162 lines
5.3 KiB
Python
162 lines
5.3 KiB
Python
from typing import Dict, List, Optional
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from pydantic import BaseModel
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try:
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from langchain_community.vectorstores import VectorStore
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except ImportError:
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raise ImportError("The 'langchain_community' library is required. Please install it using 'pip install langchain_community'.")
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from mem0.vector_stores.base import VectorStoreBase
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class OutputData(BaseModel):
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id: Optional[str] # memory id
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score: Optional[float] # distance
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payload: Optional[Dict] # metadata
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class Langchain(VectorStoreBase):
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def __init__(self, client: VectorStore, collection_name: str = "mem0"):
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self.client = client
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self.collection_name = collection_name
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def _parse_output(self, data: Dict) -> List[OutputData]:
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"""
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Parse the output data.
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Args:
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data (Dict): Output data or list of Document objects.
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Returns:
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List[OutputData]: Parsed output data.
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"""
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# Check if input is a list of Document objects
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if isinstance(data, list) and all(hasattr(doc, 'metadata') for doc in data if hasattr(doc, '__dict__')):
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result = []
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for doc in data:
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entry = OutputData(
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id=getattr(doc, "id", None),
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score=None, # Document objects typically don't include scores
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payload=getattr(doc, "metadata", {})
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)
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result.append(entry)
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return result
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# Original format handling
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keys = ["ids", "distances", "metadatas"]
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values = []
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for key in keys:
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value = data.get(key, [])
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if isinstance(value, list) and value and isinstance(value[0], list):
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value = value[0]
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values.append(value)
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ids, distances, metadatas = values
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max_length = max(len(v) for v in values if isinstance(v, list) and v is not None)
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result = []
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for i in range(max_length):
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entry = OutputData(
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id=ids[i] if isinstance(ids, list) and ids and i < len(ids) else None,
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score=(distances[i] if isinstance(distances, list) and distances and i < len(distances) else None),
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payload=(metadatas[i] if isinstance(metadatas, list) and metadatas and i < len(metadatas) else None),
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)
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result.append(entry)
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return result
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def create_col(self, name, vector_size=None, distance=None):
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self.collection_name = name
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return self.client
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def insert(self, vectors: List[List[float]], payloads: Optional[List[Dict]] = None, ids: Optional[List[str]] = None):
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"""
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Insert vectors into the LangChain vectorstore.
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"""
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# Check if client has add_embeddings method
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if hasattr(self.client, "add_embeddings"):
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# Some LangChain vectorstores have a direct add_embeddings method
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self.client.add_embeddings(
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embeddings=vectors,
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metadatas=payloads,
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ids=ids
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)
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else:
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# Fallback to add_texts method
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texts = [payload.get("data", "") for payload in payloads] if payloads else [""] * len(vectors)
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self.client.add_texts(
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texts=texts,
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metadatas=payloads,
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ids=ids
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)
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def search(self, query: str, vectors: List[List[float]], limit: int = 5, filters: Optional[Dict] = None):
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"""
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Search for similar vectors in LangChain.
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"""
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# For each vector, perform a similarity search
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if filters:
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results = self.client.similarity_search_by_vector(
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embedding=vectors,
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k=limit,
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filter=filters
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)
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else:
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results = self.client.similarity_search_by_vector(
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embedding=vectors,
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k=limit
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)
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final_results = self._parse_output(results)
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return final_results
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def delete(self, vector_id):
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"""
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Delete a vector by ID.
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"""
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self.client.delete(ids=[vector_id])
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def update(self, vector_id, vector=None, payload=None):
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"""
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Update a vector and its payload.
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"""
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self.delete(vector_id)
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self.insert(vector, payload, [vector_id])
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def get(self, vector_id):
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"""
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Retrieve a vector by ID.
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"""
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docs = self.client.get_by_ids([vector_id])
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if docs and len(docs) > 0:
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doc = docs[0]
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return self._parse_output([doc])[0]
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return None
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def list_cols(self):
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"""
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List all collections.
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"""
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# LangChain doesn't have collections
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return [self.collection_name]
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def delete_col(self):
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"""
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Delete a collection.
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"""
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self.client.delete(ids=None)
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def col_info(self):
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"""
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Get information about a collection.
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"""
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return {"name": self.collection_name}
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def list(self, filters=None, limit=None):
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"""
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List all vectors in a collection.
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"""
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# This would require implementation-specific access to the underlying store
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raise NotImplementedError("Listing all vectors not directly supported by LangChain vectorstores")
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