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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,51 @@ | ||
| from urllib.parse import urlparse | ||
|
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| from openapi_parser import parse | ||
|
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| try: | ||
| from application.parser.file.base_parser import BaseParser | ||
| except ModuleNotFoundError: | ||
| from base_parser import BaseParser | ||
|
|
||
|
|
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| class OpenAPI3Parser(BaseParser): | ||
| def init_parser(self) -> None: | ||
| return super().init_parser() | ||
|
|
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| def get_base_urls(self, urls): | ||
| base_urls = [] | ||
| for i in urls: | ||
| parsed_url = urlparse(i) | ||
| base_url = parsed_url.scheme + "://" + parsed_url.netloc | ||
| if base_url not in base_urls: | ||
| base_urls.append(base_url) | ||
| return base_urls | ||
|
|
||
| def get_info_from_paths(self, path): | ||
| info = "" | ||
| if path.operations: | ||
| for operation in path.operations: | ||
| info += ( | ||
| f"\n{operation.method.value}=" | ||
| f"{operation.responses[0].description}" | ||
| ) | ||
| return info | ||
|
|
||
| def parse_file(self, file_path): | ||
| data = parse(file_path) | ||
| results = "" | ||
| base_urls = self.get_base_urls(link.url for link in data.servers) | ||
| base_urls = ",".join([base_url for base_url in base_urls]) | ||
| results += f"Base URL:{base_urls}\n" | ||
| i = 1 | ||
| for path in data.paths: | ||
| info = self.get_info_from_paths(path) | ||
| results += ( | ||
| f"Path{i}: {path.url}\n" | ||
| f"description: {path.description}\n" | ||
| f"parameters: {path.parameters}\nmethods: {info}\n" | ||
| ) | ||
| i += 1 | ||
| with open("results.txt", "w") as f: | ||
| f.write(results) | ||
| return results |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,36 +1,42 @@ | ||
| Fortunately, there are many providers for LLMs, and some of them can even be run locally. | ||
| # Setting Up Local Language Models for Your App | ||
|
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| There are two models used in the app: | ||
| 1. Embeddings. | ||
| 2. Text generation. | ||
| Your app relies on two essential models: Embeddings and Text Generation. While OpenAI's default models work seamlessly, you have the flexibility to switch providers or even run the models locally. | ||
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| By default, we use OpenAI's models, but if you want to change it or even run it locally, it's very simple! | ||
| ## Step 1: Configure Environment Variables | ||
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| ### Go to .env file or set environment variables: | ||
| Navigate to the `.env` file or set the following environment variables: | ||
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| `LLM_NAME=<your Text generation>` | ||
| ```env | ||
| LLM_NAME=<your Text Generation model> | ||
| API_KEY=<API key for Text Generation> | ||
| EMBEDDINGS_NAME=<LLM for Embeddings> | ||
| EMBEDDINGS_KEY=<API key for Embeddings> | ||
| VITE_API_STREAMING=<true or false> | ||
| ``` | ||
|
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| `API_KEY=<api_key for Text generation>` | ||
| You can omit the keys if users provide their own. Ensure you set `LLM_NAME` and `EMBEDDINGS_NAME`. | ||
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| `EMBEDDINGS_NAME=<llm for embeddings>` | ||
| ## Step 2: Choose Your Models | ||
|
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| `EMBEDDINGS_KEY=<api_key for embeddings>` | ||
| **Options for `LLM_NAME`:** | ||
| - openai | ||
| - manifest | ||
| - cohere | ||
| - Arc53/docsgpt-14b | ||
| - Arc53/docsgpt-7b-falcon | ||
| - llama.cpp | ||
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| `VITE_API_STREAMING=<true or false (true if using openai, false for all others)>` | ||
| **Options for `EMBEDDINGS_NAME`:** | ||
| - openai_text-embedding-ada-002 | ||
| - huggingface_sentence-transformers/all-mpnet-base-v2 | ||
