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[Feature] Add support for GPTCache (#1065)

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This commit is contained in:
Deven Patel
2023-12-30 14:51:48 +05:30
committed by GitHub
parent a7e1520d08
commit 04daa1b206
10 changed files with 157 additions and 11 deletions
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42
embedchain/embedchain.py
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@@ -7,6 +7,9 @@ from typing import Any, Dict, List, Optional, Tuple, Union
from dotenv import load_dotenv
from langchain.docstore.document import Document
from embedchain.cache import (adapt, get_gptcache_session,
gptcache_data_convert,
gptcache_update_cache_callback)
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config import AddConfig, BaseLlmConfig, ChunkerConfig
from embedchain.config.base_app_config import BaseAppConfig
@@ -52,6 +55,7 @@ class EmbedChain(JSONSerializable):
"""
self.config = config
self.cache_config = None
# Llm
self.llm = llm
# Database has support for config assignment for backwards compatibility
@@ -546,9 +550,22 @@ class EmbedChain(JSONSerializable):
else:
contexts_data_for_llm_query = contexts
answer = self.llm.query(
input_query=input_query, contexts=contexts_data_for_llm_query, config=config, dry_run=dry_run
)
if self.cache_config is not None:
logging.info("Cache enabled. Checking cache...")
answer = adapt(
llm_handler=self.llm.query,
cache_data_convert=gptcache_data_convert,
update_cache_callback=gptcache_update_cache_callback,
session=get_gptcache_session(session_id=self.config.id),
input_query=input_query,
contexts=contexts_data_for_llm_query,
config=config,
dry_run=dry_run,
)
else:
answer = self.llm.query(
input_query=input_query, contexts=contexts_data_for_llm_query, config=config, dry_run=dry_run
)
# Send anonymous telemetry
self.telemetry.capture(event_name="query", properties=self._telemetry_props)
@@ -599,9 +616,22 @@ class EmbedChain(JSONSerializable):
else:
contexts_data_for_llm_query = contexts
answer = self.llm.chat(
input_query=input_query, contexts=contexts_data_for_llm_query, config=config, dry_run=dry_run
)
if self.cache_config is not None:
logging.info("Cache enabled. Checking cache...")
answer = adapt(
llm_handler=self.llm.chat,
cache_data_convert=gptcache_data_convert,
update_cache_callback=gptcache_update_cache_callback,
session=get_gptcache_session(session_id=self.config.id),
input_query=input_query,
contexts=contexts_data_for_llm_query,
config=config,
dry_run=dry_run,
)
else:
answer = self.llm.chat(
input_query=input_query, contexts=contexts_data_for_llm_query, config=config, dry_run=dry_run
)
# add conversation in memory
self.llm.add_history(self.config.id, input_query, answer)