feat: add support for ollama RAG providers (#1427)

* fix: openai env

* feat: add support for multiple RAG providers

- Added provider, model and endpoint configuration options for RAG service

- Updated RAG service to support both OpenAI and Ollama providers

- Added Ollama embedding support and dependencies

- Improved environment variable handling for RAG service configuration

Signed-off-by: wfhtqp@gmail.com <wfhtqp@gmail.com>

* fix: update docker env

* feat: rag server add ollama llm

* fix: pre-commit

* feat: check embed model and clean

* docs: add rag server config docs

* fix: pyright ignore

---------

Signed-off-by: wfhtqp@gmail.com <wfhtqp@gmail.com>

lua/avante/config.lua

@@ -35,6 +35,10 @@ M._defaults = {
   tokenizer = "tiktoken",
   rag_service = {
     enabled = false, -- Enables the rag service, requires OPENAI_API_KEY to be set
+    provider = "openai", -- The provider to use for RAG service. eg: openai or ollama
+    llm_model = "", -- The LLM model to use for RAG service
+    embed_model = "", -- The embedding model to use for RAG service
+    endpoint = "https://api.openai.com/v1", -- The API endpoint for RAG service
   },
   web_search_engine = {
     provider = "tavily",

lua/avante/rag_service.lua

@@ -1,6 +1,7 @@
 local curl = require("plenary.curl")
 local Path = require("plenary.path")
 local Utils = require("avante.utils")
+local Config = require("avante.config")
 
 local M = {}
 
@@ -32,12 +33,12 @@ end
 ---@param cb fun()
 function M.launch_rag_service(cb)
   local openai_api_key = os.getenv("OPENAI_API_KEY")
-  if openai_api_key == nil then
-    error("cannot launch avante rag service, OPENAI_API_KEY is not set")
-    return
+  if Config.rag_service.provider == "openai" then
+    if openai_api_key == nil then
+      error("cannot launch avante rag service, OPENAI_API_KEY is not set")
+      return
+    end
   end
-  local openai_base_url = os.getenv("OPENAI_BASE_URL")
-  if openai_base_url == nil then openai_base_url = "https://api.openai.com/v1" end
   local port = M.get_rag_service_port()
   local image = M.get_rag_service_image()
   local data_path = M.get_data_path()
@@ -63,13 +64,17 @@ function M.launch_rag_service(cb)
     Utils.debug(string.format("container %s not found, starting...", container_name))
   end
   local cmd_ = string.format(
-    "docker run -d -p %d:8000 --name %s -v %s:/data -v /:/host -e DATA_DIR=/data -e OPENAI_API_KEY=%s -e OPENAI_API_BASE=%s -e OPENAI_EMBED_MODEL=%s %s",
+    "docker run -d -p %d:8000 --name %s -v %s:/data -v /:/host -e DATA_DIR=/data -e RAG_PROVIDER=%s -e %s_API_KEY=%s -e %s_API_BASE=%s -e RAG_LLM_MODEL=%s -e RAG_EMBED_MODEL=%s %s",
     port,
     container_name,
     data_path,
+    Config.rag_service.provider,
+    Config.rag_service.provider:upper(),
     openai_api_key,
-    openai_base_url,
-    os.getenv("OPENAI_EMBED_MODEL"),
+    Config.rag_service.provider:upper(),
+    Config.rag_service.endpoint,
+    Config.rag_service.llm_model,
+    Config.rag_service.embed_model,
     image
   )
   vim.fn.jobstart(cmd_, {
@@ -229,6 +234,7 @@ function M.retrieve(base_uri, query)
       query = query,
       top_k = 10,
     }),
+    timeout = 100000,
   })
   if resp.status ~= 200 then
     Utils.error("failed to retrieve: " .. resp.body)