diff --git a/.changeset/sour-ways-listen.md b/.changeset/sour-ways-listen.md new file mode 100644 index 0000000..c7c663a --- /dev/null +++ b/.changeset/sour-ways-listen.md @@ -0,0 +1,5 @@ +--- +"apollo": minor +--- + +Added search service (RAG) diff --git a/.env.example b/.env.example index 368a2db..50e5c08 100644 --- a/.env.example +++ b/.env.example @@ -1,2 +1,5 @@ OPENAI_API_KEY=sk-YOUR-API-KEY-HERE -HF_ACCESS_TOKEN=hf_YOUR-API-KEY-HERE # llama2 base \ No newline at end of file +HF_ACCESS_TOKEN=hf_YOUR-API-KEY-HERE # llama2 base + +ZILLIZ_URI = https://in01-XXXXXXXXXXXXX.aws-us-west-2.vectordb.zillizcloud.com:XXXXX +ZILLIZ_TOKEN =db_admin:password (or ApiKey) \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index e301716..9fc4f0e 100644 --- a/Dockerfile +++ b/Dockerfile @@ -5,11 +5,15 @@ WORKDIR /app COPY ./pyproject.toml ./poetry.lock poetry.toml ./ COPY ./package.json bun.lockb ./ COPY ./tsconfig.json ./ +COPY ./path.config ./ COPY ./platform/ ./platform COPY 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+anthropic = "^0.34.0" +pymilvus = "^2.4.6" +langchain-text-splitters = "^0.2.4" # These dependencies should only be needed for fine tuning # use poetry install --with ft -anthropic = "^0.34.0" [tool.poetry.group.ft] optional = true diff --git a/services/search/README.md b/services/search/README.md new file mode 100644 index 0000000..6532743 --- /dev/null +++ b/services/search/README.md @@ -0,0 +1,100 @@ +## Search (RAG) + +** RAG SEARCH IS AN EXPERIMENTAL FEATURE. IT IS NOT SUPPORTED BY THE STAGING SERVER** + +The Search Service integrates Retrieval-Augmented Generation (RAG) with Apollo to enhance the accuracy and relevance of data retrieval based on user queries. By leveraging this service, clients can obtain pertinent information from documents, provided they include sufficient context in their queries. + +## Usage - Search Service + +### To make a search with `curl`: + +```bash +curl -X POST https://apollo-staging.openfn.org/services/search --json @tmp/payload.json +``` + +### With the CLI, returning to stdout: + +```bash +openfn apollo search tmp/payload.json +``` +To run directly from this repo (note that the server must be started): + +```bash +bun py search tmp/payload.json -O +``` + +## Config Paths +To make it easier to specify which documents should be embedded, we use a configuration file named `path.config` located at the root of the repository. + +### Adding Document Paths + +1. **Open the `path.config` file**: This file contains the paths to the markdown files that you want to embed into the vector database. + +2. **Specify one path per line**: Add each document path you wish to include, with one path per line. These paths should be relative to the docs folder within the repository. + +3. **Use standard glob patterns**: You can use standard glob patterns to match multiple files or directories. For example: + - `docs/jobs/*.md`: This pattern matches all markdown files in the `docs/jobs` directory. + - `docs/adaptors/**/*.md`: This pattern matches all markdown files within the `docs/adaptors` directory and any of its subdirectories. + +### Example `path.config` + +```plaintext +docs/jobs/*.md +docs/adaptors/**/*.md +``` +In this example, all markdown files under docs/jobs and docs/adaptors (including subdirectories) will be processed and embedded. + +#### Important Note: +The paths specified in the `path.config` file are relative to the `docs` directory within your repository, not from the root folder. + +By following these instructions, you can easily manage and update the list of documents to be embedded without needing to dive into the underlying code. + +## Usage - Embedding OpenFn Docs +This service also includes the embedding of OpenFn docs to a vector database. The vector database used here is Zilliz. To obtain the env variables follow these steps: + +1. Create an account on [Zilliz](https://zilliz.com/) and set up a free cluster. +2. Obtain the URL and token for the cluster and add them to the `.env` file. +3. You'll also need an OpenAI API key to generate embeddings. + +There are two ways to run the embed the docs: + +### Running the Generate Embeddings File +Use the poetry command to run the service. You will need to clone the docs repo in this case using: + +```bash +git clone --depth 1 https://github.com/OpenFn/docs.git