtype=np.float32) | ||
| .tobytes() | ||
| } | ||
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| # print(self.index) | ||
| results = self.redis_client.ft(self.index).search(query,params_dict) | ||
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| # Prepare document results | ||
| documents = [] | ||
| for result in results.docs: | ||
| documents.append( | ||
| Document( | ||
| text_content=result.content, | ||
| metadata=json.loads(result.metadata) | ||
| ) | ||
| ) | ||
| return {"documents": documents} | ||
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| def _convert_to_redis_filters(self, metadata: Optional[dict] = {}) -> str: | ||
| if metadata is None or len(metadata) == 0: | ||
| return "*" | ||
| filter_strings = [] | ||
| for key in metadata.keys(): | ||
| filter_string = "@%s:{%s}" % (key, self.escape_token(str(metadata[key]))) | ||
| filter_strings.append(filter_string) | ||
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| joined_filter_strings = " & ".join(filter_strings) | ||
| return f"({joined_filter_strings})" | ||
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| def create_index(self): | ||
| try: | ||
| # check to see if index exists | ||
| temp = self.redis_client.ft(self.index).info() | ||
| logger.info(temp) | ||
| logger.info("Index already exists!") | ||
| except: | ||
| vector_dimensions = self.embedding_model.get_embedding("sample") | ||
| # schema | ||
| schema = ( | ||
| TagField("tag"), # Tag Field Name | ||
| VectorField(self.vector_key, # Vector Field Name | ||
| "FLAT", { # Vector Index Type: FLAT or HNSW | ||
| "TYPE": "FLOAT32", # FLOAT32 or FLOAT64 | ||
| "DIM": len(vector_dimensions), # Number of Vector Dimensions | ||
| "DISTANCE_METRIC": "COSINE", # Vector Search Distance Metric | ||
| } | ||
| ) | ||
| ) | ||
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| # index Definition | ||
| definition = IndexDefinition(prefix=[DOC_PREFIX], index_type=IndexType.HASH) | ||
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| # create Index | ||
| self.redis_client.ft(self.index).create_index(fields=schema, definition=definition) | ||
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| def escape_token(self, value: str) -> str: | ||
| """ | ||
| Escape punctuation within an input string. Taken from RedisOM Python. | ||
| Args: | ||
| value (str): The input string. | ||
| Returns: | ||
| str: The escaped string. | ||
| """ | ||
| escaped_chars_re = re.compile(Redis.DEFAULT_ESCAPED_CHARS) | ||
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| def escape_symbol(match: re.Match) -> str: | ||
| return f"\\{match.group(0)}" | ||
|
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| return escaped_chars_re.sub(escape_symbol, value) | ||
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