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Support for hybrid search in Azure AI vector store (#2408)

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Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>
This commit is contained in:
Dev Khant
2025-03-20 22:57:00 +05:30
committed by GitHub
parent 8b9a8e5825
commit 8e6a08aa83
24 changed files with 275 additions and 294 deletions
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14
mem0/memory/main.py
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@@ -71,13 +71,14 @@ class Memory(MemoryBase):
if "vector_store" not in config_dict and "embedder" in config_dict:
config_dict["vector_store"] = {}
config_dict["vector_store"]["config"] = {}
config_dict["vector_store"]["config"]["embedding_model_dims"] = config_dict["embedder"]["config"]["embedding_dims"]
config_dict["vector_store"]["config"]["embedding_model_dims"] = config_dict["embedder"]["config"][
"embedding_dims"
]
try:
return config_dict
except ValidationError as e:
logger.error(f"Configuration validation error: {e}")
raise
def add(
self,
@@ -204,7 +205,8 @@ class Memory(MemoryBase):
messages_embeddings = self.embedding_model.embed(new_mem, "add")
new_message_embeddings[new_mem] = messages_embeddings
existing_memories = self.vector_store.search(
query=messages_embeddings,
query=new_mem,
vectors=messages_embeddings,
limit=5,
filters=filters,
)
@@ -222,7 +224,9 @@ class Memory(MemoryBase):
temp_uuid_mapping[str(idx)] = item["id"]
retrieved_old_memory[idx]["id"] = str(idx)
function_calling_prompt = get_update_memory_messages(retrieved_old_memory, new_retrieved_facts, self.custom_update_memory_prompt)
function_calling_prompt = get_update_memory_messages(
retrieved_old_memory, new_retrieved_facts, self.custom_update_memory_prompt
)
try:
new_memories_with_actions = self.llm.generate_response(
@@ -479,7 +483,7 @@ class Memory(MemoryBase):
def _search_vector_store(self, query, filters, limit):
embeddings = self.embedding_model.embed(query, "search")
memories = self.vector_store.search(query=embeddings, limit=limit, filters=filters)
memories = self.vector_store.search(query=query, vectors=embeddings, limit=limit, filters=filters)
excluded_keys = {
"user_id",