fix: bedrock claude do not support prompt caching (#1507)

lua/avante/providers/openai.lua

@@ -13,6 +13,8 @@ M.role_map = {
   assistant = "assistant",
 }
 
+function M:is_disable_stream() return false end
+
 ---@param tool AvanteLLMTool
 ---@return AvanteOpenAITool
 function M.transform_tool(tool)
@@ -63,14 +65,14 @@ end
 
 function M.is_o_series_model(model) return model and string.match(model, "^o%d+") ~= nil end
 
-function M.parse_messages(opts)
+function M:parse_messages(opts)
   local messages = {}
   local provider = P[Config.provider]
   local base, _ = P.parse_config(provider)
 
   -- NOTE: Handle the case where the selected model is the `o1` model
   -- "o1" models are "smart" enough to understand user prompt as a system prompt in this context
-  if M.is_o_series_model(base.model) then
+  if self.is_o_series_model(base.model) then
     table.insert(messages, { role = "user", content = opts.system_prompt })
   else
     table.insert(messages, { role = "system", content = opts.system_prompt })
@@ -100,20 +102,20 @@ function M.parse_messages(opts)
   vim.iter(messages):each(function(message)
     local role = message.role
     if role == prev_role then
-      if role == M.role_map["user"] then
-        table.insert(final_messages, { role = M.role_map["assistant"], content = "Ok, I understand." })
+      if role == self.role_map["user"] then
+        table.insert(final_messages, { role = self.role_map["assistant"], content = "Ok, I understand." })
       else
-        table.insert(final_messages, { role = M.role_map["user"], content = "Ok" })
+        table.insert(final_messages, { role = self.role_map["user"], content = "Ok" })
       end
     end
     prev_role = role
-    table.insert(final_messages, { role = M.role_map[role] or role, content = message.content })
+    table.insert(final_messages, { role = self.role_map[role] or role, content = message.content })
   end)
 
   if opts.tool_histories then
     for _, tool_history in ipairs(opts.tool_histories) do
       table.insert(final_messages, {
-        role = M.role_map["assistant"],
+        role = self.role_map["assistant"],
         tool_calls = {
           {
             id = tool_history.tool_use.id,
@@ -137,7 +139,7 @@ function M.parse_messages(opts)
   return final_messages
 end
 
-function M.parse_response(ctx, data_stream, _, opts)
+function M:parse_response(ctx, data_stream, _, opts)
   if data_stream:match('"%[DONE%]":') then
     opts.on_stop({ reason = "complete" })
     return
@@ -205,7 +207,7 @@ function M.parse_response(ctx, data_stream, _, opts)
   end
 end
 
-function M.parse_response_without_stream(data, _, opts)
+function M:parse_response_without_stream(data, _, opts)
   ---@type AvanteOpenAIChatResponse
   local json = vim.json.decode(data)
   if json.choices and json.choices[1] then
@@ -217,7 +219,7 @@ function M.parse_response_without_stream(data, _, opts)
   end
 end
 
-function M.parse_curl_args(provider, prompt_opts)
+function M:parse_curl_args(provider, prompt_opts)
   local provider_conf, request_body = P.parse_config(provider)
   local disable_tools = provider_conf.disable_tools or false
 
@@ -240,8 +242,7 @@ function M.parse_curl_args(provider, prompt_opts)
   end
 
   -- NOTE: When using "o" series set the supported parameters only
-  local stream = true
-  if M.is_o_series_model(provider_conf.model) then
+  if self.is_o_series_model(provider_conf.model) then
     request_body.max_completion_tokens = request_body.max_tokens
     request_body.max_tokens = nil
     request_body.temperature = 1
@@ -251,7 +252,7 @@ function M.parse_curl_args(provider, prompt_opts)
   if not disable_tools and prompt_opts.tools then
     tools = {}
     for _, tool in ipairs(prompt_opts.tools) do
-      table.insert(tools, M.transform_tool(tool))
+      table.insert(tools, self.transform_tool(tool))
     end
   end
 
@@ -265,8 +266,8 @@ function M.parse_curl_args(provider, prompt_opts)
     headers = headers,
     body = vim.tbl_deep_extend("force", {
       model = provider_conf.model,
-      messages = M.parse_messages(prompt_opts),
-      stream = stream,
+      messages = self:parse_messages(prompt_opts),
+      stream = true,
       tools = tools,
     }, request_body),
   }