tmp +``` + +Then run: +```bash +poetry run python services/search/generate_docs_embeddings.py tmp/docs openfn_docs +``` +Here, tmp/docs is the path to the OpenFn docs and openfn_docs is the name of the collection in the vector database. + +### Docker +Alternatively, run the Docker file directly using: + +```bash +docker build --secret id=_env,src=.env -t apollo . +``` + +**NOTE**: The docs are split into chunks which can be seen in the `tmp/split_sections` folder. + +## Implementation +The service retrieves data from job documents stored in a vector database. It uses the OpenAI Embedding Function to embed the OpenFn Documentation, allowing for relevant context retrieval and enhancing the performance of other Apollo services. You can also configure the number of chunks retrieved from the database. + +## Payload Reference +The input payload is a JSON object with the following structure: + +```js +{ + "api_key": "", + "query": "What are jobs in OpenFn?", + "limit": 10, // Custom limit for number of chunks retrieved (1 to 15). Default value is 5. (optional) + "partition_name": "openfn_docs", // Name of the partition in the vector database. (optional) +} +``` +The `partition_name` is optional and the expected values are `normal_docs` or `adaptor_docs`. + +## Response Reference +The server returns an array of relevant strings from the documents based on the provided query. \ No newline at end of file diff --git a/services/search/generate_docs_embeddings.py b/services/search/generate_docs_embeddings.py new file mode 100644 index 0000000..e1d036e --- /dev/null +++ b/services/search/generate_docs_embeddings.py @@ -0,0 +1,151 @@ +import os +import time +import argparse +from openai import OpenAI +from dotenv import load_dotenv +from pymilvus import FieldSchema, CollectionSchema, DataType, utility, Collection, connections +from utils import split_docs, read_md_files, read_paths_config, fetch_adaptor_data, process_adaptor_data, logger + +load_dotenv() + +def parse_arguments(): + """ + Parses command line arguments. + + :return: Parsed arguments + """ + parser = argparse.ArgumentParser(description="Initialize Milvus with markdown files") + parser.add_argument("repo_path", type=str, help="Path to the repository containing markdown files") + parser.add_argument("collection_name", type=str, help="Name of the Milvus collection") + return parser.parse_args() + +def create_collection(collection_name: str, max_chunk_length: int) -> Collection: + """ + Creates a Milvus collection with the specified schema. + + :param collection_name: Name of the collection to create + :param max_chunk_length: Maximum length of text field + :return: Milvus Collection instance + """ + # Define field schemas + id_field = FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True, description="Primary ID") + embedding_field = FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=1536, description="Vector") + text_field = FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=max_chunk_length, description="Text data") + + # Create collection schema + schema = CollectionSchema(fields=[id_field, embedding_field, text_field], description="Corpus collection schema") + + # Create the collection + logger.info(f"Creating collection: {collection_name}") + collection = Collection(name=collection_name, schema=schema) + logger.info("Collection created!") + + # Create partitions + collection.create_partition(partition_name="normal_docs") + collection.create_partition(partition_name="adaptor_docs") + logger.info("Partitions 'normal_docs' and 'adaptor_docs' created!") + + return collection + +def embed_documents(client: OpenAI, corpus: list, adaptor_corpus: list) -> tuple: + """ + Embeds documents using the OpenAI API. + + :param client: OpenAI client instance + :param corpus: List of normal documents to embed + :param adaptor_corpus: List of adaptor documents to embed + :return: Tuple containing embedded normal and adaptor documents + """ + logger.info("Embedding documents...") + corpus_vectors = client.embeddings.create(input=corpus, model="text-embedding-3-small").data + adaptor_vectors = client.embeddings.create(input=adaptor_corpus, model="text-embedding-3-small").data + return corpus_vectors, adaptor_vectors + +def insert_data(collection: Collection, vectors: list, texts: list, partition_name: str) -> None: + """ + Inserts data into a specified partition of the Milvus collection. + + :param collection: Milvus Collection instance + :param vectors: List of embeddings + :param texts: List of document texts + :param partition_name: Name of the partition to insert data into + """ + data = [{"embedding": vec.embedding, "text": texts[i]} for i, vec in enumerate(vectors)] + collection.insert(data=data, partition_name=partition_name) + logger.info(f"Inserted {len(data)} documents into '{partition_name}' partition.") + +def main(): + args = parse_arguments() + + # Fetch API keys and connection details + openai_key = os.getenv("OPENAI_API_KEY") + zilliz_uri = os.getenv('ZILLIZ_URI') + zilliz_token = os.getenv('ZILLIZ_TOKEN') + + # Connect to OpenAI and Milvus + logger.info("Connecting to Milvus Database...") + client = OpenAI(api_key=openai_key) + connections.connect("default", uri=zilliz_uri, token=zilliz_token, db_name="openfn_docs") + + # Read and process markdown files + docs_to_embed = read_paths_config("./path.config", args.repo_path) + md_files_content = read_md_files(docs_to_embed) + + corpus = [] + file_embeddings_count = {} + + for file_name, content in md_files_content: + sections = split_docs(file_name, content) + corpus.extend(section.page_content for section in sections) + file_embeddings_count[file_name] = len(sections) + + # Log the number of embeddings per file + for file_name, count in file_embeddings_count.items(): + logger.info(f"File: {file_name} - Generated {count} embeddings") + + logger.info(f"Normal docs split: {len(corpus)}") + + # Fetch and process adaptor data + adaptor_data = fetch_adaptor_data() + adaptor_corpus, max_chunk_length = process_adaptor_data(adaptor_data) + logger.info(f"Adaptor data split: {len(adaptor_corpus)}") + + # Combine normal and adaptor docs + logger.info(f"Total documents after adding adaptors: {len(corpus) + len(adaptor_corpus)}") + + collection_name = args.collection_name + check_collection = utility.has_collection(collection_name) + if check_collection: + utility.drop_collection(collection_name) + + # Create collection + collection = create_collection(collection_name=collection_name, max_chunk_length=max_chunk_length) + + # Embed and insert data + corpus_vectors, adaptor_vectors = embed_documents(client, corpus, adaptor_corpus) + + insert_data(collection, corpus_vectors, corpus, partition_name="normal_docs") + insert_data(collection, adaptor_vectors, adaptor_corpus, partition_name="adaptor_docs") + + # Flush and create index + logger.info("Flushing...") + start_flush = time.time() + collection.flush() + end_flush = time.time() + logger.info(f"Succeeded in {round(end_flush - start_flush, 4)} seconds!") + + logger.info("Creating index...") + default_index = {'index_type': 'IVF_FLAT', 'metric_type': 'L2', 'params': {'nlist': 1024}} + collection.create_index(field_name="embedding", index_params=default_index) + + # Load collection + t0 = time.time() + logger.info("Loading collection...") + collection.load() + t1 = time.time() + logger.info(f"Loaded collection in {round(t1 - t0, 4)} seconds!") + + logger.info("Milvus database configured successfully!") + +if __name__ == "__main__": + main() diff --git a/services/search/results/outputs_with_scores.md b/services/search/results/outputs_with_scores.md new file mode 100644 index 0000000..fc9ad7f --- /dev/null +++ b/services/search/results/outputs_with_scores.md @@ -0,0 +1,225 @@ +## Query: What are cursors? +### Threshold: 1.3 +Distance: 0.9001595973968506 +### Manual Cursors +It's often useful to manually set the cursor position - usually when testing or +debugging. Maybe yesterday's run failed and you want to repeat it, or maybe +you're testing out some new functionality and you want to experiment with +different cursors. +You can do this by setting a cursor value on input state, like this: +```js +{ +"cursor": "today", +} +``` +You can do this by triggering a manual run in the platform's +[Job Inspector](documentation/build/steps/step-editor), or you can pass the +state as input to the CLI: +```bash +$ openfn job.js -s state.json -a http +``` +
