0.1.12 to add timestamp

shohey1226 · Jul 27, 2023 · e6593e3 · e6593e3
1 parent 960acfc
commit e6593e3
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Showing 3 changed files with 8 additions and 5 deletions.
diff --git a/Gemfile.lock b/Gemfile.lock
@@ -1,7 +1,7 @@
 PATH
   remote: .
   specs:
-    llm_memory (0.1.11)
+    llm_memory (0.1.12)
       redis (~> 4.6.0)
       ruby-openai (~> 3.7.0)
       tokenizers (~> 0.3.3)

diff --git a/README.md b/README.md
@@ -54,8 +54,10 @@ end
 
 To use LLM Memory, follow these steps:
 
+If you want to use pgvector instead of Redisearch. You can use the plugin. Please check the doc and change the setup steps(2&3)
+
 1. Install the gem: gem install llm_memory
-2. Set up Redis with Redisearch module enabled - Go to [Reids Cloud](https://redis.com/redis-enterprise-cloud/overview/) and get the redis url
+2. Set up Redis with Redisearch module enabled - Go to [Reids Cloud](https://redis.com/redis-enterprise-cloud/overview/) and get the redis url.
 3. Configure LLM Memory to connect to your Redis instance
 4. Use LlmMemory::Wernicke to load data from your external sources
 5. Use LlmMemory::Hippocampus to search for relevant information based on user queries
@@ -71,12 +73,13 @@ docs = LlmMemory::Wernicke.load(:file, "/tmp/a_directory")
 # docs = [{
 #   content: "Hi there",
 #   metadata: {
-#     file_name: "a.txt"
+#     file_name: "a.txt",
+#     timestamp: "20201231235959"
 #   }
 # },,,]
 
 hippocampus = LlmMemory::Hippocampus.new
-hippocampus.memorize(docs)
+res = hippocampus.memorize(docs)
 
 query_str = "What is my name?"
 related_docs = hippocampus.query(query_str, limit: 3)

diff --git a/lib/llm_memory/version.rb b/lib/llm_memory/version.rb
@@ -1,5 +1,5 @@
 # frozen_string_literal: true
 
 module LlmMemory
-  VERSION = "0.1.11"
+  VERSION = "0.1.12"
 end