بحث RAG في TXT

from crewai_tools import TXTSearchTool

# Initialize the tool to search within any text file's content
# the agent learns about during its execution
tool = TXTSearchTool()

# OR

# Initialize the tool with a specific text file,
# so the agent can search within the given text file's content
tool = TXTSearchTool(txt='path/to/text/file.txt')

from chromadb.config import Settings

tool = TXTSearchTool(
    config={
        # Required: embeddings provider + config
        "embedding_model": {
            "provider": "openai",  # or google-generativeai, cohere, ollama, ...
            "config": {
                "model": "text-embedding-3-small",
                # "api_key": "sk-...",  # optional if env var is set (e.g., OPENAI_API_KEY or EMBEDDINGS_OPENAI_API_KEY)
                # Provider examples:
                # Google → model_name: "gemini-embedding-001", task_type: "RETRIEVAL_DOCUMENT"
                # Cohere → model: "embed-english-v3.0"
                # Ollama → model: "nomic-embed-text"
            },
        },

        # Required: vector database config
        "vectordb": {
            "provider": "chromadb",  # or "qdrant"
            "config": {
                # Chroma settings (optional persistence)
                # "settings": Settings(
                #     persist_directory="/content/chroma",
                #     allow_reset=True,
                #     is_persistent=True,
                # ),

                # Qdrant vector params example:
                # from qdrant_client.models import VectorParams, Distance
                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),

                # Note: collection name is controlled by the tool (default: "rag_tool_collection").
            }
        },
    }
)