+Manual cursors on v1 +Platform v1 does not allow input states to be freely defined, so setting a +manual cursor is a little more difficult. +You have to hard-code the manual cursor into the run so that the state cursor is +ignored: +```js +cursor('2024-03-12'); +``` +This line should be commented out in production runs. + + +INFO:search: + +Distance: 0.9147039651870728 +### Using the cursor +To use the cursor in your job, just use `state.cursor` in your queries like any +other state property. +The usage will be different depending on the adaptor you're using. Here's how +you might build a URL with query parameters with the HTTP adaptor: +```js +get(state => `/registrations?since=${state.cursor}`); +fn(/* do something good with your data */); +``` +This will read the cursor value off the state object, insert it into a string, +and pass it into a HTTP query. +Or perhaps you want to build the cursor into an object: +```js +get('registrations', state => { +query: { +fromdate: state.cursor; +} +}); +``` +The actual value of a cursor is arbitrary. You can use a string, a Date, a page +number or object, or anything you like. +You may want to advance the cursor at the end of a job ready, for the next run: +```js +cursor(state => state.cursor, { defaultValue: 'today' }); +get(`/registrations?since={date.cursor}`); +fn(/* do something good with your data */); +cursor('now'); +``` + + +INFO:search: + +Distance: 0.931585967540741 +### Cursor Options +The second argument to `cursor()` is an options object. You can use this to set +the `defaultValue` or the `key` the cursor should use (defaults to `cursor`) +```js +cursor(state => state.cursor, { defaultValue: '2024-03-12', key: 'page' }); +``` + + +INFO:search: + +Distance: 0.9570850729942322 +## Using Cursors +Sometimes it is useful to maintain a rolling cursor position on the backend +datasource. This can be used in a cron-based workflow, for example, to query the +database for new records since the last run. +In a cron workflow, OpenFn will pass the previous state into the next state - so +state persists across runs. We can take advantage of that to pick up where we +left off. +You can use the [`cursor()`](adaptors/packages/common-docs#cursor) operation, +which is built-in to most adaptors, to make cursor management easier. +
+Version support +The cursor operation was introduced to @openfn/language-common in version +1.13.0 (released April 2024). +
+
+Any adaptor which uses common 1.12.0 or less will not support the +cursor operation. Consider updating to the latest adaptor version to take advantage +of this functionality. +
+ + +INFO:search: + +Distance: 0.9877110719680786 +### Setting the cursor value +To use a cursor from a fixed date, just add a line like this to the top of your +job: +```js +cursor('2024-04-08T12:00:00.0000'); +``` +This will set the cursor to _always_ use the date you provided. +If you are using a date cursor, you can also pass in natural language strings +like "now", "today", "yesterday", "24 hours ago" or "start" (ie, the time the +job started). +:::tip Timezones +Relative dates like "today" will be converted into a Javascript Date using the +system locale. +If you're in the CLI that means times will be calculated in your local system +time; or if you're running on Lightning it'll use the Lightning system time +(usually UTC). +The cursor function will log the exact time, including the time zone, it is +using. +::: +To use a rolling or manual cursor, you should pass the cursor value from state. +You might want to include a default value too: +```js +cursor(state => state.cursor, { defaultValue: '2024-04-08T12:00:00.0000' }); +``` + + +INFO:search: + +Distance: 1.0386306047439575 +manual cursor is a little more difficult. +You have to hard-code the manual cursor into the run so that the state cursor is +ignored: +```js +cursor('2024-03-12'); +``` +This line should be commented out in production runs. +Alternatively, you can use the defaultValue option. This will work so long you +run without any initial state: +```js +cursor(state => state.cursor, { defaultValue: '2024-03-12' }); +``` +