| - huggingface_hkunlp/instructor-large | ||
| - cohere_medium | ||
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| You don't need to provide keys if you are happy with users providing theirs, so make sure you set `LLM_NAME` and `EMBEDDINGS_NAME`. | ||
| If using Llama, set `EMBEDDINGS_NAME` to `huggingface_sentence-transformers/all-mpnet-base-v2`. Download the required model and place it in the `models/` folder. | ||
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| Options: | ||
| LLM_NAME (openai, manifest, cohere, Arc53/docsgpt-14b, Arc53/docsgpt-7b-falcon, llama.cpp) | ||
| EMBEDDINGS_NAME (openai_text-embedding-ada-002, huggingface_sentence-transformers/all-mpnet-base-v2, huggingface_hkunlp/instructor-large, cohere_medium) | ||
| Alternatively, for local Llama setup, run `setup.sh` and choose option 1. The script handles the DocsGPT model addition. | ||
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| If using Llama, set the `EMBEDDINGS_NAME` to `huggingface_sentence-transformers/all-mpnet-base-v2` and be sure to download [this model](https://d3dg1063dc54p9.cloudfront.net/models/docsgpt-7b-f16.gguf) into the `models/` folder: `https://d3dg1063dc54p9.cloudfront.net/models/docsgpt-7b-f16.gguf`. | ||
| ## Step 3: Local Hosting for Privacy | ||
|
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| Alternatively, if you wish to run Llama locally, you can run `setup.sh` and choose option 1 when prompted. You do not need to manually add the DocsGPT model mentioned above to your `models/` folder if you use `setup.sh`, as the script will manage that step for you. | ||
|
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| That's it! | ||
|
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| ### Hosting everything locally and privately (for using our optimised open-source models) | ||
| If you are working with critical data and don't want anything to leave your premises. | ||
|
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| Make sure you set `SELF_HOSTED_MODEL` as true in your `.env` variable, and for your `LLM_NAME`, you can use anything that is on Hugging Face. | ||
| If working with sensitive data, host everything locally by setting `SELF_HOSTED_MODEL` to true in your `.env`. For `LLM_NAME`, use any model available on Hugging Face. | ||
| That's it! Your app is now configured for local and private hosting, ensuring optimal security for critical data. |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,51 @@ | ||
| from urllib.parse import urlparse | ||
|
|
||
| from openapi_parser import parse | ||
|
|
||
| try: | ||
| from scripts.parser.file.base_parser import BaseParser | ||
| except ModuleNotFoundError: | ||
| from base_parser import BaseParser | ||
|
|
||
|
|
||
| class OpenAPI3Parser(BaseParser): | ||
| def init_parser(self) -> None: | ||
| return super().init_parser() | ||
|
|
||
| def get_base_urls(self, urls): | ||
| base_urls = [] | ||
| for i in urls: | ||
| parsed_url = urlparse(i) | ||
| base_url = parsed_url.scheme + "://" + parsed_url.netloc | ||
| if base_url not in base_urls: | ||
| base_urls.append(base_url) | ||
| return base_urls | ||
|
|
||
| def get_info_from_paths(self, path): | ||
| info = "" | ||
| if path.operations: | ||
| for operation in path.operations: | ||
| info += ( | ||
| f"\n{operation.method.value}=" | ||
| f"{operation.responses[0].description}" | ||
| ) | ||
| return info | ||
|
|
||
| def parse_file(self, file_path): | ||
| data = parse(file_path) | ||
| results = "" | ||
| base_urls = self.get_base_urls(link.url for link in data.servers) | ||
| base_urls = ",".join([base_url for base_url in base_urls]) | ||
| results += f"Base URL:{base_urls}\n" | ||
| i = 1 | ||
| for path in data.paths: | ||
| info = self.get_info_from_paths(path) | ||
| results += ( | ||
| f"Path{i}: {path.url}\n" | ||
| f"description: {path.description}\n" | ||
| f"parameters: {path.parameters}\nmethods: {info}\n" | ||
| ) | ||
| i += 1 | ||
| with open("results.txt", "w") as f: | ||
| f.write(results) | ||
| return results |
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