feat: Enhanced Model Provider Support and Configuration Flexibility For Rag Service (#2056)

Co-authored-by: doodleEsc <cokie@foxmail.com>
Co-authored-by: pre-commit-ci-lite[bot] <117423508+pre-commit-ci-lite[bot]@users.noreply.github.com>

py/rag-service/README.md

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+# RAG Service Configuration
+
+This document describes how to configure the RAG service, including setting up Language Model (LLM) and Embedding providers.
+
+## Provider Support Matrix
+
+The following table shows which model types are supported by each provider:
+
+| Provider   | LLM Support | Embedding Support |
+| ---------- | ----------- | ----------------- |
+| dashscope  | Yes         | Yes               |
+| ollama     | Yes         | Yes               |
+| openai     | Yes         | Yes               |
+| openrouter | Yes         | No                |
+
+## LLM Provider Configuration
+
+The `llm` section in the configuration file is used to configure the Language Model (LLM) used by the RAG service.
+
+Here are the configuration examples for each supported LLM provider:
+
+### OpenAI LLM Configuration
+
+[See more configurations](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py#L130)
+
+```lua
+llm = { -- Configuration for the Language Model (LLM) used by the RAG service
+  provider = "openai", -- The LLM provider ("openai")
+  endpoint = "https://api.openai.com/v1", -- The LLM API endpoint
+  api_key = "OPENAI_API_KEY", -- The environment variable name for the LLM API key
+  model = "gpt-4o-mini", -- The LLM model name (e.g., "gpt-4o-mini", "gpt-3.5-turbo")
+  extra = {-- Extra configuration options for the LLM (optional)
+    temperature = 0.7, -- Controls the randomness of the output. Lower values make it more deterministic.
+    max_tokens = 512, -- The maximum number of tokens to generate in the completion.
+    -- system_prompt = "You are a helpful assistant.", -- A system prompt to guide the model's behavior.
+    -- timeout = 120, -- Request timeout in seconds.
+  },
+},
+```
+
+### DashScope LLM Configuration
+
+[See more configurations](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/llms/llama-index-llms-dashscope/llama_index/llms/dashscope/base.py#L155)
+
+```lua
+llm = { -- Configuration for the Language Model (LLM) used by the RAG service
+  provider = "dashscope", -- The LLM provider ("dashscope")
+  endpoint = "", -- The LLM API endpoint (DashScope typically uses default or environment variables)
+  api_key = "DASHSCOPE_API_KEY", -- The environment variable name for the LLM API key
+  model = "qwen-plus", -- The LLM model name (e.g., "qwen-plus", "qwen-max")
+  extra = nil, -- Extra configuration options for the LLM (optional)
+},
+```
+
+### Ollama LLM Configuration
+
+[See more configurations](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/llms/llama-index-llms-ollama/llama_index/llms/ollama/base.py#L65)
+
+```lua
+llm = { -- Configuration for the Language Model (LLM) used by the RAG service
+  provider = "ollama", -- The LLM provider ("ollama")
+  endpoint = "http://localhost:11434", -- The LLM API endpoint for Ollama
+  api_key = "", -- Ollama typically does not require an API key
+  model = "llama2", -- The LLM model name (e.g., "llama2", "mistral")
+  extra = nil, -- Extra configuration options for the LLM (optional) Kristin", -- Extra configuration options for the LLM (optional)
+},
+```
+
+### OpenRouter LLM Configuration
+
+[See more configurations](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/llms/llama-index-llms-openrouter/llama_index/llms/openrouter/base.py#L17)
+
+```lua
+llm = { -- Configuration for the Language Model (LLM) used by the RAG service
+  provider = "openrouter", -- The LLM provider ("openrouter")
+  endpoint = "https://openrouter.ai/api/v1", -- The LLM API endpoint for OpenRouter
+  api_key = "OPENROUTER_API_KEY", -- The environment variable name for the LLM API key
+  model = "openai/gpt-4o-mini", -- The LLM model name (e.g., "openai/gpt-4o-mini", "mistralai/mistral-7b-instruct")
+  extra = nil, -- Extra configuration options for the LLM (optional)
+},
+```
+
+## Embedding Provider Configuration
+
+The `embedding` section in the configuration file is used to configure the Embedding Model used by the RAG service.
+
+Here are the configuration examples for each supported Embedding provider:
+
+### OpenAI Embedding Configuration
+
+[See more configurations](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/base.py#L214)
+
+```lua
+embed = { -- Configuration for the Embedding Model used by the RAG service
+  provider = "openai", -- The Embedding provider ("openai")
+  endpoint = "https://api.openai.com/v1", -- The Embedding API endpoint
+  api_key = "OPENAI_API_KEY", -- The environment variable name for the Embedding API key
+  model = "text-embedding-3-large", -- The Embedding model name (e.g., "text-embedding-3-small", "text-embedding-3-large")
+  extra = {-- Extra configuration options for the Embedding model (optional)
+    dimensions = nil,
+  },
+},
+```
+
+### DashScope Embedding Configuration
+
+[See more configurations](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py#L156)
+
+```lua
+embed = { -- Configuration for the Embedding Model used by the RAG service
+  provider = "dashscope", -- The Embedding provider ("dashscope")
+  endpoint = "", -- The Embedding API endpoint (DashScope typically uses default or environment variables)
+  api_key = "DASHSCOPE_API_KEY", -- The environment variable name for the Embedding API key
+  model = "text-embedding-v3", -- The Embedding model name (e.g., "text-embedding-v2")
+  extra = { -- Extra configuration options for the Embedding model (optional)
+    embed_batch_size = 10,
+  },
+},
+```
+
+### Ollama Embedding Configuration
+
+[See more configurations](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-ollama/llama_index/embeddings/ollama/base.py#L12)
+
+```lua
+embed = { -- Configuration for the Embedding Model used by the RAG service
+  provider = "ollama", -- The Embedding provider ("ollama")
+  endpoint = "http://localhost:11434", -- The Embedding API endpoint for Ollama
+  api_key = "", -- Ollama typically does not require an API key
+  model = "nomic-embed-text", -- The Embedding model name (e.g., "nomic-embed-text")
+  extra = { -- Extra configuration options for the Embedding model (optional)
+    embed_batch_size = 10，
+  },
+},
+```