+ + +INFO:search: + +Distance: 1.1283425092697144 +### Formatting the value +If you're using a service which doesn't use standard date formats, or you wish +to map a number of input formats into a consistent standard, you can use the +`format` option. +`format` takes a function which accepts the current cursor value as an argument, +and returns a formatted or updated value. This is called just before the cursor +is assigned to state. +For example, to use a Javascript Date as your cursor: +```js +cursor('today', { format: c => new Date(c) }); +``` +The formatter will run after any natural-language processing, so you can +intercept and convert the value to whatever you need. +You can combine this with +[`dateFns.format`](https://date-fns.org/v3.6.0/docs/format) to use a custom +timestamp: +```js +cursor('today', { format: c => dateFns.format(new Date(c), 'dd/mm/yyyy') }); +``` +You can add as much logic as you wish to your formatter - it's just a regular +Javascript function +```js +cursor('today', { +format: c => { +if (typeof c === 'number') { + + +INFO:search: + +Distance: 1.1477880477905273 +cursor('today', { format: c => dateFns.format(new Date(c), 'dd/mm/yyyy') }); +``` +You can add as much logic as you wish to your formatter - it's just a regular +Javascript function +```js +cursor('today', { +format: c => { +if (typeof c === 'number') { +return { page: c, count: 20 }; +} +return c; +}, +}); +``` + +========================================================================================= + +## Query: What is langchain +### Threshold: 1.5 + +Distance: 1.391467809677124 +### Optional chaining +JavaScript is an untyped language - which is very conveient for OpenFn jobs and +usually makes life easier. +However, a common problem is that when writing long property chains, an +exception will be thrown if a property is missing. And this happens all the time +when fetching data from remote servers. +Optional chaning allows JavaScript to stop evaluating a property chain and +return undefined as the result of that whole expression: +```js +const x = a.b?.c?.d?.e; +``` +In this example, if `c`, for example, is not defined, then `x` will be given a +value of `undefined`. No exception will be thrown. +You can do this with string properties too, although the syntax is a bit +fiddlier: +```js +const x = a.b['missing-link']?.d?.e; +``` +This can also be used for optional function calls (less useful in job writing +but included for completeness): +```js +const x = a.b?.(); +``` +You can combine optional chaning with the wonderfully named **"nullish \ No newline at end of file diff --git a/services/search/search.py b/services/search/search.py new file mode 100644 index 0000000..c789313 --- /dev/null +++ b/services/search/search.py @@ -0,0 +1,159 @@ +import os +import json +from openai import OpenAI +from util import DictObj, createLogger +from pymilvus import MilvusClient + +logger = createLogger("search") + +class Payload(DictObj): + api_key: str + query: str + partition_name: str = None # Optional partition_name. Helps distinguish between normal docs and adaptor data + limit: int = 5 # Default limit value + +def validate_payload(data: Payload): + """ + Validates the payload data. + + :param data: Payload object containing the input data + :raises ValueError: If any validation checks fail + """ + if not (1 <= int(data.limit) <= 15): + raise ValueError("Limit must be between 1 and 15.") + if not data.api_key: + raise ValueError("API key is missing.") + if not data.query: + raise ValueError("Query string is missing.") + if data.partition_name and data.partition_name not in ["normal_docs", "adaptor_docs"]: + raise ValueError("Invalid partition_name. Expected values are 'normal_docs' or 'adaptor_docs'.") + +def connect_to_milvus() -> MilvusClient: + """ + Connects to the Milvus database client using environment variables. + + :return: MilvusClient instance + :raises EnvironmentError: If required environment variables are not set + """ + zilliz_uri = os.getenv('ZILLIZ_URI') + zilliz_token = os.getenv('ZILLIZ_TOKEN') + + if not zilliz_uri or not zilliz_token: + raise EnvironmentError("ZILLIZ_URI or ZILLIZ_TOKEN environment variables are not set.") + + logger.info(f"Connecting to Milvus database...") + + return MilvusClient(uri=zilliz_uri, token=zilliz_token, db_name="openfn_docs") + + +def get_search_embeddings(api_key: str, query: str) -> list: + """ + Generates embeddings for the query using OpenAI's embedding API. + + :param api_key: OpenAI API key + :param query: Query string to be embedded + :return: List of embeddings + :raises Exception: If the embedding API call fails + """ + try: + embedding_client = OpenAI(api_key=api_key) + logger.info("Encoding search string...") + response = embedding_client.embeddings.create( + model="text-embedding-3-small", + input=query + ) + return response.data[0].embedding + except Exception as e: + logger.error(f"Failed to generate embeddings: {e}") + raise + +def search_database(client: MilvusClient, search_embeddings: list, partition_name: str, limit: int) -> list: + """ + Searches the Milvus database for relevant documents using embeddings. + + :param client: MilvusClient instance + :param search_embeddings: Embeddings for the search query + :param partition_name: Optional partition name for partitioned search + :param limit: Maximum number of results to return + :return: List of search results + """ + search_params = { + "metric_type": "L2", + "params": { + "nprobe": 16, # Number of clusters to search in + "radius": 1.2, # Search radius threshold + "level": 2 # Search precision level (1-3). Higher values yield more accurate results but slower performance. + } + } + + logger.info(f"Searching database with limit={limit}...") + try: + if partition_name: + logger.info(f"Searching within partition: {partition_name}") + res = client.search( + collection_name="openfn_docs", + data=[search_embeddings], + limit=limit, + search_params=search_params, + partition_names=[partition_name], # Search within specified partition + output_fields=["text"] + ) + else: + logger.info("Searching entire collection") + res = client.search( + collection_name="openfn_docs", # Search entire collection + data=[search_embeddings], + limit=limit, + search_params=search_params, + output_fields=["text"] + ) + + return res + except Exception as e: + logger.error(f"Database search failed: {e}") + raise + +def extract_documents(res) -> list: + """ + Extracts the document texts from the search results. + + :param res: Search results from Milvus + :return: List of document texts + """ + documents = [] + logger.info("Extracting documents from search results...") + + for hits in res: + if isinstance(hits, str): + hits = json.loads(hits) # Parse string to list of dictionaries + + for hit in hits: + text = hit['entity']['text'] + distance = hit['distance'] + print(f"Distance: {distance}\n{text}\n") + documents.append(text) + + return documents + +def main(dataDict) -> str: + try: + data = Payload(dataDict) + validate_payload(data) + + # Connect to Milvus database + client = connect_to_milvus() + + # Generate embeddings for the search query + search_embeddings = get_search_embeddings(api_key=data.api_key, query=data.query) + + # Perform the search + limit = int(data.limit) + res = search_database(client, search_embeddings, data.partition_name, limit) + + # Extract documents from search results + documents = extract_documents(res) + + return documents + except Exception as e: + logger.error(f"An error occurred during execution: {e}") + raise \ No newline at end of file diff --git a/services/search/utils.py b/services/search/utils.py new file mode 100644 index 0000000..cc5eb51 --- /dev/null +++ b/services/search/utils.py @@ -0,0 +1,166 @@ +import os +import glob +import logging +import requests +from langchain_text_splitters import MarkdownHeaderTextSplitter, RecursiveCharacterTextSplitter + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger("RAG") + +def fetch_adaptor_data(url="https://raw.githubusercontent.com/OpenFn/adaptors/docs/docs/docs.json"): + """ + Fetches adaptor data from a given URL. + + :param url: URL to fetch JSON data from + :return: Parsed JSON data + :raises Exception: If the request fails + """ + try: + response = requests.get(url) + response.raise_for_status() + return response.json() + except requests.RequestException as e: + logger.error(f"Failed to fetch adaptor data: {e}") + raise + +def read_md_files(file_paths): + """ + Reads content of markdown files from given file paths. + + :param file_paths: List of file paths to markdown files + :return: List of tuples containing file path and file content + """ + docs = [] + for file_path in file_paths: + if os.path.isfile(file_path): + try: + with open(file_path, "r", encoding="utf-8") as file: + docs.append((file_path, file.read())) + except OSError as e: + logger.error(f"Error reading file {file_path}: {e}") + else: + logger.warning(f"File {file_path} does not exist.") + return docs + +def read_paths_config(config_file, repo_path): + """ + Reads file paths from a config file and resolves them using glob. + + :param config_file: Path to the configuration file containing file patterns + :param repo_path: Base repository path to prepend to the file patterns + :return: List of resolved file paths + :raises FileNotFoundError: If the config file does not exist + """ + if not os.path.isfile(config_file): + raise FileNotFoundError(f"Configuration file {config_file} does not exist.") + + paths = [] + with open(config_file, 'r') as file: + for line in file: + pattern = line.strip() + if pattern: + full_pattern = os.path.expanduser(os.path.join(repo_path, pattern)) + logger.info(f"Processing pattern: {full_pattern}") + matched_paths = glob.glob(full_pattern, recursive=True) + + if matched_paths: + paths.extend(matched_paths) + else: + logger.warning(f"No matches found for pattern: {full_pattern}") + return paths + +def write_to_file(output_dir, file_name, content, header="## Section Start:\n"): + """ + Writes content to a file in the specified directory. + + :param output_dir: Directory to store the output file + :param file_name: Name of the file to write to + :param content: Content to write to the file + :param header: Optional header to prepend to each section of content + """ + os.makedirs(output_dir, exist_ok=True) + output_file = os.path.join(output_dir, file_name) + + # Delete the output file if it already exists + if os.path.exists(output_file): + try: + os.remove(output_file) + print(f"Existing output file '{output_file}' has been deleted.") + except OSError as e: + print(f"Error deleting the file {output_file}: {e}") + return + + with open(output_file, "w", encoding="utf-8") as out_file: + for section in content: + out_file.write(f"{header}\n{section}\n\n") + logger.info(f"Content written to {output_file}") + +def split_docs(file_name, content, output_dir="./tmp/split_sections"): + """ + Splits markdown content into sections using headers and character count. + + :param file_name: Name of the file being split + :param content: Content of the markdown file + :param output_dir: Directory to store the split sections + :return: List of split sections + """ + headers_to_split_on = [("##", "Header 2"), ("###", "Header 3")] + + # Initialize splitters + markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on, strip_headers=False) + text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=256) + + # Split the document + md_header_splits = markdown_splitter.split_text(content) + splits = text_splitter.split_documents(md_header_splits) + + # Write the split sections to disk using the new write function + file_name_with_extension = f"{os.path.basename(file_name)}_sections.md" + write_to_file(output_dir, file_name_with_extension, [section.page_content for section in splits]) + + return splits + +def process_adaptor_data(adaptor_data, output_dir="./tmp/split_sections"): + """ + Processes adaptor data to extract function details and generate documentation. + + :param adaptor_data: List of adaptors with function details + :param output_dir: Directory to store the processed output + :return: Tuple of corpus (list of chunks) and max_chunk_length + """ + corpus = [] + function_data = [] + max_chunk_length = 0 + + # Separate adaptors and functions + for item in adaptor_data: + if isinstance(item, dict) and 'adaptor' in item: + # This is an adaptor + adaptor_name = item['adaptor'] + functions = item['functions'] + elif isinstance(item, list): + # This is the functions array + function_data = [fn for fn in item if fn.get("kind") == "function"] + else: + continue + + for function in functions: + for fn_detail in function_data: + if fn_detail.get("name") == function: + description = fn_detail.get("description", "No description provided") + params = ", ".join([f"{p.get('name', 'No name provided')}: {p.get('description', 'No description provided')}" for p in fn_detail.get("params", [])]) + examples = "; ".join(fn_detail.get("examples", [])) + + # Create a chunk with a description + chunk = f"Adaptor: {adaptor_name} Function: {function} Description: {description} Parameters: {params} Examples: {examples}" + corpus.append(chunk) + + # Track max chunk length + max_chunk_length = max(max_chunk_length, len(chunk)) + + function_data = [] + # Write the processed adaptor data to disk using the new write function + write_to_file(output_dir, "adaptors.md", corpus) + + return corpus, max_chunk_length